Observational Studies of the Learning Behaviour of Distance Education Students using an Asynchronous, Remote, Recording and Replay Tool

This thesis gives details of a series of studies that were designed to investigate how distance education students use courseware in their learning and how time, comfort and learning styles, should be taken into account when designing distance education courses. The online behaviour of groups of distance education students, who volunteered to take part, were observed using an asynchronous, remote recording and replay tool (AESOP) as they completed online practical exercises as part of the Open University course M206 Computing: An Object Oriented Approach. Web based questionnaires were used to determine data not obtainable from the recording software, including students’ levels of comfort with computing tasks and learning styles as measured by two well known questionnaires and another developed for the study. The profile of the times at which students study suggests the times at which they study are constrained by their personal circumstances. Time of day was not found to be a factor that affected academic performance or online behaviour. Students’ self expressed levels of comfort with computing tasks were found to be significantly related to academic performance. Significant relationships were also noted between students’ levels of preferences for the Activist and Dependent learning styles and academic performance. The Theorist, Collaborative and Visual styles were also found to be significantly related to the time students took to complete online practical work. A series of fine grained analyses looking at students' workspace arrangement, use of the notes page and sequence in which they used the course material, all raise further issues pertinent to the research and improvement in computer based instructional materials and distance education

Working Performatively with Interactive 3D Printing: An artistic practice utilising interactive programming for computational manufacturing and livecoding

This thesis explores the liminal space where personal computational art and design practices and mass-manufacturing technologies intersect. It focuses on what it could look and feel like to be a computationally-augmented, creative practitioner working with 3D printing in a more programmatic, interactive way. The major research contribution is the introduction of a future-looking practice of Interactive 3D Printing (I3DP).I3DP is articulated using the Cognitive Dimensions of Notations in terms of associated user activities and design trade-offs. Another contribution is the design, development, and analysis of a working I3DP system called LivePrinter. LivePrinter is evaluated through a series of qualitiative user studies and a personal computational art practice, including livecoding performances and 3D form-making

Working Performatively with Interactive 3D Printing: An artistic practice utilising interactive programming for computational manufacturing and livecoding

This thesis explores the liminal space where personal computational art and design practices and mass-manufacturing technologies intersect. It focuses on what it could look and feel like to be a computationally-augmented, creative practitioner working with 3D printing in a more programmatic, interactive way. The major research contribution is the introduction of a future-looking practice of Interactive 3D Printing (I3DP).I3DP is articulated using the Cognitive Dimensions of Notations in terms of associated user activities and design trade-offs. Another contribution is the design, development, and analysis of a working I3DP system called LivePrinter. LivePrinter is evaluated through a series of qualitiative user studies and a personal computational art practice, including livecoding performances and 3D form-making

Learning programming via worked-examples: the effects of cognitive load and learning styles

This research explored strategies for learning programming via worked-examples that promote schema acquisition and transfer. However, learning style is a factor in how much learners are willing to expend serious effort on understanding worked-examples, with active learners tending to be more impatient of them than reflective learners. It was hypothesised that these two learning styles might also interact with learners’ cognitive load. The research proposed a worked-example format, called a Paired-method strategy that combines a Structure-emphasising strategy with a Completion strategy. An experiment was conducted to compare the effects of the three worked-examples strategies on cognitive load measures and on learning performance. The experiment also examined the degree to which individual learning style influenced the learning process and performance. Overall, the results of the experiment were inconsistent. In comparing the effects of the three strategies, there were significant differences in reported difficulty and effort during the learning phase, with difficulty but not effort in favour of the Completion strategy. However no significant differences were detected in reported mental effort during the post-tests in the transfer phase. This was also the case for the performance on the post-tests. Concerning efficiency measures, the results revealed significant differences between the three strategy groups in terms of the learning process and task involvement, with the learning process in favour of the Completion strategy. Unexpectedly, no significant differences were observed in learning outcome efficiencies. Despite this, there was a trend in the data that suggested a partial reversal effect for the Completion strategy. Moreover, the results partially replicated earlier findings on the explanation effect. In comparing the effects of the two learning styles, there were no significant differences between active and reflective learners in the three strategy groups on cognitive load measures and on learning performance (nor between reflective learners in the Paired-method strategy and the other strategies). Finally, concerning efficiency measures, there was a significant difference between active learners in the three strategy groups on task involvement. Despite all these, effect sizes ranging from a medium to large suggested that learning styles might have interacted with learners’ cognitive load