Developing Academic Motivation Scale for Learning Information Technology (AMSLIT): A Study of Validity and Reliability
Bibliographic record
Abstract
This study aimed to develop Academic Motivation Scale for Learning Information Technology for university students. For this purpose, 120 randomly selected university students studying in different classes and faculties at KSU were invited to the study during the 2016-2017 academic year. To define the scale indicators students were asked to answer the question; “What are your motivations for learning information technologies?”. Four different academicians examined the answers in accordance with the self-determination theory and they created the item pool. After expert examinations and pilot studies, the scale was designed in Likert-type with 18 items in 6 categories. To analyze the construct validity of the scale, 824 randomly selected students among the freshmen at KSU were included in the sample of the research. Among those, 276 of the students were included in the exploratory factor analysis in the first step, 269 were involved to repeat the first step with a new sample, and 279 participated in the last step to carry out the confirmatory factor analysis. Although literature suggest three different types of motivation (extrinsic, intrinsic, and amotivation), in this study, it was found that the intrinsic and extrinsic motivation items were gathered together and expressed as a single factor named “Intrinsic-Occupational Motivation”. According to the results, the final state of the scale included 15 items in two sub-dimensions. The sub-dimensions were named as “Intrinsic-Occupational Motivation” and “Amotivation”. It is understood from the analysis that the results derived from the scale have high reliability.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".