From 6d38a75a7904f1571d4c0606efc0fc7df6b72662 Mon Sep 17 00:00:00 2001
From: zichaow <wangzichao6@gmail.com>
Date: Tue, 10 Sep 2024 10:08:38 -0700
Subject: [PATCH] add one paper to the proceedings

---
 _posts/2024-08-09-kumar24.md | 38 ++++++++++++++++++++++++++++++++++++
 ai4ed-aaai24.bib             | 11 ++++++++++-
 2 files changed, 48 insertions(+), 1 deletion(-)
 create mode 100644 _posts/2024-08-09-kumar24.md

diff --git a/_posts/2024-08-09-kumar24.md b/_posts/2024-08-09-kumar24.md
new file mode 100644
index 0000000..a055c9f
--- /dev/null
+++ b/_posts/2024-08-09-kumar24.md
@@ -0,0 +1,38 @@
+---
+title: 'Using Large Language Models for Student-Code GuidedTest Case Generation in Computer Science Education'
+booktitle: Proceedings of the 2024 AAAI Conference on Artificial Intelligence
+year: '2024'
+abstract: 'In computer science education, test cases are an integral part of programming assignments since they can be used as assessment items to test students' programming knowledgeand provide personalized feedback on student-written code. The goal of our work is to propose a fully automated approach for test case generation that can accurately measure student knowledge, which is important for two reasons. First, manually constructing testcases requires expert knowledge and is a labor-intensive process. Second, developing test cases for students, especially those who are novice programmers, is significantly different from those oriented toward professional-level software developers. Therefore, we need an automated process for test case generation to assess student knowledge and provide feedback. In this work, we propose a large language model-based approach to automatically generate test cases and show that they are good measures of student knowledge, using a publicly available dataset that contains student-written Java code. We also discuss future research directions centered on using test cases to help students.'
+layout: inproceedings
+series: Proceedings of Machine Learning Research
+publisher: PMLR
+issn: 2640-3498
+id: kumar24
+month: 0
+tex_title: 'Learning to Compare Hints: Combining Insights from Student Logs and Large
+  Language Models'
+firstpage: 170
+lastpage: 179
+page: 170-179
+order: 170
+cycles: false
+bibtex_author: Ashok Kumar, Nischal and Andrew S., Lan
+author:
+- given: Nischal
+  family: Ashok Kumar
+- given: Andrew S.
+  family: Lan
+date: 2024-08-09
+address:
+container-title: Proceedings of the 2024 AAAI Conference on Artificial Intelligence
+volume: '257'
+genre: inproceedings
+issued:
+  date-parts:
+  - 2024
+  - 8
+  - 9
+pdf: https://raw.githubusercontent.com/mlresearch/v257/main/assets/kumar24/kumar24.pdf
+extras: []
+# Format based on Martin Fenner's citeproc: https://blog.front-matter.io/posts/citeproc-yaml-for-bibliographies/
+---
diff --git a/ai4ed-aaai24.bib b/ai4ed-aaai24.bib
index 8d7ae8f..b083fc9 100644
--- a/ai4ed-aaai24.bib
+++ b/ai4ed-aaai24.bib
@@ -215,4 +215,13 @@ @inproceedings{moringen24
 }
 
 
-
+@inproceedings{kumar24,
+  title     = {Using Large Language Models for Student-Code GuidedTest Case Generation in Computer Science Education},
+  author    = {Ashok Kumar, Nischal and Andrew S., Lan},
+ booktitle = {Proceedings of the 2024 AAAI Conference on Artificial Intelligence},
+  year      = {2024},
+  volume = {257},
+  pages     = {170-179},
+  abstract  = {In computer science education, test cases are an integral part of programming assignments since they can be used as assessment items to test students' programming knowledgeand provide personalized feedback on student-written code. The goal of our work is to propose a fully automated approach for test case generation that can accurately measure student knowledge, which is important for two reasons. First, manually constructing testcases requires expert knowledge and is a labor-intensive process. Second, developing test cases for students, especially those who are novice programmers, is significantly different from those oriented toward professional-level software developers. Therefore, we need an automated process for test case generation to assess student knowledge and provide feedback. In this work, we propose a large language model-based approach to automatically generate test cases and show that they are good measures of student knowledge, using a publicly available dataset that contains student-written Java code. We also discuss future research directions centered on using test cases to help students.
+}
+}
\ No newline at end of file