Applying the Motivation, Opportunity, Ability (MOA) Model, and Self-Efficacy (S-E) to Better Understand Student Engagement on Undergraduate Event Management Programs
Bibliographic record
Abstract
Considering the motivation, opportunity, ability (MOA) model and the self-efficacy (S-E) component of the social cognitive theory (SCT), this article aims to examine through a series of four research questions whether such models can help to determine how students engage with their program of study. Furthermore, the article will determine factors that influence student engagement in event management (EM) degree programs and seek to understand how EM students engage with their reading and interact within classroom-based environments. In doing so, the article will contribute to the existing debates on inclusive teaching and learning in higher education (HE), and provide a link towards creating more professional and employable graduates. Self-efficacy refers to beliefs in one's capabilities to learn or perform at designated levels. Much research has demonstrated that self-efficacy influences academic motivation, learning, and achievement; particularly within science, technology, English, and mathematics (STEM) subjects. With this in mind, this research aims to investigate the frame conditions mentioned that surround both self and group efficacy and seeks to reveal whether the above models can be used to better understand the engagement and subsequent performance of undergraduate EM students. This analysis will enable academics to better understand the role of MOA and S-E, how these develop over a program of study, and thereby provide a boost to student self-efficacy. By doing so, the best possible educational experience and results in higher education can be achieved.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".