Dissertations / Theses on the topic 'Machine learning in education'
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Harrison, Saskia. "Dualisms in modernity : a machine for learning in." Diss., University of Pretoria, 2016. http://hdl.handle.net/2263/60179.
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This dissertation is rooted in the theory of time and place and it considers the
built environment through the lens of past, present and projected future evolution.
The project examines various themes of dualistic study within the broader subject
of time and change. Pertinent to the 21st century, the interface between man,
technology and architecture is investigated in an examination of how architecture
can intervene in the process of perpetual modernisation and the benefits or
compromising attributes it has on man. Additionally, the relationship between old
and new built fabric in architectural heritage is studied and a mediative architectural
approach is proposed. Also, the dual construct of permanence and change in
architecture is investigated.
At the dawn of the fourth industrial revolution, where the physical- and the cyber
worlds are continuously interwoven, a re-examination of learning models and the
volatile situation of higher education in South Africa is conducted in anticipation of
what technological advancement continuously presents and the impact this has
on man and the built environment.
The site of the Government Printing Works embodies a comprehensive
intersection between time, change and architecture with a rich development
history spanning over 120 years. The block tells the story of function, production
and dissemination of knowledge, and this intangible heritage is commemorated
by the proposed programme of a T.E.L. (Technology-Enabled-Learning) Centre
that blends physical and virtual learning environments and where knowledge is
distributed in a ubiquitous manner.Hierdie studie is gegrond in die teorie oor tyd en plek en dit beskou die bouomgewing deur die lens van verlede, hede en geprojekteerde toekomstige evolusie. In die wyer onderwerp van tyd en plek word verskeie temas van dualistiese studie ondersoek. Met toepassing op die 21ste eeu, word die koppelvlak tussen die mens, tegnologie en argitektuur ondersoek, deur 'n studie oor hoe argitektuur kan ingryp in the proses van onophoudelike modernisering en die voordele of nadele wat dit inhou vir die mens. Daarbenewens word die verhouding tussen ou en nuwe geboue bestudeer en 'n bemiddelinde argitektoniese benadering word voorgestel. Verder word die dubelle benadring van vastheid en verandering in argitektoniese elemente ondersoek. Aan die omvang van 'n vierde industri?le revolusie, waar die fisiese en die kuber w?relde voortdurend verweef word, word 'n herondersoek van leermodelle en die huidige wisselvallige situasie van ho?r onderwys in Suid-Afrika gedoen, in afwagting van wat tegnologiese vooruitgang voortdurend bied vir die mens en die beboude omgewing. Die terrein van die Staatsdrukkery verpersoonlik 'n omvattende kruising tussen tyd, verandering en argitektuur met 'n ryk geskiedenis van ontwikkeling wat strek oor meer as 120 jaar. Die blok vertel die verhaal van funksie, produksie en die verspreiding van kennis, en hierdie nie-tasbare erfenis is herdenk deur die voorgestelde program van 'n T.A.L. (Tegnologie Aangedrewe-Leer) Sentrum wat fisiese en virtuele leeromgewings saamsmelt en waar kennis versprei word in 'n alomteenwoordige wyse
Mini Dissertation (MArch (Prof))--University of Pretoria, 2016.
Architecture
MArch (Prof)
Unrestricted
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Hugo, Linsey Sledge. "A Comparison of Machine Learning Models Predicting Student Employment." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1544127100472053.
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Ar, Rosyid Harits. "Adaptive serious educational games using machine learning." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/adaptive-serious-educational-games-using-machine-learning(b5f5024b-c7fd-4660-997c-9fd22e140a8f).html.
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The ultimate goals of adaptive serious educational games (adaptive SEG) are to promote effective learning and maximising enjoyment for players. Firstly, we develop the SEG by combining knowledge space (learning materials) and game content space to be used to convey learning materials. We propose a novel approach that serves toward minimising experts' involvement in mapping learning materials to game content space. We categorise both content spaces using known procedures and apply BIRCH clustering algorithm to categorise the similarity of the game content. Then, we map both content spaces based on the statistical properties and/or by the knowledge learning handout. Secondly, we construct a predictive model by learning data sets constructed through a survey on public testers who labelled their in-game data with their reported experiences. A Random Forest algorithm non-intrusively predicts experiences via the game data. Lastly, it is not feasible to manually select or adapt the content from both spaces because of the immense amount of options available. Therefore, we apply reinforcement learning technique to generate a series of learning goals that promote an efficient learning for the player. Subsequently, a combination of conditional branching and agglomerative hierarchical clustering select the most appropriate game content for each selected education material. For a proof-of-concept, we apply the proposed approach to producing the SEG, named Chem Dungeon, as a case study to demonstrate the effectiveness of our proposed methods.
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Lindell, Johan. "Identifying student stuck states in programmingassignments using machine learning." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-103993.
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Intelligent tutors are becoming more popular with the increased use of computersand hand held devices in the education sphere. An area of research isinvestigating how machine learning can be used to improve the precision andfeedback of the tutor. This thesis compares machine learning clustering algorithmswith various distance functions in an attempt to cluster together codesnapshots of students solving a programming task. It investigates whethera general non-problem specific implementation of a distance function canbe used to identify when a student is stuck solving an assignment. Themachine learning algorithms compared are k-medoids, the randomly initializedalgorithm that produces a pre-defined number of clusters and affinitypropagation, a two phase algorithm with dynamic cluster sizes. Distancefunctions tried are based on the Bag of Words approach, lower level APIcalls and a problem specific distance function. This thesis could not find agood algorithm to achieve the sought goal, and lists a number of possibleerror sources linked to the data, preprocessing and algorithm. The methodologyis promising but requires a controlled environment at every level toassure data quality does not detract from the analysis in later stages.
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Griffiths, Alexander. "Forecasting failure : assessing risks to quality assurance in higher education using machine learning." Thesis, King's College London (University of London), 2017. https://kclpure.kcl.ac.uk/portal/en/theses/forecasting-failure(aacc8294-15ba-4a4a-93d6-329843dfcfd9).html.
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The landscape of UK higher education has changed significantly in the last five years. A tripling of tuition fees, the uncapping of student numbers, and an explosion in the number of ‘alternative providers’ typify a more marketised higher education sector (Brown and Carasso, 2013). With more providers than ever before competing for students, many with little experience and profitdriven motives, there is a clear danger that quality will suffer. Faced with limited resource and an expanding, fiercely independent sector, the Government sought to protect quality by asking the Quality Assurance Agency for Higher Education (QAA) to adopt a risk-based approach. The 2011 White Paper Student at the Heart of the System directed QAA to prioritise their reviews based on “an objective assessment of a basket of data, monitored continually but at arm’s length” (BIS, 2011, 3.19). There is, however, an evident dearth of empirical evidence to support such an approach . The aim of this thesis is to examine the extent to which available data can predict the outcome of quality assurance reviews, and hence prioritise them. To fulfill this aim, the outcomes of all QAA reviews comparable with its current inspection methods were gathered along with all available data that could feasibly form part of a data-driven riskbased approach to quality assurance. Using machine learning, this study shows conclusively that a risk-based approach to quality assurance, as envisioned in the 2011 White Paper, cannot work. There is no connection between the available data and the subsequent outcome of QAA reviews. The final part of this thesis therefore examines the reason why there is no connection between the available data and the outcome of QAA reivews. Three overarching and non-exclusive possibilities are identified. Concerns over the data, the review process, and the definition of ‘quality’ pose significant barriers to the operation of a successful data-driven, risk-based approach. An alternative approach to prioritising quality assurance in higher education is therefore required.
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Srivastava, Akshat. "Developing Functional Literacy of Machine Learning Among UX Design Students." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617104876484835.
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Zhu, Kevin(Kevin F. ). "An educational approach to machine learning with mobile applications." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122989.
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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 81-82).
Machine learning has increasingly become a major topic in computer science for students to learn. However, it can be quite technical and thus difficult for students to grasp, especially those in high school and under. To make machine learning and its applications more accessible to younger students, we developed a series of machine learning extensions for MIT App Inventor. MIT App Inventor is a web application for users with minimal programming experience to easily and quickly build mobile applications, and these extensions allow users to build applications that incorporate powerful machine learning functionality. These extensions were tested over a 6-week class with about 10 students and can be used as an educational tool.
by Kevin Zhu.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Meth, Deanna, and Kathryn Ecclestone. "Questioning the machine : Academics’ perceptions of tensions and trade-offs in undergraduate education at one English university." Thesis, University of Sheffield, 2016. https://eprints.qut.edu.au/201184/1/58002491.pdf.
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Exploring the proposition that in our consumer society, undergraduate students are now denied the opportunity to transform into critical thinking scholars, this case study explores academics’ beliefs about the purpose and shape of an ideal undergraduate higher education. Located in one English research-intensive university, research focuses on their perceptions of transformation as a concept, and how it is enabled or denied. Adopting a critical realist approach, the study responds to an absence of work on the effects of marketisation on curricula and pedagogy, and academics’ shifting identities in national policy and local practice. Academics’ views link to tensions in a changing higher education system, where managerialisation and marketisation have been compounded by the emergence of a global knowledge economy, massification, a new digital age, and more recently, the global financial crisis and a conservative government. Within this, and setting the context for fourteen in-depth interviews, increasingly influential ‘students as consumers’ and ‘student experience’ discourses are explored through critical examination of national and institutional policy documents. Using a presage-process-product (3P) model, the thesis links academics’ aspirations for an ideal undergraduate education which develops knowledge and intellectual approaches grounded within a discipline (product), to elements that ‘enable’ or ‘deny’ in curricula and pedagogy (process), and the wider institutional environment, such as academics’ roles, student numbers and quality processes (presage). Academics describe the ways in which they negotiate, subvert or overcome these elements. The study uses a suite of concepts including quality discourses, university psychosis, unregulated play, and models of knowledge, curriculum and pedagogy, to visualise tensions surfaced and disentangle the concept of transformation. In proposing a way forward, conclusions note the need for the university to overtly acknowledge trade-offs made, and to consider more deeply the impact of presage and process elements on academics, students, and the undergraduate education aimed for.
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Melsion, Perez Gaspar Isaac. "Leveraging Explainable Machine Learning to Raise Awareness among Preadolescents about Gender Bias in Supervised Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287554.
