Peer code review, which involves programmers examining and evaluating one other's code for quality, has been adopted in Computer Science (CS) classrooms to provide authentic learning experiences, support collaboration, develop programming skills, and facilitate peer learning. Although a body of research has explored the effectiveness of peer code review as learning activities, limited work focused on how the role of reviewer/reviewee impacts students' learning during the peer code review process. This work shares preliminary findings from the implementation of peer code review in a senior CS course. The instructor designed a double-blind peer code review activity in which students first individually review an assigned submission by completing a code review checklist and then, engage in group discussion. Through surveys, we examined students' perceptions of learning through peer code review. We found that students highly valued the opportunity to review others' solutions, thought it somewhat challenging to provide quality feedback, and had mixed opinions about being reviewed.

One of the primary goals of introductory programming courses is to develop fundamental programming competencies, including the ability to design and implement computer programs. Computer-based laboratory examinations, or lab exams, are considered a valid, efficient, and reliable assessment method for these competencies. In a lab exam, students are required to complete programming tasks in a proctored and time-limited setting with measures to prevent academic dishonesty. However, the proliferation of Generative Artificial Intelligence (GenAI) has raised new challenges for academic integrity during lab exams. In this work, we propose an approach to selectively block common GenAI tools during lab exams and share our preliminary experience using it in an introductory programming course.

Live coding has gained its prominence in Computer Science (CS) classrooms as it enhances learning experiences by providing real-time demonstrations of programming and debugging during lectures. However, live coding may also present challenges to the effectiveness and inclusiveness of the classroom environment. This experience report presents the use of live coding in an introductory level CS course to better understand its impact on novice learners and their perceptions of the strategy. We conducted a between-subjects study at a public university in the United States across two course offerings. Specifically, the instructor taught the same course in two consecutive quarters: one with live coding and one without. Through assignment and exam scores, grades, and surveys, we compared data from students who experienced live coding with those who did not. We found that while live coding prompted engagement and interaction, and students believed that it helped them learn better, there was no statistically significant difference in course performance in terms of assignment and exam scores, nor in final grades. Additionally, most students prefer to keep live coding in lectures despite the challenges it presents. We also shared challenges, lessons, and practical instructional strategies learned from this experience, in the hope that they will contribute to developing more engaging and inclusive learning experiences in CS classrooms.

GPT has demonstrated impressive capabilities in executing various natural language processing (NLP) and reasoning tasks, showcasing its potential for deductive coding in social annotations. This research explored the effectiveness of prompt engineering and fine-tuning approaches of GPT for deductive coding of context-dependent and context-independent dimensions. Coding context-dependent dimensions (i.e., Theorizing, Integration, Reflection) requires a contextualized understanding that connects the target comment with reading materials and previous comments, whereas coding context-independent dimensions (i.e., Appraisal, Questioning, Social, Curiosity, Surprise) relies more on the comment itself. Utilizing strategies such as prompt decomposition, multi-prompt learning, and a codebook-centered approach, we found that prompt engineering can achieve fair to substantial agreement with expert-labeled data across various coding dimensions. These results affirm GPT's potential for effective application in real-world coding tasks. Compared to context-independent coding, context-dependent dimensions had lower agreement with expert-labeled data. To enhance accuracy, GPT models were fine-tuned using 102 pieces of expert-labeled data, with an additional 102 cases used for validation. The fine-tuned models demonstrated substantial agreement with ground truth in context-independent dimensions and elevated the inter-rater reliability of context-dependent categories to moderate levels. This approach represents a promising path for significantly reducing human labor and time, especially with large unstructured datasets, without sacrificing the accuracy and reliability of deductive coding tasks in social annotation. The study marks a step toward optimizing and streamlining coding processes in social annotation. Our findings suggest the promise of using GPT to analyze qualitative data and provide detailed, immediate feedback for students to elicit deepening inquiries.

To address the challenge of teaching the interdisciplinary foundations of data science in computing, mathematics and statistics, we propose a mathematical logic based framework to seamlessly and coherently integrate these foundations. A 8-week module based on the framework is implemented in a high school. The results show an overall feasibility of the integrated approach.

Abstraction, which is considered the most important computational thinking skill, can be learned from programming or computational thinking learning activities. We implemented a 8-week long course to teach high school students statistics and programming. A pre- and post-test was designed to measure students’ understandings of computing and statistics. This work reports some interesting observations we made on students’ misconception of abstraction while examining students’ responses to test questions.

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.

During disasters, discover people's concerns dynamically is crucial to disaster rescue and relief. In this paper, we propose a social media based framework to analyze people's concerns, to access the importance and to track the dynamic changes of these concerns. To better understand people's concerns across platforms and to monitor the dynamics, we make comparisons between Tweets and news on the mentioned aspects and disclosed some interesting findings. Specifically, we take 2018 Camp Fire, the most destructive wildfire on record in history of California as a case study. We find that despite their keen attentions towards the disaster, social media and news media focus on different aspects of the disaster, so are the contents and dynamic changes of their concerns.

It is of paramount importance to know the situations of people who undergone disaster events and be aware of their updates, yet it is not an easy job to accomplish in the chaos of a disaster. To facilitate advanced disaster relief organization and efficient supplement distribution, we develop NiPred, a social media based need prediction prototype that predicts needs for victims across the affected area. NiPred first extracts problems and concerns posted by victims of disaster-hurricane, in our case study; then displays the statistics to offer an overview for awareness and further analysis; and last, predicts the needs such as "diaper", "boat", "canoe" and "shelter" etc. for disaster relief planning.