A Far Cry from School History: Massive Online Open Courses as a Generative Source for Historical Research
Bibliographic record
Abstract
Current research into Massive Online Open Courses (MOOCs) has neglected the potential of using learner comments for discipline-specific analysis. This article explores how MOOCs, within the historical discipline, can be used to generate, investigate, and document personal narratives, and argues that they serve as a rich platform for historical resource generation. Through these narratives, this research explores changing perceptions of learning; from learning history at school to learning about history in a MOOC. This exploration uses a qualitative thematic analysis of learner comments related to personal narratives of learning history at school from the Trinity College Dublin/Futurelearn “Irish Lives in War and Revolution: 1912-1923” MOOC. These personal narratives were generated both directly and indirectly through four pedagogical tools; reflective questions, multimedia resources, external links, and inter-learner interaction. Broad themes emerged from the analysis of personal narratives including attitudes toward history at school, biased and inadequate teaching, and MOOC teaching compared with school experiences. The analysis demonstrated that MOOCs serve as a generative repository for personal and family historical narratives, and described how MOOCs can change perceptions of teaching and learning history. This paper contributes a novel understanding of MOOCs for discipline-specific analysis, provides a framework for MOOC historical resource generation, and describes changing perceptions of learning from the perspective of MOOC learners.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".