The quality of assessment determines the quality of learning, and is characterized by validity, reliability and difficulty. Mastery of learning is generally represented by the difficulty levels of assessment items. A very large number of variables are identified in the literature to measure the difficulty level. These variables, which are not completely independent of one another, are categorized into learner dependent, learner independent, generic, non-generic and score based. This research proposes a model for predicting the difficulty level of assessment items in engineering courses using learner independent and generic variables. An ordinal regression model is developed for predicting the difficulty level, and uses six variables including three stimuli variables (item presentation, usage of technical notations and number of resources), two content related variables (number of concepts and procedures) and one task variable (number of conditions). Experimental results from three engineering courses provide around 80% accuracy in classification of items using the proposed model.
Exploiting Cognitive Structure for Adaptive Learning
University of Science and Technology of China（中国科学技术大学）；University of Tennessee（田纳西大学）；Tianjin University（天津大学）；iFLYTEK CO., LTD.（科大讯飞股份有限公司）；State Key Laboratory of Cognitive Intelligence（认知智能全国重点实验室）
Artificial Intelligence Bias on English Language Learners in Automatic Scoring
School of Teacher Education, Nanjing Normal University（南京师范大学教师教育学院）；AI4STEM Education Center, University of Georgia（佐治亚大学AI4STEM教育中心）；School of Computing, University of Georgia（佐治亚大学计算机学院）；College of Education and Human Development, George Mason University（乔治马歇尔大学教育与人类发展学院）；Educational Testing Service（教育测评服务）
Latent Confidence Alignment for LLM Self-Assessment
Department of Information Management（信息管理系）；National Sun Yat-Sen University（国立中山大学）；Kaohsiung Medical University Hospital（高雄医学大学附设医院）；Kaohsiung Medical University（高雄医学大学）
