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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
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
Hung Tan Ha; Duyen Thi Bich Nguyen; Tim Stoeckel – Language Testing, 2024
Word frequency has a long history of being considered the most important predictor of word difficulty and has served as a guideline for several aspects of second language vocabulary teaching, learning, and assessment. However, recent empirical research has challenged the supremacy of frequency as a predictor of word difficulty. Accordingly,…
Descriptors: Word Frequency, Vocabulary Skills, Second Language Learning, Second Language Instruction
Chu, Wei; Pavlik, Philip I., Jr. – International Educational Data Mining Society, 2023
In adaptive learning systems, various models are employed to obtain the optimal learning schedule and review for a specific learner. Models of learning are used to estimate the learner's current recall probability by incorporating features or predictors proposed by psychological theory or empirically relevant to learners' performance. Logistic…
Descriptors: Reaction Time, Accuracy, Models, Predictor Variables
Tempelaar, Dirk; Rienties, Bart; Nguyen, Quan – International Association for Development of the Information Society, 2019
Learning analytic models are built upon traces students leave in technology-enhanced learning platforms as the digital footprints of their learning processes. Learning analytics uses these traces of learning engagement to predict performance and provide learning feedback to students and teachers when these predictions signal the risk of failing a…
Descriptors: Learner Engagement, Outcomes of Education, Learning Processes, Learning Analytics
Levi-Gamlieli, Hadas; Cohen, Anat; Nachmias, Rafi – Technology, Instruction, Cognition and Learning, 2015
The aim of this study is to identify online learning behavior that is excessively intense as reflected in a student's overly frequent interaction with the instructor through various communication channels. Then, this study aims to use learning analytics methodologies to discover whether a student with the identified behavior also displays the same…
Descriptors: Student Behavior, Online Courses, Web Sites, Teacher Student Relationship