Diagnostic reasoning is a key component of many professions. To improve students' diagnostic reasoning skills, educational psychologists analyse and give feedback on epistemic activities used by these students while diagnosing, in particular, hypothesis generation, evidence generation, evidence evaluation, and drawing conclusions. However, this manual analysis is highly time-consuming. We aim to enable the large-scale adoption of diagnostic reasoning analysis and feedback by automating the epistemic activity identification. We create the first corpus for this task, comprising diagnostic reasoning self-explanations of students from two domains annotated with epistemic activities. Based on insights from the corpus creation and the task's characteristics, we discuss three challenges for the automatic identification of epistemic activities using AI methods: the correct identification of epistemic activity spans, the reliable distinction of similar epistemic activities, and the detection of overlapping epistemic activities. We propose a separate performance metric for each challenge and thus provide an evaluation framework for future research. Indeed, our evaluation of various state-of-the-art recurrent neural network architectures reveals that current techniques fail to address some of these challenges.
DiaCDM: Cognitive Diagnosis in Teacher-Student Dialogues using the Initiation-Response-Evaluation Framework
Lab of Artificial Intelligence for Education, East China Normal University, China（人工智能教育实验室，华东师范大学，中国）；Shanghai Institute of Artificial Intelligence for Education, East China Normal University, China（人工智能教育上海研究院，华东师范大学，中国）；XR System Application Research Center, Nanchang Research Institute, Zhejiang University, China（XR系统应用研究中心，南昌研究所，浙江大学，中国）
DrawSim-PD: Simulating Student Science Drawings to Support NGSS-Aligned Teacher Diagnostic Reasoning
Department of Computer Science, Drexel University（德雷塞尔大学计算机科学系）；College of Chemistry, Beijing Normal University（北京师范大学化学学院）；Department of Teaching and Learning, Washington State University（华盛顿州立大学教学与学习系）
Using GPT-4 to Augment Unbalanced Data for Automatic Scoring
Franklin College of Arts and Sciences, University of Georgia（佐治亚大学富兰克林文理学院）；Mary Frances Early College of Education, University of Georgia（佐治亚大学玛丽·弗朗西斯·厄尔利教育学院）
