High Self-efficacy and High Use of Electronic Information may Predict Improved Academic Performance
Bibliographic record
Abstract
A review of: Tella, Adeyinka, Adedeji Tella, C. O. Ayeni, and R. O. Omoba. “Self-efficacy and Use of Electronic Information as Predictors of Academic Performance.” Electronic Journal of Academic and Special Librarianship 8.2 (2007). 24 Apr. 2008 Objective – To determine if self-efficacy and use of electronic information jointly predicted academic performance and to determine what information sources students used most often. Design – Descriptive surveys (scales) for each of the three variables. Setting – University of Ibadan, Nigeria, a metropolitan, government-supported university with approximately 18,000 students. Subjects – Seven hundred undergraduate and graduate students randomly chosen from 7 departments of the faculty (i.e., college) of education (100 students from each department). Methods – Students completed the Morgan-Jinks Self-Efficacy Scale and the Use of Electronic Information Scale. Academic performance was measured using a general aptitude test that covered general education, English language, and mathematics. The Morgan-Jinks scale consisted of 30 items, and the academic performance test consisted of 40 items. No instrument length was provided for the Use of Electronic Information Scale, and no details on the actual content of the general aptitude test or the Use of Electronic Information Scale were provided. These surveys were completed at the university under conditions similar to that of a typical exam (i.e., no talking). All 700 subjects completed the surveys, and there was no evidence of participants providing informed consent or that they were given an opportunity to withdraw from the study. Data was analyzed using multiple regression analysis, a suitable analysis for this type of data. Main Results – Self-efficacy and use of electronic information together contributed to 9% (reported as 0.9% in the article) of the variance in academic performance, and each variable statistically significantly contributed to predicting academic performance (p
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".