Leveraging Wikipedia for educational innovation: a higher education course model for enhancing students’ competencies and collaborative knowledge creation
Bibliographic record
Abstract
Abstract The integration of Wikipedia into higher education has remained sporadic, often limited to isolated assignments rather than systematic curriculum integration. This study addresses this gap by examining an innovative university-level Wikipedia course designed to enhance student learning through active knowledge production and community engagement. Originating from a medical-focused initiative, the course represents the first for-credit, interdisciplinary Wikipedia course accessible to undergraduates at Tel Aviv University. Through three iterations involving 88 students, the course facilitated the creation of 260 new articles, garnering over 21 million views, demonstrating its broad societal impact. The course’s design, structured around peer and self-assessment, active collaboration with the Wikimedia community, and scaffolded skill development, significantly contributed to students’ academic performance, digital literacy and reflective learning. Using a mixed-methods approach, including statistical analysis of assessments and qualitative feedback, the study found a strong correlation between peer/self-evaluation and final instructor scores, underscoring the reliability of the assessment model. The findings highlight Wikipedia’s potential as a scalable educational tool, fostering open knowledge production and bridging knowledge gaps, particularly in gender representation. Future research should explore the long-term impact of Wikipedia-based learning on students’ academic and professional development, as well as the integration of emerging digital tools, such as Generative AI, to enhance collaborative learning experiences. This study contributes to the discourse on digital pedagogy, emphasizing Wikipedia’s role in transforming higher education through open-source, participatory 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".