Exploring Self-directed Learning Among Engineering Undergraduates in the Extensive Online Instruction Environment During the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic brought about unprecedented academic disruptions to postsecondary education, including engineering education.A considerable decrease in student motivation became a major issue for online learning during the pandemic.This paper attempts to address these questions: How did the online instruction environment affect engineering students' motivation and self-directed learning?How did these changes, in turn, affect their learning outcomes?We used survey data collected from a large Canadian engineering school and conceptualized self-directed learning from a social cognitive perspective to address these questions.Our findings revealed that students' selfdirected learning capabilities mediated the effects of learning environment factors on estimated grades and perceived gains in competency development; and student motivation had both direct and indirect effects on these learning outcomes.In their comments, students ascribed lack of motivation to multiple aspects of the online learning environment and felt that decreased motivation affected their learning.Our analysis demonstrated the significant role of student motivation in an online environment and suggested that the decrease in motivation became a major affective barrier to learning.Thus, the extensive online instruction during the pandemic offered both challenges and opportunities for producing self-directed learners.We recommend that engineering schools implement more interventions to help engineering students enhance their self-directed learning capabilities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".