Enhancing community of inquiry and reflective thinking skills of undergraduates through using learning analytics‐based process feedback

Abstract

This research aims to investigate into the effect of using learning analytics (LA)-based process feedback on students' perceptions of community of inquiry (teaching, social and cognitive presence) and their reflective thinking skills. By using a mixed-method research approach (QUAN + qual), this study was conducted as an experimental design with the pretest-posttest control group. A total of 104 university students who were randomly assigned to the experiment group (EG) and control group (CG) were recruited in this study. The procedure was conducted within the scope of the computing course based on the flipped classroom (FC) model. While the participants in the EG received LA-based process feedback which shows their LA results in a weekly manner, those in the CG did not get any LA-based process feedback. The data were collected through the Community of Inquiry Scale, the Reflective Thinking Scale and a semi-structured student opinion form. The findings indicated that sending feedback including the students' LA results had a statistically significant effect on the students' perceptions of community of inquiry and reflective thinking skills. Based on the findings of the study, several recommendations for teachers, instructional designers and researchers have been made.

Visualising weekly learning outcomes (VWLO) and the intention to continue using a learning management system (CIU): the role of cognitive absorption and perceived learning self-regulation

“…The literature records many attempts to improve learners' engagement and cognitive presence on the LMS platform (Yilmaz, 2020). This research adds to the literature library a new tool for improving the student intention to continue using the LMS.…”

“…The level of use can be measured by actual use or through the intention to continue using the application in the future (Hong et al, 2017). Thus, to improve learners' engagement various academic attempts have been made, such as learning analytics (Yilmaz, 2020) and a personalised recommendation system (Yilmaz & Yilmaz, 2020). However, these significant research attempts focused mainly on the current use and the current benefits of the LMS, rather than seeking to explain the variation in the intention to continue using the LMS.…”

“…The visualised weekly learning outcomes, similar to personalised recommendation system (Yilmaz & Yilmaz, 2020), are seen as a dynamic dashboard in the main interface of the LMS on which to define and visualise the learning outcomes each week. Not only have the usefulness of course learning outcomes and learners' engagement in general received little scholarly attention, but no research has examined the role of integrating learners' perceptions of an LMS and the existence of the visualised weekly learning outcome (i.e., the dashboard of the learning outcome) at the LMS interface.…”

Examination of instructors' experiences for the use of learning analytics

“…As a result of the study, they conducted to determine the impact of learners' interactions on their learning performances by using learning analytics in the online environment; Agudo-Peregrina et al (2014) found out that there was a relationship in positive direction between the two variables. The study by Yilmaz (2020) found that learning analyticsassisted feedback not only made the online learning environment more effective but also promoted the online activities to be more meaningful and enabled students to be more involved in these activities. When the results obtained from the present study and the results obtained in the literature are evaluated together, it has been determined that similar findings have been Learning analytics reached indicating that learning analytics give the instructor the opportunity for an early intervention.…”

A Learning Analytics Conceptual Framework for Augmented Reality-Supported Educational Case Studies

“…The recent advancements in the field of computing have enabled educators and practitioners to increase the effectiveness of instructional design and allowed them to collect and analyze large-scale digital datasets. One proposed solution to investigate and explore such topics involves the collection and interpretation of so-called 'big data', which are processed with the aid of machine learning (ML) models and educational data mining (EDM) techniques [6][7][8]. The so-called learning analytics (LA) discipline is an emerging field, with rapid growth over the last five years, which aims at assisting researchers and instructors to better understand learners' needs by collecting and interpreting large-scale data in a systematic and independent way.…”