Individual difference characteristics and contextual factors affecting educational attainment
Bibliographic record
Abstract
The studies in this dissertation examine cognitive and non-cognitive predictors of postsecondary educational attainment. While prior research has documented links between core cognitive abilities (e.g., processing speed, attention, fluid intelligence) and academic success, less is known about the mechanisms translating these abilities into outcomes. It also remains unclear how contextual disruptions, such as the COVID-19 pandemic impacted students’ psychosocial and academic functioning. The first study investigated the role of learning strategies, along with willingness to engage in effortful cognitive activity (Need for Cognition; NFC), as potential intermediaries between basic cognitive abilities and academic outcomes. Results showed that while standard cognitive measures did not directly predict academic performance, both NFC and model-based (goal-directed) learning strategies were significant positive predictors. Further analyses indicated that fluid intelligence and attention positively predicted NFC and model-based learning, suggesting that these abilities may facilitate the development of motivational and strategic traits that, in turn, promote academic success. These findings emphasize the importance of motivation and strategy use, even when direct associations with basic cognitive abilities are lacking. The second study complements the first by examining the broader socio-environmental challenges posed by the COVID-19 pandemic on Canadian university students, with a focus on understanding the impact of the pandemic on students’ mental health, social networks, SES, and educational attainment. Using longitudinal data collected before and during the pandemic, results revealed that while GPA slightly improved, psychosocial well-being deteriorated. Increases in substance use, smaller social networks, and reduced well-being were observed. Cross-sectional analyses further showed that greater substance use during the pandemic predicted poorer GPA, and students with pre-existing psychiatric conditions were particularly vulnerable to increased substance use. These findings suggest that students with mental health vulnerabilities may be disproportionately affected by crises, underscoring the need to address maladaptive coping to support academic success. Together, these studies highlight both individual (e.g., cognition, motivation, learning strategies) and contextual influences (e.g., pandemic disruptions) as important predictors of academic attainment. By considering internal and external factors, this dissertation provides a more comprehensive understanding of the multifaceted determinants of educational success, informing both theory and practice for optimizing university student outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".