Learning involves the integration of new information into existing knowledge. Generoting explanations to oneself (self-explaining) facilitates that integration process. Previously, self-explanation has been shown to improve the acquisition of problem-solving skills when studying worked-out examples. This study extends that finding, showing that self-explanation can also be facilitative when it is explicitly promoted, in the context of learning declarative knowledge from an expository text. Without any extensive training, 14 eighth-grade students were merely asked to self-explain after reading each line of a possage on the human circulatory system. Ten students in the control group read the same text twice, but were not prompted to self-explain. All of the students were tested for their circulatory system knowledge before and after reading the text. The prompted group had a greater gain from the pretest to the posttest. Moreover, prompted students who generated o large number of self-explanations (the high explainers) learned with greater understanding than low explainers. Understanding was assessed by answering very complex questions and inducing the function of a component when it was only implicitly stated. Understanding was further captured by a mental model onolysis of the self-explanation protocols. High explainers all achieved the correct mental model of the circulatory system, whereas many of the unprompted students as well as the low explainers did not. Three processing characteristics of self-explaining are considered as reasons for the gains in deeper understanding. Preparation of this article was supported in part by an Office of Educational Research and

This article provides one example of a method of analyzing qualitative data in an objective and quantifiable way. Although the application of the method is illustrated in the context of verbal data such as explanations, interviews, problem-solving protocols, and retrospective reports, in principle, the mechanics of the method can be adapted for coding other types of qualitative data such as gestures and videotapes. The mechanics of the method we outlined in 8 concrete step. Although verbal analyses can be used for many purposes, the main goal of the analyses discussed here is to formulate an understanding of the representation of the knowledge used in cognitive performances and how that representation changes with learning This can be contrasted with another method or analyzing verbal protocols, the goal of which is to validate the cognitive processes of human performance, often as embodied in a computational model

It is now possible to build machine learning systems whose behavior is consistent with data from human students. How can education use such simulated students? Applications that help three user groups are discussed. Teachers can practice the art of tutoring byhaving them teach a simulated student. Using a simulation instead of a real student allows teachers to see how their actions affect that student's knowledge, to undo their actions, and to try their skills on students with varying prior knowledge and learning strategies. Students can learn in collaboration with a simulated student. Because the simulated student can be simultaneously an expert and a colearner, it can scaffold and guide the human's learning in subtle ways. Instructional developers can test their instruction on simulated students. Unlike formativeevaluations with real students, a simulation-based evaluation can indicate exactly what piece of the instruction caused which pieces of knowledge, and thus help developers troubleshoot their instructional designs early in the design process. For each of these three areas of application, inherent technical limitations, existing systems and prospective systems are discussed.

The Andes physics tutoring system has a student modeler that uses Bayesian networks. Although the student modeler was evaluated once with positive results, in order to better understand it and student modeling in general, a sensitivity analysis was conducted. That is, we studied the effects on accuracy of varying both numerical parameters of the student modeler (e.g., the prior probabilities) and structural parameters (e.g., whether the tutor uses feedback; whether the tutor insists that students correct errors; whether missing entries are counted as errors). Many of the results were surprising. For instance: Leaving feedback on when testing students improved the assessor’s accuracy; Long tests harmed accuracy in certain circumstances; CAI-style user interfaces often yielded higher accuracy than ITS-style user interfaces. Furthermore, we discovered that the most important problem confronted by the Andes student modeler was not the classic assignment of credit and blame problem, which is what Bayesian student modeling was designed to solve. Rather, it is that if students do not keep moving along a solution path, knowledge that they have mastered may not get a chance to apply, and thus the student modeler can not detect it. This factor had more impact on assessment accuracy than any other numerical or structural parameter. It is arguably a problem for all student modelers, and other assessment technology as well.

This paper presents the details of a student model that enables an open learning environment to provide tailored feedback on a learner’s exploration. Open learning environments have been shown to be beneficial for learners with appropriate learning styles and characteristics, but problematic for those who are not able to explore effectively. To address this problem, we have built a student model capable of detecting when the learner is having difficulty exploring and of providing the types of assessments that the environment needs to guide and improve the learner’s exploration of the available material. The model, which uses Bayesian Networks, was built using an iterative design and evaluation process. We describe the details of this process, as it was used to both define the structure of the model and to provide its initial validation.

