The Information Seeking Behavior of Undergraduate Education Majors: Does Library Instruction Play a Role?
Bibliographic record
Abstract
Objective – This study investigated the information seeking behavior of undergraduate majors to gain a better understanding of where they find their research information (academic vs. non-academic sources) and to determine if library instruction had any impact on the types of sources used. Methods – The study used a convenience sample of 200 students currently enrolled as undergraduates at the University of Central Florida’s College of Education. A chi square test of association was conducted to determine if the proportion of undergraduate Education majors who use academic sources as compared to non-academic sources varied depending on whether the students had attended at least one library instruction session. Results – The majority of students surveyed find their research information on the freely available Web, even though they admit that academic sources are more credible. At an alpha level of .05, types of sources used for research were not statistically significantly related to whether the student attended library instruction sessions (Pearson χ2 (1, N = 200) = 1.612, p = .447, Cramer’s V = .090). Conclusion – These results are supported by other studies that indicate that today’s college students are using freely available Internet sites much more than library resources. Little to no association appears to exist between “one-shot” library instruction sessions and the sources used by students in their research. Serious consideration needs to be given to multiple library instruction sessions and to for-credit library courses over one-shot classes.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".