An article written by members of Digital Library and Distance Learning (DL2) Lab, Faculty of Computer Science Universitas Indonesia and Mercu Buana University has recently been published by Journal of Information Technology Education: Research (JITE). According to its official website, JITE is "a peer-reviewed journal that publishes scholarly articles on the use of information technology at all levels of education. This includes using technology to enhance learning, to support teaching and teaching administration."
Currenty JITE is indexed in Scopus (ranked #276/1254 in Education journals and #68/221 in general computer science [Q1]), Web of Science Emerging Sources Citation Index (ESCI), and ProQuest. This article is among the first articles published by members of DL2 Lab in JITE.
Title
Systematic Review of Self-Regulated Learning Intervention and Measurement in Higher Education: Towards a Holistic, Integrated, and Technology-Assisted Model
Authors
- Jahns Michael (Faculty of Computer Science Universitas Indonesia)
- Harry Budi Santoso (Faculty of Computer Science Universitas Indonesia)
- Kasiyah Junus (Faculty of Computer Science Universitas Indonesia)
- Sulis Sandiwarno (Faculty of Computer Science Universitas Indonesia; Mercu Buana University)
Abstract
Aim/Purpose - This study aims to analyze self-regulated learning interventions and measurement approaches in higher education as an initial contribution towards a model for holistic, integrated, and technology-assisted self-regulated learning support.
Background - Previous reviews lacked insight into how to combine self-regulated learning interventions and measurements. It is essential to understand these interventions, measurements, trends, and relationships to develop a holistic, integrated, and technology-assisted model to support self-regulated learning in higher education.
Methodology - This systematic literature review was conducted in accordance with the guidelines provided by Kitchenham and Charters for systematic literature reviews in software engineering. A total of 109 studies on self-regulated learning in higher education published between 2014 and 2023 were selected and reviewed. Data extraction on self-regulated learning interventions and measurements used by those studies was conducted. Qualitative content analysis of interventions and measurements was performed using provisional coding to produce categories, and the Jaccard Index was employed to assess the associations between these categories. Interventions were categorized into six groups: planning-based, reflection-based, training-based, prompt-based, feedback/report-based, and technology-assisted interventions. Similarly, measurements were classified into seven groups: quantitative questionnaires, qualitative questionnaires, interviews, focus group discussions, think-aloud protocols, information system data, and assessment data.
Contribution - This review examines recent trends in self-regulated learning interventions, measurements, and their interconnections in higher education. These trends inform support strategies and provide guidance to teachers, instructional designers, and decision-makers regarding self-regulated learning practices. We propose an initial model for a holistic, technology-assisted approach to supporting self-regulated learning, which should be further validated by higher education stakeholders prior to implementation. Our recommendations include training in self-regulated learning, designing instruction to foster these activities, providing suitable tools, and leveraging usage data for event-based measurement.
Findings - Identified interventions include planning-based, reflection-based, training-based, prompt-based, feedback/report-based, and technology-assisted approaches. Technology-assisted interventions are often combined with other methods, and feedback or report-based interventions frequently occur alongside technology-assisted ones. The most common measurements are self-reported quantitative questionnaires and assessment data. System logs are increasingly used to measure self-regulated learning by capturing events, while self-reports evaluate aptitude. Qualitative data, such as interviews and focus groups, are used less frequently. Notably, there is a strong association between technology-assisted interventions and the use of system logs for measurement purposes.
Recommendations for Practitioners - Higher education institutions should conduct training programs to equip teachers with the skills needed to support students’ self-regulated learning. Teachers can prompt students to use self-regulated learning strategies. Various tools, such as learning management systems and learning analytics dashboards, can aid in fostering self-regulated learning. Data from these tools can be analyzed using educational data mining approaches to provide actionable insights for both overall learning activities and data-driven feedback for students.
Recommendation for Researchers - Future research should prioritize validating the proposed model with qualitative methods and expert feedback to improve its accuracy. Create integrated measurement dashboard systems that provide students and teachers with actionable insights into self-regulated learning processes and instructional design.
Impact on Society - By encouraging self-regulated learning, this model can potentially enhance academic performance, improve learning outcomes, and equip students with the essential skills necessary for lifelong learning and success in a rapidly changing world.
Future Research - Future research will focus on validating the initial model through qualitative methods, gathering real-world insights from teachers via interviews or focus groups, and developing an integrated measurement dashboard system.