Scientific knowledge is dynamic in two senses: it changes and increases extremely rapidly, and it is thrust from the lab into the wider world and public forum almost as rapidly. This implies increasing demands on secondary school science education. Besides knowing key facts, concepts, and procedures, it is important for today’s students to understand the process by which the claims of science are generated, evaluated, and revised – an interplay between theoretical and empirical work (Dunbar & Klahr, 1989). The educational goals behind the work reported in this chapter are to improve students ’ understanding of this process and to facilitate students ’ acquisition of critical inquiry skills, while also meeting conventional subject matter learning objectives. In addition to the need to change what is taught, there are grounds to change how it is taught. Research shows that students learn better when they actively pursue understanding rather than passively

Abstract. Schools need assessments of students in order to make informed decisions. The most common assessments are tests consisting of questions or problems that can be answered in under a minute each. When schools change their instruction to maximize performance on short-item tests, the students ’ learning can suffer. To prevent this, assessments are being developed such that “teaching to the test ” will actually improve instruction. Such performance assessments, as they are called, have students work on complex, intrinsically valuable, authentic tasks. Olae is a performance assessment for Newtonian physics. It is based on student modeling, a technology developed for intelligent tutoring systems. Students solve traditional problems as well as tasks developed by cognitive psychologists for measuring expertise. Students work on a computer, which records all their work as well as their answers. This record is analyzed to form a model of the student’s physics knowledge that accounts for the students ’ actions. The model is fine-grained, in that it can report the probability of mastery of each of 290 pieces of physics knowledge. These features make Olae a rather unusual assessment instrument, so it is not immediately obvious how to evaluate it, because standard evaluations methods assume the assessment is a short-item test. This paper describes Olae (focusing on parts of it that have not been described previously), several methods for evaluating complex assessments based on student modeling such as Olae, and some preliminary results of applying these methods to Olae with a small sample of physics students. In many cases, more data would be required in order to adequately access Olae, so this 179 VanLehn & Martin paper should be viewed more as a methodological contribution than as a definitive evaluation.

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CONTENTS Page ACKNOWLEDGMENT . . . . . . . . . . . . . . . . . . iii LIST OF TABLES . . . . . . . . . . . . . . . . . . vii LIST OF FIGURES . . . . . . . . . . . . . . . . . viii LIST OF ABBREVIATIONS . . . . . . . . . . . . . . ix CHAPTER I. INTRODUCTION . . . . . . . . . . . . . . 1 1.1 CIRCSIM-Tutor . . . . . . . . . . . 1 1.2 Student Modelling . . . . . . . . . . 3 1.3 Hints . . . . . . . . . . . . . . . . 3 1.4 Organization of Thesis . . . . . . . 5 II. ITS LITERATURE . . . . . . . . . . . . . 6 2.1 Early Attempts at Student Modelling . 6 2.2 Bug and Overlay Paradigms . . . . . . 9 2.3 Other Paradigms . . . . . . . . . . . 11 2.4 Diagnosis . . . . . . . . . . . . . . 12 2.5 Hints . . . . . . . . . . . . . . . . 14 III. THE CST PROJECT . . . . . . . . . . . . . 20 3.1 Methodology . . . . . . . . . . . . . 22 3.2 The CST Domain . . . . . . . . . . . 23 3.3 Keyboard to Keyboard Tutoring Experiments . . . . . . . . . . . . 26 3.4 CST . . . . . . . . . . . . . . . . 30 3.4.1 The Domain

A basic feature of Intelligent Tutoring Systems (ITS) is their ability to represent domain knowledge that can be attributed to the student at each stage of the learning process. In this paper we present a general (first order logic) framework for the representation of this kind of knowledge acquired by the system through the analysis of the student answers. This represantation makes it possible to describe the behaviour of well known ITSs and to provide a direct implementation in a logic programming language. Moreover, we point out several improvements that can be easily achieved by exploiting the features of a declarative approach. In particular, we address the representation and use of the knowledge that the system knows not to be possessed by the student. 1