This innovative practice full paper delves into the transformative role of Learning Assistants (LAs) in Computer Science education, focusing on enhancing student engagement and improving learning outcomes. The LA model, which aligns with Vygotsky's Social Constructivist Learning Theory, fosters an environment of student-centered learning and social interaction. In a pilot study conducted in Spring 2024 at a public university, the LA program is implemented in two computer science courses. A quasi-experimental design has been used to evaluate the impact of LA-facilitated team activities on student learning outcomes. The study compares a control group receiving traditional instruction with an experimental group participating in LA-facilitated team activities. The experimental group engaged in weekly team-based activities, guided by LAs and faculty, to reinforce class concepts and promote collaboration among team members. Student engagement and learning have been evaluated using feedback from students and LAs collected through Discussion Boards (DBs). Preliminary findings suggest that LA-facilitated in-class activities promote active learning and enhance problem-solving skills. LAs provide valuable support and guidance to students, particularly those struggling to understand complex concepts. The study tested a working model of AIELA, an innovative AI-powered chatbot that assists human LAs in supporting students through knowledge-reinforcing questions and multimodal data analysis, powered by OpenAI API's gpt-4-turbo model. This research is a step towards embracing the challenges of modern CS education, inspiring further innovation in this critical field. The findings will benefit educators seeking innovative strategies to enrich student engagement and learning in engineering and computing disciplines.

This innovative practice full paper explores the use of AI technologies in user story generation. With the emergence of agile software development, generating comprehensive user stories that capture all necessary functionalities and perspectives has become crucial for software development. Every computing program in the United States requires a semester-or year-long senior capstone project, which requires student teams to gather and document technical requirements. Effective user story generation is crucial for successfully implementing software projects. However, user stories written in natural language can be prone to inherent defects such as incompleteness and incorrectness, which may creep in during the downstream development activities like software designs, construction, and testing. One of the challenges faced by software engineering educators is to teach students how to elicit and document requirements, which serve as a blueprint for software development. Advanced AI technologies have increased the popularity of large language models (LLMs) trained on large multimodal datasets. Therefore, utilizing LLM-based techniques can assist educators in helping students discover aspects of user stories that may have been overlooked or missed during the manual analysis of requirements from various stakeholders. The main goal of this research study is to investigate the potential application of OpenAI techniques in software development courses at two academic institutions to enhance software design and development processes, aiming to improve innovation and efficiency in team project-based educational settings. The data used for the study constitute student teams generating user stories by traditional methods (control) vs. student teams using OpenAI agents (treatment) such as gpt-4-turbo for generating user stories. The overarching research questions include: RQ-l) What aspects of user stories generated using OpenAI prompt engineering differ significantly from those generated using the traditional method? RQ-2) Can the prompt engineering data provide insights into the efficacy of the questions/prompts that affect the quality and comprehensiveness of user stories created by software development teams? Industry experts evaluated the user stories created and analyzed how prompt engineering affects the overall effectiveness and innovation of user story creation, which provided guidelines for incorporating AI-driven approaches into software development practices. Overall, this research seeks to contribute to the growing body of knowledge on the application of AI in software engineering education, specifically in user story generation. Investigating the use of AI technologies in user story generation could further enhance the usability of prompt engineering in agile software development environments. We plan to expand the study to investigate the long-term effects of prompt engineering on all phases of software development.

The Covid-19 pandemic, as Henry Kissinger mentions, will not only "forever alter the world order," but also potentially transform the ever-changing higher education world. The recent increase in technological innovations in information, communications, and computer technology has profoundly transformed traditional teaching-learning processes and peer-to-peer interactions for knowledge transfer. One such radical change in technology that researchers are continually working on is motivating collaborative learning and student interactions to improve their learning experiences. Collaborative Learning (CL), where students work in groups to achieve a specific learning objective, can facilitate a deep learning activity that promotes student participation. However, the potential of discussion forums is limited due to their unstructured nature in LMSs like Canvas.We propose and develop a structured discussion forum that can offer a platform to communicate and discuss problems and receive feedback, discuss solutions, and suggestions online. Students who participate in these discussion forums can benefit in multiple ways, including increased class preparedness and more active learning. The twofold objectives and outcomes include 1) analyzing discussion board data to reveal students’ interaction and their degree of participation in the course, and 2) developing a toolset to draw useful inferences from such collaboration networks. Specifically, our schema-based model can help students visualize the discussion board networks creating an engaged learning environment. Furthermore, the model can help draw valuable inferences of the patterns of student interactions and assess student participation and belonging in the course with greater precision.This paper demonstrates a schema-based discussion board model that can allow researchers to collect better-formatted discussion data and more reliable information about the posts, such as the type of posts and the relationships of each post with others. The reimagined discussion boards include the ability to classify discussion posts using various parameters, visualize the posts’ patterns of interactions, identify their relationships with other discussion posts, and precisely evaluate student participation in discussions to monitor the major topics of discussion. We believe that the result of increased participation in discussions with other students will have the effect of increasing students’ sense of belonging to the community of scholars.

