Distance Education and Academic Achievement in Business Administration: The case of the University of Akureyri
Bibliographic record
Abstract
This paper first presents the development of distance education in Icelandic universities. Its second aim is to present a detailed analysis of the distance education practice at the University of Akureyri (UNAK), Iceland. Finally, the paper aims at analysing academic achievement, as well as attitudes towards courses, among campus and distance students in business administration at UNAK. The research is based on secondary data from the university’s information system and official statistics. The findings reveal that distance education has increased significantly in Iceland in recent years. UNAK has had a leading role in developing distance education at university level in Iceland. Nearly half the students at UNAK are enrolled in distance education. Females take longer to finish their study than males, but they receive higher grades than males. Distance students take up to a year longer to finish their BSc programme than campus students. The study also has shown that distance students tend to receive lower grades in business administration at UNAK, and they are older, on average, than local students. Finally, both groups of students seem to express similar attitudes towards taught courses within the faculty. More research is needed in order to fully understand the factors behind the different achievements of distance and campus students.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".