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Machine learning systems have become ubiquitous into our society. This has raised concerns about the potential discrimination that these systems might exert due to unconscious bias present in the data, for example regarding gender and race. Whilst this issue has been proposed as an essential subject to be included in the new AI curricula for schools, research has shown that it is a difficult topic to grasp by students. This thesis aims to develop an educational platform tailored to raise the awareness of the societal implications of gender bias in supervised learning. It assesses whether using an explainable model has a positive effect in teaching the impacts of gender bias to preadolescents from 10 to 13 years old. A study was carried out at a school in Stockholm employing an online platform with a classifier incorporating Grad-CAM as the explainability technique that enables it to visually explain its own predictions. The students were divided into two groups differentiated by the use of the explainable model or not. Analysis of the answers demonstrates that preadolescents significantly improve their understanding of the concept of bias in terms of gender discrimination when they interact with the explainable model, highlighting its suitability for educational programs.Maskininlärningssystemen har blivit allmänt förekommande i vårt samhälle, vilket har lett till oro över den potentiella diskriminering som dessa system kan utöva när det gäller kön och ras. Detta med orsak av det bias som finns i datan. Även om detta problem har föreslagits som ett viktigt ämne som ska ingå i de nya AI-läroplanerna för skolor, har forskning visat att det är ett svårt ämne att förstå för studenter. Detta examensarbete syftar till att utveckla en utbildningsplattform för att öka medvetenhet om de samhälleliga konsekvenserna av könsbias inom övervakad maskinlärning. Det utvärderar huruvida användning av en förklaringsbar modell har en positiv effekt vid inlärning hos ungdomar mellan 10 och 13 år när det kommer till konsekvenserna av könsbias. En studie genomfördes på en skola i Stockholm med hjälp av en onlineplattform som använder en klassificeringsalgoritm med Grad-CAM förklaringsbar teknik som gör det möjligt för den att visuellt förklara sina egna förutsägelser. Eleverna delades in i två grupper som åtskiljdes genom att den ena gruppen använde den förklarbara modellen medan den andra inte gjorde det. Analysen av svaren visar att ungdomar markant förbättrar sin förståelse av könsdiskrimineringsbias när de interagerar med den förklarbara modellen, vilket lyfter fram dess lämplighet för användning inom utbildningsprogram.
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Ha, Minsu. "Assessing Scientific Practices Using Machine Learning Methods: Development of Automated Computer Scoring Models for Written Evolutionary Explanations." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1367505135.
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Teguim, Kamdjou Herve Donald [Verfasser], and Andreas [Akademischer Betreuer] Behr. "Application of machine learning algorithms for analysing higher education dropouts and estimating returns to education / Herve Donald Teguim Kamdjou ; Betreuer: Andreas Behr." Duisburg, 2021. http://d-nb.info/1239048696/34.
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Wood, Alicia Crowder. "Creating and Automatically Grading Annotated Questions." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/6099.
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We have created a question type that allows teachers to easily create questions, helps provide an intuitive user experience for students to take questions, and reduces the time it currently takes teachers to grade and provide feedback to students. This question type, or an "annotated" question, will allow teachers to test students' knowledge in a particular subject area by having students "annotate" or mark text and video sources to answer questions. Through user testing we determined that overall the interface and the implemented system decrease the time it would take a teacher to grade annotated quiz questions. However, there are some limitations based on the way students answered text annotated questions that would require a rewrite of the user interface and system design to decrease the grading time even more for teachers.
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McKeague-McFadden, Ikaika A. "Identifying Students at Risk of Not Passing Introductory Physics Using Data Mining and Machine Learning." Miami University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami1596214863294544.
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Cabrera, Dalmazzo David. "Machine learning and deep neural networks approach to modelling musical gestures." Doctoral thesis, Universitat Pompeu Fabra, 2020. http://hdl.handle.net/10803/670399.
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Gestures can be defined as a form of non-verbal communication associated with an intention or an emotional state articulation. They are not only intrinsically part of the human language, but also explain specific details of a body-knowledge execution. Gestures are being studied not only in the language research field but also in dance, sports, rehabilitation, and music; where the term is understood as a “learned technique of the body”. Therefore, in music education, gestures are assumed as automatic-motor abilities learned by repetitional practice, to self-teach and fine-tune the motor actions optimally. Hence, those gestures are intended to be part of the performer’s technical repertoire to take fast actions/decisions on-the flight, assuming that they are not only relevant in music expressive capabilities but also, a method for a correct ‘energy-consumption’ habit development to avoid injuries.
In this thesis, we applied state-of-the-art machine learning (ML) techniques to model violin bowing gestures in professional players. Concretely, we recorded a database of expert performers and different student levels and developed three strategies to classify and recognise those gestures in real-time: a) First, we developed a multimodal synchronisation system to record audio, video and IMU sensor data with a unified time reference. We programmed a custom C++ application to visualise the output from the ML models. We implemented a Hidden Markov Model to detect fingering disposition and bow-stroke gesture performance. b) A second approach is a system that extracts general time features from the gestures samples, creating a dataset of audio and motion data from expert performers implementing a Deep Neural Networks algorithm. To do so, we have implemented the hybrid model CNN LSTM architecture. c) Furthermore, a Melspectrogram based analysis that can read and extract patterns from only audio data, opening the option of recognising relevant information from the audio recordings without the need for external sensors to achieve similar results. All of these techniques are complementary and also incorporated into an education application as a computer assistant to enhance music-learners practice by providing useful real-time feedback. The application will be tested in a professional education institution.Els gestos es poden definir com una forma de comunicació no verbal associada a una intenció o a l’articulació d’un estat emocional. No només formen part intrínsecament del llenguatge humà, sinó que també expliquen detalls específics de l’execució del coneixement del cos. Els gestos són objecte d’estudi no només en el camp de la recerca lingüística, sinó també en la dansa, l’esport, la rehabilitació i la música; on el terme s’entén com a “tècnica apresa del cos”. Per tant, en l’educació musical, els gestos s’assumeixen com a habilitats automomotrius apreses mitjançant la pràctica repetitiva, per aprendre i ajustar les accions motrius de manera ptima. En conseqüència, aquests gestos estan destinats a formar part del repertori tècnic de l’intèrpret per prendre accions/decisions ràpides en temps real durant la interpretació, suposant que no només són rellevants en les capacitats expressives de la música, sinó que també ho són com a mètode per a un correcte desenvolupament d’hàbits (“çonsum d’energia”) per evitar lesions. En aquesta tesi, hem aplicat tècniques de Machine Learning (ML) d’última generació per modelar els gestos de proa de violí en músics professionals. Concretament, hem enregistrat una base de dades d’intèrprets experts i d’estudiants de diferents nivells i hem desenvolupat tres estratègies per classificar i reconèixer aquests gestos en temps real: a) Primer, hem desenvolupar un sistema de sincronització multimodal per enregistrar dades de sensors d’àudio, vídeo i IMU amb una referència de tamps unificada. Hem programat una aplicació C++ per visualitzar els resultats dels models ML. Hem implementat un Hidden Markov Model per detectar la disposició dels dits i la realització de gestos de l’arc. b) Un segon enfocament aplicatés un sistema que extreu les característiques generals de les seqüències de dades de les mostres de gestos, creant un conjunt de dades d’àudio i de dades de moviment d’intèrprets experts implementant un algoritme de Deep Neural Networks. Per fer-ho, hem aplicat el model híbrid d’arquitectura CNN-LSTM. c) A més, s’ha fet una anàlisi basada en l’espectrograma Mel que pot llegir i extreure patrons només de dades d’àudio, obrint l’opció de reconèixer informació rellevant dels enregistraments d’àudio sense necessitat de sensors externs per obtenir resultats similars. Totes aquestes tècniques són complementàries i s’han incorporat a una aplicació d’educació com a assistent d’ordinador per millorar la pràctica dels aprenents de música proporcionant comentaris útils en temps real. Aquesta aplicació serà provada en una institució d’educació professional.
Los gestos pueden definirse como una forma de comunicación no verbal asociada con una intención o una articulación del estado emocional. No solo forman parte intrínsec del lenguaje humano, sino que también explican detalles específicos de la ejecución del conocimiento corporal. Los gestos se están estudiando no solo en el campo de la investigación del lenguaje, sino también en danza, deportes, rehabilitación y música; donde el término se entiende como una “técnica aprendida del cuerpo”. Por tanto, en la educación musical, los gestos se asumen como habilidades motoras automáticas aprendidas mediante la práctica repetitiva, para aprender y afinar las acciones motoras de forma óptima. Por lo tanto, esos gestos están destinados a ser parte del repertorio técnico del intérprete para tomar acciones/decisiones rápidas en tiempo real, asumiendo que no solo son relevantes en las capacidades expresivas de la música sino también, como un método para desarrollar hábitos correctos de 'consumo de energía’ para evitar lesiones. En esta tesis, aplicamos técnicas de Machine Learning (ML) de última generación para modelar los gestos de arco de violín en interpretes profesionales. Concretamente, creamos una base de datos con músicos expertos y también con diferentes niveles de estudiantes, desarrollando tres estrategias para clasificar y reconocer esos gestos en tiempo real: a) Primero, desarrollamos un sistema de sincronización multimodal para grabar audio, video y datos de sensores IMU con una referencia de tiempo unificada. Programamos una aplicación C++ personalizada para visualizar el resultado de los modelos ML. Implementamos un Hidden Markov Model para detectar la disposición de los dedos y la ejecución del gestos del arco. b) Desarrollamos un sistema que extrae características de tiempo generales en todas las muestras de gestos, creando un conjunto de datos de audio y datos de movimiento de músicos expertos implementando un algoritmo Deep neural Networks; particularmente, el modelo híbrido CNN-LSTM. c) Además, un análisis basado en espectrograma Mel que puede leer y extraer patrones únicamente usando datos de audio, abriendo la opción de reconocer información relevante usando las grabaciones de audio sin la necesidad de sensores externos para lograr resultados similares. Todas estas técnicas son complementarias y también se incorporan en una aplicación educativa como asistente computacional para mejorar la práctica de los estudiantes de música, al proporcionar información útil en tiempo real. La aplicación se probará en una institución de educación profesional.
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Suárez, Chavarría Nicolás. "The impact of commuting time over educational achievement : a machine learning approach." Tesis, Universidad de Chile, 2018. http://repositorio.uchile.cl/handle/2250/164086.
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TESIS PARA OPTAR AL GRADO DE MAGÍSTER EN ECONOMÍATaking advantage of georeferenced data from Chilean students, we estimate the impact of commuting time over academic achievement. As the commuting time is an endogenous variable, we use instrumental variables and fixed effects at school level to overcome this problem. Also, as we don’t know which mode of transport the students use, we complement our analysis using machine learning methods to predict the transportation mode. Our findings suggest that the commuting time has a negative effect over academic performance, but this effect is not always significant.
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Goldstein, Adam B. "Responding to Moments of Learning." Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-theses/685.
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In the field of Artificial Intelligence in Education, many contributions have been made toward estimating student proficiency in Intelligent Tutoring Systems (cf. Corbett & Anderson, 1995). Although the community is increasingly capable of estimating how much a student knows, this does not shed much light on when the knowledge was acquired. In recent research (Baker, Goldstein, & Heffernan, 2010), we created a model that attempts to answer that exact question. We call the model P(J), for the probability that a student just learned from the last problem they answered. We demonstrated an analysis of changes in P(J) that we call “spikiness", defined as the maximum value of P(J) for a student/knowledge component (KC) pair, divided by the average value of P(J) for that same student/KC pair. Spikiness is directly correlated with final student knowledge, meaning that spikes can be an early predictor of success. It has been shown that both over-practice and under-practice can be detrimental to student learning, so using this model can potentially help bias tutors toward ideal practice schedules. After demonstrating the validity of the P(J) model in both CMU's Cognitive Tutor and WPI's ASSISTments Tutoring System, we conducted a pilot study to test the utility of our model. The experiment included a balanced pre/post-test and three conditions for proficiency assessment tested across 6 knowledge components. In the first condition, students are considered to have mastered a KC after correctly answering 3 questions in a row. The second condition uses Bayesian Knowledge Tracing and accepts a student as proficient once they earn a current knowledge probability (Ln) of 0.95 or higher. Finally, we test P(J), which accepts mastery if a student's P(J) value spikes from one problem and the next first response is correct. In this work, we will discuss the details of deriving P(J), our experiment and its results, as well as potential ways this model could be utilized to improve the effectiveness of cognitive mastery learning.
