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Nedime Selin Çöpgeven; Mehmet Firat – Journal of Educators Online, 2024
Learning processes can now be transferred to digital environments, allowing for the tracking of learners' digital footprints. The field of learning analytics focuses on the efficient use of these digital records to improve both learning experiences and processes. Dashboards are the tangible outputs of learning analytics. The use of dashboards in…
Descriptors: Electronic Learning, Distance Education, Academic Achievement, Educational Technology
Flora Ji-Yoon Jin; Bhagya Maheshi; Wenhua Lai; Yuheng Li; Danijela Gasevic; Guanliang Chen; Nicola Charwat; Philip Wing Keung Chan; Roberto Martinez-Maldonado; Dragan Gaševic; Yi-Shan Tsai – Journal of Learning Analytics, 2025
This paper explores the integration of generative AI (GenAI) in the feedback process in higher education through a learning analytics (LA) tool, examined from a feedback literacy perspective. Feedback literacy refers to students' ability to understand, evaluate, and apply feedback effectively to improve their learning, which is crucial for…
Descriptors: College Students, Student Attitudes, Artificial Intelligence, Learning Analytics
Lucas Paulsen; Euan Lindsay – Education and Information Technologies, 2024
This systematic review explores the emerging themes in the design and implementation of student-facing learning analytics dashboards in higher education. Learning Analytics has long been criticised for focusing too much on the analytics, and not enough on the learning. The review is then guided by an interest in whether these dashboards are still…
Descriptors: Learning Analytics, Educational Technology, Learning Processes, College Students
Wollny, Sebastian; Di Mitri, Daniele; Jivet, Ioana; Muñoz-Merino, Pedro; Scheffel, Maren; Schneider, Jan; Tsai, Yi-Shan; Whitelock-Wainwright, Alexander; Gaševic, Dragan; Drachsler, Hendrik – Journal of Computer Assisted Learning, 2023
Background: Learning Analytics (LA) is an emerging field concerned with measuring, collecting, and analysing data about learners and their contexts to gain insights into learning processes. As the technology of Learning Analytics is evolving, many systems are being implemented. In this context, it is essential to understand stakeholders'…
Descriptors: Foreign Countries, College Students, Learning Analytics, Expectation
Saleh Alhazbi; Afnan Al-ali; Aliya Tabassum; Abdulla Al-Ali; Ahmed Al-Emadi; Tamer Khattab; Mahmood A. Hasan – Journal of Computer Assisted Learning, 2024
Background: Measuring students' self-regulation skills is essential to understand how they approach their learning tasks in order to identify areas where they might need additional support. Traditionally, self-report questionnaires and think aloud protocols have been used to measure self-regulated learning skills (SRL). However, these methods are…
Descriptors: Learning Analytics, Independent Study, Higher Education, College Students
Smithers, Laura – Learning, Media and Technology, 2023
This article examines the work of predictive analytics in shaping the social worlds in which they thrive, and in particular the world of the first year of Great State University's student success initiative. Specifically, this article investigates the following research paradox: predictive analytics, as driven by a logic premised on predicting the…
Descriptors: Prediction, Learning Analytics, Academic Achievement, College Students
Logan Sizemore; Brian Hutchinson; Emily Borda – Chemistry Education Research and Practice, 2024
Education researchers are deeply interested in understanding the way students organize their knowledge. Card sort tasks, which require students to group concepts, are one mechanism to infer a student's organizational strategy. However, the limited resolution of card sort tasks means they necessarily miss some of the nuance in a student's strategy.…
Descriptors: Artificial Intelligence, Chemistry, Cognitive Ability, Abstract Reasoning
Esteban Villalobos; Isabel Hilliger; Carlos Gonzalez; Sergio Celis; Mar Pérez-Sanagustín; Julien Broisin – Journal of Learning Analytics, 2024