Node-based social network analysis (SNA) techniques can be used to investigate the significance of actors that play central roles in social networks where the nodes represent people, teams or stakeholders and the links represent the communication, information exchange or collaboration between these nodes (actors). This research investigates how collaborative problem-solving can help in students' learning process. We analyzed the discussion board data collected from online student discussions on Canvas, a Learning Management System (LMS), in a CS1 course of a medium-sized US University. The discussion topics were classified as classroom experiences/learning, question/answers, opinions, and comments and were used to represent the patterns of interactions in the student discussion networks. Node-based network measures were then applied to unravel the students' interaction patterns to gain insights on students' progress. The textual analysis helps find the most challenging/debated topics in a particular course, analyze the leadership and team-based qualities of a group of students, and analyze patterns and trends in female student participation. The experimental analysis reveals that participation in online discussion forums has a positive impact on the students' grades; the study of interaction patterns exhibit similar insights. In conclusion, this research study validates that the analysis of structured discussions can provide useful insights into changes in student collaboration patterns over time and students' sense of belongingness for pedagogical benefits.

A variety of cyberlearning environments and online student collaboration techniques are used in Science, Technology, Engineering and Math (STEM) courses to analyze and enhance academic performance. However, only a few research studies in the past have investigated the need for better modeling techniques of the unstructured discussion forums to enable measuring the influence of interactions in 'dynamically evolving' student collaboration networks as the course progresses. This paper focuses on discussing both current collaborative learning techniques and analyzing the need for developing an innovative network model involving more structured/goal-oriented discussion forums that portray active collaborative learning techniques. Such a structured collaborative network model can be used to investigate the impact that student collaborations have on knowledge acquisition, persistence and course outcomes. The research objective leads to the research question this study addresses on the potential of the unstructured and dynamically evolving discussion boards to measure the ability of the learners to communicate technical information effectively as the course progresses. The experimental analysis of the weighted and undirected temporal (time-varying or dynamic) student discussion network data and the inferences drawn from such an exploratory analysis have revealed the pedagogical need for and the utility of schema-based structured discussion boards. These structured discussions would enable a pragmatic study to analyze the changes in student collaboration patterns and belongingness.

Programming is a skill, often acquired through repeated practice and feedback. During traditional lectures, students not actively engaged in their own learning. It is imperative to pique students motivation and direct their focus on gaining the requisite knowledge. As the class size grows, instructors feedback is delayed that impacts student engagement and learning. Educational researchers have supported using web-based tools to help evaluate student work, provide timely feedback and increase the amount of time they spend improving their skills. Motivated by the previous work, our team has developed the SEP-CyLE (Software Engineering and Programming Cyber Learning Environment) - a cyber learning environment that contains digital learning content of software programming and testing concepts. SEP-CyLE incorporates collaborative learning, social networking and gamification-based learning engagement strategies (LESs) that has led to an improved motivation and understanding of programming concepts. This paper aims to assess the impact of different combinations of these LESs on student learning in the context of CS1 classrooms. We coordinated studies at two universities wherein different combination of LESs were utilized using SEP-CyLE in CS1 classrooms. We analyzed the impact of LESs on students' acquisition of programming concepts, their engagement and usage of SEP-CyLE. The pre and post test results indicated that the assorted LEs have shown a positive impact on student learning across all the institutions. The correlation results demonstrated that there is meaningful relationship between the LEs and the student performance.