The design and formative evaluation of computer based qualitative modelling environments for schools

This research investigated how computers might enable young learners to build models so that they can express and explore their ideas and hence they can gain understanding of the subject matter as well as developing modelling abilities. A design for a qualitative modelling environment was produced, which incorporated a simple rule-based metaphor that could be presented as a diagram. The design was founded on empirical evidence of children modelling as well as theoretical grounds. This research originated in and contributed to the Modus Project, a joint venture between King's College London and the Advisory Unit for Microtechnology in Education, Hertfordshire County Council. A prototype of the software, Expert Builder, was implemented by software engineers from the Modus team. The initial stage of evaluation, based on a questionnaire survey and widespread trialling, established that the tool could be used in a wide range of educational contexts. A detailed study of children using the qualitative modelling environment was conducted in three primary schools involving 34 pupils, aged nine to 11. They used the modelling environment within the classroom in their normal curriculum work over one school year on a variety of topics assisted by their class teacher. The modelling environment enabled cooperative groupwork and supported pupils in consolidating and extending their knowledge. A formative evaluation was used to inform the design of a revised version of the software. In addition the experiences of children using the software were analysed. A framework was developed which characterised the stages in the modelling process. Teachers in the study were observed to demonstrate the earlier stages of the modelling process and then to set tasks for the children based on the later stages of building and testing the models. The evidence suggested that the abilities to model were context dependent so that pupils as young as nine years old could undertake the whole modelling process provided that they were working on subject matter with which they were familiar. The teachers made use of computer based modelling in order to develop and reinforce pupils' understanding of various aspects of the curriculum and therefore they chose modelling tasks for the children. However in one school the children were given the opportunity to design and build models of their own choice and they demonstrated that they were able to carry out all the stages in the modelling process. A taxonomy of computer based modelling is proposed which could be used to inform decisions about the design of the modelling curriculum and could provide a basis for researchers investigating the modelling process. This would be useful for further research into the intellectual and social activities of people learning to model and for teachers seeking to develop a framework for the modelling curriculum. The National Curriculum (Department of Education and Science and the Welsh Office, 1990) specifies that early steps in computer based modelling should involve exploring models developed by others and pupils are not required to build models themselves until level 7 which is expected to be reached by more able 14 year-olds. In this thesis it is argued that a modelling curriculum should provide early opportunities for pupils to undertake the modelling process by developing simple models on familiar subject matter as well as opportunities for exploring more complex models as evidence from research reported in this thesis suggests that younger pupils are able to build models. In this way pupils will be enabled to acquire modelling capability as well as developing their understanding of a range of topics through modelling. Progression in modelling capability would involve constructing models of more complex situations and using a wider range of modelling environments

A methodology for evaluating intelligent tutoring systems

DissertationThis dissertation proposes a generic methodology for evaluating intelligent tutoring systems (ITSs), and applies it to the evaluation of the SQL-Tutor, an ITS for the database language SQL. An examination of the historical development, theory and architecture of intelligent tutoring systems, as well as the theory, architecture and behaviour of the SQL-Tutor sets the context for this study. The characteristics and criteria for evaluating computer-aided instruction (CAl) systems are considered as a background to an in-depth investigation of the characteristics and criteria appropriate for evaluating ITSs. These criteria are categorised along internal and external dimensions with the internal dimension focusing on the intrinsic features and behavioural aspects of ITSs, and the external dimension focusing on its educational impact. Several issues surrounding the evaluation of ITSs namely, approaches, methods, techniques and principles are examined, and integrated within a framework for assessing the added value of ITS technology for instructional purposes.Educational StudiesM. Sc. (Information Systems

Computer aided learning for entry level accountancy students

Available from British Library Document Supply Centre-DSC:DXN049783 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Research on Teaching and Learning In Biology, Chemistry and Physics In ESERA 2013 Conference

This paper provides an overview of the topics in educational research that were published in the ESERA 2013 conference proceedings. The aim of the research was to identify what aspects of the teacher-student-content interaction were investigated frequently and what have been studied rarely. We used the categorization system developed by Kinnunen, Lampiselkä, Malmi and Meisalo (2016) and altogether 184 articles were analyzed. The analysis focused on secondary and tertiary level biology, chemistry, physics, and science education. The results showed that most of the studies focus on either the teacher’s pedagogical actions or on the student - content relationship. All other aspects were studied considerably less. For example, the teachers’ thoughts about the students’ perceptions and attitudes towards the goals and the content, and the teachers’ conceptions of the students’ actions towards achieving the goals were studied only rarely. Discussion about the scope and the coverage of the research in science education in Europe is needed.Peer reviewe

Electronic homework: an intelligent tutoring system in mathematics.