Researchers in learning analytics have created indicators with learners' trace data as a proxy for studying learner behaviour in a college course. Student Approaches to Learning (SAL) is one of the theories used to explain these behaviours, distinguishing between deep, surface, and organized study. In Latin America, researchers have demonstrated…
Descriptors: Learning Analytics, Academic Achievement, Role Theory, Learning Processes
Brown, Alice; Lawrence, Jill; Basson, Marita; Axelsen, Megan; Redmond, Petrea; Turner, Joanna; Maloney, Suzanne; Galligan, Linda – Active Learning in Higher Education, 2023
Combining nudge theory with learning analytics, 'nudge analytics', is a relatively recent phenomenon in the educational context. Used, for example, to address such issues as concerns with student (dis)engagement, nudging students to take certain action or to change a behaviour towards active learning, can make a difference. However, knowing who to…
Descriptors: Online Courses, Learner Engagement, Learning Analytics, Intervention
Lars de Vreugd; Anouschka van Leeuwen; Marieke van der Schaaf – Journal of Computer Assisted Learning, 2025
Background: University students need to self-regulate but are sometimes incapable of doing so. Learning Analytics Dashboards (LADs) can support students' appraisal of study behaviour, from which goals can be set and performed. However, it is unclear how goal-setting and self-motivation within self-regulated learning elicits behaviour when using an…
Descriptors: Learning Analytics, Educational Technology, Goal Orientation, Learning Motivation
Karaoglan Yilmaz, Fatma Gizem – Asia-Pacific Education Researcher, 2022
The use of the flipped classroom (FC) model in higher education is becoming increasingly common. Although the FC model has many benefits, there are some limitations using this model for learners who do not have self-directed learning skills and do not have a developed learner autonomy. One of these limitations is that students with low academic…
Descriptors: Learning Analytics, Self Efficacy, Problem Solving, Flipped Classroom
Viberg, Olga; Engström, Linda; Saqr, Mohammed; Hrastinski, Stefan – Education and Information Technologies, 2022
In order to successfully implement learning analytics (LA), we need a better understanding of student expectations of such services. Yet, there is still a limited body of research about students' expectations across countries. Student expectations of LA have been predominantly examined from a view that perceives students as a group of individuals…
Descriptors: Learning Analytics, Student Attitudes, Expectation, College Students
Jones, Kyle M. L.; Goben, Abigail; Perry, Michael R.; Regalado, Mariana; Salo, Dorothea; Asher, Andrew D.; Smale, Maura A.; Briney, Kristin A. – portal: Libraries and the Academy, 2023
Higher education data mining and analytics, like learning analytics, may improve learning experiences and outcomes. However, such practices are rife with student privacy concerns and other ethics issues. It is crucial that student privacy expectations and preferences are considered in the design of educational data analytics. This study forefronts…
Descriptors: College Students, Student Attitudes, Data Collection, Learning Analytics
Anni Silvola; Amanda Sjöblom; Piia Näykki; Egle Gedrimiene; Hanni Muukkonen – Frontline Learning Research, 2023
An in-depth understanding of student experiences and evaluations of learning analytics dashboards (LADs) is needed to develop supportive learning analytics tools. This study investigates how students (N = 140) evaluated two student-facing LADs as a support for academic path-level self-regulated learning (SRL) through the concrete processes of…
Descriptors: Learning Analytics, Student Evaluation, Student Experience, Student Attitudes
Zamecnik, Andrew; Kovanovíc, Vitomir; Joksimovíc, Srécko; Grossmann, Georg; Ladjal, Djazia; Marshall, Ruth; Pardo, Abelardo – Journal of Computer Assisted Learning, 2023
Background: Maintaining cohesion is critical for teams to achieve shared goals and performance outcomes within a work-integrated learning (WIL) environment. Cohesion is an emergent state that develops over time, representing the synchrony of different behavioural interactions. Cohesive teams will exhibit such phenomena by their temporal…
Descriptors: Data Use, Group Dynamics, College Students, Cooperative Learning