Relevant bibliographies by topics / Machine learning in education

Academic literature on the topic 'Machine learning in education'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Machine learning in education"

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Kodelja, Zdenko. "Is Machine Learning Real Learning?" Center for Educational Policy Studies Journal 9, no. 3 (September 24, 2019): 11. http://dx.doi.org/10.26529/cepsj.709.

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The question of whether machine learning is real learning is ambiguous, because the term “real learning” can be understood in two different ways. Firstly, it can be understood as learning that actually exists and is, as such, opposed to something that only appears to be learning, or is misleadingly called learning despite being something else, something that is different from learning. Secondly, it can be understood as the highest form of human learning, which presupposes that an agent understands what is learned and acquires new knowledge as a justified true belief. As a result, there are also two opposite answers to the question of whether machine learning is real learning. Some experts in the field of machine learning, which is a subset of artificial intelligence, claim that machine learning is in fact learning and not something else, while some others – including philosophers – reject the claim that machine learning is real learning. For them, real learning means the highest form of human learning. The main purpose of this paper is to present and discuss, very briefly and in a simplifying manner, certain interpretations of human and machine learning, on the one hand, and the problem of real learning, on the other, in order to make it clearer that the answer to the question of whether machine learning is real learning depends on the definition of learning.
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Kim, Jihyun. "New Era of Education: Incorporating Machine Teachers into Education." Journal of Communication Pedagogy 4 (2021): 121–22. http://dx.doi.org/10.31446/jcp.2021.1.11.

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This editorial briefly discusses the potential of machine agents in education that can assist in creating more positive and meaningful teaching and learning environments. Then, it introduces three articles, two empirical research studies and one research-based instructional activity, compromising a special section on “Machine Teachers in Education” of Journal of Communication Pedagogy. Collectively, these articles help us better understand the role of machines in education and facilitate intellectual dialogues
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Lim, Daniel. "Philosophy through Machine Learning." Teaching Philosophy 43, no. 1 (2020): 29–46. http://dx.doi.org/10.5840/teachphil202018116.

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In a previous article (2019), I motivated and defended the idea of teaching philosophy through computer science. In this article, I will further develop this idea and discuss how machine learning can be used for pedagogical purposes because of its tight affinity with philosophical issues surrounding induction. To this end, I will discuss three areas of significant overlap: (i) good / bad data and David Hume’s so-called Problem of Induction, (ii) validation and accommodation vs. prediction in scientific theory selection and (iii) feature engineering and Nelson Goodman’s so-called New Riddle of Induction.
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Cui, Zhongmin. "Machine Learning and Small Data." Educational Measurement: Issues and Practice 40, no. 4 (November 25, 2021): 8–12. http://dx.doi.org/10.1111/emip.12472.

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Biswas, Rupayan, Richa Rashmi, and Upakarasamy Lourderaj. "Machine Learning in Chemical Dynamics." Resonance 25, no. 1 (January 2020): 59–75. http://dx.doi.org/10.1007/s12045-019-0922-1.

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Gómez-Pulido, Juan A., Young Park, Ricardo Soto, and José M. Lanza-Gutiérrez. "Data Analytics and Machine Learning in Education." Applied Sciences 13, no. 3 (January 20, 2023): 1418. http://dx.doi.org/10.3390/app13031418.

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The widespread application of information and communication technologies in education, especially in the context of learning management platforms, is generating a large amount of data related to the academic activities in which students and teachers participate. These data stand out not only for their quantity and heterogeneity, but also for their relationship with the behavior and performance of the educational actors. For this reason, these data must be properly stored, processed and analyzed, with the aim of extracting knowledge that can be highly useful for improving educational processes. For this purpose, this Special Issue aims to present cutting-edge research on the application of advanced data analysis and machine learning techniques in education [...]
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Hazzan, Orit, and Koby Mike. "Teaching core principles of machine learning with a simple machine learning algorithm." ACM Inroads 13, no. 1 (March 2022): 18–25. http://dx.doi.org/10.1145/3514217.

