This article presents a computational model of the learning of diagnostic knowledge based on observations of human operators engaged in a real-world troubleshooting task. We present a model of problem solving and learning in which the reasoner introspects about its own performance on the problem solving task, identifies what it needs to learn to improve its performance, formulates learning goals to acquire the required knowledge, and pursues its learning goals using multiple learning strategies. The model is implemented in a computer system which provides a case study based on observations of troubleshooting operators and protocol analysis of the data gathered in the test area of an operational electronics manufacturing plant. The model is intended as a computational model of human learning; in addition, it is computationally justified as a uniform, extensible framework for multistrategy learning. Technical Report GIT-CC-93/67, College of Computing, Georgia Institute of Technology, A...

. Problem-solving methods are means to describe the inference process of knowledge-based systems. During the last years, a number of these problemsolving methods have been identified that can be reused for building new systems. However, problem-solving methods require specific types of domain knowledge and introduce specific restrictions on the tasks that can be solved by them. These requirements and restrictions are assumptions that play a key role in reusing problem-solving methods, in acquiring domain knowledge, and in defining the problem that can be tackled by the knowledge-based systems. In the paper, we discuss the different roles, assumptions play in the development process of knowledge-based systems and provide a survey of assumptions used by diagnostic problem solving. We show how such assumptions introduce target and bias for goal-driven machine learning and knowledge discovery techniques. 1 INTRODUCTION During the last years, Problem-solving methods (PSMs) have become quit...

This study aimed to explore the effects of marking and metacognition-evaluated feedback (MEF) in computerbased testing (CBT) on student performance and review behavior. Marking is a strategy, in which students place a question mark next to a test item to indicate an uncertain answer. The MEF provided students with feedback on test results classified as correct answers with and without marking or incorrect answers with and without marking. The study analyzed 454 ninth graders randomly assigned to three groups: G mm (marking + MEF), G mu (marking), and G uu (none). Each group was further categorized into three subgroups based on their English ability. Results showed that marking improved medium-ability examinees ’ test scores. This was a promising finding because the medium-ability students were the very target group that had the most potential for improvement. Additionally, MEF was found to be beneficial as well in that it encouraged students to use marking skills more frequently and to review answer-explanations of the test items. The follow-up interviews indicated that providing adaptive and detailed AEs for low-ability students were necessary. The present study reveals the potential of integrating marking and adaptive feedbacks into the design of learning functions that are worth implementing in CBT systems.