Kay G. Schulze

Bio: Kay G. Schulze is an academic researcher from United States Naval Academy. The author has contributed to research in topics: TUTOR & Intelligent tutoring system. The author has an hindex of 11, co-authored 20 publications receiving 1134 citations.

The Andes Physics Tutoring System: Lessons Learned

Abstract: The Andes system demonstrates that student learning can be significantly increased by upgrading only their homework problem-solving support. Although Andes is called an intelligent tutoring system, it actually replaces only the students' pencil and paper as they do problem-solving homework. Students do the same problems as before, study the same textbook, and attend the same lectures, labs and recitations. Five years of experimentation at the United States Naval Academy indicates that Andes significantly improves student learning. Andes' key feature appears to be the grain-size of interaction. Whereas most tutoring systems have students enter only the answer to a problem, Andes has students enter a whole derivation, which may consist of many steps, such as drawing vectors, drawing coordinate systems, defining variables and writing equations. Andes gives feedback after each step. When the student asks for help in the middle of problem-solving, Andes gives hints on what's wrong with an incorrect step or on what kind of step to do next. Thus, the grain size of Andes' interaction is a single step in solving the problem, whereas the grain size of a typical tutoring system's interaction is the answer to the problem. This report is a comprehensive description of Andes. It describes Andes' pedagogical principles and features, the system design and implementation, the evaluations of pedagogical effectiveness, and our plans for dissemination.

Interactive Conceptual Tutoring in Atlas-Andes

Abstract: Atlas-Andes is a dialogue enhanced model tracing tutor (MTT) integrating the Andes Physics tutoring system (Gertner ~ VanLelm 2000) with the Atlas tutorial dialogue system (Freedman et al. 2000). Andes is a MTT that presents quantitative physics problems to students. Each problem solving action entered by students is highlighted either red or green to indicate whether it. was correct or not. This basic feedback is terlned red-greeu feedback. Furthermore, when students get stuck in the midst of problem solving and request help, Andes provides hint sequences designed to help them achieve the goal of soh’ing the problem as quickly as possible. Atlas provides Andes with the capability of leading students through directed lines of reasoning that teach basic physics conceptual knowledge, such as Newton’s Laws. The purpose of these directed lines of reasoning is to provide a solid foundation in conceptual physics to promote deep learning and to enable students to develop meaningful problem solving strategies. While students in elementary mechanics courses have demonstrated an ability to master the skills required to solve quantitative physics problems, a nmnber of studies have revealed that the same students perform very poorly when faced with qualitative physics problems (Halloun & Hestenes 1985b; 1985a; Hake 1998). Furthermore, the naive conceptions of physics that they bring with them when they begin a formal study of physics do not change significantly by the time they finish their classes (Halloun & Hestenes 1985b). Similarly, MTTs in a wide range of domains have commonly been criticized for failing to encourage deep learning (VanLehn et al. 2000). If students do not reflect upon the hints they are given, but instead simply continue guessing until they perform an action that receives positive feedback, they tend to learn the right actions for the wrong reasons (Aleveu, Koedinger, K~ Cross 1999;

The Andes Physics Tutoring System: Five Years of Evaluations

Abstract: Andes is a mature intelligent tutoring system that has helped hundreds of students improve their learning of university physics. It replaces pencil and paper problem solving homework. Students continue to attend the same lectures, labs and recitations. Five years of experimentation at the United States Naval Academy indicates that it significantly improves student learning. This report describes the evaluations and what was learned from them.

Fading and deepening : The next steps for andes and other model-tracing tutors

Abstract: Model tracing tutors have been quite successful in teaching cognitive skills; however, they still are not as competent as expert human tutors. We propose two ways to improve model tracing tutors and in particular the Andes physics tutor. First, tutors should fade their scaffolding. Although most model tracing tutors have scaffolding that needs to be gradually removed (faded), Andes' scaffolding is already faded, and that causes student modeling difficulties that adversely impact its tutoring. A proposed solution to this problem is presented. Second, tutors should integrate the knowledge they currently teach with other important knowledge in the task domain in order to promote deeper learning. Several types of deep learning are discussed, and it is argued that natural language processing is necessary for encouraging such learning. A new project, Atlas, is developing natural language based enhancements to model tracing tutors that are intended to encourage deeper learning.