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Roy, Sayan, and Debanjan Rana. "Machine Learning in Nonlinear Dynamical Systems." Resonance 26, no. 7 (July 2021): 953–70. http://dx.doi.org/10.1007/s12045-021-1194-0.

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N. B. Sultangazina, M. A. Ermaganbetova, Zh. B. Akhayeva, and A. B. Zakirova. "Artificial intelligence and machine learning." Bulletin of Toraighyrov University. Physics & Mathematics series, no. 3.2021 (September 27, 2021): 24–33. http://dx.doi.org/10.48081/wcct7602.

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Artificial Intelligence (AI) is an area of ​​research driven by innovation and development that culminates in computers, machines with human-like intelligence characterized by cognitive ability, learnability, adaptability and decision-making ability. The study found that AI is widely adopted and used in education, especially by educational institutions, in various forms. This article reviewed articles by various scientists from different countries. The paper discusses the prospects for the application of artificial intelligence and machine learning technologies in education and in everyday life. The history of the development of artificial intelligence is described, technologies of machine learning and neural networks are analyzed. An overview of already implemented projects for the use of artificial intelligence is given, a forecast of the most promising, according to the authors, directions for the development of artificial intelligence technologies for the next period is given. This article provides an analysis of how educational research is being transformed into an experimental science. AI is combined with the study of science into new ‘digital laboratories’, in which ownership of data, as well as power and authority in the production of educational knowledge, are redistributed between research complexes of computers and scientific knowledge.
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An, Chang. "Student Status Supervision in Ideological and Political Machine Teaching Based on Machine Learning." E3S Web of Conferences 275 (2021): 03028. http://dx.doi.org/10.1051/e3sconf/202127503028.

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Under the premise of active in the field of machine learning, this paper takes online teaching system of ideological and Political education as an example to study machine learning and machine teaching system. In order to specifically understand the current situation of the construction and application of machine teaching based on supervised teaching of ideological and political theory courses in local colleges and universities, this experiment first conducted a statistical analysis of the learning results of the surveyed classes in two semesters from March 2020 to December 2020. The experimental data show that there is a positive interaction between teachers and students. Most students use the interactive communication mode of machines, while a small number of students use real-time interactive discussions with teachers. The experimental results show that the excellent rate of ABC classes in the first semester is 80%, 82% and 90%, respectively, through the machine-supervised teaching mode. Therefore, supervised machine learning can greatly help students improve their academic performance. In the future, we should further explore the application of other personalized and extensible machine learning methods in quality education.
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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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Books on the topic "Machine learning in education"

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Bauer, James J. The runaway learning machine: Growing up dyslexic. Minneapolis, MN: Educational Media Corp., 1992.

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Gábor, Lugosi, ed. Prediction, learning, and games. Cambridge: Cambridge University Press, 2006.

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Joan, Bliss, Säljö Roger 1948-, Light Paul, and European Association for Research on Learning and Instruction, eds. Learning sites: Social and technological resources for learning. Amsterdam: Pergamon, 1999.

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Paul, Reid. Learning about simple machines. Niagara Falls, N.Y: T4T Learning Materials, 1998.

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Web-based learning: Men & machines : proceedings of the first Interational Conference on Web-Based Learning in China, ICWL, 2002, Hong Kong, 17-19 August 2002. New Jersey: World Scientific, 2002.

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Christian), Stephan Frank (Frank, Vovk Vladimir 1960-, Zeugmann Thomas, and SpringerLink (Online service), eds. Algorithmic Learning Theory: 21st International Conference, ALT 2010, Canberra, Australia, October 6-8, 2010. Proceedings. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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Topolsky, Nikolay, and Valeriy Vilisov. Methods, models and algorithms in security systems: machine learning, robotics, insurance, risks, control. ru: Publishing Center RIOR, 2021. http://dx.doi.org/10.29039/02072-2.

