Motivational Design for Web-Based Instruction in Health Professions Education: Protocol for a Systematic Review and Directed Content Analysis
Bibliographic record
Abstract
BACKGROUND: Web-based instruction plays an essential role in health professions education (HPE) by facilitating learners' interactions with educational content, teachers, peers, and patients when they would not be feasible in person. Within the unsupervised settings where web-based instruction is often delivered, learners must effectively self-regulate their learning to be successful. Effective self-regulation places heavy demands on learners' motivation, so effective web-based instruction must be designed to instigate and maintain learners' motivation to learn. Models of motivational design integrate theories of motivation with design strategies intended to create the conditions for motivated engagement. Teachers can use such models to develop their procedural and conceptual knowledge in ways that help them design motivating instruction in messy real-world contexts. Studies such as randomized controlled trials (RCTs) and other quasi-experimental designs that compare different motivational design strategies play a critical role in advancing models of motivational design. Synthesizing the evidence from those studies can identify effective strategies and help teachers and researchers understand the mechanisms governing why strategies work, for whom, and under what circumstances. OBJECTIVE: The planned review aims to analyze how studies comparing motivational design strategies for web-based instruction in HPE support and advance models of motivational design by (1) controlling for established risks to internal validity, (2) leveraging authentic educational contexts to afford ecological validity, (3) drawing on established theories of motivation, (4) investigating a wide breadth of motivational constructs, and (5) analyzing mediators and moderators of strategy effects. METHODS: The planned review will use database searching, registry searching, and hand searching to identify studies comparing motivational design strategies for web-based instruction, delivered to learners in HPE. Studies will be considered from 1990 onward. Two team members will independently screen studies and extract data from the included studies. During extraction, we will record information on the design characteristics of the studies, the theories of motivation they are informed by, the motivational constructs they target, and the mediators and moderators they consider. RESULTS: We have executed our database and registry searches and have begun screening titles and abstracts. CONCLUSIONS: By appraising the characteristics of studies that have focused on the motivational design of web-based instruction in HPE, the planned review will produce recommendations that will ensure impactful programs of future research in this crucial educational space. TRIAL REGISTRATION: PROSPERO CRD42022359521; https://tinyurl.com/57chuzf6. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42681.
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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 | 0.088 | 0.113 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.016 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.062 | 0.007 |
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".