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Jiang Xiaxia; Li Yahong; Kuang Ziyi; Yu Jiajun – Journal of Computer Assisted Learning, 2025
Background: Video conferencing technology has moved online education into a new stage of real-time video interaction. However, shortcomings such as students' lack of concentration and substantive engagement during video conferencing greatly limit the improvement of online learning effectiveness. According to social presence theory and the…
Descriptors: College Faculty, College Students, Electronic Learning, Distance Education
Ute Mertens; Marlit A. Lindner – Journal of Computer Assisted Learning, 2025
Background: Educational assessments increasingly shift towards computer-based formats. Many studies have explored how different types of automated feedback affect learning. However, few studies have investigated how digital performance feedback affects test takers' ratings of affective-motivational reactions during a testing session. Method: In…
Descriptors: Educational Assessment, Computer Assisted Testing, Automation, Feedback (Response)
Hilliger, Isabel; Ruipérez-Valiente, José A.; Alexandron, Giora; Gaševic, Dragan – Journal of Computer Assisted Learning, 2022
Background: Online learning has grown significantly during the past two decades, and COVID-19 pandemic has expedited this process. However, previous research has shown how academic dishonesty is more prevalent under these modalities. Therefore, there is the challenge of performing trustworthy remote assessments, in order to obtain valid and…
Descriptors: Online Courses, Ethics, Student Evaluation, Evaluation Methods
Héctor J. Pijeira-Díaz; Shashank Subramanya; Janneke van de Pol; Anique de Bruin – Journal of Computer Assisted Learning, 2024
Background: When learning causal relations, completing causal diagrams enhances students' comprehension judgements to some extent. To potentially boost this effect, advances in natural language processing (NLP) enable real-time formative feedback based on the automated assessment of students' diagrams, which can involve the correctness of both the…
Descriptors: Learning Analytics, Automation, Student Evaluation, Causal Models