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The monograph examines topical issues of decision support and management in safety systems for fire and emergency situations through the use of innovative approaches and tools for operations research, artificial intelligence, robotics and management methods in organizational systems. The monograph is intended for faculty, researchers, graduate students (adjuncts) and doctoral students, as well as for undergraduates, students and listeners of educational organizations, all those who are interested in the problems of decision support and management in security systems.
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1949-, Kennewell Steve, Parkinson John 1947-, and Tanner Howard 1951-, eds. Learning to teach ICT in the secondary school. London: RoutledgeFalmer, 2002.

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Polyakova, Anna, Tat'yana Sergeeva, and Irina Kitaeva. The continuous formation of the stochastic culture of schoolchildren in the context of the digital transformation of general education. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1876368.

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The material presented in the monograph shows the possibilities of continuous teaching of mathematics at school, namely, the significant potential of modern information and communication technologies, with the help of which it is possible to form elements of stochastic culture among students. Continuity in learning is considered from two positions: procedural and educational-cognitive. In addition, a distinctive feature of the book is the presentation of the digital transformation of general education as a way to overcome the "new digital divide". Methodological features of promising digital technologies (within the framework of teaching students the elements of the probabilistic and statistical line) that contribute to overcoming the "new digital divide": artificial intelligence, the Internet of Things, additive manufacturing, machine learning, blockchain, virtual and augmented reality are described. The solution of the main questions of probability theory and statistics in the 9th grade mathematics course is proposed to be carried out using a distance learning course built in the Moodle distance learning system. The content, structure and methodological features of the implementation of the stochastics course for students of grades 10-11 of a secondary school are based on the use of such tools in the educational process as an online calculator for plotting functions, the Wolfram Alpha service, Google Docs and Google Tables services, the Yaklass remote training, the Banktest website.<url>", interactive module "Galton Board", educational website "Mathematics at school". It will be interesting for students, undergraduates, postgraduates, mathematics teachers, as well as specialists improving their qualifications in the field of pedagogical education.
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David, Meister, ed. The Russian theory of activity: Current applications to design and learning. Mahwah, N.J: Lawrence Erlbaum Associates, 1997.

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Book chapters on the topic "Machine learning in education"

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Chai, Mengqiu, Yun Lin, and Ying Li. "Machine Learning and Modern Education." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 41–46. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93719-9_6.

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Tang, Lin, and Lin Liu. "National Defense Education Resource Recommender of High Education Institutions Based on Knowledge-Aware Generative Adversarial Network." In Machine Learning for Cyber Security, 326–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62463-7_30.

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Hao, Jiangang. "Supervised Machine Learning." In Methodology of Educational Measurement and Assessment, 159–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74394-9_9.

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Wong, Pak Chung. "Unsupervised Machine Learning." In Methodology of Educational Measurement and Assessment, 173–93. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74394-9_10.

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Woods, Paul. "Machine Learning and Spiritualities for Urban Living." In Postdigital Science and Education, 163–79. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09405-7_10.

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Zhou, Jiaji, Heng Luo, Quanfeng Luo, and Liping Shen. "Attentiveness Detection Using Continuous Restricted Boltzmann Machine in E-Learning Environment." In Hybrid Learning and Education, 24–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03697-2_3.

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Vehmas, Juha, Arnob Islam Khan, Vasilii Kaliteevskii, and Leonid Chechurin. "Learning Analytics Overview: Academic Approach and Machine Learning Possibilities." In Digital Teaching and Learning in Higher Education, 123–43. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00801-6_6.

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Zhou, Weiwei. "Education for Bilingual Children in the Age of Artificial Intelligence." In Machine Learning for Cyber Security, 436–42. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62460-6_39.

