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Jing Chen; Bei Fang; Hao Zhang; Xia Xue – Interactive Learning Environments, 2024
High dropout rate exists universally in massive open online courses (MOOCs) due to the separation of teachers and learners in space and time. Dropout prediction using the machine learning method is an extremely important prerequisite to identify potential at-risk learners to improve learning. It has attracted much attention and there have emerged…
Descriptors: MOOCs, Potential Dropouts, Prediction, Artificial Intelligence
Ünal Çakiroglu; Seval Bilgi – Interactive Learning Environments, 2024
The aim of this explanatory study is to identify the causes of intrinsic cognitive load in programming process. For this purpose, a method based on two dimensions; programming knowledge types (syntactic, semantic, and strategic) and programming constructs was proposed. The proposed method was tested with high school students enrolled in Computer…
Descriptors: Cognitive Processes, Difficulty Level, Programming, Interaction
Akbar Bahari; Rui Li – Interactive Learning Environments, 2024
Given the important technology-assisted language learning (TALL)-empowered affordances, a comprehensive understanding of how these TALL tools facilitate learning across different language components, e.g., phonology, morphology, syntax, semantics, and pragmatics, could not only present the panoramic scenery of the subject matter but also inform…
Descriptors: Language Acquisition, Phonology, Morphology (Languages), Syntax
Chi-Tung Chen; Chih-Ming Chen; Hsiao-Ting Tsai – Interactive Learning Environments, 2024
This study utilised the instant semantic analysis and feedback system (ISAFS) to assist learners in the online discussion learning activities of socio-scientific issues (SSIs) and to document their learning process behaviours for behavioural analyses. The aim was to understand the learners' discussion behaviours during the ISAFS assisted learning…
Descriptors: Behavior Patterns, Electronic Learning, Discussion, Instructional Effectiveness