Beyond Grades: Harnessing Digital Badges to Champion Holistic Skill Development and Celebrate Active Engagement across a Large Enrollment Organic Chemistry Module
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide In a technology-enhanced learning environment and underpinned by a unique hybrid pedagogic model that borrows from gamification, constructivism, and experiential learning approaches, badges were purposefully used to foster engagement. This approach promoted the development of a mindset that identifies and appreciates the worth of a portfolio of practical and general skills developed across an entire introductory organic chemistry lab course. Within the subthemes of General Laboratory Skills, Purification and Characterization Skills, and Professional Desk-Based Skills, ten key microskills that align with course objectives were identified. A visually attractive badge icon that clearly illustrates the specific achievement was created for each. Development of each skill was presented as a standalone short-term goal to be rewarded with an individual task-completion badge. Award criteria included effort and engagement with structured prelab activities, including LearnSci lab sims, instructional videos and online quizzes, hands-on laboratory experience, and postlab reporting. The broad range afforded students opportunities to construct their knowledge and skills across different scenarios, both on and off campus. Award criteria were judiciously selected for their compatibility with our Virtual Learning Environment, Moodle, and its badges plugin. In this way, the logistical demands of validation and badge issuance for a large enrollment class were serviced by technology. Across two academic cycles, ∼3,250 badges were awarded to ∼370 students. Survey responses show that participants found this hybrid pedagogic approach useful for highlighting skill development and evidencing achievement. Students considered it an attractive teaching method that positively impacted on their education and enabled them to make links between in-curriculum skill acquisition and competency for employment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".