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Swaminathan, Ranjini. "SPEECH AND LANGUAGE TECHNOLOGIES FOR SEMANTICALLY LINKED INSTRUCTIONAL CONTENT." Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/201498.
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Recent advances in technology have made it possible to offer educational content online in the form of e-learning systems. The Semantically Linked Instructional Content (SLIC) system, developed at The University of Arizona,is one such system that hosts educational and technical videos online.This dissertation proposes the integration of speech and language technologies with the SLIC system.Speech transcripts are being used increasingly in video browsing systems to help understand the video content better and to do search on the content with text queries. Transcripts are especially useful for people with disabilities and those who have a limited understanding of the language of the video. Automatic Speech Recognizers (ASRs) are commonly used to generate speech transcripts for videos but are not consistent in their performance. This issue is more pronounced in a system like SLIC due to the technical nature of talks with words not seen in the ASR vocabulary and many speakers with different voices and accents making recognition harder.The videos in SLIC come with presentation slides that contain words specific to the talk subject and the speech transcript itself can be considered to be composed of these slide words interspersed with other words. Furthermore, the errors in the transcript are words that sound similar to what was actually spoken; notes instead of nodes for example. The errors that occur due to misrecognized slide words can be fixed if we know which slide words were actually spoken and where they occur in the transcript. In other words, the slide words are matched or aligned with the transcript.In this dissertation two algorithms are developed to phonetically align transcript words with slide words based on a Hidden Markov Model and a Hybrid hidden semi-Markov model respectively. The slide words constitute the hidden states and the transcript words are the observed states in both models. The alignment algorithms are adapted for different applications such as transcript correction (as already mentioned), search and indexing, video segmentation and closed captioning. Results from experiments conducted show that the corrected transcripts have improved accuracy andyield better search results for slide word queries.
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Rhees, Brooke Ellen. "A Semi-Automatic Grading Experience for Digital Ink Quizzes." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6245.
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Teachers who want to assess student learning and provide quality feedback are faced with a challenge when trying to grade assignments quickly. There is currently no system which will provide both a fast-to-grade quiz and a rich testing experience. Previous attempts to speed up grading time include NLP-based text analysis to automate grading and scanning in documents for manual grading with recyclable feedback. However, automated NLP systems all focus solely on text-based problems, and manual grading is still linear in the number of students. Machine learning algorithms exist which can interactively train a computer quickly classify digital ink strokes. We used stroke recognition and interactive machine learning concepts to build a grading interface for digital ink quizzes, to allow non-text open-ended questions that can then be semiautomatically graded. We tested this system on a Computer Science class with 361 students using a set of quiz questions which their teacher provided, evaluated its effectiveness, and determined some of its limitations. Adaptations to the interface and the training process as well as further work to resolve intrinsic stroke perversity are required to make this a truly effective system. However, using the system we were able to reduce grading time by as much as 10x for open-ended responses.
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Fazenbaker, Canon. "EXPLORING IMPACT OF EDUCATIONAL AND ECONOMIC FACTORS ON NATIONAL INTELLECTUAL PRODUCTIVITY USING MACHINE LEARNING METHODS." VCU Scholars Compass, 2009. http://scholarscompass.vcu.edu/etd/642.
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The patent process is representative of a nationwide means for innovations and new ideas to be recognized. The U.S. Patents Office, since its inception in 1790, has issued nearly five million patents. These patents span from the U.S. Patent #1, which was for an improvement "in the making of Pot ash and Pearl ash by a new Apparatus and Process" to today's patents which deal with technologies and mediums that were unimaginable at the Patent Offices' inception. The purpose of this study is to determine what social and economic factors at the federal level have the highest impact on national productivity measured by the number of patents applied for and/or granted each year. Using Machine Learning algorithms and predictive analysis on fifty years worth of data to determine what macroeconomic and educational factors have the most impact on patents. The first part of this study describes the methods and algorithms used during this research. The second part of this study discusses the results and what those results reveal about the impact of education and economic factors as they relate to national creativity / intellectual productivity. The goal of this study is to determine what factors affect national intellectual productivity in a given year. This data will be useful for governments, both local and federal, when faced with educational and economic issues.
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Gallego-Durán, Francisco J. "Estimating difficulty of learning activities in design stages: A novel application of Neuroevolution." Doctoral thesis, Universidad de Alicante, 2015. http://hdl.handle.net/10045/53697.
Full textAbstract:
In every learning or training environment, exercises are the basis for practical learning. Learners need to practice in order to acquire new abilities and perfect those gained previously. However, not every exercise is valid for every learner: learners require exercises that match their ability levels. Hence, difficulty of an exercise could be defined as the amount of effort that a learner requires to successfully complete the exercise (its learning cost). Too high difficulties tend to discourage learners and make them drop out, whereas too low difficulties are perceived as unchallenging, resulting in loss of interest. Correctly estimating difficulties is hard and error-prone problem that tends to be done manually using domain-expert knowledge. Underestimating or overestimating difficulty generates a problem for learners, increasing dropout rates in learning environments. This paper presents a novel approach to improve difficulty estimations by using Neuroevolution. The method is based on measuring the computational cost that Neuroevolution algorithms require to successfully complete a given exercise and establishing similarities with previously gathered information from learners. For specific experiments presented, a game called PLMan has been used. PLMan is a PacMan-like game in which users have to program the Artificial Intelligence of the main character using a Prolog knowledge base. Results show that there exists a correlation between students’ learning costs and those of Neuroevolution. This suggests that the approach is valid, and measured difficulty of Neuroevolution algorithms may be used as estimation for student's difficulty in the proposed environment.
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Walonoski, Jason A. "Visual Feedback for Gaming Prevention in Intelligent Tutoring Systems." Digital WPI, 2006. https://digitalcommons.wpi.edu/etd-theses/23.
Full textAbstract:
A major issue in Intelligent Tutoring Systems is off-task student behavior, especially performance-based gaming, where students systematically exploit tutor behavior in order to advance through a curriculum quickly and easily, with as little active thought directed at the educational content as possible. The goal of this research was to explore the phenomena of off-task gaming behavior within the Assistments system, as well as to develop a passive visual indicator to deter and prevent off-task gaming behavior without active intervention via graphical feedback to the student and teachers. Traditional active intervention approaches were also constructed for comparison purposes, and machine-learned gaming-detection models were developed as a potential invocation and evaluation mechanism. Passive graphical interventions have been well received by teachers, and results are suggestive that they are effective at reducing off-task gaming behavior.
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Pardos, Zachary Alexander. "Predictive Models of Student Learning." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-dissertations/185.
Full textAbstract:
In this dissertation, several approaches I have taken to build upon the student learning model are described. There are two focuses of this dissertation. The first focus is on improving the accuracy with which future student knowledge and performance can be predicted by individualizing the model to each student. The second focus is to predict how different educational content and tutorial strategies will influence student learning. The two focuses are complimentary but are approached from slightly different directions. I have found that Bayesian Networks, based on belief propagation, are strong at achieving the goals of both focuses. In prediction, they excel at capturing the temporal nature of data produced where student knowledge is changing over time. This concept of state change over time is very difficult to capture with classical machine learning approaches. Interpretability is also hard to come by with classical machine learning approaches; however, it is one of the strengths of Bayesian models and aids in studying the direct influence of various factors on learning. The domain in which these models are being studied is the domain of computer tutoring systems, software which uses artificial intelligence to enhance computer based tutorial instruction. These systems are growing in relevance. At their best they have been shown to achieve the same educational gain as one on one human interaction. Computer tutors have also received the attention of White House, which mentioned an tutoring platform called ASSISTments in its National Educational Technology Plan. With the fast paced adoption of these data driven systems it is important to learn how to improve the educational effectiveness of these systems by making sense of the data that is being generated from them. The studies in this proposal use data from these educational systems which primarily teach topics of Geometry and Algebra but can be applied to any domain with clearly defined sub-skills and dichotomous student response data. One of the intended impacts of this work is for these knowledge modeling contributions to facilitate the move towards computer adaptive learning in much the same way that Item Response Theory models facilitated the move towards computer adaptive testing.
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Olsen, Grace. "Fundamental Work Toward an Image Processing-Empowered Dental Intelligent Educational System." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/2052.
Full textAbstract:
Computer-aided education in dental schools is greatly needed in order to reduce the need for human instructors to provide guidance and feedback as students practice dental procedures. A portable computer-aided educational system with advanced digital image processing capabilities would be less expensive than current computer-aided dental educational systems and would also address some of their limitations. This dissertation outlines the development of novel components that would be part of such a system. This research includes the design of a novel image processing technique, the Directed Active Shape Model algorithm, which is used to locate the tooth and drilled preparation from a digital image, and also to measure the exact size, shape and location of the drilled preparation in relation to the expected preparation. The use of statistical measures taken from the digital images to provide feedback about the smoothness and depth of the dental preparation is also detailed. This research also includes the design and testing of a posture-monitoring component for a portable educational system. Maintaining proper posture is critical for dental practitioners, because poor posture can affect not only the dental practitioner's health, but also the quality of the practitioner's work. The algorithms and techniques designed for use in the dental education support system could also be applied in the design of computer-aided educational systems for the development of procedural skills in many other fields, and in the design of systems to support practicing dentists.
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Al-Hamad, Salah Madhi Ahmad. "Blended learning system for further and higher education mechanical engineering courses in Bahrain." Thesis, University of Huddersfield, 2013. http://eprints.hud.ac.uk/id/eprint/19028/.
