A Case Study: Enhancing the Learning Process and the Quality of the Students' Work in Online Courses Using Multiple High Impact Practices and Civic Strategies
Bibliographic record
Abstract
Background: Employing High Impact Practices (HIPs) in the curriculum will keep the students engaged and will boost their learning outcomes. Study Aim: Explore the students’ feedback on two HIPs practices and measure the impact of the studied HIPs on the course’s outcomes. Methodology: Two HIPs strategies were applied to the graduate students of an advanced online eight –week course. A cross-sectional study by conducting an online Survey-Monkey survey to assess the studied HIPs. The interventions were the group leadership and scaffolding the course’s final project strategies. Results and Discussion: 53% (n=17) of students responded. 53% very liked and liked the leadership group HIP. 82% found the leadership group HIP helpful to present and establish discussions with their classmates. 76.5% either strongly agreed or agreed that the group leadership HIP helped them as presenters and discussion moderators, as well as in handling the weekly assignments. 52.94% agreed and strongly agreed that the leadership HIP helped them, as an audience, to understand the curriculum and assisted them in handling the weekly assignments and projects. 94.12% agreed or strongly agreed that the dissemination of the final project's sections on the weekly assignments helped them understand and write the final project effectively. The average final project grades of the class under study was 97% in comparison to 90% of the same course the instructor taught in Spring 2018.Conclusion: Introducing HIPs strategies will make the students feel that they owned the learning process and enhanced their creativity, self-confidence, and self-efficacy skills.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".