Discovering the Motivations of Students when Using an Online Learning Tool
Bibliographic record
Abstract
In an educational setting, the use of online learning tools impacts student performance. Motivation and beliefs play an important role in predicting student decisions to use these learning tools. However, IT-personality entailing playfulness on the web, perceived personal innovativeness, and enjoyment may have an impact on motivations. In this study, we investigate the influence of IT-personality traits on motivation and beliefs. The study includes 95 participants. A survey was conducted after using the learning tool for one semester. Assessment of the psychometric properties of the scales proved acceptable and confirmatory factor analysis supported the proposed hypotheses. With the exception of the impact of enjoyment on motivation, all other hypotheses demonstrate behavior different from other contexts: playfulness on the web and perceived personal innovativeness have little to no impact on motivation; motivation in turn has the opposite strong and significant effect on beliefs. Specifically, we found that motivation has a strong impact on students’ attitudes and consequently attitudes were found to determine intentions where the variance explained is 50% (attitude) and 28% (intentions). These results give way to interesting interpretations as they relate to learning.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".