Examining In-Person and Asynchronous Information-Seeking Behavior Instruction Among First-Year Engineering Students
Bibliographic record
Abstract
This is a complete evidence-based practice paper.The current COVID-19 global pandemic has required educators to pioneer online instruction even as they deliver it.This shift has particularly impacted first-year programs, in which training engineering students to find reliable information is fundamental to their professional development and ABET and CEAB accreditation criteria.Typically, information seeking is taught in person so that instructors and librarians can directly observe and guide student behavior, a practice still evolving but well-established by research.However, the effects of online information-seeking training and the sudden transition on students' learning are very poorly understood.Even less is known about the use of asynchronous instructional methods.This paper significantly enhances existing knowledge by directly examining the efficacy of in-person and asynchronous online instructional modalities.For 60 students in a mandatory engineering-communication course, we deployed an enhanced online baseline-assessment exercise to understand students' existing information-seeking behavior.Librarians then deployed an asynchronous online lesson to teach engineering research practices, critical evaluation, and information literacy.We evaluated the extent to which the online lesson impacted student information-seeking behavior and compared it to existing data from the prior year's classroom version.Our results demonstrate that the asynchronous learning module significantly enhanced the students' critical evaluation of sources and student outcomes were comparable with results in the previous synchronous course.These results have dramatic implications for how we understand students' baseline information-seeking behaviors, pedagogical design to bring about meaningful changes in students' use of sources, and how course design can incorporate effective asynchronous online delivery in diverse models.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".