Impact of Lecturer’s Discourse for Students’ Video Engagement: Video Learning Analytics Case Study of MOOCs

Abstract

Lecture videos are amongst the most widely used instructional methods in present Massive Open Online Courses (MOOCs) and other digital educational platforms. As the main form of instruction, students’ engagement behaviour with videos directly impacts the students’ success or failure and accordingly, in-video dropouts positively correlate with dropout from MOOCs. The primary focus of previous video learning analytics studies is on analysing video engagement behaviour using explicit factors (i.e. views or annotations). Limited research studies focus on implicit video learning analytics (e.g. pause, seek, content type) and their impact on students’ success, with existing studies addressing video interactions and their relationship with visual transitions. We aim to explore the association between video interactions and non-visual (i.e. verbal or discourse) transition. This research focuses on text (e.g. cohesion and syntactic complexity) and spoken (e.g. speaking rate) discourse features of lecture videos. We conduct a fine-grained analysis of 3.4 million video interactions of two AdelaideX MOOCs – Programming (Code101x) and Cyber, Surveillance and Security (Cyber101x). According to our results, some discourse features (e.g. lexical diversity and causal connectives) demonstrate statistically significant correlation with video interactions. We present insights for educational video design implications based on discourse processing theories.

Mining Big Data in Education: Affordances and Challenges

“…They found that students in instructor-facilitated courses demonstrated higher levels of interaction and used more personal pronouns, whereas students in peer-facilitated courses exhibited lower levels of engagement and used fewer personal pronouns. Atapattu and Falkner (2018) used NLP on MOOC lecture videos to find that discourse features of the lecture video content are related to student interactions with the videos (e.g., pausing, seeking). Joksimović et al (2015) examined course-related participation patterns of MOOC students on Twitter, Facebook, and blogs.…”

“…Such data can also give information on relationships and networks within an online community. Studies that use textual analysis may help instructors design courses and activities to improve student engagement and to facilitate peer-to-peer learning (e.g., Atapattu & Falkner, 2018;Gelman et al, 2016;Slater et al, 2016). However, the applicability of various tools (e.g., Coh-Metrix and Linguistic Inquiry and Word Count) has not been tested extensively in all educational settings (Fesler et al, 2019).…”

Visual analytics of video‐clickstream data and prediction of learners' performance using deep learning models in MOOCs' courses

“…Then, they introduced a methodology based on the experience gained from learner clickstreams and showed multiaspects for predicting the quality of individual videos and improving CFA outcomes, which emphasize the ability to associate performance with behavior. Atapattu and Falkner [2] studied the association between learner video interactions and the discourse features of video transcript patterns. They concentrated on features of MOOC video content for language and discourse.…”

Can MOOC Instructor Be Portrayed by Semantic Features? Using Discourse and Clustering Analysis to Identify Lecture-Style of Instructors in MOOCs

“…Cohesion is considered an important theoretical construct that measures the overlap between propositions in the textbase. It provides linguistic clues to make connections between an adjacent pair of sentences (Atapattu and Falkner, 2018 ). Higher level of cohesion in text has been found to facilitate comprehension for many readers (Gernsbacher, 1990 ) and is particularly important to low-knowledge readers (McNamara, 2001 ).…”