Predicting Success in the Introduction to Computers Course: GPA vs. Student's Self-Efficacy Scores
Bibliographic record
Abstract
This study examines whether students’ final grades in an introductory college business computing class correlate with their self-reported computer skill levels provided at the beginning of the course. While significant research effort has been devoted to studying the effects of student selfefficacy on course outcomes and studying the moderating effects of various demographic variables (such as age and gender) and experience variables (such as computer access at home), there is a dearth of studies examining a student’s grade-point-average (GPA) as a predictor of final course success in the introductory computing class. For the fundamentals of computer applications course at the medium-size state college, student self-perceptions of their own computer abilities explained very little of the variation in the final course grade outcomes. GPA, however, was a more powerful predictor (adjusted R 2 = 0.365) of the final class grade as well as the students’ grades on individual course modules. Students’ perceptions of their own computer abilities added very little additional predictive value, increasing the full model’s adjusted R 2 only to 0.393. Given the predictive power of GPA relative to course success, discussion is included concerning ways to use this information to offer additional assistance to lower performing students. The study contributes to the existing literature and refutes the value of self-assessment of skills and abilities as a sole predictor of success. Although the literature has suggested non-traditional or adult students may have more difficulty with the computer course, our findings do not support this. Areas for future research are suggested.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".