Evaluation of Basic Science Independent Learning Modules Implemented in a First Year Medical School Curriculum
Bibliographic record
Abstract
Introduction Medical education is shifting to a competency‐based education (CBE) model on a global scale. The Schulich School of Medicine & Dentistry implemented a renewed curriculum for the 2019–2020 academic year, adopting a CBE model at the undergraduate medical education (UME) level. One of the key design principles of CBE is to develop a learner‐centred environment that fosters active learning. Active learning modalities often require more content delivery outside of the classroom via online independent learning modules (ILs). Literature on the implementation and evaluation of ILs within an UME integrative curriculum is limited and conflicting, highlighting the need for the investigation of implementation strategies and evaluations of ILs. The purpose of this project has two aims: i) determine which IL format, Articulate Rise 360 vs. Articulate Storyline 360, first‐year medical students perceive to be more effective to enhance their learning; ii) determine which elements of each learning module format are perceived to be more effective at fostering learning. Methods Basic science ILs implemented in a Foundations of Medicine course were evaluated. After each learning module students were given the opportunity to provide feedback on each IL via an anonymous questionnaire. The questionnaire consisted of 6 Likert scale questions, two fill in the blank questions, and two short answer questions. Likert data were analyzed using unpaired 2‐tailed t‐tests and short answer data will be coded and themed for analysis. Results Preliminary data indicate that Articulate Rise 360 and Articulate Storyline 360 scored similarly on Likert scales with respect to visual design, navigation, learning objective achievement, and interactivity. Articulate Storyline 360 scored significantly higher than Articulate Rise 360 on the Likert scale question evaluating the effectiveness of the embedded knowledge checks with 69.9% of students indicating that knowledge checks in Storyline 360 ILs were “Very valuable” or “Extremely valuable”, compared to 47.9% for the Rise 360 modules. When analyzing the ILs based on length of completion, students perceived ILs that took under 60 minutes to complete to have significantly more effective visual designs, more relevant examples, and enable achievement of learning objectives when compared to ILs that take over 60 minutes to complete. The short answer data has yet to be analyzed. Discussion First year medical students preferred knowledge checks in Articulate Storyline 360 ILs. This may be due to the enhanced interactivity of Storyline 360. Rise 360 is more ridged in its structure and its ability to create interactive elements, whereas Storyline 360 has an enhanced ability to build a variety of question formats. Data indicates that students feel shorter ILs are more effective at accomplishing most or all learning objectives. This may be due to students losing focus or being overwhelmed with extraneous information with longer ILs. Preliminary results suggest that faculty creating new ILs should limit the length to under 60 total minutes and utilize a software that allows for a variety of knowledge checks. Qualitative analysis of the short answer questions will enable further investigation into student preferences of IL attributes.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".