How do Strengths-Based Schooling and Caregiver Stress Impact Student Engagement?
Bibliographic record
Abstract
The term twice-exceptional (2E) refers to individuals who are both gifted and face some type of learning challenge. 2E students often face unique social-emotional challenges that may result in lower levels of school engagement. Previous literature suggests a high level of engagement prevents dropping-out, leads to better grades, is related to lower rates of depression, and can be a protective factor against suicidal behaviors. Research suggests that a collaborative, strengths-based school approach is most effective for improving outcomes of 2E students. In this approach school material is presented in a way that aligns with students’ talents and interests, and decisions about the students’ education involve the student, their family, and any relevant professionals. Strong parent-school relationships may mediate levels of parental stress, which has been shown to impact student engagement. Because of the benefits associated with higher school engagement, we will explore whether this approach is also beneficial for typically-developing students. With this study, we aim to determine the relationship(s) between student engagement, task-strength alignment, and caregiver stress by recruiting 2E students, typical students, and their caregivers. Students’ strengths will be assessed and compared to tasks planned by their teachers. We will then observe their level of engagement during these tasks. Survey data on caregiver stress will be collected at various points in the study. We hypothesize that across both groups, student engagement will be higher when task-strength alignment is higher. We also predict there will be an inverse relationship between student engagement and caregiver stress in both groups. Presented in absentia on April 27, 2020 at Student Research Day at MacEwan University in Edmonton, Alberta. (Conference cancelled) Faculty Mentor: Michele Moscicki Department: Psychology
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".