Full textAbstract:
Teaching and learning processes that are being followed globally by education providers consist of conventional face-to-face approach. Various socio-economic indicators have increased the pressure on Engineering Education in Bahrain in order to equip the students with both cognitive and psychomotor skills that are required by the labour market. The globalisation, along with the interdependence of various economies, has resulted in creating an extra dimension to the higher order of skills requirements. Hence, there is a need to develop new teaching and learning (T & L) methodologies that can comply with the ever increasing demands of the industry, regarding the skills of engineering students. In this study, the author has presented a comparison between various teaching and learning methodologies being implemented on the students of Higher National Diploma at Sheikh Khalifa Institute (SKI), Kingdom of Bahrain. The author reviewed the effectiveness of the conventional teaching and learning methodology by comparing the pre-results with post-results. The same has been carried out on two novel T & L methodologies developed in these study i.e. computer-assisted instructions (CAI) and Blended Learning method, on imparting higher order of cognitive and psychomotor skills to engineering students. The study has been conducted on various groups of Higher National Diploma (HND) students at SKI. The study makes use of various questionnaires design especially for both the students and the teachers about their views on different T & L methodologies being implemented. It has been observed that computer-assisted instructions, when used with the conventional T & L methodology, perform superiorly than blended e-learning method or the conventional method alone. Hence, it has been recommended that this novel T & L method be used in the future to Higher National Diploma students at SKI. Further to the development of a novel T & L methodology that performs better than the conventional T & L method, novel mathematical models have been developed for T & L methodology for both the cognitive and psychomotor domains. These mathematical models are based on the findings of the present study. These mathematical models explain the learning process of the students at microscopic level, in contrast to the conventional macroscopic evaluation method where only the marks obtained by the students indicate the quantitative learning. Furthermore, a novel Blended Learning package (containing tutorials for various Mechanical Engineering modules) has been developed based on the students-centred learning, considering institutional, pedagogical and technological contexts of service and product implementation. In this perspective, the novel Blended Learning package has been designed and developed in order to minimise/close the gaps between higher education at SKI and the requirements of the labour market.
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Alvarado, Mantecon Jesus Gerardo. "Towards the Automatic Classification of Student Answers to Open-ended Questions." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39093.
Full textAbstract:
One of the main research challenges nowadays in the context of Massive Open Online Courses (MOOCs) is the automation of the evaluation process of text-based assessments effectively. Text-based assessments, such as essay writing, have been proved to be better indicators of higher level of understanding than machine-scored assessments (E.g. Multiple Choice Questions). Nonetheless, due to the rapid growth of MOOCs, text-based evaluation has become a difficult task for human markers, creating the need of automated systems for grading.
In this thesis, we focus on the automated short answer grading task (ASAG), which automatically assesses natural language answers to open-ended questions into correct and incorrect classes. We propose an ensemble supervised machine learning approach that relies on two types of classifiers: a response-based classifier, which centers around feature extraction from available responses, and a reference-based classifier which considers the relationships between responses, model answers and questions.
For each classifier, we explored a set of features based on words and entities. For the response-based classifier, we tested and compared 5 features: traditional n-gram models, entity URIs (Uniform Resource Identifier) and entity mentions both extracted using a semantic annotation API, entity mention embeddings based on GloVe and entity URI embeddings extracted from Wikipedia. For the reference-based classifier, we explored fourteen features: cosine similarity between sentence embeddings from student answers and model answers, number of overlapping elements (words, entity URI, entity mention) between student answers and model answers or question text, Jaccard similarity coefficient between student answers and model answers or question text (based on words, entity URI or entity mentions) and a sentence embedding representation.
We evaluated our classifiers on three datasets, two of which belong to the SemEval ASAG competition (Dzikovska et al., 2013). Our results show that, in general, reference-based features perform much better than response-based features in terms of accuracy and macro average f1-score. Within the reference-based approach, we observe that the use of S6 embedding representation, which considers question text, student and model answer, generated the best performing models. Nonetheless, their combination with other similarity features helped build more accurate classifiers. As for response-based classifiers, models based on traditional n-gram features remained the best models.
Finally, we combined our best reference-based and response-based classifiers using an ensemble learning model. Our ensemble classifiers combining both approaches achieved the best results for one of the evaluation datasets, but underperformed on the remaining two. We also compared the best two classifiers with some of the main state-of-the-art results on the SemEval competition. Our final embedded meta-classifier outperformed the top-ranking result on the SemEval Beetle dataset and our top classifier on SemEval SciEntBank, trained on reference-based features, obtained the 2nd position.
In conclusion, the reference-based approach, powered mainly by sentence level embeddings and other similarity features, proved to generate the most efficient models in two out of three datasets and the ensemble model was the best on the SemEval Beetle dataset.
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Bulgarov, Florin Adrian. "Toward Supporting Fine-Grained, Structured, Meaningful and Engaging Feedback in Educational Applications." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1404562/.
Full textAbstract:
Recent advancements in machine learning have started to put their mark on educational technology. Technology is evolving fast and, as people adopt it, schools and universities must also keep up (nearly 70% of primary and secondary schools in the UK are now using tablets for various purposes). As these numbers are likely going to follow the same increasing trend, it is imperative for schools to adapt and benefit from the advantages offered by technology: real-time processing of data, availability of different resources through connectivity, efficiency, and many others. To this end, this work contributes to the growth of educational technology by developing several algorithms and models that are meant to ease several tasks for the instructors, engage students in deep discussions and ultimately, increase their learning gains.
First, a novel, fine-grained knowledge representation is introduced that splits phrases into their constituent propositions that are both meaningful and minimal. An automated extraction algorithm of the propositions is also introduced. Compared with other fine-grained representations, the extraction model does not require any human labor after it is trained, while the results show considerable improvement over two meaningful baselines.
Second, a proposition alignment model is created that relies on even finer-grained units of text while also outperforming several alternative systems. Third, a detailed machine learning based analysis of students' unrestricted natural language responses to questions asked in classrooms is made by leveraging the proposition extraction algorithm to make computational predictions of textual assessment. Two computational approaches are introduced that use and compare manually engineered machine learning features with word embeddings input into a two-hidden layers neural network. Both methods achieve notable improvements over two alternative approaches, a recent short answer grading system and DiSAN – a recent, pre-trained, light-weight neural network that obtained state-of-the-art performance on multiple NLP tasks and corpora.
Fourth, a clustering algorithm is introduced in order to bring structure to the feedback offered to instructors in classrooms. The algorithm organizes student responses based on three important aspects: propositional importance classifications, computational textual understanding of student understanding and algorithm similarity metrics between student responses. Moreover, a dynamic cluster selection algorithm is designed to decide which are the best groups of responses resulting from the cluster hierarchy. The algorithm achieves a performance that is 86.3% of the performance achieved by humans on the same task and dataset.
Fifth, a deep neural network is built to predict, for each cluster, an engagement response that is meant to help generate insightful classroom discussion. This is the first ever computational model to predict how engaging student responses will be in classroom discussion. Its performance reaches 86.8% of the performance obtained by humans on the same task and dataset. Moreover, I also demonstrate the effectiveness of a dynamic algorithm that can self-improve with minimal help from the teachers, in order to reduce its relative error by up to 32%.
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Orliac, Charlotte. "Modèles et outils pour la conception de Learning Games en Réalité Mixte." Phd thesis, INSA de Lyon, 2013. http://tel.archives-ouvertes.fr/tel-00952892.
Full textAbstract:
Les Learning Games sont des environnements d'apprentissage, souvent informatisés, qui utilisent des ressorts ludiques pour catalyser l'attention des apprenants et ainsi faciliter leur apprentissage. Ils ont des atouts indéniables mais présentent également certaines limites, comme des situations d'apprentissage trop artificielles. Ces limites peuvent être dépassées par l'intégration d'interactions en Réalité Mixte dans les Learning Games, que nous appelons alors des Mixed Reality Learning Games (MRLG). La Réalité Mixte, qui combine environnements numériques et objets réels, ouvre de nouvelles possibilités d'interactions et d'apprentissage qui gomment les limites précédentes et qu'il faut repérer et explorer. Dans ce contexte, nous nous intéressons au processus de conception des MRLG. Dans un premier temps, nous présentons une étude sur l'utilisation de la Réalité Mixte dans les domaines de l'apprentissage et du jeu, incluant un état de l'art des MRLG. Cette étude montre que, malgré de nombreux atouts, la conception des MRLG reste difficile à maîtriser. En effet, il n'existe ni méthode ni outil adapté à la conception de ce type d'environnements. Dans un second temps, nous analysons et modélisons l'activité de conception des MRLG à travers la littérature et des expériences de conception, dont une menée dans le cadre du projet SEGAREM. Cette démarche révèle des verrous spécifiques tels que l'absence d'aide à la modélisation (ou formalisation), à la créativité et à la vérification de la cohérence des idées. Nous éclairons nos réponses à ces besoins par un recensement des outils utilisés dans les domaines liés aux MRLG : situations d'apprentissage, jeux et environnements de la Réalité Mixte. Ceci nous amène à proposer deux outils conceptuels : un modèle de description de MRLG (f-MRLG) et des aides à la créativité sous la forme de propositions puis de recommandations. Le modèle de description a pour objectif de formaliser l'ensemble des éléments constituant un MRLG, mais aussi d'être un moyen d'identifier les éléments à définir, de structurer et de vérifier les idées. Les listes de propositions et recommandations ont pour but d'aider le concepteur à faire des choix cohérents par rapport à la situation d'apprentissage visée, en particulier en ce qui concerne les types de jeux et les dispositifs de Réalité Mixte. Une première évaluation de ces propositions a conduit à leur amélioration. Ces propositions sont à l'origine de la conception et du développement d'un outil auteur informatisé : MIRLEGADEE (Mixed Reality Learning Game DEsign Environment). MIRLEGADEE est basé sur LEGADEE, un environnement auteur pour la conception de Learning Games. Une expérimentation auprès de 20 enseignants et concepteurs de formation a validé le bienfondé de cet outil qui guide effectivement les concepteurs dans les phases amont du processus de conception de MRLG malgré des limites pour l'accompagnement de tâches complexes.
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Arafat, Md Yasin. "Three Essays on the Evolution of the Determinants of Educational Attainment and its Consequences." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/99465.
Full textAbstract:
The dissertation focuses on the different determinants of education, their effects on the educational outcome, and the overall effect of education on the lifetime consequences.
The first chapter focuses on the inequality of educational opportunity across different demographic factors. This chapter employs a broader set of social factors to provide fresh insights into the inequality situation in the USA relative to those of the extant literature. The chapter employs polynomial trends for the effects of social factors to identify long-term trends in the determinants of the differences in attainment of each of four achievements (high school graduation, some college, college graduation, and post-college work) across different endogenous social groups. Using the Panel Study of Income Dynamics (PSID) data for the years of 1968-2013, we show how inequality of educational opportunity and its determinants have evolved over the years. The chapter utilizes the machine-learning process and logistic regression model to identify inequality of opportunity.
The second chapter examines the age demographic distribution of graduates across cohorts from 1940 until 1990. Using the PSID data, the paper explored the first and second moment of the age of graduating from high school and college across the US. To deal with the data deficiencies, a large part of the chapter dealt with data preparation. The chapter provides a unique method of extracting information on the graduating age of the individuals both from high school and from college. The results show a large dispersion across the full sample. The data truncated to a standard length, however, provides a much smaller dispersion and much smaller moments. The chapter concludes that as the time passes, people tend to attain education at a younger age.
The third chapter investigates the trends of the contribution of different factors of income starting from 1910 cohort. Following Mincer (1974), a wave of papers studied how various factors contribute to the earnings of individuals. This paper contributes to that literature in three ways: (i) using the PSID data, it computes the actual working experience of the individuals, (ii) it studies the cohorts who were born in 1910 or afterwards, unlike the existing papers, and (iii) it adds two variables�"technological progress and the occupation with which individuals start their careers�"to an extended Mincerian equation. The results re-emphasize the importance of education in lifetime earnings. The results also show that while some of the determinants of income have become more important over the years, other factors have not changed much in importance.PHD
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Olofsson, Nina, and Nivin Fakih. "A Machine Learning Approach to Dialogue Act Classification in Human-Robot Conversations : Evaluation of dialogue act classification with the robot Furhat and an analysis of the market for social robots used for education." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175705.
