Learning analytics of humanities course: reader profiles in critical reading activity

Abstract

This study investigates learner’s reading behaviors in a critical reading task in humanities course using learning analytics techniques. A Critical Analysis of Literature and Cinema course was selected as a context. The course activities evolved over 10 years, and for this instance, some face-to-face classroom critical reading activities were migrated to online mode by using BookRoll, a learning analytics enhanced eBook platform. Students (n=22 out of the 50 registered) accessed Hayavadana, an Indian play uploaded on BookRoll, and attempted to identify performative elements and cultural references in the text and highlight them. In this study, we analyze learner’s reading logs gathered in the learning record store linked to BookRoll during that activity. We extend our previous work where we identify four online reading profiles: effortful, strategic, wanderers, and check-out, based on learner’s clickstream interactions and time spent with the content. We validate the profiles with qualitative interview data collected from the learners and illustrate the quantified learning behaviors of each of those profiles based on an engagement metric. Our work aims to initiate further discussion related to the application of learning analytics in humanities courses both to probe into the learning behaviors of the students and thereby enhance the experiences with the use of interactive learning environments and data-driven services.

Learning behavioral patterns of students with varying performance in a high school mathematics course using an e-book system

“…"ReviseNote" and "NoteToAnnotation" behaviors illustrated that students took notes to expand their knowledge. This finding indicates that, when performing cognitive activities, students stopped at the stage of expanding knowledge and did not take for teachers (e.g., Chen & Su, 2019;Majumdar et al, 2021). Employing tracking learning data allows students to obtain personalized scaffolding to optimize the learning process when necessary in SRL (e.g., Lim et al, 2023).…”

E-book-based learning activity during COVID-19: engagement behaviors and perceptions of Japanese junior-high school students

“…In that, 3,682,944 logs were of the target contents, but we excluded 2,867,481 logs because students’ operation was too quick (less than 3 s) or too long (longer than 20 min), which implies students’ off-task behavior on the material. The thresholds were determined by the previous studies on e-book based learning interactions (Majumdar, Bakilapadavu, et al, 2021 a, Majumdar, Flanagan, et al, 2021 b; Yin et al, 2019 ). Finally, we included 815,463 logs for our analysis (Fig.…”

“…In behavior analysis, we only targeted the completion rate of students in terms of contents. However, several other metrics can be used to gauge the learning engagement of e-book readers in general (Akçapınar et al, 2019 ) and specifically during ERT period (Majumdar, Bakilapadavu, et al, 2021 a; Majumdar, Flanagan, et al, 2021 b). In particular, the school teacher pointed out the impact of differences in the density of teaching materials in different subjects.…”

The Role of Metacognition (Metacomprehension) and Inferential Ability on Reading Comprehension Ability

“…Another aspect that interferes with the low level of metacomprehension accuracy is that teachers may use different ways of assessing students' understanding levels. Distraction level theory assumes that, when a reader assesses his or her own understanding, it is influenced by cues that originate from the reading disorder (Majumdar et al, 2021;Martins & Capellini, 2021). Based on inferential assumptions, the metacomprehension assessment was carried out based on the example of the disorder.…”