Enhancing online learning experiences: A cross-sectional study on medical students engagement and challenges
Bibliographic record
Abstract
Background. Digital learning tools have become integral to higher education, offering students enhanced accessibility, flexibility, and engagement. However, their effectiveness depends on usability, reliability, and alignment with academic needs. Limited research has explored students’ experiences with these tools across diverse educational systems. This study examines the frequency of use, perceived benefits, and challenges of digital learning tools among university and college students in multiple countries.Methods. A cross-sectional survey was conducted among 103 Kazakhstan, Russia, South Korea, Turkey, Germany, the USA, and Canada medical students. A structured questionnaire assessed demographic characteristics, usage patterns, and perceptions of digital learning tools. Responses were meas-ured using a fivepoint Likert scale, and statistical analyses, including correlation analysis, were performed to identify key factors influencing student satisfaction and engagement.Results. The study revealed a high engagement rate with digital learning tools, with 73.1% of students frequently utilizing online resources. Despite this, students reported significant challenges: 57.2% struggled to find reliable information, 62.8% questioned content accuracy, and 71.5% found tools lacking interactivity. Correlation analysis indicated that perceived reliability and usability strongly influenced student satisfaction and motivation.Conclusions. Findings highlight the need for improvements in digital learning tools to enhance accuracy, engagement, and personalization. Addressing these challenges can optimize student learning experiences and contribute to more effective, student-centered educational strategies. Future research should explore interventions that enhance content credibility and interactive learning features.Keywords: Digital learning tools; student engagement; online education; higher education; usability; content reliability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".