Factors that influence the connection between engineering self-efficacy and growth within academic, social and spiritual life habits
Bibliographic record
Abstract
First year engineering students begin their degree with pre-conceived notions of how the year will go, with respect to their academics, in addition to their social and spiritual lives. This thereby gives way to a loss of self-efficacy, associated with both engineering itself and their own self-concept led by that initial disconnection. Thus, it is important to understand what factors influence the connections between engineering self-efficacy and their academic, social, and spiritual life-habits. Life habits can be defined as any set of factors encouraging the growth of an individual, affecting an individual’s life, ranging from learning strategies to self-perception of oneself and everything in between. Previous research has explored the stressors specific to students in first year engineering and how this affects students’ wellbeing overall [1] - although not specific to the motivational belief that is self-efficacy and the effect it has on their entire life. Using an inductive thematic analysis [2] on responses written by students who completed a series of self-reflections after participating in Mental Wellness and Engineering Attributes seminars offered in their first year Engineering courses, this research explores the factors that influence the connection between self-efficacy and an individual’s personal growth as described through life habits. The five themes that were found were social/spiritual wellness in terms of a support system, a fixed academic mindset with an “all or nothing” behavior, the inability to cope with transitioning and adapting out of their previous institutions, harmful expectations, and the importance of finding a balance in their everyday lives. Given these findings, the connection between self-efficacy and life habits is prevalent both negatively and positively for first year engineering students. The results suggest that individuals in their first year of engineering are caught off guard by the difficulty of the program, leading to a loss of self-efficacy and the development of new negative learning strategies - until they discover how to succeed in engineering.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".