Revitalizing ENGL 2302 in Response to ChatGPT and Pandemic-Era Disruptions
Bibliographic record
Abstract
In the summer of 2023, we set out to refresh ENGL 2302: Literatures and Cultures 1500-1700 in response to two forces disrupting the teaching and learning experience: AI Chatbots, and students’ performance struggles in test environments. In response to the first, we developed a multi-stage Final Project that asked students, working in groups, to assess the utility of ChatGPT for literary studies. Students used ChatGPT to generate a response to an essay prompt, critiqued its output, wrote a revised version, and then reflected on the experience of using ChatGPT. To support this initiative, we created a series of instructions, templates, and rubrics. For the second issue, we developed a suite of resources to prepare students for tests/exams, including two new formative activities: The Sight Passage Analyses (group assignments) The Test/Exam Preparation Modules (H5P lessons that introduce students to the style of the test questions, give them opportunities to practice answering them, and provide them with study tips for each question type) While it is difficult to establish causality, when we compared student performance in the previous cohort to the cohort that experienced the above interventions, we observed some promising results, including: Improved test/exam scores (a 10% increase in students earning As, Bs, and Cs on Test 1; a 24% increase on Test 2; and a 16% increase on the Exam) Improved final grade scores (a 24% increase in students earning an A- or better, and an 8% reduction in students earning an F)
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".