Thesis (MEd. Computer-integrated Education (School of Teacher Training))--University of Pretoria, 2002. / Includes bibliographical references.

published_or_final_version / Education / Master / Master of Education

published_or_final_version / Education / Master / Master of Science in Information Technology in Education

One of the fundamental concerns of instructional design is pedagogical sequencing which is a practice of organizing course materials according to the underlying knowledge structure and concept dependency. In the conventional settings, like the secondary schools or tertiary institution, instructors are required to interpret learning materials by their own domain knowledge. But in many online learning systems, analyzing and interpreting learning materials are very challenging due to the lack of instructional contexts and pedagogical attributes of the learning units. The learning objects and learning object metadata (LOM) are learning technologies to formalize the concept of learning unit and standardizing the specification of learning object annotation framework. The learning object is aimed to provide a solution for reuse and sharing of learning materials, and to provide infrastructure for pedagogical design. The LOM has been widely adopted in various learning systems, methodologies and system frameworks proposed to solve instructional design problem based on the pedagogical information as provided in the LOM. However, an empirical study showed that most real-life learning objects do not provide necessary pedagogical information. Thus, it is not clear how the issue of incomplete metadata and hence incomplete pedagogical information will affect those LOM based methods. A new approach to reconstruct the underlying knowledge structure based on information extracted from LOM and data mining techniques is proposed. The main idea of the approach is to reconstruct knowledge structure by the context of learning materials. Intrinsically, the vector space model and the k-means clustering algorithm are applied to reconstruct the knowledge graph based on keyword extraction techniques, and concept dependency relations are extracted from the obtained knowledge graph. Then, the genetic algorithm is applied to optimize for a learning path that satisfies most of the obtained concept dependencies. Furthermore, the performance of applying different semantic interpreters and rule extraction methodology are carefully tested and compared. Experimental results revealed that learning paths generated by the proposed approach are very similar to learning paths designed by human instructors. / published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

Problem-Based Learning (PBL) has been widely adopted as an important approach to medical education in order to help students master knowledge by solving authentic problems. However, the effect of PBL on students’ construction of a well-organized knowledge base is found not satisfactory. It is assumed that learning a concept found in a problem will automatically result in that concept being used to solve other problems, which is not always the case. Knowledge gained from practice is found difficult to retain and reuse as a result of contextualization and dynamic aspects of actual problem-solving practice. Reciprocity between practice and knowledge has been insufficiently investigated in existing studies. The purpose of this study was to address the challenge by aligning knowledge construction with problem solving through the design of a computer-based cognitive tool and implementation of the tool into an online learning environment. The tool consists of the argument mapping technique to represent the problem-solving processes and the concept mapping technique to represent the knowledge constructed from the problem-solving experience, so called dual-mapping tool. This study adopted a design-based research paradigm with two rounds of design and evaluation to explore how the proposed dual-mapping learning (DML) environment could be designed to externalize and connect the problem-solving and knowledge-construction processes and to evaluate how effectively the DML environment could support PBL. The design of the DML environment was underpinned by the cognitive apprenticeship model, which is widely used in situated learning contexts such as PBL. The model highlights the importance of offering an authentic learning context, externalizing complex cognitive processes, and providing expert guidance on learning processes. Accordingly, the proposed DML environment consists of an authentic problem context for exploration, a dual-mapping tool for articulation and reflection of problem-solving and knowledge-construction processes, and expert support for modeling, coaching, and scaffolding these complex processes. The evaluation study aimed to investigate the effectiveness of the DML environment in terms of its acceptance by students, students’ problem-solving and knowledge-construction performances, and its impact on learning emotions and motivation to learn. Medical students from two medical schools in Mainland China participated in the study to use the DML environment. Multiple-source data was collected from questionnaire surveys, pre-and post-competency tests, semi-structured interviews, and log file data of online learning records, and was analyzed through descriptive statistical analysis, analysis of means, correlation analysis, analysis of variance, and content analysis. The evaluation results suggested that the students found the DML environment useful, and that the DML environment was effective in improving clinical problem-solving and medical-knowledge construction performance, as well as activating positive emotions and motivation in PBL. The findings of the study have practical implications for educators and learning technology designers as well as theoretical implications for educational researchers. / published_or_final_version / Education / Doctoral / Doctor of Philosophy

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The statistical mentor system incorporates a knowledge base into an educational tool for novices in statistical decision making to use in choosing a statistical technique. The novices are students in a business school curriculum who are expected to learn the basic statistical processes in business applications. The purpose of the system is to stimulate learning of the data analysis process on the part of the novice, usually a difficult task. The system acts as a consultant to the novice and approaches the task using a top-down problem solving strategy rather than the traditional bottom-up strategy used by novices. The heart of the system is the rule base for differentiating between statistics. These rules were built by gathering expertise from two experts in statistical analysis. The rules are based on five questions which the data can answer, as well as the type of data, the number of variables, and any dependent/independent relationships which exist between the variables. The knowledge base consists of five rule sets and can be represented either by condition/conclusion rules or by a set of multi-dimensional tables. Twenty-nine statistics and the rules for choosing them are in the rules sets. The knowledge base was used to define the logic incorporated in the consultant system in order to aid the user in selecting a correct technique. A dialogue mode is employed in the consultant to determine which conditions are true for the problem and data set. The rule sets are then checked to find the conclusion satisfying the conditions. The computer mentor was tested against the usual textbook mentor method (search through a textbook until one finds a statistic that looks promising) with two different groups of subjects, 25 undergraduates and 19 doctoral students. The results were that the computer-assisted students in both samples correctly solved a larger proportion of problems and had a higher average number of problems correct than did the textbook assisted groups.

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