The Role of Motivation in the Use of Lecture Behaviors in the Online Classroom
Bibliographic record
Abstract
Aim/Purpose: Extant research provides conflicting information regarding the role that lecture behaviors play within e-learning lectures. This study sought to understand what role motivation plays in increasing the likelihood that students engage in lecture behaviors in general, and how motivation affects the differing types of lecture behaviors. Background: The growth of online learning has increased the importance of video lectures as a means of delivering content. As with offline lectures, students may find it useful to adapt and change the way they interact with lectures to improve their learning. One possible approach that allows students to effectively manage any challenges they have in understanding a lesson is to initiate lecture behaviors to alter the flow of information. Methodology: In the present study, a survey was administered to cyber university students (n = 2434) in order to examine at the relationship between intrinsic goal orientation (a type of motivation) and levels of lecture behaviors. Contribution: This research fills an important gap by showing the effects that motivation can have on how students interact with video lectures and suggests the ways in which students engaging in specific lecture behaviors do so in order to gain a better understanding of the content. As lecture behaviors are an important part of how students are interacting with this important and new method of teaching, it is important to understand which characteristics make students more likely to engage in lecture behaviors. Findings: Students who have higher levels of motivation are more likely to engage in lecture behaviors. These lecture behaviors may include splitting attention between media sources, pausing the video lecture, rewatching parts of the video lecture, and diverting attention to obtain better audio or visual clarity. Recommendations for Practitioners: Instead of just tracking students’ viewing progress on each course lecture video, instructors should further endeavor to measure their students’ use lecture behaviors in relation to online course lecture content. Doing so can provide valuable insight into students’ level of engagement with course lecture materials and overall levels of intrinsic goal orientation. Recommendation for Researchers: Researchers need to start factoring in how student characteristics interact with instructional engagement when investigating online learning. Impact on Society: Improvement in our understanding of online learning helps improve the quality of instruction, which provides a net gain for society. Future Research: This paper is a broad overview using a survey, so future research should focus on a more detailed analysis of lecture behaviors, possibly using controlled experiments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".