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

An integrated environment for problem solving and program development

A framework for an integrated problem solving and program development environment that addresses the needs of students learning programming is proposed. Several objectives have been accomplished: defining the tasks required for program development and a literature review to determine the actual difficulties involved in learning those tasks. A comprehensive Study of environments and tools developed to support the learning of problem solving and programming was then performed, covering programming environments, debugging aids, intelligent tutoring systems, and intelligent programming environments. This was followed by a careful analysis and critique of these systems, which uncovered the limitations that have prevented them from accomplishing their goals. Next, an extensive study of problem solving methodologies developed in this century was carried out and a common model for problem solving was produced. The tasks of program development were then integrated with the common model for problem solving. Then, the cognitive activities required for problem solving and program development were identified and also integrated with the common model to form a Dual Common Model for problem Solving and Program Development. This dual common model was then used to define the functional specifications for a problem solving and program development environment which was designed, implemented, tested, and integrated into the curriculum. The development of the new environment for learning problem solving and programming was followed by the planning of a cognitively oriented assessment method and the development of related instruments to evaluate the process and the product of problem solving. A detailed statistical experiment to study the effect of this environment on students\u27 problem solving and program development skills, including system testing by protocol analysis, and performance evaluation of students based on research hypotheses and questions, was also designed, implemented and the result reported

Expert System Technology and Concept Instruction: Training Educators to Accurately Classify Learning Disabled Students

Many learning disabled student being served by the public school systems have been inaccurately classified. Training and research efforts are needed to assist members of the multidisciplinary team in making more accurate learning disabilities classification decisions. CLASS.LD2, a computer-based expert system, was designed to assist multidisciplinary teams by providing second-opinion advice regarding the appropriateness of a learning disabilities classification for individual student cases. The existing expert system, CLASS.LD2, was combined with strategies for effective concept instruction to create an instructional package entitled LO.Trainer. The purpose of this study was (a) to develop a computer-based instructional package combining expert system technology and strategies for effective concept instruction and (b) to test the effectiveness of the instructional package against another system application. The training application against which the instructional package was compared consisted of users running consultations with the original expert system. Of specific interest was (a) the effectiveness of both training programs across experienced and inexperienced teachers, (b) the performance of the experienced as compared with the inexperienced teachers regardless of the training program used, (c) whether an interaction between level of experience and training program occurred, ( d) which training program was more effective for the experienced teachers, and (e) which training program was more effective for the inexperienced teachers. Ninety-seven students from three universities served as subjects and were randomly assigned to one of the two treatment groups. Subjects who completed the LO.Trainer materials scored statistically (p \u3c .05) and educationally higher (SMD = + 0.96) on the posttest than those who ran CLASS.LD2 consultations. Statistical and educational significance were al so obtained across the experienced and inexperienced subjects when considered alone. An interaction, although not statistically significant (p \u3c .05), was obtained between group and experience level. Although there exist many similarities between the processes of building expert systems and concept analysis, incorporating both to develop an effective training tool had not previously been demonstrated. Results of this study indicated that the two fields, successfully combined, can create an effective and efficient training tool

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

Is technology always helpful?: A critical review of the impact on learning outcomes of education technology in supporting formative assessment in schools

While education technology has been widely used in classrooms, and considerable investments have been made to support its use in the UK, the evidence base for many such rapidly changing technologies is weak, and their efficacy is unclear. The aim of this paper is to systematically review and synthesise empirical research on the use of technology in formative assessment, to identify approaches that are effective in improving pupils’ learning outcomes. The review involved a search of 11 major databases, and included 55 eligible studies. The results suggest promising evidence that digitally delivered formative assessment could facilitate the learning of maths and reading for young children, but there is no good evidence that it is effective for other subjects, or for older children, or that it is any more effective than formative assessment without technology. The review found no good evidence that learner response systems work in enhancing children’s academic attainment, and there is no evidence supporting the effectiveness of such technologies that embed gaming features. Much research in this area is of poor quality. More rigorous studies using causal designs are thus urgently needed. Meantime, there should be no rush to use technology on the basis of improving attainment

Explicit Feedback Within Game-based Training: Examining The Influence Of Source Modality Effects On Interaction

