Becoming engineers: how students leverage relationships between documents and learning activities
Bibliographic record
Abstract
Learners participate in complex environments that are comprised of diverse and distributed people, information, and tools. To better understand why this situation presents challenges for learners, and to examine how they seek to overcome these challenges, a two-part study was conducted. This research explores undergraduate engineers’ information interactions through a mixed methods study. Questionnaire responses and interviews with students were analyzed to investigate how undergraduate engineers seek, manage, and interact with discipline-specific information and how they share documents with their peers. This work included questionnaire responses from 103 students enrolled in undergraduate engineering programs at a large university in Canada. Follow-up interviews with 18 of these respondents extended accounts of students' experiences. The findings contribute to understandings of how undergraduate engineers navigate complex information environments. Given that students have access to a substantial amount of information communicated in many ways, their ability to select and apply information was found to be integral to their participation in these environments. Results identified and described the latent relationship between learning tasks and document genres. It was also found that students regularly collaborate through social media and other backchannels to sidestep their instructors’ efforts to monitor and control how and what information they share. Findings suggest implications for understanding how students develop awareness about pairing documents with the learning activities in which they are engaged. While students are coping with complex information environments, they are not necessarily using the expected document genres, suggesting areas for adjustments in curriculum, information literacy instruction, and theoretical synthesis.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".