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In the wake of the Covid-19 pandemic, most universities had to switch to "emergency online learning". At the same time, academics were in search of means to combat "the infodemic", a wave of misinformation rolling over the world, affecting social and political life, and undermining efforts to deal with the pandemic. In the framework of emergency online learning, we propose an educational sciences undergraduate online course addressing fake news illiteracy by giving students an insight into the form and effects of fake news with a focus on framing. The course was built upon current fake news research and the problem-based learning approach. The research questions addressed students' perceptions of critical design elements, their fake news credibility test performance, and their academic achievement. A total of N = 102 undergraduate students participated in the course. Among various design elements, students indicated that online communication and feedback was most appealing. On the other hand, for future course iterations, they suggested improvements to the task descriptions. Fake news credibility decreased significantly (F (1, 36) = 62.64, p < 0.000, partial η 2 = 0.64) and final course papers were on average good to very good, indicating strong academic achievement. The study suggests that problem-based online courses can be appropriate learning environments, even in the context of "emergency online learning" and, furthermore, that they can serve as an instrument for combating fake news illiteracy. • We address emergency online learning and infodemic during Covid-19 pandemic. • We review current literature on online news cognitive processing and problem-based instructional design. • We design, develop and test an educational sciences undergraduate online problem-based course on fake news. • Student feedback on the appeal of certain design elements was mostly positive; improvements of others were suggested. • Student fake news credibility assessment strongly improved; course papers reflected good to very good academic achievement. [ABSTRACT FROM AUTHOR]

Flipped classroom is a relatively new model in education that primarily focuses on learner-centered instructions. In other words, the model allows both management and teachers to build a more active and dynamic learning environment on the campus. The current paper tries to document the implementation of the flipped classroom model in two big data courses. Here, the course contents have been curated from a couple of websites with different contents including videos and short books as well as reports. The mixed-method approach was applied while analyzing the student perceptions in demographically two different groups. It was found that students of both groups responded positively to the flipped classroom, with each focusing on their specific goals. Consequently, the first group focused on the academic achievement whereas the second group with managerial jobs focused on solving problems in their workplaces. Students of both groups, although preferred TED talks and documentaries, they were opposed to university-like videos and O'Reilly short books and reports. Meanwhile, the use of English language contents turned out to be both a challenge and an opportunity for students. [ABSTRACT FROM AUTHOR]

A university education is widely considered essential to social advancement. Ensuring students pass their courses and graduate on time have thus become issues of concern. This paper proposes a reduced training vector-based support vector machine (RTV-SVM) capable of predicting at-risk and marginal students. It also removes redundant training vectors to reduce the training time and support vectors. To examine the effectiveness of the proposed RTV-SVM, 32,593 university students on seven courses were chosen for performance evaluation. Analysis reveals that the RTV-SVM achieved a training vector reduction of at least 59.7% without altering the margin or accuracy of the classifier. Moreover, the results showed the proposed method to be capable of achieving overall accuracy of 92.2–93.8% and 91.3–93.5% in predicting at-risk and marginal students, respectively. • Proposed algorithm predicts both marginal and at-risk university students. • Training vectors reduce by more than 59% while maintaining the prediction accuracy. • Overall accuracy of 91.3–93.5% in predicting marginal university students. • Overall accuracy of 92.2–93.8% in predicting at-risk university students. [ABSTRACT FROM AUTHOR]