This research aims to enhance Simulation-Based Training (SBT) applications to support training events in the absence of live instruction. The overarching purpose is to explore available tools for integrating intelligent tutoring communications in game-based learning platforms and to examine theory-based techniques for delivering explicit feedback in such environments. The primary tool influencing the design of this research was the Generalized Intelligent Framework for Tutoring (GIFT), a modular domain-independent architecture that provides the tools and methods to author, deliver, and evaluate intelligent tutoring technologies within any training platform. Influenced by research surrounding Social Cognitive Theory and Cognitive Load Theory, the resulting experiment tested varying approaches for utilizing an Embodied Pedagogical Agent (EPA) to function as a tutor during interaction in a game-based environment. Conditions were authored to assess the tradeoffs between embedding an EPA directly in a game, embedding an EPA in GIFT’s browser-based Tutor-User Interface (TUI), or using audio prompts alone with no social grounding. The resulting data supports the application of using an EPA embedded in GIFT’s TUI to provide explicit feedback during a game-based learning event. Analyses revealed conditions with an EPA situated in the TUI to be as effective as embedding the agent directly in the game environment. This inference is based on evidence showing reliable differences across conditions on the metrics of performance and self-reported mental demand and feedback usefulness items. This research provides source modality tradeoffs linked to tactics for relaying training relevant explicit information to a user based on real-time performance in a game

AGENT-BASED MODELING AND SIMULATION APPROACHES IN STEM EDUCATION RESEARCH

The development of best practices that deliver quality STEM education to all students, while minimizing achievement gaps, have been solicited by several national agencies. ABMS is a feasible approach to provide insight into global behavior based upon the interactions amongst agents and environments. In this review, we systematically surveyed several modeling and simulation approaches and discussed their applications to the evaluation of relevant theories in STEM education. It was found that ABMS is optimal to simulate STEM education hypotheses, as ABMS will sensibly present emergent theories and causation in STEM education phenomena if the model is properly validated and calibrated

Parameters and Structure of Neural Network Databases for Assessment of Learning Outcomes

The purpose of this study is to determine the methodology, develop a theory of construction, put into practice algorithmization and implement the functionality of a hybrid intelligent system for assessment of educational outcomes of trainees on the basis of the identified keyword parameters and structure of the artificial neural network using expert systems and fuzzy simulation; to develop a methodology for the construction of structural-logic, hierarchical, functional and fractal schemes for structuring databases of the didactic field of learning elements; to determine the content, structure of parameters and database components, selection criteria and the content of complexes of educational standards. The methodology of introducing intelligent systems into mathematical education is on the basis of the Hegelian triad: thesis (implementation of the coherence principle) – antithesis (implementation of principles of the fractality and historiogenesis) – synthesis (implementation of the principles of self-organization and reflection of the complex system inversion integrity). Requirements for the organization and construction of the artificial neural network for assessment of personal achievements on the basis of fuzzy simulation have been developed. In the direction of using elements of fractal geometry, the technological structures of clusters that constitute the basis of generalized structures have been developed. In particular, it is revealed that the didactic field of learning elements is equipped with a system of multi-level hierarchical databases of exercises, motivational-applied, research, practice-oriented tasks using expert systems and integration of mathematical, information, natural-science and humanities knowledge and procedures

The Effect of a Supplemental, Web Based Program on Student Achievement in a Suburban New York State School District

Since the inception of the Common Core, school districts have sought to implement effective and fiscally responsible ways to support under-performing general education students. The need for supplemental assistance for struggling learners has emerged, particularly when tied to high-stakes testing. To offer appropriate assistance, school districts analyzed the possible ways through which they could support students and boost achievement. One popular method for providing intervention services and supplemental instruction is web-based learning. This study examined the effect of one particular supplemental web based program on student performance. It examined students who used the Castle Learning Online Program at a suburban high school in southern New York State, measured by Regents examinations and final course average. The study explores the explanatory valuables within the sample of students using Castle, such as gender and ethnicity. The results indicated that the use of the Castle program had a statistically significant impact on student performance as measured by New York State Regents examination scores and student final course averages. Furthermore, the results indicate a statistically significant impact in academic performance when controlling for gender and ethnicity. The results and empirical evidence outlined in this study, as well as the recommendations for practice provided in this dissertation, can assist school districts and school administrators in their decision making process regarding web-based programs and interventions for students