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Papamitsiou, Zacharoula; Pappas, Ilias O.; Sharma, Kshitij; Giannakos, Michail N. – IEEE Transactions on Learning Technologies, 2020
Investigating and explaining the patterns of learners' engagement in adaptive learning conditions is a core issue towards improving the quality of personalized learning services. This article collects learner data from multiple sources during an adaptive learning activity, and employs a fuzzy set qualitative comparative analysis (fsQCA) approach…
Descriptors: Undergraduate Students, Individualized Instruction, Learner Engagement, Reaction Time
Meng, Lingling; Zhang, Wanxue; Chu, Yu; Zhang, Mingxin – IEEE Transactions on Learning Technologies, 2021
With the rapid advancement of education, personalized learning has gained considerable attention in recent years. Learning path plays an important role in this area and has attracted great concern. Many generating mechanisms have been proposed from different perspectives for assisting learning. Some methods focus on learners' interest, while some…
Descriptors: Educational Diagnosis, Individualized Instruction, Learning Processes, Cognitive Ability
Wan, Han; Zhong, Zihao; Tang, Lina; Gao, Xiaopeng – IEEE Transactions on Learning Technologies, 2023
Small private online courses (SPOCs) have influenced teaching and learning in China's higher education. Learning management systems (LMSs) are important components in SPOCs. They can collect various data related to student behavior and support pedagogical interventions. This research used feature engineering and nearest neighbor smoothing models…
Descriptors: Online Courses, Learning Management Systems, Higher Education, Student Behavior
An Early Feedback Prediction System for Learners At-Risk within a First-Year Higher Education Course
Baneres, David; Rodriguez-Gonzalez, M. Elena; Serra, Montse – IEEE Transactions on Learning Technologies, 2019
Identifying at-risk students as soon as possible is a challenge in educational institutions. Decreasing the time lag between identification and real at-risk state may significantly reduce the risk of failure or disengage. In small courses, their identification is relatively easy, but it is impractical on larger ones. Current Learning Management…
Descriptors: Prediction, Feedback (Response), At Risk Students, College Freshmen
Brinton, Christopher G.; Rill, Ruediger; Ha, Sangtae; Chiang, Mung; Smith, Robert; Ju, William – IEEE Transactions on Learning Technologies, 2015
We present the design, implementation, and preliminary evaluation of our Adaptive Educational System (AES): the Mobile Integrated and Individualized Course (MIIC). MIIC is a platform for personalized course delivery which integrates lecture videos, text, assessments, and social learning into a mobile native app, and collects clickstream-level…
Descriptors: Individualized Instruction, Electronic Learning, Online Courses, Student Attitudes
Yao, Ching-Bang – IEEE Transactions on Learning Technologies, 2017
Although m-learning applications have been widely researched, few studies have investigated applying adaptive learning content to various learning environments and efficient input interfaces. This study combined a context-aware mechanism, which can be used to provide suitable learning information anytime and anyplace by using GPS technology, with…
Descriptors: Electronic Learning, Educational Technology, Usability, Individualized Instruction

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