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Using Learners' Problem-Solving Processes in Computer-Based Assessments for Enhanced Learner Modeling: A Deep Learning Approach
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Fu Chen; Chang Lu; Ying Cui – Education and Information Technologies, 2024
Successful computer-based assessments for learning greatly rely on an effective learner modeling approach to analyze learner data and evaluate learner behaviors. In addition to explicit learning performance (i.e., product data), the process data logged by computer-based assessments provide a treasure trove of information about how learners solve…
Descriptors: Computer Assisted Testing, Problem Solving, Learning Analytics, Learning Processes
Predicting Students' Academic Performance by Mining the Educational Data through Machine Learning-Based Classification Model
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Nayak, Padmalaya; Vaheed, Sk.; Gupta, Surbhi; Mohan, Neeraj – Education and Information Technologies, 2023
Students' academic performance prediction is one of the most important applications of Educational Data Mining (EDM) that helps to improve the quality of the education process. The attainment of student outcomes in an Outcome-based Education (OBE) system adds invaluable rewards to facilitate corrective measures to the learning processes.…
Descriptors: Predictor Variables, Academic Achievement, Data Collection, Information Retrieval
Feature Optimization and Machine Learning for Predicting Students' Academic Performance in Higher Education Institutions
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Aom Perkash; Qaisar Shaheen; Robina Saleem; Furqan Rustam; Monica Gracia Villar; Eduardo Silva Alvarado; Isabel de la Torre Diez; Imran Ashraf – Education and Information Technologies, 2024
Developing tools to support students, educators, intuitions, and government in the educational environment has become an important task to improve the quality of education and learning outcomes. Information and communication technology (ICT) is adopted by educational institutions; one such instance is video interaction in flipped teaching.…
Descriptors: Academic Achievement, Colleges, Artificial Intelligence, Predictor Variables
Investigating Features That Play a Role in Predicting Gifted Student Engagement Using Machine Learning: Video Log and Self-Report Data
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Gülay Öztüre Yavuz; Gökhan Akçapinar; Hatice Çirali Sarica; Yasemin Koçak Usluel – Education and Information Technologies, 2024
This study aims to develop a predictive model for predicting gifted students' engagement levels and to investigate the features that are important in such predictions. Features reflecting students' emotions, social-emotional learning skills, learning approaches and video-watching behaviours were used in the prediction models. The study group…
Descriptors: Secondary School Students, Academically Gifted, Gifted Education, Learner Engagement
An Investigation on the Correlation of Learner Styles and Learning Objects Characteristics in a Proposed Learning Objects Management Model (LOMM)
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Wanapu, Supachanun; Fung, Chun Che; Kerdprasop, Nittaya; Chamnongsri, Nisachol; Niwattanakul, Suphakit – Education and Information Technologies, 2016
The issues of accessibility, management, storage and organization of Learning Objects (LOs) in education systems are a high priority of the Thai Government. Incorporating personalized learning or learning styles in a learning object management system to improve the accessibility of LOs has been addressed continuously in the Thai education system.…
Descriptors: Correlation, Cognitive Style, Resource Units, Foreign Countries
Mobile Learning Technology Acceptance in Saudi Arabian Higher Education: An Extended Framework and a Mixed-Method Study
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Alasmari, Talal; Zhang, Ke – Education and Information Technologies, 2019
This article reports a two-phase study with (1) an extended framework on mobile learning technology acceptance, and (2) a mixed-method research investigating college students on the use and embracement of mobile learning technology acceptance in Saudi Arabia. The research extended the Unified Theory of Acceptance and Use of Technology (UTAUT)…
Descriptors: Foreign Countries, Handheld Devices, Telecommunications, Teaching Methods