by Lee Fong-lok.Thesis (Ph.D.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 309-323).Questionnaires and some appendixes in Chinese.TABLE OF CONTENTS --- p.iiTABLES --- p.viiFIGURES --- p.viiiACKNOWLEDGMENTS --- p.ixABSTRACT --- p.xiChapter 1 --- INTRODUCTION --- p.1HOW COMPUTERS CAN HELP OUR CHILDREN --- p.2How Human Tutors Tutor --- p.7"Can Computers "" Think""?" --- p.11Intelligent Tutoring Systems --- p.17ELECTRONIC HOMEWORK --- p.18A Personal Tutor to Students --- p.18The Present Study 226}0ؤ An Investigation into Electronic Homework --- p.23How to Build up Electronic Homework --- p.25Effect of using Electronic Homework --- p.29The Future of Electronic Homework --- p.29CHAPTER SUMMARY --- p.30Chapter 2 --- REPRESENTATION OF KNOWLEDGE --- p.32OVERVIEW --- p.32HOW KNOWLEDGE IS REPRESENTED --- p.33SYMBOLIC EXPRESSIONS OR NEURAL NETWORKS --- p.36PROCEDURAL AND DECLARATIVE KNOWLEDGE --- p.37On Evidence Supporting the Procedural- Declarative Knowledge Distinction --- p.39Distinction of Knowledge --- p.49EXPLICIT VERSUS IMPLICIT KNOWLEDGE --- p.52DEGREE OF SOPHISTICATION VERSUS PROCEDURALIZATION --- p.53NOTATION OF KNOWLEDGE --- p.59What Should Be Done But Not What Is Actually Done --- p.62CHAPTER SUMMARY --- p.63Chapter 3 --- WHAT KNOWLEDGE TO INCORPORATE AND HOW --- p.67OVERVIEW --- p.67SEPARATE STORAGE FOR DIFFERENT TYPES OF KNOWLEDGE --- p.69DIFFERENT TYPES OF KNOWLEDGE --- p.70The Expert module --- p.71The Student Module --- p.78The Tutoring Module --- p.85The Communication Module --- p.121CHAPTER SUMMARY --- p.124Chapter 4 --- PROBLEM COMPLEXITY AND INDIVIDUAL DIFFERENCES --- p.127OVERVIEW --- p.127COGNITIVE DIFFICULTY OR SIMPLE ITEM DIFFICULTY RATIO --- p.129DIFFICULTY LEVEL OBTAINED BEFORE TEST ADMINISTRATION --- p.130OTHER MEASURES OF PROBLEM DIFFICULTY --- p.131Complexity of Problems --- p.132Problem Complexity Level --- p.133INDIVIDUAL DIFFERENCES --- p.133Chapter 5 --- HOW TO IMPLEMENT AND EVALUATE THE SYSTEM…… --- p.136OVERVIEW --- p.136KNOWLEDGE ACQUISITION --- p.140Expert Module --- p.141Student Module --- p.142Tutoring Module --- p.149Problem Difficulty --- p.155IMPLEMENTATION --- p.161Implementation of Knowledge into Computer Tutor --- p.161EVALUATION --- p.162Formative Evaluation --- p.162Summative Evaluation --- p.163CHAPTER SUMMARY --- p.167Chapter 6 --- KNOWLEDGE ACQUIRED --- p.169OVERVIEW --- p.169EXPERT MODULE --- p.170STUDENT MODULE --- p.172Mal-rules --- p.172Strategies for Handling Mal-rules --- p.176Understanding the Errors --- p.177Section Summary --- p.209TUTORING MODULE --- p.210Effects of tutoring --- p.210Scores in Posttest and Ceiling Effect --- p.214Effects of Practice and Tutoring Methods on Retention test --- p.214How Experienced Teachers Perceive --- p.221CHAPTER SUMMARY --- p.228Chapter 7 --- PROBLEM DIFFICULTY --- p.230OVERVIEW --- p.230RESULTS OF DIFFERENT MEASURES OF PROBLEM DIFFICULTY --- p.231Students' estimation of Item Difficulty --- p.232Item Difficulty Ratio --- p.234Teachers' Estimation of Problem Difficulty --- p.234Predicted Complexity --- p.237CORRELATION AMONG THE VARIOUS MEASURES OF PROBLEM DIFFICULTY --- p.243How students rate the problems --- p.245PREDICTING THE PROBLEM DIFFICULTY MEASURES --- p.246About the Three Measures --- p.249Practical Considerations --- p.252PROBLEM COMPLEXITY --- p.254USING PROBLEM COMPLEXITY IN ELECTRONIC HOMEWORK --- p.258CHAPTER SUMMARY --- p.258Chapter 8 --- SYSTEM EVALUATION --- p.259OVERVIEW --- p.259THE EVALUATION --- p.260Formative Evaluation --- p.260Summative Evaluation --- p.270DISCUSSION --- p.288Who Benefit From Using The System --- p.288Hardware Constraints --- p.289Human-computer interface --- p.289Effect on the use of Electronic Homework --- p.290Expert-Novice Differences --- p.292CHAPTER SUMMARY --- p.293Chapter 9 --- CONCLUSIONS AND DISCUSSION --- p.294OVERVIEW --- p.294THEORETICAL ASPECTS --- p.295Why and how do students make errors? --- p.296What makes an expert tutor? --- p.302KNOWLEDGE OBTAINED --- p.304CAN ELECTRONIC HOMEWORK HELP STUDENTS AND TEACHERS? --- p.305Purposes of the Evaluation --- p.305Results of The Evaluation --- p.306SUGGESTIONS --- p.306Machine Learning --- p.307Input Systems --- p.307Better understanding of Human Problem Solving Process --- p.307REFERENCES --- p.309Appendix A: Mal-rule Collecting Tests ……… --- p.324Appendix B: Test on Solving Algebraic Equations --- p.334Appendix C: Tutoring Scripts --- p.336Appendix D: Manipulative Rules Used In Solving Algebraic Equations --- p.338Appendix E: Remediation Rules Used In Solving Algebraic Equations --- p.339Appendix F: List of Mal-rules --- p.341Appendix G: Teachers' Estimation of Problem Difficulty --- p.344Appendix H: Learning Process Questionnaire --- p.349Appendix I: Questionnaire on the Use of Electronic Homework --- p.344Appendix J: Teachers' Perception on Electronic Homework --- p.347Appendix K: Students' Perception on the Use of Electronic Homework in Formative Evaluation --- p.346Appendix L: Results of Students' Perception on Electronic Homework --- p.347Appendix M: Students' Scores in Learning Process Questionnaire --- p.349Appendix N: Homework 1 --- p.355Appendix O: Homework 2 --- p.358Appendix P: Students' Retention Test Scores --- p.361Appendix Q: Results of Teachers' Perception on Electronic Homework --- p.366Appendix R: Transcript of Students' Interview --- p.368Appendix S: Installation and Source Code --- p.40

