Student engagement has an important role in academic achievement in all learning contexts, including e-learning environments. The extent of monitoring and promoting student engagement in e-learning affects the quality of education and is a determining factor for ensuring student’s success. Log data of students’ activities recorded in a learning management system (LMS) can be used to measure their level of engagement in the online teaching–learning process. No previous studies have been found stating a consistent and systematically raised list of LMS-based student engagement indicators, so this systematized review aimed to fulfill this gap. The authors performed an advanced search in the PubMed, Ovid, Google Scholar, Scopus, Web of Science, ProQuest, Emerald, and ERIC databases to retrieve relevant original peer-reviewed articles published until the end of June 2021. Reviewing the 32 included articles resulted in 27 indicators that were categorized into three themes and six categories as follows: (a) log-in and usage (referring to LMS, access to course material), (b) student performance (assignments, assessments), and (c) communication (messaging, forum participation). Among the categories, access to course material and messaging were the most and the least mentioned, respectively.

Background: Studying medicine is perceived to be stressful, and a high level of stress may have a negative effect on cognitive functioning and mental health of the students. Objectives: In this study, we assessed perceived stress and its severity, sources and determinants. Patients and Methods: In this cross-sectional study, we assessed the perceived stress and its severity, sources and demographic variables in 341 (136 males, 205 females) randomly selected medical students of Tehran university of medical sciences, Iran, in October 2013. A self-administered valid and reliable questionnaire with a possible range of scores from 0 to 56 was used to collect the data. Stress sources were determined using logistic regression analysis. Results: The overall perceived stress mean was 32.02 (SD = 5.08). Eighty-three percent of the medical students perceived stress. Students in clinical phase perceived more stress than basic sciences ones [OR 2.25, 95% CI 1.809 - 4.032]. Logistic regression analysis showed demographic (including gender and residential status), psychosocial and academic factors and the phase of study as sources of stress. Conclusions: The results of this study show that most of the medical students declared perceived stress. So, a change in medical education environment and empowering students to effectively cope with the perceived stress sources and their families to support their children is needed. Keywords: Students, Medical; Stress, Psychological

Universities assess their academic learning environment to improve students’ learning. Students’ experience in e-learning environment is different from face-to-face educational environment. So, in this study a specific valid and reliable instrument was devised for assessing perception of e-students from educational environment, that is, educational atmosphere. Firstly, we devised the primary instrument based on factors constituting educational atmosphere. Then Instrument’s content and construct validity were assessed. Also, Cronbach’s alpha and test–retest were used for studying the internal consistency and reliability of the instrument respectively. The final instrument named ‘e-learning educational atmosphere measure’ (EEAM) consisted of 40 items covering six factors, including programme effectiveness, teaching quality, ethics and professionalism, learner support, safety and convenience, and awareness of the rules, which accounted for 68.53% of variances. Content validity ratio was more than 0.51 and content validity index score of all questions was above 0.81. Test–retest reliability was 0.85 (p = 0.001) and Cronbach’s alpha was 0.943. Assessing educational atmosphere in e-learning settings by EEAM could provide managers and investors with useful information to settle an effective education system by prioritising the necessary changes.

Background: With the increasing popularity of e-learning programs, educational stakeholders are attempting to promote critical thinking in the virtual education system. This study aimed to explore the experiences of both the instructors and the students about critical thinking promotion within the virtual education system. Methods: This qualitative study recruited the instructors and students from four academic disciplines provided by the Virtual School of Tehran University of Medical Sciences (Tehran, Iran). All programs were master’s degree programs and utilized a blended (combination of e-learning and face to face) training. Semistructured interviews with the participants were used to collect data. Results: The participants had a variety of experiences about how to promote critical thinking. These experiences were conceptualized in four main themes, namely, instructional design, educational leadership and management, local evidence, and belief systems. Conclusion: The present study clarified the factors affecting critical thinking promotion in e-learning. Not only the instructors but also the educational designers and leaders can benefit from our findings to improve the quality of virtual education programs and promote critical thinking.

Digital storytelling (DST), which combines traditional storytelling with digital tools, can provide a narrative pedagogy that promotes critical thinking (CT). However, we found no previous study in medical education. The aim of the study was to investigate if DST can promote CT and, if so, which CT skills were improved. Thirty-two students participated in a non-equivalent control group pretest-posttest research study, with 16 in each group. The participants were fifth-year medical students on a hematology rotation. We compared the routine instructional method (control group) with DST (intervention group). The measures of CT used for the pre- and post-test in both groups was the Health Science Reasoning Test (HRST) and knowledge test. We also evaluated the satisfaction of the students in DST group. We used Paired and independent t-tests for comparing the mean scores. To eliminate the confounding effect of pre-test on the results of the intervention, the ANCOVA test was used. There was no significant difference in the overall CT pretest scores (P-value = 0.51) between the control and intervention groupsbut the difference was significant for the post-test scores (P-value = 0.03). Although post-test scores showed a significant increase (P-value = 0.002) compared to pre-test scores in the intervention group, no significant increase was observed in the control group (P-value = 0.26). Most students considered that DST improved their CT, deep learning, communication skills and team-working. The study demonstrated that DST promoted CT. We recommend the use of DST to promote CT in clinical education placements.

Microlearning is recommended to be implemented within the context of a wider teaching–learning ecosystem, especially in real working environments. Task-based learning is used in clinical education setting. This study aims at assessing the effect of an integrated approach of microlearning with task-based learning on medical students’ knowledge and performance in Ear, Nose and Throat clerkship rotation. A total of 59 final-year medical students participated in this quasi-experimental study which included two control groups (routine teaching and task-based learning methods) and one intervention group (combined microlearning and task-based learning method). Pre- and post-tests of students’ knowledge and performance were assessed through a multiple-choice question test and a Direct Observation Procedural Skills (DOPS) instrument, respectively. Performing Analysis of Covariance for knowledge post-test scores among three groups revealed significant differences (F = 3.423, p-value = 0.040) and the intervention group had the highest score. Analyzing DOPS results showed that the intervention group achieved significantly higher scores compared to the control ones for all the expected tasks (p-values = 0.001). The findings of the present study show that the combined strategy of microlearning with task-based learning is an effective clinical teaching method for enhancing medical students’ knowledge and performance in a real working environment.