Exploring autonomous learning capacity from a self‐regulated learning perspective using learning analytics

AbstractThe importance of learning design in education is widely acknowledged in the literature. Should learners make effective use of opportunities provided in a learning design, especially in online environments, previous studies have shown that they need to have strong skills for self-regulated learning (SRL). The literature, which reports the use of learning analytics (LA), shows that SRL skills are best exhibited in choices of learning tactics that are reflective of metacognitive control and monitoring. However, in spite of high significance for evaluation of learning experience, the link between learning design and learning tactics has been under-explored. In order to fill this gap, this paper proposes a novel learning analytic method that combines three data analytic techniques, including a cluster analysis, a process mining technique, and an epistemic network analysis. The proposed method was applied to a dataset collected in a massive open online course (MOOC) on teaching in flipped classrooms which was offered on a Chinese MOOC platform to pre- and in-service teachers. The results showed that the application of the approach detected four learning tactics (Search oriented, Content and assessment oriented, Content oriented and Assessment oriented) which were used by MOOC learners. The analysis of tactics’ usage across learning sessions revealed that learners from different performance groups had different priorities. The study also showed that learning tactics shaped by instructional cues were embedded in different units of study in MOOC. The learners from a high-performance group showed a high level of regulation through strong alignment of the choices of learning tactics with tasks provided in the learning design. The paper also provides a discussion about implications of research and practice.

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Using learning analytics to explore self-regulated learning in flipped blended learning music teacher education

British Journal of Educational Technology ◽

10.1111/bjet.12590 ◽

2017 ◽

Vol 50 (1) ◽

pp. 114-127 ◽

Cited By ~ 7

Author(s):

Amanda P. Montgomery ◽

Amin Mousavi ◽

Michael Carbonaro ◽

Denyse V. Hayward ◽

William Dunn

Keyword(s):

Teacher Education ◽

Blended Learning ◽

Learning Analytics ◽

Music Teacher ◽

Music Teacher Education ◽

Self Regulated Learning ◽

Regulated Learning

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Learning Analytics and Technology Through Teaching

Journal of Emerging Technologies in Accounting ◽

10.2308/jeta-2020-056 ◽

2021 ◽

Author(s):

Matthew Kaufman ◽

Kristi Yuthas

Keyword(s):

Learning Analytics ◽

Data Analytics ◽

New Technologies ◽

Teaching Approach ◽

Knowledge Gap ◽

Technical Knowledge ◽

Self Regulated Learning ◽

Logical Sequence ◽

Regulated Learning ◽

Pedagogical Framework

Data analytics problems, methods and software are changing rapidly. Learning how to learn new technologies might be the most important skill for students to develop in an analytics course. We present a pedagogical framework that promotes self-regulated learning and metacognition and three student-driven assignments that can be used in accounting analytics and other courses that incorporate technology. The assignment can be used by faculty who do not have training in analytics. The assignments adopt a learn-through-teaching approach that helps students: 1) define a conceptual or technical knowledge gap; 2) identify resources available for filling that gap; 3) work independently to acquire the desired knowledge; 4) break knowledge into components and arrange in a logical sequence; and 5) reinforce knowledge by presenting to others in an accessible manner. These assignments equip students with confidence and capabilities that will enable them to keep up with advances in technology.

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