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Clinical reasoning is a critical yet complex cognitive process of diagnostic and therapeutic decision-making in medical practice that has long challenged precise understanding and assessment. Sequential analysis can be used to uncover patterns and trends in clinical practices, contributing to improved training and ultimately leading to better patient care outcomes. In this study, 21 board-certified anesthesiologists participated in a simulated-based learning scenario requiring them to promptly recognize patient’s condition and initiate appropriate treatment. They were assigned into either the low-performing or high-performing group based on their performance. We utilized Markov Chain Transition Matrix, a robust statistical model for sequential data, to analyze participants’ practices using team reflection behavioral observation system and identified statistically significant differences between their transition matrices. The high-performing group had a much higher transition probability from evaluating information to implementation and from planning to planning. The implications are then discussed. 

Engineering design that requires mathematical analysis, scientific understanding, and technology is critical for preparing students for solving engineering problems. In simulated design environments, students are expected to learn about science and engineering through their design. However, there is a lack of understanding concerning linking science concepts with design problems to design artifacts. This study investigated how 99 high school students applied science concepts to solarize their school using a computer-aided engineering design software, aiming to explore the interaction between students’ science concepts and engineering design behaviors. Students were assigned to three groups based on their design performance: the achieving group, proficient group, and emerging group. By mining log activities, we explored the interactions among students’ application of science concepts, engineering design behaviors, design iterations, and their design performance. We found that the achieving group has a statistically higher number of design iterations than the other two performance groups. We also identified distinctive transition patterns in students’ applying science concepts and exercising design behaviors among three groups. The implications of this study are then discussed. 

Abstract: There is a consensus to introduce data science to secondary schools. However, data science is interdisciplinary in nature and not easy to teach in K−12 settings. We proposed a new approach to integrate mathematics, statistics, and programming—the foundations of data science—for high school students based on set theory and logic. We developed an 8-week data science foundation course and implemented it in a public high school. We conducted semistructured group interviews to collect students’ feedback on the course and the new approach. Students thought the approach could well connect the topics and helped them learn the interdisciplinary content. 

Participating in online communities has significant benefits to students learning in terms of students’ motivation, persistence, and learning outcomes. However, maintaining and supporting online learning communities is very challenging and requires tremendous work. Automatic support is desirable in this situation. The purpose of this work is to explore the use of deep learning algorithms for automatic text generation in providing emotional and community support for a massive online learning community, Scratch. Particularly, state-of-art deep learning language models GPT-2 and recurrent neural network (RNN) are trained using two million comments from the online learning community. We then conduct both a readability test and human evaluation on the automatically generated results for offering support to the online students. The results show that the GPT-2 language model can provide timely and human-written like replies in a style genuine to the data set and context for offering related support. 

Novice programming learners encounter programming errors on a regular basis. Resolving programming errors, which is also known as debugging, is not easy yet important to programming learning. Students with poor debugging ability hardly perform well on programming courses. A debugging learning trajectory which identifies learning goals, learning pathways, and instructional activities will benefit debugging learning activities development. This study aims to develop a debugging learning trajectory for text-based programming learners. This is accomplished through (1) analyzing programming errors in a logic programming learning environment and (2) examining existing literature on debugging analysis.