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Han, Feifei; Ellis, Robert A.; Pardo, Abelardo – IEEE Transactions on Learning Technologies, 2022
This article uses digital traces to help identify students' online learning strategies by making a clear distinction between the descriptive features (the proportional distribution of students' different online learning actions) and quantitative aspects (the total number of the online learning sessions), a distinction that has not been properly…
Descriptors: Electronic Learning, Learning Strategies, Student Behavior, Educational Environment
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Ellis, Robert A.; Han, Feifei; Pardo, Abelardo – IEEE Transactions on Learning Technologies, 2019
Collaboration is an increasingly important and difficult skill for graduate engineers to develop. While universities provide some measures of collaboration ability of students on graduation, there is still some dissatisfaction with the level of preparedness of students for collaborative activity in the workplace. This paper presents a case study…
Descriptors: Engineering Education, College Freshmen, Blended Learning, Cooperation
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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
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Deho, Oscar Blessed; Joksimovic, Srecko; Li, Jiuyong; Zhan, Chen; Liu, Jixue; Liu, Lin – IEEE Transactions on Learning Technologies, 2023
Many educational institutions are using predictive models to leverage actionable insights using student data and drive student success. A common task has been predicting students at risk of dropping out for the necessary interventions to be made. However, issues of discrimination by these predictive models based on protected attributes of students…
Descriptors: Learning Analytics, Models, Student Records, Prediction
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Pardo, Abelardo; Gasevic, Dragan; Jovanovic, Jelena; Dawson, Shane; Mirriahi, Negin – IEEE Transactions on Learning Technologies, 2019
The success of the flipped classroom (FC) is effectively reliant on the level of student engagement with the preparatory activities prior to attending face-to-face teaching sessions. Information about the nature and level of student engagement with these activities can help instructors make informed decisions regarding how to best support student…
Descriptors: Educational Technology, Technology Uses in Education, Homework, Video Technology
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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
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Sun, Geng; Shen, Jun – IEEE Transactions on Learning Technologies, 2014
Mobile learning is an emerging trend that brings many advantages to distributed learners, enabling them to achieve collaborative learning, in which the virtual teams are usually built to engage multiple learners working together towards the same pedagogical goals in online courses. However, the socio-technical mechanisms to enhance teamwork…
Descriptors: Teamwork, Computer Software, Online Courses, Teaching Methods