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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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Hernandez, Josefina; Rodriguez, Fernanda; Hilliger, Isabel; Perez-Sanagustin, Mar – IEEE Transactions on Learning Technologies, 2019
The effectiveness of remedial mathematics courses in post-secondary education has been a controversial topic for years. Higher Education institutions need their students to have basic understandings of the subjects to be imparted in the first semesters, but since they come with different backgrounds and prior knowledge, this is not always possible…
Descriptors: Online Courses, Remedial Mathematics, Adoption (Ideas), Student Attitudes
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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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Wan, Han; Liu, Kangxu; Yu, Qiaoye; Gao, Xiaopeng – IEEE Transactions on Learning Technologies, 2019
Most educational institutions adopted the hybrid teaching mode through learning management systems. The logging data/clickstream could describe learners' online behavior. Many researchers have used them to predict students' performance, which has led to a diverse set of findings, but how to use insights from captured data to enhance learning…
Descriptors: Educational Practices, Learner Engagement, Identification, Study Habits
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Conijn, Rianne; Snijders, Chris; Kleingeld, Ad; Matzat, Uwe – IEEE Transactions on Learning Technologies, 2017
With the adoption of Learning Management Systems (LMSs) in educational institutions, a lot of data has become available describing students' online behavior. Many researchers have used these data to predict student performance. This has led to a rather diverse set of findings, possibly related to the diversity in courses and predictor variables…
Descriptors: Blended Learning, Predictor Variables, Predictive Validity, Predictive Measurement
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Pérez-Sanagustin, Mar; Hernández-Leo, Davinia; Santos, Patricia; Kloos, Carlos Delgado; Blat, Josep – IEEE Transactions on Learning Technologies, 2014
Visits to museums and city tours have been part of higher and secondary education curriculum activities for many years. However these activities are typically considered "less formal" when compared to those carried out in the classroom, mainly because they take place in informal or non-formal settings. Augmented Reality (AR) technologies…
Descriptors: Blended Learning, Computer Simulation, Management Systems, Telecommunications
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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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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