Throughout life, we continuously accumulate data, information and knowledge. The ability to recall much of this accumulated knowledge commonly deteriorates with time, though some forms part of what is referred to as tacit knowledge. In the context of education, students access and interact with a teacher’s knowledge in order to create their own, and may have their own data, information and knowledge that could be added to teacher’s knowledge for everyone’s benefit. The realization that students can contribute to enhancing personal knowledge is an important cornerstone in developing a mentor (teacher, tutor and facilitator) focused knowledge system. The research presented in this thesis discusses an integrated framework that manages an individual’s personal data, information and knowledge and enables it to be enhanced by others, in the context of a blended teaching and learning environment. Existing related models, structures, systems and current practices are discussed. The core outcomes of this thesis include: • the virtualMe framework that can be utilized when developing Web based teaching and learning systems; • the sniplet content model that can be used as the basis for sharing information and knowledge; • an annotation framework used to manage knowledge acquisition; and • a multimedia object (MMO) model that: o allows for related media artefacts to be intuitively grouped in a logical collection; o includes a meta-data schema that encompasses other metadata structures, and manages context and referencing; and o includes a model allowing component parts to be reaggregated if they are separated. The virtualMe framework provides the ability to retain context while transferring the content from one person to another and from one place to another. The framework retains the content’s original context and then allows the receiver to customise the content and metadata so that the content becomes that person’s knowledge. A mechanism has been created for such contextual transfer of content (context retained by the metadata).

This study was motivated by a desire to address the challenges of introductory programming courses. Ethiopian universities teach such courses in large classes (80+ students) and students complain about the difficulty of the courses and teaching variation of instructors. The study was set to explore optimum course and learning environment design approaches. The research question raised was: how can a blended learning approach be used to improve large class teaching of programming? In an action design research approach, the study was initiated with redesigning two consecutive courses and a supportive blended learning environment on the basis of existing learning theories and educational design frameworks. Two cycles of action research were conducted for a dual goal of refinement and evaluation of the intervention. The action research was conducted during the 2012/13 academic year with 240 students at the beginning. A predominantly quantitative first cycle of action research produced a mixed outcome. The students’ marks from assessment activities were fairly close to results from two other international universities. A pre- and post-implementation survey of students’ approach to learning showed a slight class level change towards the deep learning approach. Conversely, some students were found at-risk (not progressing well) and certain technologies, particularly program visualisation tools, were found underutilised. The second action research cycle aimed to explain the result from the first round. A grounded action research evaluation of data from focus group discussions, interviews and participants’ memos identified plausible factors for meaningful programming learning in a large class. These factors were use of collaborative and pair programming; alignment of learning and assignment activities; integrated use of e-learning; and use of large class strategies like student mentors and team teaching. A critical realist interpretation of the result of the action research suggested that students can learn programming in large classes, 200+ in this study, with a course and learning environment design that keeps them engaged in learning and assessment activities. The study concludes that improved learning of programming can be possible with the use of students as mentors and changed role-dynamics of instructors, which presupposes adaptation of suitable pedagogical approaches and use of technologies. / School of Computing / D. Litt. et Phil. (Information Systems)

This study was motivated by a desire to address the challenges of introductory programming courses. Ethiopian universities teach such courses in large classes (80+ students) and students complain about the difficulty of the courses and teaching variation of instructors. The study was set to explore optimum course and learning environment design approaches. The research question raised was: how can a blended learning approach be used to improve large class teaching of programming? In an action design research approach, the study was initiated with redesigning two consecutive courses and a supportive blended learning environment on the basis of existing learning theories and educational design frameworks. Two cycles of action research were conducted for a dual goal of refinement and evaluation of the intervention. The action research was conducted during the 2012/13 academic year with 240 students at the beginning. A predominantly quantitative first cycle of action research produced a mixed outcome. The students’ marks from assessment activities were fairly close to results from two other international universities. A pre- and post-implementation survey of students’ approach to learning showed a slight class level change towards the deep learning approach. Conversely, some students were found at-risk (not progressing well) and certain technologies, particularly program visualisation tools, were found underutilised. The second action research cycle aimed to explain the result from the first round. A grounded action research evaluation of data from focus group discussions, interviews and participants’ memos identified plausible factors for meaningful programming learning in a large class. These factors were use of collaborative and pair programming; alignment of learning and assignment activities; integrated use of e-learning; and use of large class strategies like student mentors and team teaching. A critical realist interpretation of the result of the action research suggested that students can learn programming in large classes, 200+ in this study, with a course and learning environment design that keeps them engaged in learning and assessment activities. The study concludes that improved learning of programming can be possible with the use of students as mentors and changed role-dynamics of instructors, which presupposes adaptation of suitable pedagogical approaches and use of technologies. / School of Computing / D. Litt. et Phil. (Information Systems)