There is a Significant Relationship Between Computer Attitudes and Library Anxiety Among African American Graduate Students
Bibliographic record
Abstract
Objective – To investigate whether African American students’ computer attitudes predict levels of library anxiety. Design – A user study in which two instruments were administered to a group of graduate students to measure computer attitudes and library anxiety. Setting – The College of Education at an historically black college and university in the United States of America. Subjects – Ninety-four, predominantly female, African American graduate students, ranging in age from 22-62 years old, and enrolled in either a statistics or a measurement course. Methods – Two instruments, the Computer Attitude Scale (CAS) and the Library Anxiety Scale (LAS) were administered to all the study participants. The Computer Anxiety Scale contains forty Likert-type items that assess individuals’ attitudes toward computers and their use. It includes four scales which can be administered separately: 1. Anxiety or fear of computers 2. Confidence in the ability to use computers 3. Liking or enjoying working with computers 4. Computer usefulness The LAS contains forty-three, 5-point, Likert-format items that assess levels of library anxiety experienced by college students. It also has five subscales as follows: 1. Barriers with staff 2. Affective barriers 3. Comfort with the library 4. Knowledge of the library 5. Mechanical barriers Main results – There were twenty correlations between the library anxiety subscale scores and the computer attitude subscale scores. Four of these correlations were statistically significant. Liking or enjoying working with computers was statistically significantly linked to affective barriers, comfort with the library, and knowledge of the library. There was also a statistically significant association between an attitude of computer usefulness and knowledge of the library. Conclusion – These findings suggest that in this group of students there is a medium to strong relationship between computer attitudes and library anxiety.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".