CS-Guide: Leveraging LLMs and Student Reflections to Provide Frequent, Scalable Academic Monitoring Feedback to Computer Science Students
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911334419922944 |
|---|---|
| author | Chacko, Samuel Jacob Wang, An-I Andy Perez-Felkner, Lara Haiduc, Sonia Whalley, David Liu, Xiuwen |
| author_facet | Chacko, Samuel Jacob Wang, An-I Andy Perez-Felkner, Lara Haiduc, Sonia Whalley, David Liu, Xiuwen |
| contents | Computer Science (CS) departments often serve large student populations, making timely academic monitoring and personalized feedback difficult. While the recommended counselor-to-student ratio is 250:1, it often exceeds 350:1 in practice, leading to delays in support and interventions. We present CS-Guide, which leverages Large Language Models (LLMs) to deliver scalable, frequent academic feedback. Weekly, students interact with CS-Guide through self-reported grades and reflective journal entries, from which CS-Guide extracts quantitative and qualitative features and triggers tailored interventions (e.g., academic support, health and wellness referrals). Thus, CS-Guide uniquely integrates learning analytics, LLMs, and actionable interventions using both structured and unstructured student-generated data.
We evaluated CS-Guide on a four-year, ~20K-entry longitudinal dataset, and it achieved up to a 97% F1 score in recommending interventions for first-year students. This shows that CS-Guide can enhance advising systems with scalable, consistent, timely, and domain-specific feedback. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19866 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CS-Guide: Leveraging LLMs and Student Reflections to Provide Frequent, Scalable Academic Monitoring Feedback to Computer Science Students Chacko, Samuel Jacob Wang, An-I Andy Perez-Felkner, Lara Haiduc, Sonia Whalley, David Liu, Xiuwen Computers and Society I.2.1; I.2.7; J.1; K.3.1; K.3.2 Computer Science (CS) departments often serve large student populations, making timely academic monitoring and personalized feedback difficult. While the recommended counselor-to-student ratio is 250:1, it often exceeds 350:1 in practice, leading to delays in support and interventions. We present CS-Guide, which leverages Large Language Models (LLMs) to deliver scalable, frequent academic feedback. Weekly, students interact with CS-Guide through self-reported grades and reflective journal entries, from which CS-Guide extracts quantitative and qualitative features and triggers tailored interventions (e.g., academic support, health and wellness referrals). Thus, CS-Guide uniquely integrates learning analytics, LLMs, and actionable interventions using both structured and unstructured student-generated data. We evaluated CS-Guide on a four-year, ~20K-entry longitudinal dataset, and it achieved up to a 97% F1 score in recommending interventions for first-year students. This shows that CS-Guide can enhance advising systems with scalable, consistent, timely, and domain-specific feedback. |
| title | CS-Guide: Leveraging LLMs and Student Reflections to Provide Frequent, Scalable Academic Monitoring Feedback to Computer Science Students |
| topic | Computers and Society I.2.1; I.2.7; J.1; K.3.1; K.3.2 |
| url | https://arxiv.org/abs/2512.19866 |