Fading and Deepening: The Next Steps for Andes and other Model-Tracing Tutors

Abstract: Model tracing tutors have been quite successful in teaching cognitive skills; however, they still are not as competent as expert human tutors We propose two ways to improve model tracing tutors and in particular the Andes physics tutor First, tutors should fade their scaffolding Although most model tracing tutors have scaffolding that needs to be gradually removed (faded), Andes' scaffolding is already "faded," and that causes student modeling difficulties that adversely impact its tutoring A proposed solution to this problem is presented Second, tutors should integrate the knowledge they currently teach with other important knowledge in the task domain in order to promote deeper learning Several types of deep learning are discussed, and it is argued that natural language processing is necessary for encouraging such learning A new project, Atlas, is developing natural language based enhancements to model tracing tutors that are intended to encourage deeper learning

Focus on Formative Feedback

Abstract: This article reviews the corpus of research on feedback, with a focus on formative feedback—defined as information communicated to the learner that is intended to modify his or her thinking or behavior to improve learning According to researchers, formative feedback should be nonevaluative, supportive, timely, and specific Formative feedback is usually presented as information to a learner in response to some action on the learner’s part It comes in a variety of types (eg, verification of response accuracy, explanation of the correct answer, hints, worked examples) and can be administered at various times during the learning process (eg, immediately following an answer, after some time has elapsed) Finally, several variables have been shown to interact with formative feedback’s success at promoting learning (eg, individual characteristics of the learner and aspects of the task) All of these issues are discussed This review concludes with guidelines for generating formative feedback

Focus on formative feedback

Abstract: This paper reviews the corpus of research on feedback, with a particular focus on formative feedback—defined as information communicated to the learner that is intended to modify the learner's thinking or behavior for the purpose of improving learning. According to researchers in the area, formative feedback should be multidimensional, nonevaluative, supportive, timely, specific, credible, infrequent, and genuine (e.g., Brophy, 1981; Schwartz & White, 2000). Formative feedback is usually presented as information to a learner in response to some action on the learner's part. It comes in a variety of types (e.g., verification of response accuracy, explanation of the correct answer, hints, worked examples) and can be administered at various times during the learning process (e.g., immediately following an answer, after some period of time has elapsed). Finally, there are a number of variables that have been shown to interact with formative feedback's success at promoting learning (e.g., individual characteristics of the learner and aspects of the task). All of these issues will be discussed in this paper. This review concludes with a set of guidelines for generating formative feedback.

The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems

Abstract: This article is a review of experiments comparing the effectiveness of human tutoring, computer tutoring, and no tutoring. “No tutoring” refers to instruction that teaches the same content without tutoring. The computer tutoring systems were divided by their granularity of the user interface interaction into answer-based, step-based, and substep-based tutoring systems. Most intelligent tutoring systems have step-based or substep-based granularities of interaction, whereas most other tutoring systems (often called CAI, CBT, or CAL systems) have answer-based user interfaces. It is widely believed as the granularity of tutoring decreases, the effectiveness increases. In particular, when compared to No tutoring, the effect sizes of answer-based tutoring systems, intelligent tutoring systems, and adult human tutors are believed to be d = 0.3, 1.0, and 2.0 respectively. This review did not confirm these beliefs. Instead, it found that the effect size of human tutoring was much lower: d = 0.79. Moreover, the eff...

Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments

Abstract: We study the incidence (rate of occurrence), persistence (rate of reoccurrence immediately after occurrence), and impact (effect on behavior) of students' cognitive-affective states during their use of three different computer-based learning environments. Students' cognitive-affective states are studied using different populations (Philippines, USA), different methods (quantitative field observation, self-report), and different types of learning environments (dialogue tutor, problem-solving game, and problem-solving-based Intelligent Tutoring System). By varying the studies along these multiple factors, we can have greater confidence that findings which generalize across studies are robust. The incidence, persistence, and impact of boredom, frustration, confusion, engaged concentration, delight, and surprise were compared. We found that boredom was very persistent across learning environments and was associated with poorer learning and problem behaviors, such as gaming the system. Despite prior hypothesis to the contrary, frustration was less persistent, less associated with poorer learning, and did not appear to be an antecedent to gaming the system. Confusion and engaged concentration were the most common states within all three learning environments. Experiences of delight and surprise were rare. These findings suggest that significant effort should be put into detecting and responding to boredom and confusion, with a particular emphasis on developing pedagogical interventions to disrupt the ''vicious cycles'' which occur when a student becomes bored and remains bored for long periods of time.