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Agrawal, Rakesh. "Enriching Education through Data Mining." In Machine Learning and Knowledge Discovery in Databases, 1–2. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23780-5_1.

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Hsieh, Yi-Zeng, Mu-Chun Su, and Yu-Lin Jeng. "The Jacobian Matrix-Based Learning Machine in Student." In Emerging Technologies for Education, 469–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71084-6_55.

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Conference papers on the topic "Machine learning in education"

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Wu, Janel. "Machine Learning in Education." In 2020 International Conference on Modern Education and Information Management (ICMEIM). IEEE, 2020. http://dx.doi.org/10.1109/icmeim51375.2020.00020.

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Young, Ramsey, and Jonathan Ringenberg. "Machine Learning." In SIGCSE '19: The 50th ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3287324.3293806.

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Pantic, Maja, and Reinier Zwitserloot. "Active Learning of Introductory Machine Learning." In Proceedings. Frontiers in Education. 36th Annual Conference. IEEE, 2006. http://dx.doi.org/10.1109/fie.2006.322738.

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Prieto-Valdes, Juan, and Elena Gortcheva. "MATHEMATICS FOR MACHINE LEARNING." In 16th International Technology, Education and Development Conference. IATED, 2022. http://dx.doi.org/10.21125/inted.2022.2134.

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Vartiainen, Henriikka, Tapani Toivonen, Ilkka Jormanainen, Juho Kahila, Matti Tedre, and Teemu Valtonen. "Machine learning for middle-schoolers: Children as designers of machine-learning apps." In 2020 IEEE Frontiers in Education Conference (FIE). IEEE, 2020. http://dx.doi.org/10.1109/fie44824.2020.9273981.

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Fröhlich, Martin, Stefan Krauss, and Sven Hilbert. "Using Machine Learning to Predict Mathematical Performance." In Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.icots11.t3b3.

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In empirical educational research, it is of great interest to predict student performance. In contrast to other disciplines, however, machine learning methods for identifying promising predictors are not yet widely used. We will use a machine learning approach to study the effect of learning strategies and cooperative behaviors of German mathematics students with respect to exam grades. A small outlook will be given on how machine learning methods can be integrated into the education of young researchers in empirical educational research.
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Diamant, Emanuel. "Learning to Understand Image Content: Machine Learning Versus Machine Teaching Alternative." In 2006 International Conference on Information Technology: Research and Education. IEEE, 2006. http://dx.doi.org/10.1109/itre.2006.381526.

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Maris, Jo-Mae. "Validation of the Learning Machine." In InSITE 2005: Informing Science + IT Education Conference. Informing Science Institute, 2005. http://dx.doi.org/10.28945/2869.

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As Web-based courses become more prevalent, tools need to be created that go beyond electronic page turning. The tools should allow for easy development of Web-based interactive instruction. The Learning Machine is data-driven tutorial software that is based on behavioral education philosophy. Development and presentation use the same database, but separate scripts, so that changes to content do not require changes to the presentation script. This decoupling enables content providers to concentrate on course development. This paper validates the effectiveness of Learning Machine tutorials as compared with classroom lectures. The experiment conducted to validate the Learning Machine tutorials showed that the tutorials were at least as good as classroom lectures.
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Gupta, Megha, and Gunjan Batra. "Investigation of Machine Learning Assistance to Education." In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2021. http://dx.doi.org/10.1109/iccmc51019.2021.9418364.

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Hu, Wei, Yining Li, Fang Liu, and Tianyi Liu. "Machine-Learning based MOOC Education Data Analysis." In 2021 IEEE 3rd International Conference on Computer Science and Educational Informatization (CSEI). IEEE, 2021. http://dx.doi.org/10.1109/csei51395.2021.9477747.

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Reports on the topic "Machine learning in education"

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Benelli, Gabriele. Data Science and Machine Learning in Education. Office of Scientific and Technical Information (OSTI), July 2022. http://dx.doi.org/10.2172/1882567.

