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Record W4213458470 · doi:10.2196/34751

Real-life Evaluation of an Interactive Versus Noninteractive e-Learning Module on Chronic Obstructive Pulmonary Disease for Medical Licentiate Students in Zambia: Web-Based, Mixed Methods Randomized Controlled Trial

2022· article· en· W4213458470 on OpenAlexvenueno aff
Elena Schnieders, Freda Röhr, Misho Mbewe, Aubrey Shanzi, Astrid Berner-Rodoreda, Sandra Barteit, Valérie R. Louis, Petros Andreadis, Gardner Syakantu, Florian Neuhann

Bibliographic record

VenueJMIR Medical Education · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPulmonary diseaseRandomized controlled trialWeb applicationComputer scienceDiseaseMedicineMultimediaPhysical therapyWorld Wide WebPathologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: e-Learning for health professionals in many low- and middle-income countries (LMICs) is still in its infancy, but with the advent of COVID-19, a significant expansion of digital learning has occurred. Asynchronous e-learning can be grouped into interactive (user-influenceable content) and noninteractive (static material) e-learning. Studies conducted in high-income countries suggest that interactive e-learning is more effective than noninteractive e-learning in increasing learner satisfaction and knowledge; however, there is a gap in our understanding of whether this also holds true in LMICs. OBJECTIVE: This study aims to validate the hypothesis above in a resource-constrained and real-life setting to understand e-learning quality and delivery by comparing interactive and noninteractive e-learning user satisfaction, usability, and knowledge gain in a new medical university in Zambia. METHODS: We conducted a web-based, mixed methods randomized controlled trial at the Levy Mwanawasa Medical University (LMMU) in Lusaka, Zambia, between April and July 2021. We recruited medical licentiate students (second, third, and fourth study years) via email. Participants were randomized to undergo asynchronous e-learning with an interactive or noninteractive module for chronic obstructive pulmonary disease and informally blinded to their group allocation. The interactive module included interactive interfaces, quizzes, and a virtual patient, whereas the noninteractive module consisted of PowerPoint slides. Both modules covered the same content scope. The primary outcome was learner satisfaction. The secondary outcomes were usability, short- and long-term knowledge gain, and barriers to e-learning. The mixed methods study followed an explanatory sequential design in which rating conferences delivered further insights into quantitative findings, which were evaluated through web-based questionnaires. RESULTS: Initially, 94 participants were enrolled in the study, of whom 41 (44%; 18 intervention participants and 23 control participants) remained in the study and were analyzed. There were no significant differences in satisfaction (intervention: median 33.5, first quartile 31.3, second quartile 35; control: median 33, first quartile 30, second quartile 37.5; P=.66), usability, or knowledge gain between the intervention and control groups. Challenges in accessing both e-learning modules led to many dropouts. Qualitative data suggested that the content of the interactive module was more challenging to access because of technical difficulties and individual factors (eg, limited experience with interactive e-learning). CONCLUSIONS: We did not observe an increase in user satisfaction with interactive e-learning. However, this finding may not be generalizable to other low-resource settings because the post hoc power was low, and the e-learning system at LMMU has not yet reached its full potential. Consequently, technical and individual barriers to accessing e-learning may have affected the results, mainly because the interactive module was considered more difficult to access and use. Nevertheless, qualitative data showed high motivation and interest in e-learning. Future studies should minimize technical barriers to e-learning to further evaluate interactive e-learning in LMICs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.038
GPT teacher head0.534
Teacher spread0.495 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

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