Connecting Learner Motivation to Learner Progress and Completion in Massive Open Online Courses | Relier la motivation de l’apprenant à ses progrès et à l’achèvement des cours en ligne ouverts à tous
Bibliographic record
Abstract
We examined how massive open online courses (MOOC) learners’ motivational factors, self-efficacy, and task-value related to their course progress and achievement, as informed by learners’ initial course completion intention. In three individual MOOCs, learners completed a pre-course survey to report their levels of task-value and self-efficacy and to indicate their intention to complete each course topic. Using clustering techniques, we identified two distinct groups of learners in the three MOOCs based on self-efficacy and task-value variables: higher-motivation group and lower-motivation group. The higher-motivation group achieved significantly higher grades in two of the MOOCs, and also adhered to their initial completion intention significantly more so than the lower-motivation group. We posit that MOOC completion research should consider learners’ topic-level interest as one success criterion. Further research can clarify perceived task-value in relation to learners’ existing knowledge, their learning goals, and learning outcomes related to the MOOC participation.Nous avons examiné comment, dans les cours en ligne ouverts à tous (CLOT), les facteurs de motivation des apprenants, leur autoefficacité et leur valeur tâche étaient reliés à leurs progrès et à leur achèvement du cours selon l’intention initiale d’achèvement du cours des apprenants. Dans trois CLOT, les apprenants ont rempli un sondage avant le début du cours pour indiquer leur degré de valeur tâche et d’autoefficacité, ainsi que leur intention de compléter chaque sujet du cours. À l’aide de techniques agglomératives, nous avons cerné deux groupes distincts d’apprenants dans trois CLOT selon les variables de la valeur tâche et de l’autoefficacité : un groupe à plus forte motivation, et un groupe dont la motivation était plus faible. Le groupe dont la motivation était plus élevée a obtenu des notes considérablement plus élevées dans deux CLOT et, dans deux cours, ont adhéré à leur intention initiale d’achèvement considérablement plus que le groupe dont la motivation était moindre. Nous posons en principe que la recherche sur l’achèvement des CLOT devrait tenir compte de l’intérêt des apprenants sur le plan des sujets comme étant un critère de réussite. De plus amples recherches pourraient clarifier la valeur tâche perçue relativement aux connaissances préalables des apprenants, à leurs objectifs d’apprentissage et aux résultats d’apprentissage liés à la participation aux CLOT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".