Open learner models in supporting self-regulated learning in higher education: A systematic literature review. (September 2020)

• Record Type: Journal Article
• Authors: Hooshyar, Danial; Pedaste, Margus; Saks, Katrin; Leijen, Äli; Bardone, Emanuele; Wang, Minhong
• Is Part Of: Computers & education. Volume 154(2020)
• Publication Date: 2020-09
• Subjects: Open learner model -- Systematic review -- Self-regulated learning -- Higher education; Education -- Data processing -- Periodicals; Education -- Periodicals; Computers -- Periodicals; Computer-Assisted Instruction -- Periodicals; Éducation -- Informatique -- Périodiques; Electronic journals; 370.285
• DOI: 10.1016/j.compedu.2020.103878
• Languages: English
• ISSNs: 0360-1315

Abstract

Abstract: The open learner model (OLM) represents the knowledge or skill levels of learners in various ways, encouraging learners to actively participate in thinking about and crafting their own learning. Despite the important roles that OLMs play in higher education to support the learning process and self-regulated learning (SRL) in particular, there are few studies systematically reviewing OLM technology in higher education, and investigating their potential to foster self-regulated learning. Therefore, we carried out a systematic review of a 30-year sample of OLM studies in higher education and identified 64 articles that study the use of OLMs in supporting SRL. Our findings show that OLMs have been mainly used to support learners' cognition and a bit less metacognition and motivation; however, emotional support has been rarely provided. The most supported ones are Appraisal and Performance phases; Preparation of learning is enhanced by OLMs not so often. Although learners can edit or negotiate with their learning model in advanced ways, a simple inspectable OLM is more preferred. Reliance on unobservable nodes is less favored in modeling techniques in OLMs because such methods are highly dependent on expert authoring, thereby time-intensive and costly. Comparison and color-coding are two most-used features in OLMs, where the comparison feature is often used for enhancing learners' engagement and motivation. Highlights: Among area of SRL, OLMs support cognition and then metacognition and motivation. OLMs mostly ignore emotion area of SRL. Preparation phase of SRL is largely neglected by OLMs. Simple inspectable OLMs are more preferred than advanced OLMs. OLM's challenge: presentation, transparency, granularity, control level, and theorizing.