Collaborative Work and the Future of Humanities Teaching
Bibliographic record
Abstract
This article explores the degree to which student collaborations on research and writing assignments can effectively realize learning outcomes. The assignment, in this case, encouraged students to contribute discrete parts of a research project in order to develop their complementary abilities: researching, consulting, drafting, and revising. The outcomes for students included appreciation for their individual expertise, and experience combining discrete contributions into a result that surpasses the sum of its parts. In the course, we gave students preliminary guidance for establishing team objectives and roles for the duration of this assignment and asked them to evaluate their learning experience at the end. In this paper, we analyze the students’ quantitative and qualitative feedback, and suggest ways to structure and supervise collaborative assignments so that students develop their expertise and complementary skills. We suggest that collaborative work such as this is essential for advanced undergraduates in the humanities, where collaborations are less common than in other disciplines. Moreover, we conclude that future humanities instructors should be open to the benefits of collaborative research and writing. This article will be of interest to instructors who wish to develop collaborative assignments that improve students’ disciplinary expertise, engagement with course materials, and outreach to audiences beyond the academy. Cet article explore la mesure dans laquelle le travail en collaboration des étudiants en matière de recherche et de rédaction de devoirs peut aboutir à des résultats d’apprentissage efficaces. Dans le cas présent, le devoir demandé devait encourager les étudiants à contribuer à des sections distinctes d’un projet de recherche afin de développer leurs compétences complémentaires : mener à bien la recherche, consulter, préparer un brouillon et réviser. Pour les étudiants, les résultats comprenaient l’appréciation de leur expertise individuelle et l’expérience d’incorporer des contributions distinctes à un résultat qui dépassait la somme de ses parties. Dans le cours, nous avons fourni aux étudiants une orientation préliminaire pour établir les objectifs et les rôles du groupe pour la durée de ce devoir et nous leur avons demandé à la fin d’évaluer leur expérience d’apprentissage. Dans cet article, nous analysons la rétroaction qualitative et quantitative des étudiants et suggérons des manières de structurer et de superviser les devoirs en collaboration afin de permettre aux étudiants de développer leur expertise et leurs compétences complémentaires. Nous suggérons que le travail en collaboration tel que celui présenté ici est essentiel pour les étudiants de niveau avancé au premier cycle en sciences humaines, où le travail en collaboration est moins commun que dans d’autres disciplines. De plus, nous concluons que les futurs instructeurs en sciences humaines devraient se montrer ouverts aux avantages de la recherche et de la rédaction en collaboration. Cet article intéressera les instructeurs qui désirent créer des devoirs en collaboration pour améliorer l’expertise disciplinaire des étudiants, leur intérêt dans la matière enseignée et les activités de rayonnement pour des auditoires au-delà de l’université.
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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 | 0.032 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.041 |
| Scholarly communication | 0.026 | 0.025 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".