Barriers to twice-exceptional student engagement during COVID-19: A case-study of factors affecting school engagement
Bibliographic record
Abstract
In spring 2020, K-12 schools suspended in-person classes due to the COVID-19 pandemic. Schools reopened in-person classes in the fall. Many parents were concerned about the impact of school closures and subsequent re-entry on childrens’ school engagement. Previous literature suggests high engagement in school prevents dropping-out, leads to better grades, and is related to lower depression rates. Some students face unique challenges that may result in lower school engagement, such as twice-exceptional (2E) students. 2E refers to individuals who are both gifted and face some type of learning challenge. A strengths-based approach is suggested as most effective for improving 2E student outcomes. In this approach, material is presented in a way that aligns with students’ talents and interests. In addition to learning challenges impacting engagement, external factors, such as caregiver stress, may also impact student engagement. The COVID-19 pandemic has increased stress for many caregivers. In this study, we investigated whether a strengths-based approach was associated with higher engagement in 2E students. Students’ strengths were assessed and compared to tasks planned by their teachers. Students’ engagement during these tasks was then observed by the research team. Further, we explored the relationship between caregiver stress and student engagement using surveys collected in parallel with student observations. This study was designed to serve as a pilot study for future research on factors related to engagement. Preliminary findings provide weak support that higher task-strength alignment is positively associated with student engagement and that higher caregiver stress is negatively associated with student engagement. Department: Psychology Faculty Mentor: Michele Moscicki
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".