Full textAbstract:
The interest in social robots has grown dramatically in the last decade. Several studies have investigated the potential markets for such robots and how to enhance their human-like abilities. Both of these subjects have been investigated in this thesis using the company Furhat Robotics, and their robot Furhat, as a case study. This paper explores how machine learning could be used to classify dialogue acts in human-robot conversations, which could help Furhat interact in a more human-like way. Dialogue acts are acts of natural speech, such as questions or statements. Several variables and their impact on the classification of dialogue acts were tested. The results showed that a combination of some of these variables could classify 73 % of all the dialogue acts correctly. Furthermore, this paper analyzes the market for social robots which are used for education, where human-like abilities are preferable. A literature study and an interview were conducted. The market was then analyzed using a SWOT-matrix and Porter’s Five Forces. Although the study showed that the mentioned market could be a suitable target for Furhat Robotics, there are several threats and obstacles that should be taken into account before entering the market.Intresset för sociala robotar har ökat drastiskt under det senaste årtiondet. Ett flertal studier har undersökt hur man kan förbättra robotars mänskliga färdigheter. Vidare har studier undersökt potentiella marknader för sådana robotar. Båda dessa aspekter har studerats i denna rapport med företaget Furhat Robotics, och deras robot Furhat, som en fallstudie. Mer specifikt undersöker denna rapport hur maskininlärning kan användas för att klassificera talhandlingar i människa-robot- konversationer, vilket skulle kunna hjälpa Furhat att interagera på ett mer mänskligt sätt. Talhandlingar är indelningar av naturligt språk i olika handlingar, såsom frågor och påståenden. Flertalet variabler och deras inverkan på klassificeringen av talhandlingar testades i studien. Resultatet visade att en kombination av några av dessa variabler kunde klassificera 73 % av alla talhandlingar korrekt. Vidare analyserar denna rapport marknaden för sociala robotar inom utbildning, där mänskliga färdigheter är att föredra. En litteraturstudie och en intervju gjordes. Marknaden analyserades sedan med hjälp av en SWOT-matris och Porters femkraftsmodell. Fastän studien visade att den ovannämnda marknaden skulle kunna vara lämplig för Furhat Robotics finns ett flertal hot och hinder som företaget måste ta hänsyn till innan de tar sig in på marknaden.
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Lagerqvist, Gustaf, and Anton Stålhandske. "Recommendation systems for recruitment within an educational context." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-42902.
Full textAbstract:
Alongside the evolution of the recruitment process, different types of recommendation systems have been developed. The purpose of this study is to investigate recommendation systems within educational contexts, successful implementations of recommendation system architecture patterns, and alternatives to previous experience when evaluating candidates. The study is conducted through two separate methods; A literature review with a qualitative approach and design science research methodology focused on design and development, demonstration and evaluation. The literature review shows that, for recommendation systems, a layered architecture built within a microservice ecosystem is successfully utilized and has multiple beneficial aspects such as improved scalability, maintainability and security. Through design science research methodology, this study shows a suggested approach to implementing a layered architecture in combination with KNN and hybrid filtering. To avoid the lapse of suitable candidates, caused by demanding previous experience, this study shows an alternative approach to recruitment, within an educational context, through the use of soft skills. Within the study, this approach is successfully used to evaluate and compare students, but the same approach could possibly be applied to evaluate and compare companies. Moving forward, this study could be further expanded by looking into possible biases arising as a result of using AI and choices made during this study, as well as weighting of student-attributes.
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Ferreira, Lucas Daniel. "Técnicas de aprendizado de máquina aplicadas à classificação de estudantes a partir de estilos de aprendizagem." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-24102018-151910/.
Full textAbstract:
Com efeito, diversos estudos nas áreas de psicologia cognitiva e pedagogia apontam que cada indivíduo possui diferentes maneiras de captar, processar, analisar e organizar informações durante o processo de aprendizado, o que fundamenta o conceito de Estilos de Aprendizagem (EA). Em vista disso, diversos sistemas educacionais adaptativos foram propostos com o intuito de proporcionar conteúdo personalizado em seus cursos. Porém, em boa parte dos casos, estes sistemas fazem uso de questionários para identificar os estilos de aprendizagem, e este método pode mostrar-se inviável em algumas situações. Isso ocorre pois o preenchimento dos questionários demanda um esforço adicional por parte do aluno, além fazer uma avaliação estática dos EA, desconsiderando possíveis variações em suas preferências ao longo do tempo. Supõe-se que uma estratégia de detecção automática e dinâmica dos EA baseada no comportamento dos estudantes pode ser mais proveitosa neste sentido, pois é isenta destas limitações. Deste modo, a proposta neste trabalho é investigar diferentes técnicas relacionadas ao aprendizado de máquina (especialmente algoritmos de classificação) aplicadas à predição automática dos estilos de aprendizagem de estudantes, a partir de suas interações em um ambiente virtual de ensino. Dentre os inúmeros modelos de EA propostos na literatura, optou-se por usar o modelo de Felder-Silverman como base para os experimentos. Como estudo de caso, foram rastreadas as interações de 105 estudantes de um curso de pós-graduação em fonoaudiologia ministrado integralmente pelo sistema Moodle. Além disso, estes alunos foram solicitados a responder ao questionário ILS, o qual indica a preferência de cada indivíduo de acordo com o modelo de Felder-Silverman. Para a construção dos conjuntos de dados, foram coletadas informações como a quantidade de visitas, tempo gasto e interação dos usuários em cada tipo de recurso (recursos de vídeo, formulários de avaliação, fórum, etc.). Estes conjuntos de dados no formato atributo-valor serviram de entrada para quatro algoritmos de classificação: Naïve Bayes, aprendizado baseado em instâncias (kNN), Redes Neurais Artificiais (MultiLayer Perceptron) e Árvores de Decisão (J48), combinados com métodos de seleção de atributos e executados em validação cruzada. Para fins de experimentação, foram avaliadas as taxas de acertos e erros dos algoritmos em relação aos resultados apontados pelo questionário ILS, em cada umas das dimensões do modelo de Felder-Silverman. Os resultados apontaram para o uso de mais de um classificador - Naïve Bayes e aprendizagem baseada em instância - dependendo da dimensão do estilo de aprendizagem. A metodologia aplicada foi comparada com sete trabalhos correlatos da literatura; Os resultados demonstraram uma performance superior aos trabalhos anteriores em quase todas as dimensões. Portanto, o presente trabalho contribui para o contexto da informática aplicada à educação, especificamente no que diz respeito à implementação de sistemas educacionais adaptativos, com base em uma análise comparativa entre diferentes técnicas aplicadas ao mesmo problema. Sendo assim, as conclusões obtidas devem colaborar para o aprimoramento do processo de modelagem de estudantes. Além disso, são levantadas discussões a respeito dos resultados, que podem auxiliar na direção de futuros trabalhos da área.In fact, several studies in the areas of cognitive psychology and pedagogy point out that each individual has different ways of capturing, processing, analyzing and organizing information during the learning process, which supports the concept of Learning Styles (LS). Therefore, several adaptive educational systems were proposed with the aim of providing personalized content in their courses. However, in most cases, these systems use questionnaires to identify learning styles, and this method may prove unfeasible in some situations. This is because filling in the questionnaires requires an additional effort on the part of the student, besides, this approach makes a static evaluation of the LS, disregarding possible variations in their preferences over time. It is assumed that an automatic and dynamic detection of LS based on student behavior may be more useful in this sense, since it is exempt from these limitations. In this way, the proposal in this work is to investigate different techniques related to machine learning (especially classification algorithms) applied to the automatic prediction of student learning styles, based on their interactions in a virtual teaching environment. Among the many LS models proposed in the literature, we chose to use the Felder-Silverman model (FSLSM). As a case study, the interactions of 105 students from a post-graduate course in speech therapy were studied. In addition, these students were asked to respond to the ILS questionnaire, which indicates the preference of each individual according to FSLSM. In order to construct the data sets, information was collected such as the number of visits, time spent and user interaction in each type of resource (video resources, evaluation forms, forum, etc.). These data sets in the attribute-value format served as input to four classification algorithms: Naïve Bayes, instance-based learning (kNN), MultiLayer Perceptron and Decision Trees (J48), combined with attribute selection methods and executed in cross-validation. For the experimentation, the accuracy and error rates of the algorithms were evaluated in relation to the results indicated by the ILS questionnaire, in each one of FSLSM dimensions. Our results pointed out to the use of more than one classifier, Naïve Bayes and Instance-based Learning, depending on the learning style dimension. We compared our methodology to seven works of the literature; the results demonstrated a performance superior to the previous works in almost every dimension. The present work contributes to the context of informatics applied to education, specifically with regard to the implementation of adaptive educational systems, based on a comparative analysis of different methods applied to the same problem. Therefore, the conclusions obtained should contribute to the improvement of the student modeling process. In addition, discussions are held regarding the results, which may assist in the direction of future work in this area.
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Silvestre, Cerdà Joan Albert. "Different Contributions to Cost-Effective Transcription and Translation of Video Lectures." Doctoral thesis, Universitat Politècnica de València, 2016. http://hdl.handle.net/10251/62194.
Full textAbstract:
[EN] In recent years, on-line multimedia repositories have experiencied a strong
growth that have made them consolidated as essential knowledge assets, especially
in the area of education, where large repositories of video lectures have been
built in order to complement or even replace traditional teaching methods.
However, most of these video lectures are neither transcribed nor translated
due to a lack of cost-effective solutions to do so in a way that gives accurate
enough results. Solutions of this kind are clearly necessary in order to make
these lectures accessible to speakers of different languages and to people with
hearing disabilities. They would also facilitate lecture searchability and
analysis functions, such as classification, recommendation or plagiarism
detection, as well as the development of advanced educational functionalities
like content summarisation to assist student note-taking.
For this reason, the main aim of this thesis is to develop a cost-effective
solution capable of transcribing and translating video lectures to a reasonable
degree of accuracy. More specifically, we address the integration of
state-of-the-art techniques in Automatic Speech Recognition and Machine
Translation into large video lecture repositories to generate high-quality
multilingual video subtitles without human intervention and at a reduced
computational cost. Also, we explore the potential benefits of the exploitation
of the information that we know a priori about these repositories, that is,
lecture-specific knowledge such as speaker, topic or slides, to create
specialised, in-domain transcription and translation systems by means of
massive adaptation techniques.
The proposed solutions have been tested in real-life scenarios by carrying out
several objective and subjective evaluations, obtaining very positive results.
