This innovative practice paper presents an LLM-supported technique to help instructors respond effectively to periodic students' reflections. Efficient communication between instructors and students is integral to supporting a productive learning environment. Recognizing the significance of understanding students' perceptions and challenges, we present the initial implementation of a system to help instructors analyze and respond to students' feedback promptly and effectively. This research is inspired by and extends prior works where instructors sent progress check emails to students, with some works finding that such communication increased students' motivation. To collect feedback, we administer regular student reflections throughout the semester that capture how students feel about the course and uncover the challenges they face. This regular feedback-gathering approach allows instructors to better track their students' progress and respond to comments throughout the semester to provide guidance. However, reading and responding to each reflection manually in the context of their overall learning experience can be time consuming. To address this challenge, we introduce an LLM-based automated approach that generates tailored, performance-contextualized responses to student reflections that can be used to guide first-contact interventions. The generated reflection responses (GRRs) address issues discussed in student reflections and provide advice, support, course information, and follow-up questions to the students. Additionally, they provide feedback to students based on their accomplishments and behavioral data within the learning management system (LMS), such as submission patterns. In this work, we discuss our method of generating responses based on students' reflections and their LMS behavior. We also present example scenarios of the proposed approach. Preliminary results indicate that this approach can help instructors facilitate positive educational interactions with students and that the participating students view the interventions favorably, fostering a constructive learning environment. This work provides an initial presentation of our large language model-based response generation method to motivate further investigation into AI-assisted student support mechanisms

To provide an education experience tailored to students’ needs, instructors’ awareness of the students’ challenges is essential. Course evaluations or intermittent feedback prompts can help facilitate this awareness and provide students an avenue of self-expression. My work, however, focuses on the analysis of student feedback to extract students’ emotional states as related to learning from periodic written student reflections. Emotion detection of students is a hot topic in education and has been explored through various perspectives, whether to predict student performance or attrition, to identify students for academic intervention. My work explores the use of emotionally-charged reflections to supplement traditional means of analysis and provide instructors with context for students’ behavior to help them develop such interventions. To address this vision, I present a framework to analyze reflections over time (ROTF) and investigate ways to convey and analyze reflections using text-processing and machine learning.

Without a sense of belonging, students may become disheartened and give up when faced with new challenges. Moreover, with the sudden growth of remote learning due to COVID-19, it may be even more difficult for students to feel connected to the course and peers in isolation. Therefore, we propose a recommendation system to build connections between students while recommending solutions to challenges. This pilot system utilizes students’ reflections from previous semesters, asking about learning challenges and potential solutions. It then generates sentence embeddings and calculates cosine similarities between the challenges of current and prior students. The possible solutions given by previous students are then recommended to present students with similar challenges. Self-reflection encourages students to think deeply about their learning experiences and benefit both learners and instructors. This system has the potential to allow reflections also to help future learners. By demonstrating that previous students encountered and overcame similar challenges, we could help improve students’ sense of belonging. We then perform user studies to evaluate this system’s potential and find that participants rated 70% of the recommended solutions as useful. Our findings suggest an increase in students’ sense of membership and acceptance, and a decrease in the desire to withdraw.