Students with Non-Proficient Information Seeking Skills Greatly Over-Estimate Their Abilities
Bibliographic record
Abstract
A Review of: Gross, Melissa, and Don Latham. "Attaining Information Literacy: An Investigation of the Relationship between Skill Level, Self-Estimates of Skill, and Library Anxiety." Library & Information Science Research 29.3 (2007): 332-53. Objective – The objective of this study is an investigation of the relationship between students’ self-assessment of their information literacy skills and their actual skill level, as well as an analysis of whether library anxiety is related to information skill attainment. Design – Quantitative research design (Information Literacy Test (ILT), Library Anxiety Scale (LAS), pre and post surveys). Setting – Florida State University, United States. Subjects – Students, incoming freshmen. Methods – Information literacy skills were measured using the Information Literacy Test (ILT), presenting subjects with 65 multiple choice items designed around four of the five ACRL information literacy standards, in which students were expected to: 1) determine the nature and extent of the information needed; 2) access needed information effectively and efficiently; 3) evaluate information and its sources critically and incorporates selected information into his/her knowledge base system; 4) understand many of the economic, legal and social issues surrounding the use of information and accesses and uses information ethically and legally. The ILT categorized participant scores as non-proficient (
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".