Motivation and Knowledge: Pre-Assessment and Post-Assessment of MOOC Participants From an Energy and Sustainability Project
Bibliographic record
Abstract
Understanding factors promoting or preventing participants’ completion of a massive open online course (MOOC) is an important research topic, as attrition rates remain high for this environment. Motivation and digital skills have been identified as aspects promoting student engagement in a MOOC, and they are considered necessary for success. However, evaluation of these factors has often relied on tools for which the psychometric properties have not been explored; this suggests that researchers may be working with potentially inaccurate information for judging participants’ profiles. Through a set of analyses (t-test, exploratory factor analysis, correlation), this study explores the relationship between information collected by administering valid and reliable pre and post instruments to measure traits of MOOC attendees. The findings from this study support previously reported outcomes concerning the strong relationships among motivation, previous knowledge, and perceived satisfaction factors for MOOC completers. Moreover, this study provides evidence of the feasibility of developing valid assessments for evaluation purposes.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".