The high dropout rate of first year students is a major source of concern for the Department of Higher Education and Training and for Higher Education Institutions (HEI’s). Research indicated that students’ Self-Regulated Learning (SRL) skills and strategies play a significant role in achieving academic success at universities. Thus, the main aim of this study was to develop strategies for the development of SRL skills of first year university students. In order to achieve the research aim and objectives an extensive literature review was conducted on SRL and the relationship between SRL skills and the academic achievement of students at HEI’s. For the purposes of the empirical investigation, a mixed-method approach was followed. In the quantitative part of the investigation, the results of the Learning and Study Strategies Inventory (LASSI), which was administered to the 2007 cohort of first year students (n=2421) at the Potchefstroom Campus of the North-West University, were analysed to determine whether the subscales in the LASSI significantly predicted academic success and to identify variables that related to the first year students’ learning and study skills and academic achievement. In the qualitative part of the research, interviews were conducted during 2010, with a selected group of participants from the 2007 cohort of first year students who were then in their fourth year of study. The questions in the interviews were based on questions in the Self-Regulated Learning Inventory Schedule (SRLIS), and the aims were to explore the participants’ experiences with their studies and to determine which SRL skills, in addition to the skills assessed by the LASSI, influenced their studies and academic achievement. The quantitative analysis of the LASSI results revealed that: • Motivation, Time management and Information processing were the best LASSI predictors of the first year students’ academic success. • The independent biographical variables Grade 12 marks, age and gender correlated better with the first year students’ academic achievement than the LASSI subscales did. The qualitative investigation revealed that: • Successful students realised at the onset of their studies that they had to adapt their study methods to meet the challenges that studying at a university requires. • Successful students could differentiate between the different types of study material and could adapt their study methods accordingly. They could also adapt their study methods when the volume of the study material differed. • Successful students applied a repertoire of study methods in a flexible manner, and managed their time well. • Successful students conveyed knowledge of themselves as students, as well as of the different requirements that study at a university implicates. • Most of the successful students received information from parents, lecturers or principals about different study methods and could describe their learning styles and preferences clearly. • Some of the successful students could accurately infer which questions could be expected in the exam papers, and knew how and why these questions were asked. • Successful students set realistic academic goals for themselves. • Unsuccessful students did not consider their own study preferences or the academic requirements of the university. • Unsuccessful students did not manage their time well and were not motivated. On the basis of the findings, strategies were proposed for the development of SRL skills of first year students at universities. The strategies are presented as a compulsory programme that first year students have to complete in the first semester. / Thesis (PhD (Teaching and Learning))--North-West University, Potchefstroom Campus, 2012

Knowledge compilation improves search-intensive problem-solvers that are easily specified but inefficient. One promising approach improves efficiency by constructing a database of problem-instance/best-action pairs that replace problem-solving search with efficient lookup. The database is constructed by reverse enumeration- expanding the complete search space backwards, from the terminal problem instances. This approach has been used successfully in counter-planning to construct perfect problem-solvers for sub domains of chess and checkers. However, the approach is limited to small problems because both the space needed to store the database and the time needed to generate the database grow exponentially with problem size. This thesis addresses these problems through two mechanisms. First, the space needed is reduced through an abstraction mechanism that is especially suited to counter-planning domains. The search space is abstracted by representing problem states as equivalence classes with respect to the goal achieved and the operators as equivalence classes with respect to how they influence the goals. Second, the time needed is reduced through a hueristic best-first control of the reverse enumeration. Since with larger problems it may be impractical to run the compiler to completion, the search is organized to optimize the tradeoff between the time spent compiling a domain and the coverage achieved over that domain. These two mechanisms are implemented in a system that has been applied to problems in chess and checkers. Empirical results demonstrate both the strengths and weaknesses of the approach. In most problems and 80/20 rule was demonstrated, where a small number of patterns were identified early that covered most of the domain, justifying the use of best-first search. In addition, the method was able to automatically generate a set of abstract rules that had previously required two person-months to hand engineer. / Graduation date: 1992

Thesis (M.S.)--Rochester Institute of Technology, 1991. / Typescript. Includes bibliographical references (leaves 115-116).

Thesis (M.S.)--Rochester Institute of Technology, 1991. / Typescript. Includes bibliographical references.

Thesis (M.S.)--Rochester Institute of Technology, 1992. / Typescript. Bibliography: leaves 240-248.

Thesis (PhD) -- Macquarie University, Division of Information and Communication Sciences, Department of Computing, 2007. / "Submitted January 27 2007, revised July 27 2007". Bibliography: p. 281-304.

Thesis (Ph. D.)--Open University. BLDSC no. DX202524.

Report (M.S.)--Virginia Polytechnic Institute and State University. M.S. 1992. / Vita. Abstract. Includes bibliographical references (leaves 67-69). Also available via the Internet.

Thesis (Ph.D.) - University of Queensland, 2005. / Includes bibliography.

Thesis (Ph. D.)--University of Washington, 2005. / Vita. Includes bibliographical references (p. 125-139).