Towards tutoring systems that detect students' motivation: an investigation

Institute for Communicating and Collaborative SystemsThe use of Artificial Intelligence techniques in the development of educational software brought the hope of developing systems that would become personalised to each student and thus be of more benefit to him or her. But despite their added complexity, these Intelligent systems (ITSs, ILEs, ICALLs, etc.) do not always succeed in engaging the student. While a lot of effort has been spent investigating how to accommodate an instructional interaction to the student's knowledge, almost no work has been done in trying to accommodate the instruction to the student's motivational state. This is surprising, given the immense impact that a student's motivation has in his or her learning. The little previous work dealing explicitly with motivation in tutoring systems has focused mainly on the strategies that an Intelligent Tutoring System (ITS) could use to motivate the student. In this dissertation we focus on the prior (but we believe, fundamental), task of detecting the student's motivational state, on which the mentioned strategies could be used. We argue that the available theories of motivation in education are not specific enough and are of limited usefulness in order to implement a motivation detection component in an ITS. Thus, we argue for the need of empirical studies that can help us elicit formalised motivation diagnosis knowledge. To this effect, we discuss a number of empirical studies we performed in order to inform the design of an ITS simulation that detects the motivational state of a student. The main aspects of the motivation diagnosis architecture presented in this dissertation are a motivation self-report component and a motivation diagnosis component based on human teachers' motivation diagnosis knowledge, elicited via one of the mentioned empirical studies. This architecture was implemented as an ITS simulation in order to help us evaluate these motivation diagnosis techniques. The evaluation showed that, although not perfect, the motivation diagnosis techniques introduced in this dissertation seem to offer a reasonable level of accuracy in detecting a student's motivational state, and although the approach presented is not the only possible one and many aspects of this work can still be improved, we believe that it offers a promising step towards tutoring systems that care

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

Towards a synthesis of multimedia and intelligent tutoring systems : a dissertation presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science at Massey University

Multimedia is being used in almost every field. This study is about the use of multimedia in the area of intelligent tutoring systems. This project studies the advantages and disadvantages of interactive multimedia and intelligent tutoring systems, and analyses the ways of combining these technologies in search of an interesting, learnable, flexible, compelling and technology-enhanced educational tool. Educational packages need to be evaluated for effectiveness. When it comes to computer-based instruction, technical concerns such as multimedia effects are taken seriously and there is not enough emphasis on its educational value. There is not much concern about the appropriateness of the instruction method to the computer medium. This research proposes a framework for evaluating educational packages which include a number of issues. Several pieces of educational software were evaluated using this framework and Diagnosis for crop protection, a multimedia software package that aids in teaching the process of diagnosing crop problems, was selected for modification, as a practical application of the theoretical work. We studied different multimedia system development models and methodologies. We also analysed the cognitive issues and intelligent features that enhance the learnability. Finally, the appropriate intelligent features and other factors that could enhance Diagnosis for crop protection to be a more 'active knowledge constructing' environment have been identified. The current version of Diagnosis for crop protection was represented using an appropriate methodology and the proposed changes were described in detail

Intelligent and adaptive tutoring for active learning and training environments

Active learning facilitated through interactive and adaptive learning environments differs substantially from traditional instructor-oriented, classroom-based teaching. We present a Web-based e-learning environment that integrates knowledge learning and skills training. How these tools are used most effectively is still an open question. We propose knowledge-level interaction and adaptive feedback and guidance as central features. We discuss these features and evaluate the effectiveness of this Web-based environment, focusing on different aspects of learning behaviour and tool usage. Motivation, acceptance of the approach, learning organisation and actual tool usage are aspects of behaviour that require different evaluation techniques to be used

Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments

ITS for Teaching TOEFL

Abstract: An e-learning system is increasingly gaining popularity in the academic community because of several benefits of learning anywhere anyplace and anytime. An Intelligent Tutoring System (ITS) is a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher.(ITSB) is the tutoring system Builder Which designed and improved to help teachers in building intelligent tutoring system in many fields .In this paper we have an example and an evaluating are presented of building an intelligent tutoring system for teaching TOEFL using ITSB tool

An Intelligent Tutoring System for Learning TOEFL

An e-learning system is increasingly gaining popularity in the academic community because of several benefits of learning anywhere anyplace and anytime. An Intelligent Tutoring System (ITS) is a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher.(ITSB) is the tutoring system Builder Which designed and improved to help teachers in building intelligent tutoring system in many fields. In this paper, we have an example and an evaluating are presented of building an intelligent tutoring system for teaching TOEFL using ITSB tool

Personalised correction, feedback, and guidance in an automated tutoring system for skills training

In addition to knowledge, in various domains skills are equally important. Active learning and training are effective forms of education. We present an automated skills training system for a database programming environment that promotes procedural knowledge acquisition and skills training. The system provides support features such as correction of solutions, feedback and personalised guidance, similar to interactions with a human tutor. Specifically, we address synchronous feedback and guidance based on personalised assessment. Each of these features is automated and includes a level of personalisation and adaptation. At the core of the system is a pattern-based error classification and correction component that analyses student input