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Walden, Victoria Grace, and Kate Marrison, eds. Recommendations for using Artificial Intelligence and Machine Learning for Holocaust Memory and Education. REFRAME, January 2023. http://dx.doi.org/10.20919/elvh8804.

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Cilliers, Jacobus, Eric Dunford, and James Habyarimana. What Do Local Government Education Managers Do to Boost Learning Outcomes? Research on Improving Systems of Education (RISE), March 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/064.

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Decentralization reforms have shifted responsibility for public service delivery to local government, yet little is known about how their management practices or behavior shape performance. We conducted a comprehensive management survey of mid-level education bureaucrats and their staff in every district in Tanzania, and employ flexible machine learning techniques to identify important management practices associated with learning outcomes. We find that management practices explain 10 percent of variation in a district's exam performance. The three management practices most predictive of performance are: i) the frequency of school visits; ii) school and teacher incentives administered by the district manager; and iii) performance review of staff. Although the model is not causal, these findings suggest the importance of robust systems to motivate district staff, schools, and teachers, that include frequent monitoring of schools. They also show the importance of surveying subordinates of managers, in order to produce richer information on management practices.
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Filmer, Deon, Vatsal Nahata, and Shwetlena Sabarwal. Preparation, Practice, and Beliefs: A Machine Learning Approach to Understanding Teacher Effectiveness. Research on Improving Systems of Education (RISE), December 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/084.

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This paper uses machine learning methods to identify key predictors of teacher effectiveness, proxied by student learning gains linked to a teacher over an academic year. Conditional inference forests and the least absolute shrinkage and selection operator are applied to matched student-teacher data for Math and Kiswahili from Grades 2 and 3 in 392 schools across Tanzania. These two machine learning methods produce consistent results and outperform standard ordinary least squares in out-of-sample prediction by 14-24 percent. As in previous research, commonly used teacher covariates like teacher gender, education, experience, and so forth are not good predictors of teacher effectiveness. Instead, teacher practice (what teachers do, measured through classroom observations and student surveys) and teacher beliefs (measured through teacher surveys) emerge as much more important. Overall, teacher covariates are stronger predictors of teacher effectiveness in Math than in Kiswahili. Teacher beliefs that they can help disadvantaged and struggling students learn (for Math) and they have good relationships within schools (for Kiswahili), teacher practice of providing written feedback and reviewing key concepts at the end of class (for Math), and spending extra time with struggling students (for Kiswahili) are highly predictive of teacher effectiveness, as is teacher preparation on how to teach foundational topics (for both Math and Kiswahili). These results demonstrate the need to pay more systematic attention to teacher preparation, practice, and beliefs in teacher research and policy.
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Pikilnyak, Andrey V., Nadia M. Stetsenko, Volodymyr P. Stetsenko, Tetiana V. Bondarenko, and Halyna V. Tkachuk. Comparative analysis of online dictionaries in the context of the digital transformation of education. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4431.

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The article is devoted to a comparative analysis of popular online dictionaries and an overview of the main tools of these resources to study a language. The use of dictionaries in learning a foreign language is an important step to understanding the language. The effectiveness of this process increases with the use of online dictionaries, which have a lot of tools for improving the educational process. Based on the Alexa Internet resource it was found the most popular online dictionaries: Cambridge Dictionary, Wordreference, Merriam–Webster, Wiktionary, TheFreeDictionary, Dictionary.com, Glosbe, Collins Dictionary, Longman Dictionary, Oxford Dictionary. As a result of the deep analysis of these online dictionaries, we found out they have the next standard functions like the word explanations, transcription, audio pronounce, semantic connections, and examples of use. In propose dictionaries, we also found out the additional tools of learning foreign languages (mostly English) that can be effective. In general, we described sixteen functions of the online platforms for learning that can be useful in learning a foreign language. We have compiled a comparison table based on the next functions: machine translation, multilingualism, a video of pronunciation, an image of a word, discussion, collaborative edit, the rank of words, hints, learning tools, thesaurus, paid services, sharing content, hyperlinks in a definition, registration, lists of words, mobile version, etc. Based on the additional tools of online dictionaries we created a diagram that shows the functionality of analyzed platforms.
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Belafi, Carmen. Where There’s a Will There’s a Way: The Role of Political Will in Creating/Producing/Shaping Education Systems for Learning. Research on Improving Systems of Education (RISE), July 2022. http://dx.doi.org/10.35489/bsg-rise-ri_2022/043.

