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Chun Yan Enoch Sit; Siu-Cheung Kong – Journal of Educational Computing Research, 2024
Educational process mining aims (EPM) to help teachers understand the overall learning process of their students. Although deep learning models have shown promising results in many domains, the event log dataset in many online courses may not be large enough for deep learning models to approximate the probability distribution of students' learning…
Descriptors: Learning Processes, Learning Analytics, Algorithms, Guidelines
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Qing Wang; Xizhen Cai – Journal of Statistics and Data Science Education, 2024
Support vector classifiers are one of the most popular linear classification techniques for binary classification. Different from some commonly seen model fitting criteria in statistics, such as the ordinary least squares criterion and the maximum likelihood method, its algorithm depends on an optimization problem under constraints, which is…
Descriptors: Active Learning, Class Activities, Classification, Artificial Intelligence
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Conijn, Rianne; Kahr, Patricia; Snijders, Chris – Journal of Learning Analytics, 2023
Ethical considerations, including transparency, play an important role when using artificial intelligence (AI) in education. Explainable AI has been coined as a solution to provide more insight into the inner workings of AI algorithms. However, carefully designed user studies on how to design explanations for AI in education are still limited. The…
Descriptors: Ethics, Writing Evaluation, Artificial Intelligence, Essays
Nathan H. Bean – ProQuest LLC, 2022
Learning to program has long been known to be a difficult task, requiring a student to develop both fluency in the syntax and grammar of a formal programming language and learn the problem-solving approaches and techniques of computational thinking. The successful teaching strategies of the past have involved maintaining small teacher-student…
Descriptors: Algorithms, Coding, Supplementary Education, Programming
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Parhizkar, Amirmohammad; Tejeddin, Golnaz; Khatibi, Toktam – Education and Information Technologies, 2023
Increasing productivity in educational systems is of great importance. Researchers are keen to predict the academic performance of students; this is done to enhance the overall productivity of educational system by effectively identifying students whose performance is below average. This universal concern has been combined with data science…
Descriptors: Algorithms, Grade Point Average, Interdisciplinary Approach, Prediction
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Zhongzhou Chen; Tom Zhang; Michelle Taub – Journal of Learning Analytics, 2024
The current study measures the extent to which students' self-regulated learning tactics and learning outcomes change as the result of a deliberate, data-driven improvement in the learning design of mastery-based online learning modules. In the original design, students were required to attempt the assessment once before being allowed to access…
Descriptors: Learning Analytics, Algorithms, Instructional Materials, Course Content
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Liew, Tze Wei; Tan, Su-Mae; Kew, Si Na – Information and Learning Sciences, 2022
Purpose: This study aims to examine if a pedagogical agent's expressed anger, when framed as a feedback cue, can enhance mental effort and learning performance in a multimedia learning environment than expressed happiness. Design/methodology/approach: A between-subjects experiment was conducted in which learners engaged with a multimedia learning…
Descriptors: Teaching Methods, Multimedia Instruction, Psychological Patterns, Emotional Response
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Lee, Alwyn Vwen Yen; Luco, Andrés Carlos; Tan, Seng Chee – Educational Technology & Society, 2023
Although artificial Intelligence (AI) is prevalent and impacts facets of daily life, there is limited research on responsible and humanistic design, implementation, and evaluation of AI, especially in the field of education. Afterall, learning is inherently a social endeavor involving human interactions, rendering the need for AI designs to be…
Descriptors: Essays, Scoring, Writing Evaluation, Computer Software
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Song, Xueshu – International Journal of Instructional Media, 1992
Describes the development and validation of a microcomputer software system that enhances undergraduate students' interests in becoming engineering graduate students. The development of a database with information on engineering graduate programs is discussed, and a model that matches individual and institutional needs using Southwell's Relaxation…
Descriptors: Academic Advising, Algorithms, College Programs, Computer Software Development