Modelling human teaching tactics and strategies for tutoring systems

One of the promises of ITSs and ILEs is that they will teach and assist learning in an intelligent manner. Historically this has tended to mean concentrating on the interface, on the representation of the domain and on the representation of the student’s knowledge. So systems have attempted to provide students with reifications both of what is to be learned and of the learning process, as well as optimally sequencing and adjusting activities, problems and feedback to best help them learn that domain. We now have embodied (and disembodied) teaching agents and computer-based peers, and the field demonstrates a much greater interest in metacognition and in collaborative activities and tools to support that collaboration. Nevertheless the issue of the teaching competence of ITSs and ILEs is still important, as well as the more specific question as to whether systems can and should mimic human teachers. Indeed increasing interest in embodied agents has thrown the spotlight back on how such agents should behave with respect to learners. In the mid 1980s Ohlsson and others offered critiques of ITSs and ILEs in terms of the limited range and adaptability of their teaching actions as compared to the wealth of tactics and strategies employed by human expert teachers. So are we in any better position in modelling teaching than we were in the 80s? Are these criticisms still as valid today as they were then? This paper reviews progress in understanding certain aspects of human expert teaching and in developing tutoring systems that implement those human teaching strategies and tactics. It concentrates particularly on how systems have dealt with student answers and how they have dealt with motivational issues, referring particularly to work carried out at Sussex: for example, on responding effectively to the student’s motivational state, on contingent and Vygotskian inspired teaching strategies and on the plausibility problem. This latter is concerned with whether tactics that are effectively applied by human teachers can be as effective when embodied in machine teachers

Tutorial Dialog in an Equation Solving Intelligent Tutoring System

This thesis makes a contribution to Intelligent Tutoring Systems (ITS) architectures. A new intelligent tutoring system is presented for the domain of solving linear equations. This system is novel, because it is the first intelligent equation-solving tutor that combines a cognitive model of the domain with a model of dialog-based tutoring. The tutorial model is novel because it is based on the observation of an experienced human tutor and captures tutorial strategies specific to the domain of equation-solving. In this context, a tutorial dialog is the equivalent of breaking down problems into simpler steps and then asking new questions to the student before proceeding to the next navigational step. The resulting system, named E-tutor, was compared, via a randomized controlled experiment, to an algebra ITS similar to the“Cognitive Tutor by Carnegie Learning, Inc®. The Cognitive Tutor can provide traditional model-tracing feedback and buggy messages to students, but does not engage students in dialog. Preliminary results using a very small sample size, i.e., teaching equation solving to 15 high school students, showed that E-Tutor with dialog capabilities performed better than E-tutor without dialog. This result showed an effect size of 0.4 standard deviations for overall learning by condition. This set of preliminary results, though not statistically significant, shows promising opportunities to improve learning performance by adding tutorial dialog capabilities to ITSs. However, significant further validation is required, specifically, adding greater numbers and variations of the work to our sample size, before this approach can be deemed successful. The system is available at www.wpi.edu/~leenar/E-tutor

Supporting Constructive Learning with a Feedback Planner

A promising approach to constructing more effective computer tutors is implementing tutorial strategies that extend over multiple turns. This means that computer tutors must deal with (1) failure, (2) interruptions, (3) the need to revise their tactics, and (4) basic dialogue phenomena such as acknowledgment. To deal with these issues, we need to combine ITS technology with advances from robotics and computational linguistics. We can use reactive planning techniques from robotics to allow us to modify tutorial plans, adapting them to student input. Computational linguistics will give us guidance in handling communication management as well as building a reusable architecture for tutorial dialogue systems. A modular and reusable architecture is critical given the difficulty in constructing tutorial dialogue systems and the many domains to which we would like to apply them. In this paper, we propose such an architecture and discuss how a reactive planner in the context of this architecture can implement multi-turn tutorial strategies

Investigating Learning in an Intelligent Tutoring System through Randomized Controlled Experiments

