Investigation of Factors Affecting Information Literacy Student Learning Outcomes Fails to Undercover Significant Findings. A Review of: Detlor, B., Julien, H., Willson, R., Serenko, A., & Lavallee, M. (2011). Learning outcomes of information literacy instruction at business schools. Journal of the American Society for Information Science and Technology, 62(3), 572-585.
Bibliographic record
Abstract
Objective – To ascertain the factors influencing student learning during information literacy instruction (ILI) and create a theoretical model based on those factors. Design – Mixed methodology consisting of interviews and an assessment test. Setting – Three Canadian business schools. Subjects – Seven librarians, 4 library administrators, 16 business faculty, and 52 undergraduate business students were interviewed, and the Standardized Assessment of Information Literacy Skills (SAILS) test was administered to 1,087 undergraduate business students across three different business schools. Methods – The authors used an interview script to conduct interviews with librarians, library administrators, business school faculty, and undergraduate business school students at three business schools in Canada. The authors also administered the SAILS test to undergraduate business students at the same three Canadian business schools. Main Results – ILI works best when it is related to an assignment, part of the curriculum, periodically evaluated, adequatelyfunded, timely, mandatory, interactive, uses handouts, provides the proper amount of information, and favourably viewed within the school. ILI student learning outcomes are affected by whether the students find the ILI beneficial and relevant, their year in the program, gender, status as international or domestic student, and overall academic achievement. Conclusion – Creation of theoretical model consisting of the three main factors influencing student learning outcomes in information literacy instruction: learning environment, information literacy components, and student demographics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".