Cheol Il Lim

Bio: Cheol Il Lim is an academic researcher. The author has contributed to research in topics: Web application & Self-regulated learning. The author has an hindex of 3, co-authored 5 publications receiving 78 citations.

Analytics of communities of inquiry: Effects of learning technology use on cognitive presence in asynchronous online discussions

Abstract: This paper describes a study that looked at the effects of different technology-use profiles on educational experience within communities of inquiry, and how they are related to the students' levels of cognitive presence in asynchronous online discussions. Through clustering of students (N = 81) in a graduate distance education engineering course, we identified six different profiles: 1) task-focused users, 2) content-focused no-users, 3) no-users, 4) highly intensive users, 5) content-focused intensive users, and 6) socially-focused intensive users. Identified profiles significantly differ in terms of their use of learning platform and their levels of cognitive presence, with large effect sizes of 0.54 and 0.19 multivariate η2, respectively. Given that several profiles are associated with higher levels of cognitive presence, our results suggest multiple ways for students to be successful within communities of inquiry. Our results also emphasize a need for a different instructional support and pedagogical interventions for different technology-use profiles.

Analytics for Learning Design: A Layered Framework and Tools.

Abstract: The field of learning design studies how to support teachers in devising suitable activities for their students to learn. The field of learning analytics explores how data about students' interactions can be used to increase the understanding of learning experiences. Despite its clear synergy, there is only limited and fragmented work exploring the active role that data analytics can play in supporting design for learning. This paper builds on previous research to propose a framework (analytics layers for learning design) that articulates three layers of data analytics—learning analytics, design analytics and community analytics—to support informed decision‐making in learning design. Additionally, a set of tools and experiences are described to illustrate how the different data analytics perspectives proposed by the framework can support learning design processes. [ABSTRACT FROM AUTHOR]

Identification of ‘at risk’ students using learning analytics: the ethical dilemmas of intervention strategies in a higher education institution

Abstract: Learning analytics is an emerging field in which sophisticated analytic tools are used to inform and improve learning and teaching. Researchers within a regional university in Australia identified an association between interaction and student success in online courses and subsequently developed a learning analytics system aimed at informing learning and teaching practices. Contemporary literature draws attention to ethical considerations and moral tensions in the implementation of learning analytics. This paper presents a case study of the ethical implications of a learning analytics implementation at CQUniversity, a regional Australian university. There was an institutional assumption that student data, consensually gathered at enrollment could be analyzed beyond the scope of the original consent. Further, academics were using the data in a manner not intended by the designers of the learning analytic system, and academics interpreted the student’s individualized data to label students based on their estimate of success. The learning analytics system is still being used and the ethical findings from this paper have implications for CQUniversity, academics and students. In order to resolve the ethical dilemmas the university could increase transparency of the process to students and obtain consent at multiple levels throughout the student journey.