Introduction to a Class-based Online Writing Environment: Gwrit (Game of Writing)
Bibliographic record
Abstract
The Game of Writing (GWrit) is an online writing environment where students can comment on each other’s writing and where they get rewards for on task activity (gamification). This paper brings together research on GWrit from the following perspectives: gamification, analytic tools, user habits, evaluation and task completion structures. First we introduce the way the system was designed to support experimenting with gamification and show the gamification rule editing environment we are developing. Second, we discuss the ways we are evaluating the interface of GWrit using Cognitive Walkthrough and Heuristic evaluation approaches. Third, we look at user behavior based on Google Analytics and compare this to the behavior expected and desired. Finally, we discuss the role task completion structures play in motivating learning. Compared with traditional ways of providing peer review, GWrit offers a different way of teaching writing. Our research shows that the major features of GWrit–including the gamification components, mutual study environment–have been recognized by users. We found that features tied to grades were used more frequently than those not tied to grades. Assignment deadlines, one of the task completion structures applied in GWrit, play an effective role in motivating learning. We end by describing potential improvements for the system from both programming and design perspectives. “Game of Writing” (GWrit) est un environnement d’écriture en ligne où les étudiants peuvent faire des commentaires sur l’écriture de chacun d’entre eux et où ils reçoivent des récompenses dues à leur travail axé à la tâche (ludification). Cette étude rassemble la recherche sur GWrit, venant des perspectives suivantes: la ludification, les outils d’analyse, les habitudes des utilisateurs, l’évaluation et les structures d’achèvement de tâches. Premièrement, nous présentons la façon dont le système a été conçu pour encourager l’expérimentation sur la ludification et pour montrer l’environnement de montage de règles de ludification que nous développons. Deuxièmement, nous discutons les manières dont nous évaluons l’interface de GWrit en employant les approches d’inspection cognitive et d’évaluation heuristique. Troisièmement, nous considérons le comportement des utilisateurs observé avec l’outil Google Analytics et le comparons au comportement attendu et désiré. Finalement, nous discutons du rôle que les structures d’achèvement de tâches jouent dans la motivation d’apprentissage. Comparé aux stratégies d’évaluation collégiale traditionnelles, GWrit en offre une nouvelle visée à l’enseignement de l’écriture. Notre recherche démontre que les utilisateurs reconnaissent les principales caractéristiques de GWrit – ce qui inclut des éléments de ludification et un environnement d’étude mutuelle. Nous avons trouvé que les éléments liés aux notes ont été utilisés plus fréquemment que ceux qui n’y ont pas été liés. Les délais de devoirs, une des structures d’achèvement de tâches employées dans GWrit, jouent un rôle efficace dans la motivation d’apprentissage. Pour conclure, nous décrivons des améliorations potentielles pour ce système en considérant les perspectives de programmation et de conception. Mots-clés: environnement d’écriture en ligne; ludification; analyses d’écriture; l’évaluation de système; recherche sur la motivation
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.055 | 0.019 |
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".