In the United States, many students are doing poorly on new high-stakes standards-based tests that are required by the No Child Left Behind Act of 2002. Teachers are expected to cover more material to address all of the topics covered in standardized tests, and instructional time is more precious than ever. Educators want to know that the interventions that they are using in their classrooms are effective for students of varying abilities. Many educational technologies rely on tutored problem solving, which requires students to work through problems step-by-step while the system provides hints and feedback, to improve student learning. Intelligent tutoring researchers, education scientists and cognitive scientists are interested in knowing whether tutored problem solving is effective and for whom. Intelligent tutoring systems have the ability to adapt to individual students but need to know what types of feedback to present to individual students for the best and most efficient learning results. This dissertation presents an evaluation of the ASSISTment System, an intelligent tutoring system for the domain of middle school mathematics. In general, students were found to learn when engaging in tutored problem solving in the ASSISTment System. Students using the ASSISTment System also learned more when compared to paper-and-pencil problem-solving. This dissertation puts together a series of randomized controlled studies to build a comprehensive theory about when different types of tutoring feedback are more appropriate in an intelligent tutoring system. Data from these studies were used to analyze whether interactive tutored problem solving in an intelligent tutoring system is more effective than less interactive methods of allowing students to solve problems. This dissertation is novel in that it presents a theory that designers of intelligent tutoring systems could use to better adapt their software to the needs of students. One of the interesting results showed is that the effectiveness of tutored problem solving in an intelligent tutoring system is dependent on the math proficiency of the students. Students with low math proficiency learned more when they engaged in interactive tutoring sessions where they worked on one step at a time, and students with high math proficiency learned more when they were given the whole solution at once. More interactive methods of tutoring take more time versus less interactive methods. The data showed that it is worth the extra time it takes for students with low math proficiency. The main contribution of this dissertation is the development of a comprehensive theory of when educational technologies should use tutored problem solving to help students learn compared to other feedback mechanisms such as hints on demand, worked out solutions, worked examples and educational web pages

Tutoring instrument flight: patterns of instructor and student communication

Individual tutoring has been successful in facilitating learning in domains such as LISP, physics, and algebra. These tasks are static in that problems do not change while the student is trying to solve them. Dynamic tasks such as flying, where the problem changes spontaneously over time, represent different challenges for tutors. To understand tutoring in dynamic tasks, we conducted a field observation of students being given messages by a flight instructor. Five low flight time student pilots were asked to perform nine instrument flight tasks while being tutored by an instructor pilot in both a virtual simulator flight and in a real airplane flight. The data from our study were compared to two prominent models of one-on-one tutoring. Only a small portion of the utterances made by the tutor or by the student matched previous accounts, suggesting that a new approach is needed to address tutoring during instrument flight instruction

Teachers\u27 Conceptions of Mathematics and Intelligent Tutoring System Use

The purpose of this mixed-methods study was to investigate the relationship between teachers’ conceptions of mathematics and their use of intelligent tutoring systems for mathematics instruction. Intelligent tutoring systems are adaptive computer programs which administer mathematics instruction to students based on their cognitive state. A conception is a mixture of beliefs and knowledge. The participants in this study were 93 junior high school mathematics teachers from three school districts in the Midwest. Data were gathered using a two-part online survey. The first part of the survey contained questions about their use of intelligent tutoring systems, graphing calculators, Desmos and dynamic geometry software. The second part of the survey contained Likert questions from the teachers’ version of the Conceptions of Mathematics Inventory. Desmos is a website providing interactive classroom activities and a user-friendly graphing calculator. Dynamic geometry software is a class of interactive geometry programs. The quantitative analysis revealed no statistically significant interactions between teachers’ conception scores and intelligent tutoring system use, or between teachers’ conception scores and how intelligent tutoring systems were used. There were statistically significant interactions between teachers’ conception scores and their use of graphing calculators, Desmos, and dynamic geometry software. The qualitative analysis revealed that teachers used intelligent tutoring systems for differentiation. Teachers used graphing calculators, Desmos, and dynamic geometry software for visual, computational, and exploratory purposes. Teachers exclusively using intelligent tutoring systems to incorporate technology should also incorporate technology which promotes student exploration

