LAUSR

One of the main problems encountered in the online learning process is the low or absence of students’ engagement. They may face problems with behavioral engagement, cognitive engagement, emotional engagement in online learning environments. It is thought that the problems related to students' engagements can be overcome with personalized metacognitive feedback support based on learning analytics. In this research, the effect of personalized metacognitive feedback support based on learning analytics in online learning for recommendation and guidance was investigated on student engagement. The research was designed in conformity with experimental design, and it was performed on 68 first graders at a university in Turkey. The procedure was conducted within the scope of the Computing II Course based on online learning. The participants were randomly apportioned to experimental and control groups. Students in the experimental group were provided with personalized metacognitive feedback support based on learning analytics for recommendation and guidance. This support was not given to the control group. The personalized metacognitive feedback support used in this research consists of two basic components. These; (a) Learning analytics reports created with data obtained from students' weekly learning management system usage. (b) The second component of the feedback messages is the recommendations messages prepared personalized for each participant based on learning analytics reports. The data of the study was obtained by the students’ engagement scale which is used as pretest and posttest. The findings of the study revealed that the experimental group students' engagement was higher than the control group. Based on the research findings, it was seen that providing personalized metacognitive feedback based on learning analytics to students in online learning would improve students’ engagement. Therefore, it can be said that providing personalized metacognitive feedback based on learning analytics in online learning is a useful approach. This research has a novel and unique value in examining the effect of personalized metacognitive feedback based on learning analytics on students’ engagement. In line with the findings obtained from the research, various suggestions were made for educators, administrators, and researchers.