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This Insight Note argues that political will is a decisive factor in explaining both the homogeneity in the expansion of schooling and the heterogeneity in the expansion of learning, and introduces three takeaways as necessary conditions for a meaningful and sustained prioritisation of learning: The highest authorities of a country have the political will to prioritise learning. The highest authorities of a country want to get every child learning (as they define learning goals as universal goals). The highest authorities of a country adopt a long-horizon view to reap the benefits of a learning-oriented system for the long haul. That said, there are good reasons why many disdain appeals to ‘political will’ and why political will is described as “the slipperiest concept in the policy lexicon” (Hammergren 1998: 12).1 My purpose is not to invoke political will as a deus ex machina or an exogenously given characteristic of a country like its latitude. I explore what political will is (and who needs to have it), how one can identify it, and how it arises in different political regime types. To make “because they wanted it” a workable and useful insight, we need to dig deeper into questions such as: “How do some countries come to want to and others not?” and “What, if anything, can be done to foment the wanting to?” As will be shown, the standard RISE Systems Framework is only partially capable of explaining how the political willof the highest authorities of a state is formed. Therefore, an additional typology of political regimes is introduced to distinguish between different modalities of rule and state-society relations. This typology not only helps in separating different ways in which political will is formed, but also outlines different pathways for how political will may be fostered, depending on the type of regime. The Insight Note is structured as follows: Section 2 defines the term ‘political will’. Section 3 hones in on the three key points and offers empirical examples of political will and prioritisation of learning. Section 4 discusses the formation of political will in different political contexts and highlights the strengths and weaknesses of the RISE Systems Framework in capturing the different modalities of political will formation. Section 5 introduces a typology of political regimes that can help guide analysis on how political will is formed in different regime types and how interventions to create political will may have to look different depending on the type of political system. Section 6 concludes.
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Balyk, Nadiia, Yaroslav Vasylenko, Vasyl Oleksiuk, and Galina Shmyger. Designing of Virtual Cloud Labs for the Learning Cisco CyberSecurity Operations Course. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3177.

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The article is devoted to the study of the problem of the cybersecurity basics teaching. The training of the ICT-specialties students using the course “CCNA Cyber Operations” of the network academy Cisco is considered. At present, many universities have similar academies, while others can open them. On the basis of free software platforms Apache CloudStack and EVE-NG Community authors designed and implemented a virtual cloud laboratory. It operates according to the “IaaS” model. Thanks to the technology of embedded virtualization, the work of many virtual machines, storing of their status, traffic analysis and visualization of network topologies are maintained. The article describes the experience of teaching students of the specialty “Pedagogical education. ICT” in the course “CCNA Cyber Operations” with the use of virtual cloud laboratories. The authors have been conducted a survey of students who studied at the course. Its purpose was to determine how much they satisfied were with the course. Statistical processing of the results was performed on the basis of the Rasch model using the software MiniSteps.
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Vesselinov, Velimir Valentinov. Machine Learning. Office of Scientific and Technical Information (OSTI), January 2019. http://dx.doi.org/10.2172/1492563.

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Valiant, L. G. Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada283386.

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Chase, Melissa P. Machine Learning. Fort Belvoir, VA: Defense Technical Information Center, April 1990. http://dx.doi.org/10.21236/ada223732.

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