Bite-sized and peer-assisted video-based learning in statistics education: benefits on attainment, attitudes and preferences of university students

The use and acceptance of online learning have increased following the COVID-19 pandemic. This mixed-methods study examined learners' preferences and performance in online learning interactions in relation to two factors: 'bite-sized' learning and 'presenter status' in instructional videos. University students (N = 18) without a mathematical background utilised bite-sized online learning episodes focusing on statistics. Each episode included a 10-minute instructional video followed by an assessment. The videos implemented three alternative 'presenter-status' conditions: lecturer, student-imitating-lecturer or student-peer-tutor. Individual students completed three episodes, one from each presenter-status condition (counterbalanced). Participants presented high performance in the post-episode assessments, irrespective of presenter status. Students also reported remarkably positive views towards bite-sized learning in user-satisfaction questionnaires. Finally, qualitative analysis of open-ended responses and interviews uncovered three main themes: positive learning experiences, divergent attitudes towards video-based learning, and differential preferences for presenter status. These findings have clear implications for teaching and learning in higher education.

Can Personality Traits Predict Students' Satisfaction with Blended Learning during the COVID-19 Pandemic?

The present study aimed to assess the impact of personality traits on student satisfaction with blended learning which many higher education institutions have adopted since the COVID-19 pandemic in the UK. Personality traits were assessed using the International Personality Item Pool and student satisfaction was recorded on a 7-point Likert scale. Data analysis of 72 undergraduate students revealed that low extraversion and high neuroticism predicted higher levels of student satisfaction. Implications are discussed considering the current pandemic with a view of increasing student satisfaction and in-turn improving National Student Survey results that impact on Teaching Excellence Framework scores and league tables. © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.

Predicting peak and cumulative ventilator need for COVID-19 in the US: Development of an epidemiological model

Rationale: The COVID-19 pandemic has caused major challenges for healthcare. The availability of mechanical ventilators was particularly problematic early on, with discussion about the appropriateness of invasive mechanical (IMV) vs. non-invasive ventilation (NIV) in the management of COVID-19. Various prediction models were developed, but few projected COVID-19-related ventilator use. We aimed to estimate peak and cumulative IMV and NIV need in the US through May 2021. Methods: We used a modified Susceptible-Infected-Recovered model with four additional compartments: exposed (E), in-hospital NIV, in-hospital IMV, and NIV or CPAP (HNIV/CPAP) treated out-of-hospital, based on published ventilator use patterns for COVID-19. Three scenarios (best-, moderate-, and worst-case) were modeled to reflect levels of intervention (e.g., shelter-in-place) effectiveness and compliance. Results included initial peak date, peak ventilator events, ventilator shortfall and timing of subsequent waves. Model performance was compared with estimates of IMV use back-calculated from observed mortality data, assuming a 75% mortality rate for patients on an IMV in the ICU based on published estimates. Results: At the start of the pandemic, the US was estimated to have 62,188 full-featured IMV;22,976 NIV;88,462 non full-featured devices in hospitals for use as surge devices;plus 12,700 in the Strategic National Stockpile. The moderate-case scenario aligned most closely to back-calculated IMV use based on observed mortality, and suggested that initial peak ventilator need occurred around May 1, 2020, requiring 94,472 inhospital ventilators (40,930 IMV;53,541 NIV) and 7,931 HNIV/CPAP. In this scenario, the US had sufficient IMV (+21,258) but there may have been a shortfall of 30,565 in-hospital NIV, triggering surge use in some locations. The US is estimated to have 1,915,217 cumulative ventilator use events by May 2021, of which 688,549 are IMV events and 1,226,668 are NIV+HNIV/CPAP events. At least two subsequent waves were estimated to occur prior to May 2021: fall of 2020 and early 2021. Conclusion: We estimated the US may need to utilize surge devices to have sufficient in-hospital NIV during peak need periods. Although IMV supplies seem generally adequate, improved strategies to track and share equipment, i.e., move ventilators from centers with surplus to centers in need, should be developed. This model may inform resource planning and allocation for which patients require ventilators in subsequent waves. Further validation will be beneficial using observed data for hospitalization, ventilator utilization and mortality, as well as adjustments for shifting trends in clinical practice such as use of high-flow nasal cannula.