The main outcome derived from this thesis, The transLectures-UPV
Platform, has been publicly released as an open-source software, and, at the
time of writing, it is serving automatic transcriptions and translations for
several thousands of video lectures in many Spanish and European
universities and institutions.[ES] Durante estos últimos años, los repositorios multimedia on-line han experimentado un gran crecimiento que les ha hecho establecerse como fuentes fundamentales de conocimiento, especialmente en el área de la educación, donde se han creado grandes repositorios de vídeo charlas educativas para complementar e incluso reemplazar los métodos de enseñanza tradicionales. No obstante, la mayoría de estas charlas no están transcritas ni traducidas debido a la ausencia de soluciones de bajo coste que sean capaces de hacerlo garantizando una calidad mínima aceptable. Soluciones de este tipo son claramente necesarias para hacer que las vídeo charlas sean más accesibles para hablantes de otras lenguas o para personas con discapacidades auditivas. Además, dichas soluciones podrían facilitar la aplicación de funciones de búsqueda y de análisis tales como clasificación, recomendación o detección de plagios, así como el desarrollo de funcionalidades educativas avanzadas, como por ejemplo la generación de resúmenes automáticos de contenidos para ayudar al estudiante a tomar apuntes. Por este motivo, el principal objetivo de esta tesis es desarrollar una solución de bajo coste capaz de transcribir y traducir vídeo charlas con un nivel de calidad razonable. Más específicamente, abordamos la integración de técnicas estado del arte de Reconocimiento del Habla Automático y Traducción Automática en grandes repositorios de vídeo charlas educativas para la generación de subtítulos multilingües de alta calidad sin requerir intervención humana y con un reducido coste computacional. Además, también exploramos los beneficios potenciales que conllevaría la explotación de la información de la que disponemos a priori sobre estos repositorios, es decir, conocimientos específicos sobre las charlas tales como el locutor, la temática o las transparencias, para crear sistemas de transcripción y traducción especializados mediante técnicas de adaptación masiva. Las soluciones propuestas en esta tesis han sido testeadas en escenarios reales llevando a cabo nombrosas evaluaciones objetivas y subjetivas, obteniendo muy buenos resultados. El principal legado de esta tesis, The transLectures-UPV Platform, ha sido liberado públicamente como software de código abierto, y, en el momento de escribir estas líneas, está sirviendo transcripciones y traducciones automáticas para diversos miles de vídeo charlas educativas en nombrosas universidades e instituciones Españolas y Europeas.
[CAT] Durant aquests darrers anys, els repositoris multimèdia on-line han experimentat un gran creixement que els ha fet consolidar-se com a fonts fonamentals de coneixement, especialment a l'àrea de l'educació, on s'han creat grans repositoris de vídeo xarrades educatives per tal de complementar o inclús reemplaçar els mètodes d'ensenyament tradicionals. No obstant això, la majoria d'aquestes xarrades no estan transcrites ni traduïdes degut a l'absència de solucions de baix cost capaces de fer-ho garantint una qualitat mínima acceptable. Solucions d'aquest tipus són clarament necessàries per a fer que les vídeo xarres siguen més accessibles per a parlants d'altres llengües o per a persones amb discapacitats auditives. A més, aquestes solucions podrien facilitar l'aplicació de funcions de cerca i d'anàlisi tals com classificació, recomanació o detecció de plagis, així com el desenvolupament de funcionalitats educatives avançades, com per exemple la generació de resums automàtics de continguts per ajudar a l'estudiant a prendre anotacions. Per aquest motiu, el principal objectiu d'aquesta tesi és desenvolupar una solució de baix cost capaç de transcriure i traduir vídeo xarrades amb un nivell de qualitat raonable. Més específicament, abordem la integració de tècniques estat de l'art de Reconeixement de la Parla Automàtic i Traducció Automàtica en grans repositoris de vídeo xarrades educatives per a la generació de subtítols multilingües d'alta qualitat sense requerir intervenció humana i amb un reduït cost computacional. A més, també explorem els beneficis potencials que comportaria l'explotació de la informació de la que disposem a priori sobre aquests repositoris, és a dir, coneixements específics sobre les xarrades tals com el locutor, la temàtica o les transparències, per a crear sistemes de transcripció i traducció especialitzats mitjançant tècniques d'adaptació massiva. Les solucions proposades en aquesta tesi han estat testejades en escenaris reals duent a terme nombroses avaluacions objectives i subjectives, obtenint molt bons resultats. El principal llegat d'aquesta tesi, The transLectures-UPV Platform, ha sigut alliberat públicament com a programari de codi obert, i, en el moment d'escriure aquestes línies, està servint transcripcions i traduccions automàtiques per a diversos milers de vídeo xarrades educatives en nombroses universitats i institucions Espanyoles i Europees.
Silvestre Cerdà, JA. (2016). Different Contributions to Cost-Effective Transcription and Translation of Video Lectures [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/62194
TESIS
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Andersson, Viktor. "Machine Learning in Logistics: Machine Learning Algorithms : Data Preprocessing and Machine Learning Algorithms." Thesis, Luleå tekniska universitet, Datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-64721.
Full textAbstract:
Data Ductus is a Swedish IT-consultant company, their customer base ranging from small startups to large scale cooperations. The company has steadily grown since the 80s and has established offices in both Sweden and the US. With the help of machine learning, this project will present a possible solution to the errors caused by the human factor in the logistic business.A way of preprocessing data before applying it to a machine learning algorithm, as well as a couple of algorithms to use will be presented.Data Ductus är ett svenskt IT-konsultbolag, deras kundbas sträcker sig från små startups till stora redan etablerade företag. Företaget har stadigt växt sedan 80-talet och har etablerat kontor både i Sverige och i USA. Med hjälp av maskininlärning kommer detta projket att presentera en möjlig lösning på de fel som kan uppstå inom logistikverksamheten, orsakade av den mänskliga faktorn.Ett sätt att förbehandla data innan den tillämpas på en maskininlärning algoritm, liksom ett par algoritmer för användning kommer att presenteras.
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Doudagiri, Vivek Reddy. "Extracting Temporally-Anchored Knowledge from Tweets." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1157588/.
Full textAbstract:
Twitter has quickly become one of the most popular social media sites. It has 313 million monthly active users, and 500 million tweets are published daily. With the massive number of tweets, Twitter users share information about a location along with the temporal awareness. In this work, I focus on tweets where author of the tweets exclusively mentions a location in the tweet. Natural language processing systems can leverage wide range of information from the tweets to build applications like recommender systems that predict the location of the author. This kind of system can be used to increase the visibility of the targeted audience and can also provide recommendations interesting places to visit, hotels to stay, restaurants to eat, targeted on-line advertising, and co-traveler matching based on the temporal information extracted from a tweet. In this work I determine if the author of the tweet is present in the mentioned location of the tweet. I also determine if the author is present in the location before tweeting, while tweeting, or after tweeting. I introduce 5 temporal tags (before the tweet but > 24 hours; before the tweet but < 24 hours; during the tweet is posted; after the tweet is posted but < 24 hours; and after the tweet is posted but > 24 hours). The major contributions of this paper are: (1) creation of a corpus of 1062 tweets containing 1200 location named entities, containing annotations whether author of a tweet is or is not located in the location he tweets about with respect to 5 temporal tags; (2) detailed corpus analysis including real annotation examples and label distributions per temporal tag; (3) detailed inter-annotator agreements, including Cohen's kappa, Krippendorff's alpha and confusion matrices per temporal tag; (4) label distributions and analysis; and (5) supervised learning experiments, along with the results.
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Marulcu, Ismail. "Investigating the impact of a LEGO-based, engineering-oriented curriculum compared to an inquiry-based curriculum on fifth graders' content learning of simple machines." Thesis, Boston College, 2010. http://hdl.handle.net/2345/1532.
Full textAbstract:
Thesis advisor: Michael BarnettThis mixed method study examined the impact of a LEGOTM-based, engineering-oriented curriculum compared to an inquiry-based curriculum on fifth graders' content learning of simple machines. This study takes a social constructivist theoretical stance that science learning involves learning scientific concepts and their relations to each other. From this perspective, students are active participants, and they construct their conceptual understanding through the guidance of their teacher. With the goal of better understanding the use of engineering education materials in classrooms the National Academy of Engineering and National Research Council in the book "Engineering in K-12 Education" conducted an in-depth review of the potential benefits of including engineering in K-12 schools as (a) improved learning and achievement in science and mathematics, (b) increased awareness of engineering and the work of engineers, (c) understanding of and the ability to engage in engineering design, (d) interest in pursuing engineering as a career, and (e) increased technological literacy (Katehi, Pearson, & Feder, 2009). However, they also noted a lack of reliable data and rigorous research to support these assertions. Data sources included identical written tests and interviews, classroom observations and videos, teacher interviews, and classroom artifacts. To investigate the impact of the design-based simple machines curriculum compared to the scientific inquiry-based simple machines curriculum on student learning outcomes, I compared the control and the experimental groups' scores on the tests and interviews by using ANCOVA. To analyze and characterize the classroom observation videotapes, I used Jordan and Henderson's (1995) method and divide them into episodes. My analyses revealed that the design-based Design a People Mover: Simple Machines unit was, if not better, as successful as the inquiry-based FOSS Levers and Pulleys unit in terms of students' content learning. I also found that students in the engineering group outperformed students in the control group in regards to their ability to answer open-ended questions when interviewed. Implications for students' science content learning and teachers' professional development are discussed
Thesis (PhD) — Boston College, 2010
Submitted to: Boston College. Lynch School of Education
Discipline: Teacher Education, Special Education, Curriculum and Instruction
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Collazo, Santiago Bryan Omar. "Machine learning blocks." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100301.
Full textAbstract:
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references.
This work presents MLBlocks, a machine learning system that lets data scientists explore the space of modeling techniques in a very easy and efficient manner. We show how the system is very general in the sense that virtually any problem and dataset can be casted to use MLBlocks, and how it supports the exploration of Discriminative Modeling, Generative Modeling and the use of synthetic features to boost performance. MLBlocks is highly parameterizable, and some of its powerful features include the ease of formulating lead and lag experiments for time series data, its simple interface for automation, and its extensibility to additional modeling techniques. We show how we used MLBlocks to quickly get results for two very different realworld data science problems. In the first, we used time series data from Massive Open Online Courses to cast many lead and lag formulations of predicting student dropout. In the second, we used MLBlocks' Discriminative Modeling functionality to find the best-performing model for predicting the destination of a car given its past trajectories. This later functionality is self-optimizing and will find the best model by exploring a space of 11 classification algorithms with a combination of Multi-Armed Bandit strategies and Gaussian Process optimizations, all in a distributed fashion in the cloud.
by Bryan Omar Collazo Santiago.
M. Eng.
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Ye, Liang. "A machine learning approach to fundraising success in higher education." Thesis, 2017. http://hdl.handle.net/1828/8028.
