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Zhang, Jingjing; Huang, Yicheng; Gao, Ming – Journal of Learning Analytics, 2022
Network analytics has the potential to examine new behaviour patterns that are often hidden by the complexity of online interactions. One of the varied network analytics approaches and methods, the model of collective attention, takes an ecological system perspective to exploring the dynamic process of participation patterns in online and flexible…
Descriptors: Network Analysis, Video Technology, MOOCs, Attention Control
Gallego-Romero, Jesús Manuel; Alario-Hoyos, Carlos; Estévez-Ayres, Iria; Delgado Kloos, Carlos – Educational Technology Research and Development, 2020
Massive Open Online Courses (MOOCs) can be enhanced with the so-called learning-by-doing, designing the courses in a way that the learners are involved in a more active way in the learning process. Within the options for increasing learners' interaction in MOOCs, it is possible to integrate (third-party) external tools as part of the instructional…
Descriptors: Learner Engagement, Student Behavior, Learning Analytics, Online Courses
Wells, Jason; Spence, Aaron; McKenzie, Sophie – Journal of Information Technology Education: Research, 2021
Aim/Purpose: This paper focuses on understanding undergraduate computing student-learning behaviour through reviewing their online activity in a university online learning management system (LMS), along with their grade outcome, across three subjects. A specific focus is on the activity of students who failed the computing subjects. Background:…
Descriptors: Student Participation, Undergraduate Students, At Risk Students, Academic Failure
Šaric-Grgic, Ines; Grubišic, Ani; Šeric, Ljiljana; Robinson, Timothy J. – International Journal of Distance Education Technologies, 2020
The idea of clustering students according to their online learning behavior has the potential of providing more adaptive scaffolding by the intelligent tutoring system itself or by a human teacher. With the aim of identifying student groups who would benefit from the same intervention in AC-ware Tutor, this research examined online learning…
Descriptors: Learning Analytics, Intelligent Tutoring Systems, Grouping (Instructional Purposes), Undergraduate Students
Er, Erkan – Online Submission, 2022
Time management is an important self-regulation strategy that can improve student learning and lead to higher performance. Students who can manage their time effectively are more likely to exhibit consistent engagement in learning activities and to complete course assignments in a timely manner. Well planning of the study time is an essential part…
Descriptors: Programming, Time Management, Computer Science Education, Integrated Learning Systems
Farrow, Elaine; Moore, Johanna D.; Gaševic, Dragan – Journal of Learning Analytics, 2022
By participating in asynchronous course discussion forums, students can work together to refine their ideas and construct knowledge collaboratively. Typically, some messages simply repeat or paraphrase course content, while others bring in new material, demonstrate reasoning, integrate concepts, and develop solutions. Through the messages they…
Descriptors: Asynchronous Communication, Computer Mediated Communication, Group Discussion, Learning Analytics
Abe, Kousuke; Tanaka, Tetsuo; Matsumoto, Kazunori – International Association for Development of the Information Society, 2020
The authors are developing and using a fill-in workbook system that allows faculty members to ascertain the attitude of all students to classes including students who are not active, and to improve lectures through well-timed and appropriate actions. In this paper, in order to help teachers improve lessons and teaching materials, and to help…
Descriptors: Student Attitudes, Educational Improvement, Instructional Materials, Learning Management Systems
Grey, Simon; Gordon, Neil – New Directions in the Teaching of Physical Sciences, 2018
In this paper, we argue that, where we measure student attendance, this creates an extrinsic motivator in the form of a reward for (apparent) engagement and can thus lead to undesirable behaviour and outcomes. We go on to consider a number of other mechanisms to assess or encourage student engagement -- such as interactions with a learning…
Descriptors: Attendance, Measurement, Learner Engagement, Student Behavior