Rethinking teaching strategies : a framework and demonstration through augmenting Maple

In this work, an interdisciplinary approach has been adopted for the study of • teaching strategies of an Intelligent Tutoring System, in the paradigm of multiple teaching strategies, and • the use of Computer Algebra Systems (CAS) in teaching problem solving in university mathematics. As a result, the SIMTA (Styles Implemented by Methods Tactics Actions) theoretical framework has been developed to support and sustain teaching strategies in the paradigm of multiple teaching strategies. TeLoDe (TEaching Linear Ordinary Differential Equations), is a prototype Intelligent Tutoring System, teaching the ,solution of linear second order differential equations with constant coefficients in a novel way. This novel way, which has been empirically tested, has been achieved by augmenting Maple and represents an alternative use of CASs where the human lecturer and Maple are interlocked in a symbiotic and interdependent manner. In SIMTA, the contemporary concept of teaching strategy is rethought and proposed to be viewed at two fundamental levels: • the organisational level • and the operational level. The organisational level deals with the structure of the teaching strategy whereas the operational level deals with the manifestation of that structure. In SIMTA the organisational level is represented by a triple generic structure, method, tactic(s), action(s). A method is a mechanism for structuring the subject matter (e.g. analogy, examples, generalisation, specialisation). Likewise, a tactic is a mechanism for facilitating the interaction (e.g. explicit interaction, implicit interaction). An action is a low level activity such as display this message, ask this question. In SIMTA, the exact manifestation of the above generic structures (analogies, examples, implicit interaction, explicit interaction) depends on the concept of style: different styles result in different manifestations of the same generic structures. Thus, in SIMTA the concept of multiple teaching strategies is seen as merely a collection of teaching strategies manifested under the same style. These strategies operate with the aim of offering alternative representations of the same task at hand and ensuring that the lea~er is active by activating, directing and maintaining exploration. To help demonstrate the feasibility of SIMTA, two styles, the expository style and the , guided discovery style have been formed. The expository style draws on Ausubel's theory of meaningful learning, whereas, the guided discovery style draws on Bruner's work. These styles have been implemented in TeLoDe. TeLoDe, incorporates a teaching strategy module, based on a style, and declarative knowledge. Its purpose is threefold: (i) to serve as a research tool for the SIMTA framework, (ii) to serve as a prototype, demonstrating clearly how a 'second generation' CAS which undertakes the procedural aspect of mathematics allowing the human tutor to concentrate on its conceptual aspect, could be developed, (iii) to demonstrate how Maple and human lecturers are given clear roles which are, nevertheless, interdependent in carrying out the teaching of university mathematics. Two small-scale empirical studies were carried out in order to test SIMTA and TeLoDe respectively. The first study involved lecturers whereas the second study was carried out in a classroom environment. The results found from these studies demonstrate that TeLoDe has a potential as a teaching tool for problem solving in university mathematics in a novel way

The effect of multiple knowledge sources on learning and teaching

Current paradigms for machine-based learning and teaching tend to perform their task in isolation from a rich context of existing knowledge. In contrast, the research project presented here takes the view that bringing multiple sources of knowledge to bear is of central importance to learning in complex domains. As a consequence teaching must both take advantage of and beware of interactions between new and existing knowledge. The central process which connects learning to its context is reasoning by analogy, a primary concern of this research. In teaching, the connection is provided by the explicit use of a learning model to reason about the choice of teaching actions. In this learning paradigm, new concepts are incrementally refined and integrated into a body of expertise, rather than being evaluated against a static notion of correctness. The domain chosen for this experimentation is that of learning to solve "algebra story problems." A model of acquiring problem solving skills in this domain is described, including: representational structures for background knowledge, a problem solving architecture, learning mechanisms, and the role of analogies in applying existing problem solving abilities to novel problems. Examples of learning are given for representative instances of algebra story problems. After relating our views to the psychological literature, we outline the design of a teaching system. Finally, we insist on the interdependence of learning and teaching and on the synergistic effects of conducting both research efforts in parallel