Full textAbstract:
New donor acquisition and current donor promotion are the two major programs in fundraising for higher education, and developing proper targeting strategies plays an important role in the both programs. This thesis presents machine learning solutions as targeting strategies for the both programs based on readily available alumni data in almost any institution. The targeting strategy for new donor acquisition is modeled as a donor identification problem. The Gaussian na ̈ıve bayes, random forest, and support vector machine algorithms are used and evaluated. The test results show that having been trained with enough samples, all three algorithms can distinguish donors from rejectors well, and big donors are identified more often than others.While there is a trade off between the cost of soliciting candidates and the success of donor acquisition, the results show that in a practical scenario where the models are properly used as the targeting strategy, more than 85% of new donors and more than 90% of new big donors can be acquired when only 40% of the candidates are solicited. The targeting strategy for donor promotion is modeled as a promising donor(i.e., those who will upgrade their pledge) prediction problem in machine learning.The Gaussian na ̈ıve bayes, random forest, and support vector machine algorithms are tested. The test results show that all the three algorithms can distinguish promising donors from non-promising donors (i.e., those who will not upgrade their pledge).When the age information is known, the best model produces an overall accuracy of 97% in the test set. The results show that in a practical scenario where the models are properly used as the targeting strategy, more than 85% of promising donors can be acquired when only 26% candidates are solicited.Graduate
liangye714@gmail.com
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(11197713), Nesibe Karakis. "PREDICTORS OF EARLY POSTSECONDARY STEM PERSISTENCE OF HIGH-ACHIEVING STUDENTS: AN EXPLANATORY STUDY USING MACHINE LEARNING TECHNIQUES." Thesis, 2021.
Find full textAbstract:
This study investigated high-achieving and non-high-achieving students’ persistence in STEM fields using nationally representative data from the High School Longitudinal Study of 2009 for the years 2009, 2012, 2013, 2013-2014, and 2016. The results indicated that approximately 70% of high-achieving and non-high-achieving students continued their initial STEM degrees within 3 years of college enrollment. The study revealed that the most important predictors of STEM persistence were: math proficiency level, school belonging, school engagement, school motivation, school problems, science self-efficacy, credits earned in computer sciences, GPA in STEM courses, credits earned in STEM courses, and credits earned in Advanced Placement/International Baccalaureate (AP/IB) courses. Based on the results, math proficiency was the most important variable in the study for both high-achieving and non-high-achieving students. Even though credits earned in AP/IB combined were among the most important variables, they were two times more important for high-achieving students (6.86% vs. 3.37%). Regarding demographic information related variables, socioeconomic status was the most important variable among gender, ethnicity, and urbanicity in models predicting STEM persistence and had higher importance for non-high-achieving students. Furthermore, Hispanic students' proportion of persistence differed from other underrepresented populations’ persistence. Non-high-achieving Hispanic students had the highest persistence rate, similar to well-represented populations (i.e., White, Asian). Machine learning methods used in the study including random forest and artificial neural network provided good accuracy for both achievement groups. Random forest accuracy was over 82% with the Synthetic Minority Over-Sampling Technique (SMOTE) dataset, while artificial neural network accuracy was over 92%.
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(11197911), Dylan James Imbus. "An Industrial-Grade Cyber-Physical Platform for Introducing Machine Learning Concepts." Thesis, 2021.
Find full textAbstract:
Industry
4.0 holds many promises for manufacturers; however, a shortage of qualified
employees has prevented a swift adoption of the revolution's new technologies.
Engineer and Economist Klaus Schwab argues Education 4.0 is the key to addressing
the employee shortage and preparing future generations for the shifting labor
market. To support Education 4.0, classes must allow students to engage emerging
technologies that help bridge Operational Technology (OT) and Informational
Technology (IT). The thesis detailed an educational laboratory that
demonstrates the application of data analytics (an IT tool) and optimize the
performance of a cyber-physical system composed of industrial (OT) components. The
lab experience focuses on a disc's controlled positioning (levitating) using a
PLC-based PID controller and a VFD. The activity requires students to capture
data of a moving discs, create a machine learning function representing the
disc's movement, and use the machine learning function for classification and
PID optimization problems. A comparative analysis of a PID cycle ensures a regressions
model accurately represents the physical model using measurements including
peak-overshoot, rise time, settling time, and the flight plots' Means of their
Squared Error. Further, the study examines multiple ML models each built using various
features to identify the systems relevant and redundant data.
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"Automatic Classification of Small Group Dynamics using Speech and Collaborative Writing." Doctoral diss., 2020. http://hdl.handle.net/2286/R.I.63021.
Full textAbstract:
abstract: Students seldom spontaneously collaborate with each other. A system that can measure collaboration in real time could be useful, for example, by helping the teacher locate a group requiring guidance. To address this challenge, the research presented here focuses on building and comparing collaboration detectors for different types of classroom problem solving activities, such as card sorting and handwriting.
Transfer learning using different representations was also studied with a goal of building collaboration detectors for one task can be used with a new task. Data for building such detectors were collected in the form of verbal interaction and user action logs from students’ tablets. Three qualitative levels of interactivity were distinguished: Collaboration, Cooperation and Asymmetric Contribution. Machine learning was used to induce a classifier that can assign a code for every episode based on the set of features. The results indicate that machine learned classifiers were reliable and can transfer.Dissertation/Thesis
Doctoral Dissertation Computer Science 2020
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Chen, Yi-Chang, and 陳怡璋. "Applying Cognitive Learning to Enhance XCS to Construct a Dual-Mode Learning Mechanism of Knowledge-Education and Machine-Learning— an Example of Knowledge Learning on Finance Prediction." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/30993626182353077173.
Full textAbstract:
博士國立交通大學
資訊管理研究所
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From 1956, the learning definitions of Artificial Intelligence and Psychology to human mind/behavior are obviously different. Owing to the rapid development of the computing power, we have potential to enhance the learning mechanism. This work tries to apply the learning process of the cognition structure defined in Cognitive Psychology to enhance or modify the development of AI, of which the learning models are almost based on trial and error style. However, this kind of learning style is definably given to the experience behavior of stimulus and response in Psychology. Thus, the relative AI models based on such style are design as an experience-adaptation system. For better ones, e.g. evolution-base algorithms, they belonged to the system with more powerful computing power to the dynamical environment. Even so, it was considered not only outside environment but also internal parameter tuning. As for the entire learning process, it has never been enhanced. That is, various original AI models are easily to be developed to their own close-form problem. To the unclose-form problems, their distinct results only come from huge amounts of experiments and tuning their model’s parameters. As the result, it is not easy to make clear for the explanation to why or how. The desirable cognitive learning of cognitive psychology is the development that has started since 1986. The relative literatures have pointed out that teaching-base education would increase the learning efficiency, but trial and error style is not sufficient to learning. That is the reason we enhance the AI learning process to develop a dual-perspective learning mechanism. Furthermore, since XCS is a better accuracy model of AI, we have applied it as a basement and involve the enhanced model proposed to develop an intelligence-learning model. Finally, this work is designed a test of the more complex problem, which is constructing a finance prediction knowledge model. By comparing to the accuracy and accumulative profit of XCS, R-R XCS and E&R-R XCS respectively, the results obtain the obvious outcome. That is, the proposed learning framework has enhanced the original mechanism.
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Afonso, Ana Beatriz Antunes. "Assessing the impact of academic achievement determinants using Machine Learning methods: evidence from a European country." Master's thesis, 2022. http://hdl.handle.net/10362/132859.
Full textAbstract:
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsAcademic achievement has been of great interest for researchers since the 1950s, although only recently have data science methods started to be applied more systematically. This paper uses the mathematics and Portuguese national exams of the population in Portugal in the 2018/2019 academic year to evaluate the performance between methods and compare what factors affect the results of these exams differently. Furthermore, a new approach is presented to deal with the "black-box" dilemma by creating a set of prototypes with a simple statistical approach applied through Neural Networks, providing an approximation of how much a variable impacts a grade.
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(11237160), Abel Andres Reyes Angulo. "EXPLORATION OF NOVEL EDUCATIONAL TOOLS BASED ON VISUALIZATION." Thesis, 2021.
Find full textAbstract:
The dynamic on how teaching is performed has changed abruptly in the past few years. Even before the COVID-19 pandemic, class modalities were changing. Instructors were adopting new modalities for lectures, like online and hybrid classes, and the use of collaborative resources were getting more popular over time. The current situation was just a catalyst of an event that was already started, which is the beginning of a new era for education.
This new education era implies new areas of study and the implementation of tools that promote an efficient learning process by adapting to everything involved in this change. Sciences, technology, engineering, and mathematics education (STEM) and healthcare fields are areas with noticeable demand for professionals in industry around the world. Therefore, the need to have more people academically prepared in these areas is highly prioritized. New tools to be used for learning to complement the mentioned field must show features related to the adoption of new technologies as well as the fact that this is currently a digital era. Emergent specialities like artificial intelligence and data science are traditionally being taught at the university level, due to the complexity of some concepts and the background needed to develop skills related to these areas. However, with the current technology available, tools can be used as complementary learning resources for complex subjects. Visualization helps the users to learn by sharpening the sense of sight and making evident things that are hard to illustrate by words or numbers. Therefore, the use of software for education based on visualization could be the new tools needed for these emergent specialities aligned to this new educational era. Features like intractability, gaming, and multimedia resources can help to make these tools more robust and completed.
In this work, the implementations of novel educational tools based on visualization for emergent specialization areas like machine learning in STEM and pathophysiology in heathcare were explored. This work summarizes the implementation of three different projects to illustrate the general purpose of this work, showing the relevance of the mentioned areas and proposes educational tools based on visualization, adapting the proposal for each speciality and having in mind different target populations. The projects related to each of the proposed tools includes the analysis to elaborate the content within the tool, the review of the software development, and the testing sessions to identify strengths and weaknesses of the tools. The tools are intended to be designed as frameworks in such a way that the deliverable content could be customized over the time and cover different educational needs.
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"VIPLE Extensions in Robotic Simulation, Quadrotor Control Platform, and Machine Learning for Multirotor Activity Recognition." Master's thesis, 2018. http://hdl.handle.net/2286/R.I.51679.
Full textAbstract:
abstract: Machine learning tutorials often employ an application and runtime specific solution for a given problem in which users are expected to have a broad understanding of data analysis and software programming. This thesis focuses on designing and implementing a new, hands-on approach to teaching machine learning by streamlining the process of generating Inertial Movement Unit (IMU) data from multirotor flight sessions, training a linear classifier, and applying said classifier to solve Multi-rotor Activity Recognition (MAR) problems in an online lab setting. MAR labs leverage cloud computing and data storage technologies to host a versatile environment capable of logging, orchestrating, and visualizing the solution for an MAR problem through a user interface. MAR labs extends Arizona State University’s Visual IoT/Robotics Programming Language Environment (VIPLE) as a control platform for multi-rotors used in data collection. VIPLE is a platform developed for teaching computational thinking, visual programming, Internet of Things (IoT) and robotics application development. As a part of this education platform, this work also develops a 3D simulator capable of simulating the programmable behaviors of a robot within a maze environment and builds a physical quadrotor for use in MAR lab experiments.Dissertation/Thesis
Masters Thesis Computer Science 2018
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Stern, Mia Keryn. "Using adaptive hypermedia and machine learning to create intelligent Web -based courses." 2001. https://scholarworks.umass.edu/dissertations/AAI3027261.
