Using the personal learning environment to support self-regulated learning strategies: a systematic literature review

ABSTRACT

This study presents a panoramic overview of research conducted between 2010 and 2020 on how the Personal Learning Environment (PLE) supports Self-Regulated Learning (SRL). We elaborate on why the PLE is suitable for SRL, discuss its research themes, explain how the PLE supports SRL and outline the available PLE platforms. The review method we used includes defining keywords, creating inclusion and exclusion criteria, selecting data sources, collecting relevant articles and performing content analysis and synthesis. This study categorises four main findings. The first is that there are four PLE research themes: adoption; integration; model, architecture and framework; and recommenders. The PLE is suitable to support SRL because they share similar ideas regarding personalisation and ownership, the need to integrate formal and informal learning and the need for student-centred learning environments covering flexibility and features. Furthermore, the PLE acts as a tool and a recommender for students. The final finding is that the available PLE platforms have the following characteristics: they act as a personal assistant; they support students in planning, monitoring and evaluating the learning process; they promote collaboration between students; and they act as a widget-based application. Finally, this study also proposes future research topics.

KEYWORDS:

• Self-Regulated learning
• personal learning environment
• blended learning
• online learning
• systematic literature review

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by The Research Grant: Publikasi Terindeks Internasional (PUTI) Prosiding 2020, Number: NKB-3465/UN2.RST/HKP.05.00/2020, funded by DRPM Universitas Indonesia.Direktorat Riset and Pengembangan, Universitas Indonesia

Notes on contributors

Baginda Anggun Nan Cenka

Baginda Anggun Nan Cenka received his bachelor and master's degree from the Faculty of Computer Science, Universitas Indonesia. Currently, he is a PhD student whose research is personal learning environment. His research interests include educational data mining, educational process mining, learning analytics, online learning and intelligent tutoring systems.

Harry B. Santoso

Harry B. Santoso is a faculty member in the Faculty of Computer Science, Universitas Indonesia (UI). He received his BS and MS in Computer Science from the Universitas Indonesia (UI) and his PhD from the Department of Engineering Education, Utah State University, USA. He has taught several courses at UI (e.g. computer-assisted instruction, computer and society, and human–computer interaction). He is also Head of the Digital Library and Distance Learning Laboratory. His research interests include learning personalisation, metacognition, multimedia content, user experience and distance learning.

Kasiyah Junus

Kasiyah Junus received her BS in Mathematics at Gadjah Mada University, MS in Computer Science from the University of Western Ontario, Canada and doctorate from the University of Indonesia. She is a member of the Digital Library and Distance Learning Laboratory and a lecturer in the Faculty of Computer Science, Universitas Indonesia. Her research interests include online learning, community of inquiry and metacognition.