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Record W2438974830 · doi:10.1002/lary.26040

Motivation in computer‐assisted instruction

2016· article· en· W2438974830 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Laryngoscope · 2016
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsWestern University
Fundersnot available
KeywordsRelevance (law)CurriculumNoveltyTest (biology)CLARITYMedical educationMathematics educationAction (physics)PsychologyMainstreamComputer scienceMedicinePedagogySocial psychology

Abstract

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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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Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.990
Threshold uncertainty score0.100

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.199
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it