Motivation in computer‐assisted instruction
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Computer-aided instruction (CAI) is defined as instruction in which computers play a central role as the means of information delivery and direct interaction with learners. Computer-aided instruction has become mainstream in medical school curricula. For example, a three-dimensional (3D) computer module of the larynx has been created to teach laryngeal anatomy. Although the novelty and educational potential of CAI has garnered much attention, these new technologies have been plagued with low utilization rates. Several experts attribute this problem to lack of motivation in students. Motivation is defined as the desire and action toward goal-oriented behavior. Psychologist Dr. John Keller developed the ARCS theory of motivational learning, which proposed four components: attention (A), relevance (R), concentration (C), and satisfaction (S). Keller believed that motivation is not only an innate characteristic of the pupil; it can also be influenced by external factors, such as the instructional design of the curriculum. Thus, understanding motivation is an important step to designing CAI appropriately. Keller also developed a 36-item validated instrument called the Instructional Materials Motivation Survey (IMMS) to measure motivation. The objective of this study was to study motivation in CAI. Medical students learning anatomy with the 3D computer module will have higher laryngeal anatomy test scores and higher IMMS motivation scores. Higher anatomy test scores will be positively associated with higher IMMS scores. STUDY DESIGN: Prospective, randomized, controlled trial. METHODS: After obtaining institutional review board approval, 100 medical students (mean age 25.5 ± 2.5, 49% male) were randomized to either the 3D computer module (n = 49) or written text (n = 51). Information content was identical in both arms. Students were given 30 minutes to study laryngeal anatomy and then completed the laryngeal anatomy test and IMMS. Students were categorized as either junior (year 1 and 2) or senior (year 3 and 4). RESULTS: There were no significant differences in anatomy scores based on educational modality. There was significant interaction of educational modality by year [F(1,96) = 4.12, P = 0.045, ω(2) = 0.031]. For the total score, there was a significant effect of year [F(1,96) = 22.28, P < 0.001, ω(2) = 0.178], with seniors (15.4 ± 2.6) scoring significantly higher than juniors (12.8 ± 3.1). For the motivational score, the total IMMS score had two significant effects. With educational modality [F(1,96) = 5.18, P = 0.025, ω(2) = 0.041], the 3D group (12.4 ± 2.8) scored significantly higher than the written text group (11.7 ± 3.2). With year [F(1,96) = 25.31, P < 0.001, ω(2) = 0.198], seniors (13.4 ± 3.0) scored significantly higher than juniors (10.8 ± 2.5). Pearson's correlation showed positive associations (r = 0.22-0.91) between anatomy scores and IMMS motivation scores (P < 0.05). CONCLUSION: Computer-aided instruction conferred no measurable educational benefit over traditional written text in medical students; however, CAI was associated with higher motivational levels. Computer-aided instruction was found to have a greater positive impact on senior medical students with higher anatomy and motivational scores. Higher anatomy scores were positively associated with higher motivational scores. Computer-aided instruction may be better targeted toward senior students. LEVEL OF EVIDENCE: N/A. Laryngoscope, 126:S5-S13, 2016.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.006 | 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".