Student Learning and Performance in Information Systems Courses: The Role of Academic Motivation
Bibliographic record
Abstract
ABSTRACT Despite the need for information technology knowledge in the business world today, enrollments in information systems (IS) courses have been consistently declining. Student performance in lower level IS courses and student assumptions about the level of difficulty of the courses seem to be reasons for lower enrollments. To understand how student motivation may explain learning outcomes in introductory IS courses, this study investigates the impact of intrinsic and extrinsic academic motivations as framed by self‐determination theory on two measures of learning outcomes: (1) student self‐reported measures of learning and (2) actual grades obtained in courses and course components. Using 269 student responses collected in a second‐year undergraduate core course and a first‐year MBA core course, both of which are offered in a traditional face‐to‐face classroom environment, study hypotheses are analyzed. Results indicate that the motivational model explains both the affective and cognitive perceptions of learning held by students. In examining overall grades and grades in course components, the motivational model, however, was unable to sufficiently explain student performance. Data also indicate that there are significant differences between undergraduate and graduate students in terms of their motivation and learning outcomes.
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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 | 0.004 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".