%0 Journal Article %1 Fan2021 %A Fan, Yizhou %A Matcha, Wannisa %A Uzir, Nora'ayu Ahmad %A Wang, Qiong %A Gasević, Dragan %D 2021 %J International Journal of Artificial Intelligence in Education %K MOOCs analytics design learning self-regulated %R 10.1007/s40593-021-00249-z %T Learning Analytics to Reveal Links Between Learning Design and Self-Regulated Learning %U https://doi.org/10.1007/s40593-021-00249-z %X The 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.

@article{Fan2021, abstract = {The 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.}, added-at = {2021-05-25T09:40:38.000+0200}, author = {Fan, Yizhou and Matcha, Wannisa and Uzir, Nora'ayu Ahmad and Wang, Qiong and Ga{\v{s}}evi{\'{c}}, Dragan}, biburl = {https://www.bibsonomy.org/bibtex/29816b0f5718da24b3f0a61bb27a9fb51/yish}, day = 21, description = {Learning Analytics to Reveal Links Between Learning Design and Self-Regulated Learning | SpringerLink}, doi = {10.1007/s40593-021-00249-z}, interhash = {1c2ca8181051421a4fa7492e904fbfed}, intrahash = {9816b0f5718da24b3f0a61bb27a9fb51}, issn = {1560-4306}, journal = {International Journal of Artificial Intelligence in Education}, keywords = {MOOCs analytics design learning self-regulated}, month = may, timestamp = {2021-05-25T09:40:38.000+0200}, title = {Learning Analytics to Reveal Links Between Learning Design and Self-Regulated Learning}, url = {https://doi.org/10.1007/s40593-021-00249-z}, year = 2021 }