Meaningful Learner Information for MOOC Instructors Examined Through a Contextualized Evaluation Framework
Bibliographic record
Abstract
Improving STEM MOOC evaluation requires an understanding of the current state of STEM MOOC evaluation, as perceived by all stakeholders. To this end, we investigated what kinds of information STEM MOOC instructors currently use to evaluate their courses and what kinds of information they feel would be valuable for that purpose. We conducted semi-structured interviews with 14 faculty members from a variety of fields and research institutions who had taught STEM MOOCs on edX, Coursera, or Udacity. Four major themes emerged related to instructors' desires: (1) to informally assess learners as an instructor might in a traditional classroom, (2) to assess learners’ attainment of personal learning goals, (3) to obtain in-depth qualitative feedback from learners, and (4) to access more detailed learner analytics regarding the use of course materials. These four themes contribute to a broader sentiment expressed by the instructors that they have access to a wide variety of quantitative data for use in evaluation, but are largely missing the qualitative information that plays a significant role in traditional evaluation. Finally, we provide our recommendations for MOOC evaluation criteria, based on these findings.
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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 | 0.036 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".