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David P. Reid; Timothy D. Drysdale – IEEE Transactions on Learning Technologies, 2024
The designs of many student-facing learning analytics (SFLA) dashboards are insufficiently informed by educational research and lack rigorous evaluation in authentic learning contexts, including during remote laboratory practical work. In this article, we present and evaluate an SFLA dashboard designed using the principles of formative assessment…
Descriptors: Learning Analytics, Laboratory Experiments, Electronic Learning, Feedback (Response)
Viswanathan, Sree Aurovindh; VanLehn, Kurt – IEEE Transactions on Learning Technologies, 2018
Effective collaboration between student peers is not spontaneous. A system that can measure collaboration in real-time may be useful, as it could alert an instructor to pairs that need help in collaborating effectively. We tested whether superficial measures of speech and user interface actions would suffice for measuring collaboration. Pairs of…
Descriptors: Cooperative Learning, Data Collection, Data Analysis, Speech Communication
Kostopoulos, Georgios; Karlos, Stamatis; Kotsiantis, Sotiris – IEEE Transactions on Learning Technologies, 2019
Educational data mining has gained a lot of attention among scientists in recent years and constitutes an efficient tool for unraveling the concealed knowledge in educational data. Recently, semisupervised learning methods have been gradually implemented in the educational process demonstrating their usability and effectiveness. Cotraining is a…
Descriptors: Academic Achievement, Case Studies, Usability, Data Analysis
Moreno-Marcos, Pedro Manuel; Alario-Hoyos, Carlos; Munoz-Merino, Pedro J.; Estevez-Ayres, Iria; Kloos, Carlos Delgado – IEEE Transactions on Learning Technologies, 2019
One of the characteristics of massive open online courses (MOOCs) is that the overall number of social interactions tend to be higher than in traditional courses, hindering the analysis of social learning. Learners typically ask or answer questions using the forum. This makes messages a rich source of information, which can be used to infer…
Descriptors: Large Group Instruction, Online Courses, Educational Technology, Technology Uses in Education
Fincham, Ed; Gasevic, Dragan; Jovanovic, Jelena; Pardo, Abelardo – IEEE Transactions on Learning Technologies, 2019
Research into self-regulated learning has traditionally relied upon self-reported data. While there is a rich body of literature that has extracted invaluable information from such sources, it suffers from a number of shortcomings. For instance, it has been shown that surveys often provide insight into students' perceptions about learning rather…
Descriptors: Study Habits, Learning Strategies, Independent Study, Educational Research
Liu, Qingtang; Zhang, Si; Wang, Qiyun; Chen, Wenli – IEEE Transactions on Learning Technologies, 2018
Teachers' online discussion text data shed light on their reflective thinking. With the growing scale of text data, the traditional way of manual coding, however, has been challenged. In order to process the large-scale unstructured text data, it is necessary to integrate the inductive content analysis method and educational data mining…
Descriptors: Information Retrieval, Data Collection, Data Analysis, Discourse Analysis
Niemann, Katja; Wolpers, Martin – IEEE Transactions on Learning Technologies, 2015
In this paper, we introduce a new way of detecting semantic similarities between learning objects by analysing their usage in web portals. Our approach relies on the usage-based relations between the objects themselves rather then on the content of the learning objects or on the relations between users and learning objects. We then take this new…
Descriptors: Educational Technology, Technology Uses in Education, Resource Units, Data Collection
Pardo, Abelardo; Han, Feifei; Ellis, Robert A. – IEEE Transactions on Learning Technologies, 2017
Self-regulated learning theories are used to understand the reasons for different levels of university student academic performance. Similarly, learning analytics research proposes the combination of detailed data traces derived from technology-mediated tasks with a variety of algorithms to predict student academic performance. The former approach…
Descriptors: Student Centered Learning, Learning Theories, College Students, Academic Achievement
Tempelaar, Dirk T.; Rienties, Bart; Nguyen, Quan – IEEE Transactions on Learning Technologies, 2017
Studies in the field of learning analytics (LA) have shown students' demographics and learning management system (LMS) data to be effective identifiers of "at risk" performance. However, insights generated by these predictive models may not be suitable for pedagogically informed interventions due to the inability to explain why students…
Descriptors: Student Behavior, Integrated Learning Systems, Personality, Educational Research