Full textAbstract:
This work focuses on Web-based intelligent instructional systems and research issues associated with the development of student modeling in an adaptive hypermedia system. The framework is iMANIC (intelligent Multimedia Asynchronous Networked Individualized Courseware), in which courses originating from existing video-taped lectures provide an initial set of slides, audio, and class notes. However, the existing course structure is initially linear, which, though usable, is not optimal for a WWW presentation. Web courses are used asynchronously and thus can provide a more individualized and interactive learning experience than can live courses. Therefore, we investigate ways in which personalized instruction can be delivered via the WWW. The domain organization used in iMANIC supports a non-linear, individualized course. However, once we introduce a non-linear topic structure, the “lost in hyperspace” problem might arise, in which students become confused about what to study next and how to remember where they have been. To combat these problems, adaptive navigation techniques are used to help guide the student through the course material. The original class material is presented so that each student sees the same content. This does not take into account learning differences of individual learners. However, iMANIC can consider those differences and adapt the information presented to each user. This adaptive content is achieved through a two phase approach which considers the user's level of understanding and the content that matches the user's preferences. A Naïve Bayes Classifier is used to learn the student's preferences by observing what type of content he chooses to see. An empirical study of the iMANIC system was conducted during 2000/2001 with 24 students learning Unix Network Programming. Results from this study show distinct differences in students' learning styles and provide evidence that using the same teaching strategies for each student cannot adequately support all students. This is demonstrated through two examples. The first shows that there is not a consistent direction for the correlation between time spent studying and quiz performance. The second shows that using the same parameters for the Naïve Bayes Classifier for every student results in poor overall performance of the classifier.
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Mirzaeibonehkhater, Marzieh. "Developing a dynamic recommendation system for personalizing educational content within an E-learning network." Thesis, 2018. https://doi.org/10.7912/C2KD30.
Full textAbstract:
Indiana University-Purdue University Indianapolis (IUPUI)This research proposed a dynamic recommendation system for a social learning environment entitled CourseNetworking (CN). The CN provides an opportunity for the users to satisfy their academic requirement in which they receive the most relevant and updated content. In our research, we extracted some implicit and explicit features from the system, which are the most relevant user feature and posts features. The selected features are used to make a rating scale between users and posts so that represent the link between user and post in this learning management system (LMS). We developed an algorithm which measures the link between each user and post for the individual. To achieve our goal in our system design, we applied natural language processing technique (NLP) for text analysis and applied various classi cation technique with the aim of feature selection. We believe that considering the content of the posts in learning environments as an impactful feature will greatly affect to the performance of our system. Our experimental results demonstrated that our recommender system predicts the most informative and relevant posts to the users. Our system design addressed the sparsity and cold-start problems, which are the two main challenging issues in recommender systems.
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"Effects of the Presence of Audio and Type of Game Controller on Learning of Rhythmic Accuracy." Doctoral diss., 2017. http://hdl.handle.net/2286/R.I.46362.
Full textAbstract:
abstract: Guitar Hero III and similar games potentially offer a vehicle for improvement of musical rhythmic accuracy with training delivered in both visual and auditory formats and by use of its novel guitar-shaped interface; however, some theories regarding multimedia learning suggest sound is a possible source of extraneous cognitive load while playing so players may score higher with sound turned off. Also, existing studies have shown that differences in the physical format of interfaces affect learning outcomes. This study sought to determine whether (a) the game’s audio content affects rhythmic accuracy, and (b) the type of game controller used affects learning of rhythmic accuracy. One hundred participants were randomly assigned in approximately equal numbers (ns = 25) to the four cells of a 2x2 between-subjects design. The first variable was the audio content of the game with two levels: on or off. The second variable was the type of game controller: the standard guitar-style controller or tablet interface. Participants across all conditions completed a pre- and post-test with a system that required them to tap along with repeated rhythmic patterns on an electronic drum pad. Statistical evidence showed better outcomes with a tablet controller with respect to input time error, reduction of extra notes played, and reduction of missed notes; however, the guitar-style controller produced superior outcomes in terms of avoiding missed notes and was associated with higher satisfaction by participants. When audio was present better outcomes were achieved at multiple factor-levels of reduction of missed responses, but superior outcomes in input time error were seen without audio. There was no evidence to suggest an interaction between controller type and the presence or absence of audio.Dissertation/Thesis
Doctoral Dissertation Educational Technology 2017
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"Spoken Dialogue In Face-to-Face And Remote Collaborative Learning Environments." Master's thesis, 2014. http://hdl.handle.net/2286/R.I.25915.
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abstract: Research in the learning sciences suggests that students learn better by collaborating with their peers than learning individually. Students working together as a group tend to generate new ideas more frequently and exhibit a higher level of reasoning. In this internet age with the advent of massive open online courses (MOOCs), students across the world are able to access and learn material remotely. This creates a need for tools that support distant or remote collaboration. In order to build such tools we need to understand the basic elements of remote collaboration and how it differs from traditional face-to-face collaboration.
The main goal of this thesis is to explore how spoken dialogue varies in face-to-face and remote collaborative learning settings. Speech data is collected from student participants solving mathematical problems collaboratively on a tablet. Spoken dialogue is analyzed based on conversational and acoustic features in both the settings. Looking for collaborative differences of transactivity and dialogue initiative, both settings are compared in detail using machine learning classification techniques based on acoustic and prosodic features of speech. Transactivity is defined as a joint construction of knowledge by peers. The main contributions of this thesis are: a speech corpus to analyze spoken dialogue in face-to-face and remote settings and an empirical analysis of conversation, collaboration, and speech prosody in both the settings. The results from the experiments show that amount of overlap is lower in remote dialogue than in the face-to-face setting. There is a significant difference in transactivity among strangers. My research benefits the computer-supported collaborative learning community by providing an analysis that can be used to build more efficient tools for supporting remote collaborative learning.Dissertation/Thesis
Masters Thesis Computer Science 2014
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Jacob, David Lázaro. "The main features that influence the academic success of bachelors’ students at Nova School of Business and Economics." Master's thesis, 2022. http://hdl.handle.net/10362/132927.
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Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe prediction of academic success is a major topic in higher education, especially among the academic community. In this dissertation, we are going to present a data mining approach taking into consideration the features that are the most relevant in terms of successful academic achievement of the Bachelors’ programs at Nova School of Business and Economics (Nova SBE). Initially, we are going to perform a literature review in order to understand the framework of academic success and also to make a summary of previous research on the field of educational data mining when used to assess student success. Subsequently, the empirical approach will start being developed with the extraction of socio-economic, socio-demographic, and academic data of students, which will result in our main dataset. Later, and after the data discovery, data cleansing, and transformation activities, a set of features are going to be taken into consideration according to their relevance for the subject. Based on the dataset containing these features, several predictive data-driven techniques are going to be applied, resulting in models which are going to be assessed in order to understand if the selected features are relevant enough to answer our problem or if there is a need to substitute them by other attributes. This process will result in several iterations that will confer credibility and robustness to the model that demonstrates the best performance in classifying students’ academic success. In the end, it is intended that the insights extracted from the model will provide the school key stakeholders with enough knowledge to capacitate them to take actions that will result in the maximization of the students learning success.
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(6630578), Yellamraju Tarun. "n-TARP: A Random Projection based Method for Supervised and Unsupervised Machine Learning in High-dimensions with Application to Educational Data Analysis." Thesis, 2019.
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Analyzing the structure of a dataset is a challenging problem in high-dimensions as the volume of the space increases at an exponential rate and typically, data becomes sparse in this high-dimensional space. This poses a significant challenge to machine learning methods which rely on exploiting structures underlying data to make meaningful inferences. This dissertation proposes the n-TARP method as a building block for high-dimensional data analysis, in both supervised and unsupervised scenarios.The basic element, n-TARP, consists of a random projection framework to transform high-dimensional data to one-dimensional data in a manner that yields point separations in the projected space. The point separation can be tuned to reflect classes in supervised scenarios and clusters in unsupervised scenarios. The n-TARP method finds linear separations in high-dimensional data. This basic unit can be used repeatedly to find a variety of structures. It can be arranged in a hierarchical structure like a tree, which increases the model complexity, flexibility and discriminating power. Feature space extensions combined with n-TARP can also be used to investigate non-linear separations in high-dimensional data.
The application of n-TARP to both supervised and unsupervised problems is investigated in this dissertation. In the supervised scenario, a sequence of n-TARP based classifiers with increasing complexity is considered. The point separations are measured by classification metrics like accuracy, Gini impurity or entropy. The performance of these classifiers on image classification tasks is studied. This study provides an interesting insight into the working of classification methods. The sequence of n-TARP classifiers yields benchmark curves that put in context the accuracy and complexity of other classification methods for a given dataset. The benchmark curves are parameterized by classification error and computational cost to define a benchmarking plane. This framework splits this plane into regions of "positive-gain" and "negative-gain" which provide context for the performance and effectiveness of other classification methods. The asymptotes of benchmark curves are shown to be optimal (i.e. at Bayes Error) in some cases (Theorem 2.5.2).
In the unsupervised scenario, the n-TARP method highlights the existence of many different clustering structures in a dataset. However, not all structures present are statistically meaningful. This issue is amplified when the dataset is small, as random events may yield sample sets that exhibit separations that are not present in the distribution of the data. Thus, statistical validation is an important step in data analysis, especially in high-dimensions. However, in order to statistically validate results, often an exponentially increasing number of data samples are required as the dimensions increase. The proposed n-TARP method circumvents this challenge by evaluating statistical significance in the one-dimensional space of data projections. The n-TARP framework also results in several different statistically valid instances of point separation into clusters, as opposed to a unique "best" separation, which leads to a distribution of clusters induced by the random projection process.
The distributions of clusters resulting from n-TARP are studied. This dissertation focuses on small sample high-dimensional problems. A large number of distinct clusters are found, which are statistically validated. The distribution of clusters is studied as the dimensionality of the problem evolves through the extension of the feature space using monomial terms of increasing degree in the original features, which corresponds to investigating non-linear point separations in the projection space.
A statistical framework is introduced to detect patterns of dependence between the clusters formed with the features (predictors) and a chosen outcome (response) in the data that is not used by the clustering method. This framework is designed to detect the existence of a relationship between the predictors and response. This framework can also serve as an alternative cluster validation tool.
The concepts and methods developed in this dissertation are applied to a real world data analysis problem in Engineering Education. Specifically, engineering students' Habits of Mind are analyzed. The data at hand is qualitative, in the form of text, equations and figures. To use the n-TARP based analysis method, the source data must be transformed into quantitative data (vectors). This is done by modeling it as a random process based on the theoretical framework defined by a rubric. Since the number of students is small, this problem falls into the small sample high-dimensions scenario. The n-TARP clustering method is used to find groups within this data in a statistically valid manner. The resulting clusters are analyzed in the context of education to determine what is represented by the identified clusters. The dependence of student performance indicators like the course grade on the clusters formed with n-TARP are studied in the pattern dependence framework, and the observed effect is statistically validated. The data obtained suggests the presence of a large variety of different patterns of Habits of Mind among students, many of which are associated with significant grade differences. In particular, the course grade is found to be dependent on at least two Habits of Mind: "computation and estimation" and "values and attitudes."