Дисертації з теми "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.
Повний текст джерелаHierdie 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
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.
Повний текст джерелаAr, 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.
Повний текст джерелаLindell, 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.
Повний текст джерелаGriffiths, 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.
Повний текст джерелаSrivastava, 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.
Повний текст джерелаZhu, 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.
Повний текст джерелаThesis: 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
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.
Повний текст джерелаMelsion, 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.
Повний текст джерелаMaskininlärningssystemen 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.
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.
Повний текст джерелаTeguim, 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.
Повний текст джерелаWood, Alicia Crowder. "Creating and Automatically Grading Annotated Questions." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/6099.
Повний текст джерелаMcKeague-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.
Повний текст джерелаCabrera, 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.
Повний текст джерелаEls 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.
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.
Повний текст джерелаTaking 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.
Goldstein, Adam B. "Responding to Moments of Learning." Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-theses/685.
Повний текст джерелаSwaminathan, Ranjini. "SPEECH AND LANGUAGE TECHNOLOGIES FOR SEMANTICALLY LINKED INSTRUCTIONAL CONTENT." Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/201498.
Повний текст джерелаRhees, Brooke Ellen. "A Semi-Automatic Grading Experience for Digital Ink Quizzes." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6245.
Повний текст джерелаFazenbaker, 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.
Повний текст джерелаGallego-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.
Повний текст джерелаWalonoski, Jason A. "Visual Feedback for Gaming Prevention in Intelligent Tutoring Systems." Digital WPI, 2006. https://digitalcommons.wpi.edu/etd-theses/23.
Повний текст джерелаPardos, Zachary Alexander. "Predictive Models of Student Learning." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-dissertations/185.
Повний текст джерелаOlsen, Grace. "Fundamental Work Toward an Image Processing-Empowered Dental Intelligent Educational System." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/2052.
Повний текст джерелаAl-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/.
Повний текст джерелаAlvarado, 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.
Повний текст джерелаBulgarov, 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/.
Повний текст джерелаOrliac, 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.
Повний текст джерелаArafat, 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.
Повний текст джерелаPHD
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.
Повний текст джерелаIntresset 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.
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.
Повний текст джерелаFerreira, 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/.
Повний текст джерелаIn 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.
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.
Повний текст джерела[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
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.
Повний текст джерелаData 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.
Doudagiri, Vivek Reddy. "Extracting Temporally-Anchored Knowledge from Tweets." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1157588/.
Повний текст джерелаMarulcu, 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.
Повний текст джерелаThis 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
Collazo, Santiago Bryan Omar. "Machine learning blocks." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100301.
Повний текст джерелаThis 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.
Ye, Liang. "A machine learning approach to fundraising success in higher education." Thesis, 2017. http://hdl.handle.net/1828/8028.
Повний текст джерелаGraduate
liangye714@gmail.com
(11197713), Nesibe Karakis. "PREDICTORS OF EARLY POSTSECONDARY STEM PERSISTENCE OF HIGH-ACHIEVING STUDENTS: AN EXPLANATORY STUDY USING MACHINE LEARNING TECHNIQUES." Thesis, 2021.
Знайти повний текст джерелаThis 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%.
(11197911), Dylan James Imbus. "An Industrial-Grade Cyber-Physical Platform for Introducing Machine Learning Concepts." Thesis, 2021.
Знайти повний текст джерела"Automatic Classification of Small Group Dynamics using Speech and Collaborative Writing." Doctoral diss., 2020. http://hdl.handle.net/2286/R.I.63021.
Повний текст джерелаDissertation/Thesis
Doctoral Dissertation Computer Science 2020
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.
Повний текст джерела國立交通大學
資訊管理研究所
93
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.
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.
Повний текст джерелаAcademic 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.
(11237160), Abel Andres Reyes Angulo. "EXPLORATION OF NOVEL EDUCATIONAL TOOLS BASED ON VISUALIZATION." Thesis, 2021.
Знайти повний текст джерела"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.
Повний текст джерелаDissertation/Thesis
Masters Thesis Computer Science 2018
Stern, Mia Keryn. "Using adaptive hypermedia and machine learning to create intelligent Web -based courses." 2001. https://scholarworks.umass.edu/dissertations/AAI3027261.
Повний текст джерелаMirzaeibonehkhater, Marzieh. "Developing a dynamic recommendation system for personalizing educational content within an E-learning network." Thesis, 2018. https://doi.org/10.7912/C2KD30.
Повний текст джерелаThis 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.
"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.
Повний текст джерелаDissertation/Thesis
Doctoral Dissertation Educational Technology 2017
"Spoken Dialogue In Face-to-Face And Remote Collaborative Learning Environments." Master's thesis, 2014. http://hdl.handle.net/2286/R.I.25915.
Повний текст джерелаDissertation/Thesis
Masters Thesis Computer Science 2014
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.
Повний текст джерелаThe 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.
(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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