The work partially reported here concerned the development ot a prototype Expert System for giving advice about Statistics experiments, called ASA, and an inference engine to support ASA, called ABASE. This involved discovering what knowledge was necessary for performing the task at a satis? factory level of competence, working out how to represent this knowledge in a computer, and how to process the representations efficiently. Two areas of Statistical knowledge are described in detail: the classification of measure? ments and statistical variables, and the structure of elementary statistical experiments. A knowledge representation system based on lattices is proposed, and it is shown that such representations are learnable by computer programs, and lend themselves to particularly efficient implementation. ABASE was influenced by MBASE, the inference engine of MECHO [Bundy et al 79a]. Both are theorem provers working on typed function-free Horn clauses, with controlled creation of new entities. Their type systems and proof procedures are radically different, though, and ABASE is "conversational" while MBASE is not.

The topic of integrating the formal learning of graduates with their informal workplace learning has been debated for many years. In practice, however, these two types of learning still exist as two polar opposites, especially in the field of statistics. This study examined the effectiveness of an internship programme as a means to integrate the formal learning of statisticians with their informal workplace learning. Based on the findings, a framework was proposed to ensure integration between the formal and informal workplace learning of statisticians. The study employed a mixed-method approach that combined quantitative and qualitative data collection. Quantitative data were collected from respondents (statisticians who participated in the internship programme and were permanently employed at the time of the study) through an online survey, and qualitative data were collected from managers (who served as mentors to the interns) through conducting semi-structured, one-on-one interviews. Documents pertaining to the internship programme in place at Statistics South Africa were also studied. Many developing countries still lack the capacity to produce, analyse and use the range of quality statistics required to support effective development progress. This study suggested a new way of integrating the formal learning with the informal workplace learning of statisticians, which involved adding a practical component (an internship) to the formal learning of statisticians, therefore, combining an internship during formal learning with an internship upon the completion of formal learning. In this way, statisticians are better capacitated because they can apply their formally obtained knowledge and skills in practice both while being engaged in their formal studies as well as after completing their studies, ensuring integration between formal and informal workplace learning. Crucial factors that would play a role in ensuring success are, for instance, if intern statisticians are placed in areas that are commensurate with their formal learning (thus ensuring utilisation of their skills) and if the level of the tasks assigned to them matches their cognitive ability. Support for interns’ informal learning in the form of mentorship, developmental assignments and training and development opportunities are also crucial to ensure integration between their formal learning and informal workplace learning. / Educational Leadership and Management / D. Ed. (Education Management)