First-Year Students’ Research Challenges: Does Watching Videos on Common Struggles affect Students’ Research Self-Efficacy?
Bibliographic record
Abstract
Abstract Objective – The purpose of this quantitative study was to measure the impact of providing research struggle videos on first-year students’ research self-efficacy. The three-part video series explicated and briefly addressed common first-year roadblocks related to searching, evaluating, and caring about sources. The null hypothesis tested was that students would have similar research self-efficacy scores, regardless of exposure to the video series. Methods – The study was a quasi-experimental, nonequivalent control group design. The population included all 22 sections (N = 359) of First-Year Writing affiliated with the FASTrack Learning Community at the University of Mississippi. Of 22 sections, 12 (N = 212) served as the intervention group exposed to the videos, while the other 10 (N = 147) served as the control group. A research self-efficacy pretest – posttest measure was administered to all students. In addition, all 22 sections, regardless of control or intervention status, received a face-to-face one-shot library instruction session. Results – As a whole, this study failed to reject the null hypothesis. Students exposed to the research struggle videos reported similar research self-efficacy scores as students who were not exposed to the videos. A significant difference, however, did exist between all students’ pretest and posttest scores, suggesting that something else, possibly the in-person library session, did have an impact on students’ research self-efficacy. Conclusion – Although students’ research self-efficacy may have increased due to the presence of an in-person library session, this current research was most interested in evaluating the effect of providing supplemental instruction via struggle videos for first-year students. As this was not substantiated, it is recommended that researchers review the findings and limitations of this current study in order to identify more effective approaches in providing instructional support for first-year students’ research struggles.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".