Developing a Pedagogy for Interactive Learning.

Recent developments in higher education have seen the demise of much didactic, teacher-directed instruction which was aimed mainly towards lower-level educational objectives. This traditional educational approach has been largely replaced by methods which feature the teacher as an originator or facilitator of interactive and learner-centred learning - with higher-level aims in mind. The origins of, and need for, these changes are outlined, leading into an account of the emerging pedagogical approach to interactive learning, featuring facilitation and reflection. Some of the main challenges yet to be confronted effectively in consolidating a sound and comprehensive pedagogical approach to interactive development of higher level educational aims are outlined.

Understanding what drives informal learning at work: A resource-based view.

The resource-based view identifies a number of factors that may influence employees’ informal learning. In a cross-sectional survey of 113 German employees in the energy sector, we examined a number of potential predictors of informal learning and a more positive informal learning attitude. The results showed that proactive help-seeking and professional self-efficacy were positive predictors of informal learning. Employees who were older, who enjoyed learning, sought help and were self-efficacious learners had a more positive attitude towards formal learning. Employees who had a more positive attitude about informal learning rated organisational learning provisions as less important, potentially due to being proactive help-seekers. Managers rated organisational learning resources as less important than non-managerial employees. However, managers also reported higher professional self-efficacy. These circumstances may also influence their decision-making regarding the need to provide learning resources to others in the workplace.

On Clonal Selection.

Clonal selection has been a dominant theme in many immune-inspired algorithms applied to machine learning and optimisation. We examine existing clonal selections algorithms for learning from a theoertical and empirical perspective and assert that the widely accepted computational interpretation of clonal selection is compromised both algorithmically andbiologically. We suggest a more capable abstraction of the clonal selection principle grounded in probabilistic estimation and approximation and demonstrate how it addresses some of the shortcomings in existing algorithms. We further show that by recasting black-box optimisation as a learning problem, the same abstraction may be re-employed; thereby taking steps toward unifying the clonal selection principle and distinguishing it from natural selection.