Beyond rules: development and evaluation of knowledge acquisition systems for educational knowledge-based modelling

The technology of knowledge-based systems undoubtedly offers potential for educational modelling, yet its practical impact on today's school classrooms is very limited. To an extent this is because the tools presently used in schools are EMYCIN -type expert system shells. The main argument of this thesis is that these shells make knowledge-based modelling unnecessarily difficult and that tools which exploit knowledge acquisition technologies empower learners to build better models. We describe how such tools can be designed. To evaluate their usability a model-building course was conducted in five secondary schools. During the course pupils built hundreds of models in a common range of domains. Some of the models were built with an EMYCIN -type shell whilst others were built with a variety of knowledge acquisition systems. The knowledge acquisition systems emerged as superior in important respects. We offer some explanations for these results and argue that although problems remain, such as in teacher education, design of classroom practice, and assessment of learning outcomes, it is clear that knowledge acquisition systems offer considerable potential to develop improved forms of educational knowledge-based modelling

Integrating a software engineering approach and instructional factors in instructional software development--illustrated by a prototype in theoretical computer science

This dissertation is a multi-disciplinary study, which integrates a software engineering approach with instructional factors in the decision-making, analysis, design and development processes of instructional software. Software engineering models, tools and representations are used in the process of software construction. With reference to the fundamental characteristics of the software product, several disciplines and factors, from both instructional and computing perspectives are considered, and the most appropriate approach/es selected. Software engineering, instructional design and instructional theory are considered as pillars of courseware engineering. The object-oriented design paradigm and a prototyping life-cycle model are found to be most suitable for development of computer-aided instruction. The conceptual study is illustrated by prototype development of a component-based multi-activity practice environment in theoretical Computer Science. It offers perusal or practice, in various instructional modes, according to the user's preferred learning style or need.ComputingM. Sc. (Information Systems

Evaluation of computer aided instruction: Assessing the value and effectiveness of operational systems

This thesis investigated a number of performance measures for computer-aided instruction (CAI) systems. These "evaluation metrics" are intended to assess the worth and value of teaching systems. An operational accounting tutor (which teaches marginal costing) was used to develop the metrics and a replication study was conducted on Application Program Tutor (a tutoring system designed to teach courses). Although, CAI is a mature technology which has evolved in a variety of fields and forms since the 1950s, its potential remains untapped. Factors attributed to this include resistance from teachers, lack of student involvement in design, and insufficient imagination in curriculum design. Inadequate system standards and a deficiency of good software tools, lack of documentation, maintenance and education value have also been key limiting factors. The overall picture seems akin to a cottage industry than a co-ordinated enterprise. Evaluation is significant, to developers and users in this field, because in the short-term it improves the usability and life-span of the numerous systems that have been developed and in the long-term it focuses attention (away from the impetus to deliver) towards issues of appropriateness and quality in system design. Different traditions of evaluation are explored, including the selection criteria used in educational technology and the impact of the quality philosophy on software engineering. This research was conducted using the Before-after Two-group design on forty-two accountancy students, where a conventionally taught group was compared with the accountancy tutor group. Performance on a number of marginal (or variable) costing problems was measured before and after both groups were taught. Moreover, the experimental group was given a questionnaire to complete (which was designed to capture their assessment of the system). The results derived from the well- crafted questionnaire were indicative of the systems strengths and weaknesses and supplied useful criteria for future research