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Community colleges provide broad access to a college degree due to their less expensive tuition, greater course time offerings, and more open admission policies compared to four-year universities as reported (Juszkiewicz, 2015). These institutions have great potential to diversify who chooses STEM, such as engineering. Such diverse representation is important, because students from different backgrounds can contribute new perspectives that foster creative and innovative approaches to solving problems. However, approximately 40% of the students who enter community college in the U.S. leave before graduation (Xu & Jaggars, 2011). Harnessing the power of social media to reduce attrition, we tested a pilot intervention in which community college students in engineering courses completed a reflection exercise after watching YouTube videos of former engineering students from similar backgrounds who successfully transferred to a four-year college. Quasi-experimental analyses (N = 537) show that students in the intervention received higher engineering course grades and had greater odds of enrolling in an engineering course in the subsequent term compared to those who were not in the intervention. Additionally, we find support for the hypothesis that the intervention has larger benefits for women. Results suggest that hearing from other students about their engineering experience can be a useful tool for improving community college students’ choices and performance.

 

Despite theoretical benefits of replayability in educational games, empirical studies have found mixed evidence about the effects of replaying a previously passed game (i.e., elective replay) on students’ learning. Particularly, we know little about behavioral features of students’ elective replay process after experiencing failures (i.e., interruptive elective replay) and the relationships between these features and learning outcomes. In this study, we analyzed 5th graders’ log data from an educational game, ST Math, when they studied fractions—one of the most important but challenging math topics. We systematically constructed interruptive elective replay features by following students’ sequential behaviors after failing a game and investigated the relationships between these features and students’ post-test performance, after taking into account pretest performance and in-game performance. Descriptive statistics of the features we constructed revealed individual differences in the elective replay process after failures in terms of when to start replaying, what to replay, and how to replay. Moreover, a Bayesian multi-model linear regression showed that interruptive elective replay after failures might be beneficial for students if they chose to replay previously passed games when failing at a higher, more difficult level in the current game and if they passed the replayed games. 

With the rising of remote work and schooling, the adaption of emerging technologies to teach the concepts of cybersecurity becomes critical. In this work, we present the concept, design, and prototype of a Mixed Reality-based cybersecurity education application on phishing, so that school children could be exposed to the subject remotely and practice to differentiate malicious from genuine messages. 

School closures during the COVID-19 pandemic presented a threat to student learning and motivation. Suspension of achievement testing created a barrier to understanding the extent of this threat. Leveraging data from a mathematics learning software as a substitute assessment, we found that students had lower engagement with the software during the pandemic, but students who did engage had increased performance. Students also experienced changes in motivation: lowered mathematics expectancy, but also lower emotional cost for mathematics. Results illustrate the potential and pitfalls of using educational technology data in lieu of traditional assessments and draw attention to access and motivation during at-home schooling. 

With the shortage of cybersecurity professionals, there is a critical need to train more young-generation cybersecurity professionals to fill the gap. In this work, we designed interactive activities that make abstract cybersecurity concepts more tangible by using exciting new mixed reality (MR) technology to teach cybersecurity skills and raise the potential interest in cybersecurity careers for middle school students. We plan to analyze the immersive experience, situational interest, and workload after the experiment to study the participants' learning performance and user experience. 

To understand instruction during the spring 2020 transition to emergency distance learning (EDL), we surveyed a sample of instructors teaching undergraduate EDL courses at a large university in the southwest. We asked them how frequently they used and how confident they were in their ability to implement each of nine promising practices, both for their spring 2020 EDL course and a time when they previously taught the same course face-to-face (F2F). Using latent class analysis, we examined how behavioral frequencies and confidence clustered to form meaningful groups of instructors, how these groups differed across F2F and EDL contexts, and what predicted membership in EDL groupings. Results suggest that in the EDL context, instructors fell into one of three profiles in terms of how often they used promising practices: Highly Supportive, Instructor Centered, and More Detached. When moving from the F2F to EDL context, instructors tended to shift “down” in terms of their profile—for example, among F2F Highly Supportive instructors, 34% shifted to the EDL Instructor Centered profile and 30% shifted to the EDL More Detached Profile. Instructors who reported lower self-efficacy for EDL practices were also more likely to end up in the EDL More Detached profile. These results can assist universities in understanding instructors' needs in EDL, and what resources, professional development, and institutional practices may best support instructor and student experiences.