Bibliographic record
Abstract
Abstract This chapter presents the findings of a two-year project on the inclusion of a digital story (DS) project in a higher education language class as a tool to enhance student learning while exploring the affective domain. The DS project has been implemented as a high impact practice (HIP) in two large introductory L2 classes to challenge traditional formal settings for literacy and learning (Kozma, 2003). Through the DS, the learning experience becomes pleasurable, thus the context becomes personally relevant (Handler Miller, 2008). By moving beyond traditional word processing to a multimodal way of conveying stories, the depth of the writing process is explored, and reflection becomes an integral part of the procedure. As a result, the addition of this project has increased student engagement, promoted collaborative learning, and enhanced student-faculty interaction producing higher grades (Nelson Laird, Chen, & Kuh, 2008). The pre- and post-project questionnaires were presented to students in order to measure their technical skills and their overall learning experience. The questionnaires were also valuable tools to evaluate the self-perceived overall learning experience, as well as the process of creating the DS. The project’s formative evaluation, learning objectives, and learning outcomes were also examined.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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".