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Paul Prinsloo; Mohammad Khalil; Sharon Slade – Journal of Computing in Higher Education, 2024
Central to the institutionalization of learning analytics is the need to understand and improve student learning. Frameworks guiding the implementation of learning analytics flow from and perpetuate specific understandings of learning. Crucially, they also provide insights into how learning analytics acknowledges and positions itself as entangled…
Descriptors: Learning Analytics, Data, Ecology, Models
Xing, Wanli; Zhu, Gaoxia; Arslan, Okan; Shim, Jaesub; Popov, Vitaliy – Journal of Computing in Higher Education, 2023
Engagement is critical in learning, including computer-supported collaborative learning (CSCL). Previous studies have mainly measured engagement using students' self-reports which usually do not capture the learning process or the interactions between group members. Therefore, researchers advocated developing new and innovative engagement…
Descriptors: Learning Analytics, Cooperative Learning, Learner Engagement, Learning Motivation
Yuqin Yang; Yewen Chen; Xueqi Feng; Daner Sun; Shiyan Pang – Journal of Computing in Higher Education, 2024
Helping students gradually develop collective knowledge is critical but generally faces great challenges. Employing a quasi-experimental design, this study investigated the impacts and mechanisms of analytics-supported reflective assessment on the collective knowledge advancement of undergraduates. The experimental group (n = 55) engaged in…
Descriptors: Undergraduate Students, Learning Processes, Learning Analytics, Learner Engagement
Amida, Ademola; Herbert, Michael J.; Omojiba, Makinde; Stupnisky, Robert – Journal of Computing in Higher Education, 2022
The purpose of this mixed-methods study was to explore factors affecting faculty members' motivation to use learning analytics (LA) to improve their teaching. In the quantitative phase, 107 faculty members completed an online survey about their motivation to use LA. The results showed that cost, utility, attainment value, and competence all…
Descriptors: Teacher Motivation, Teacher Effectiveness, College Faculty, Learning Analytics
López-Zambrano, Javier; Lara, Juan A.; Romero, Cristóbal – Journal of Computing in Higher Education, 2022
One of the main current challenges in Educational Data Mining and Learning Analytics is the portability or transferability of predictive models obtained for a particular course so that they can be applied to other different courses. To handle this challenge, one of the foremost problems is the models' excessive dependence on the low-level…
Descriptors: Learning Analytics, Prediction, Models, Semantics
Muljana, Pauline Salim; Luo, Tian – Journal of Computing in Higher Education, 2021
Studies in learning analytics (LA) have garnered positive findings on learning improvement and advantages for informing course design. However, little is known about instructional designers' perception and their current state of LA-related adoption. This qualitative study explores the perception of instructional designers in higher education…
Descriptors: Learning Analytics, Instructional Design, Higher Education, Attitudes
Karaoglan Yilmaz, Fatma Gizem – Journal of Computing in Higher Education, 2022
This research examined the effect of learning analytics (LA) on students' metacognitive awareness and academic achievement in an online learning environment. In this study, a mixed methods approach was used and applied as a quasi-experimental design. The results of LA were sent to students weekly in LA group (experimental group) via learning…
Descriptors: Learning Analytics, Feedback (Response), Metacognition, Academic Achievement
Jones, Kyle M. L.; VanScoy, Amy; Bright, Kawanna; Harding, Alison; Vedak, Sanika – Journal of Computing in Higher Education, 2022
Learning analytics tools are becoming commonplace in educational technologies, but extant student privacy issues remain largely unresolved. It is unknown whether faculty care about student privacy and see privacy as valuable for learning. The research herein addresses findings from a survey of over 500 full-time higher education instructors. In…
Descriptors: College Faculty, Teacher Attitudes, College Students, Privacy
Tsiakmaki, Maria; Kostopoulos, Georgios; Kotsiantis, Sotiris; Ragos, Omiros – Journal of Computing in Higher Education, 2021
Predicting students' learning outcomes is one of the main topics of interest in the area of Educational Data Mining and Learning Analytics. To this end, a plethora of machine learning methods has been successfully applied for solving a variety of predictive problems. However, it is of utmost importance for both educators and data scientists to…
Descriptors: Active Learning, Predictor Variables, Academic Achievement, Learning Analytics
Li, Xu; Ouyang, Fan; Chen, WenZhi – Journal of Computing in Higher Education, 2022
Group formation is a critical factor which influences collaborative processes and performances in computer-supported collaborative learning (CSCL). Automatic grouping has been widely used to generate groups with heterogeneous attributes and to maximize the diversity of students' characteristics within a group. But there are two dominant challenges…
Descriptors: Computer Assisted Instruction, Cooperative Learning, Group Dynamics, Grouping (Instructional Purposes)
Monllaó Olivé, David; Huynh, Du Q.; Reynolds, Mark; Dougiamas, Martin; Wiese, Damyon – Journal of Computing in Higher Education, 2020
Both educational data mining and learning analytics aim to understand learners and optimise learning processes of educational settings like Moodle, a learning management system (LMS). Analytics in an LMS covers many different aspects: finding students at risk of abandoning a course or identifying students with difficulties before the assessments.…
Descriptors: Identification, At Risk Students, Potential Dropouts, Online Courses
Hriez, Raghda Fawzey; Al-Naymat, Ghazi – Journal of Computing in Higher Education, 2021
Depicting the reason for the mismatch between instructor expectations of students' performance in advanced courses and their actual performance has been a challenging issue for a long time, which raises the question of why such a mismatch exists. An implicit reason for this mismatch is the student's weakness in prerequisite course skills. To solve…
Descriptors: Required Courses, Advanced Courses, Graphs, Outcomes of Education
Rizvi, Saman; Rienties, Bart; Rogaten, Jekaterina; Kizilcec, René F. – Journal of Computing in Higher Education, 2020
Studies on engagement and learning design in Massive Open Online Courses (MOOCs) have laid the groundwork for understanding how people learn in this relatively new type of informal learning environment. To advance our understanding of how people learn in MOOCs, we investigate the intersection between learning design and the temporal process of…
Descriptors: Online Courses, Learning Processes, Science Education, Learner Engagement
Klein, Carrie; Lester, Jaime; Rangwala, Huzefa; Johri, Aditya – Journal of Computing in Higher Education, 2019
Learning analytics (LA) tools promise to improve student learning and retention. However, adoption and use of LA tools in higher education is often uneven. In this case study, part of a larger exploratory research project, we interviewed and observed 32 faculty and advisors at a public research university to understand the technological incentives…
Descriptors: Learning Analytics, Barriers, Incentives, Adoption (Ideas)