The significance of the reflective practitioner in blended learning

This is a case study paper concerned with the introduction of blended learning on a part-time higher education programme for mature students. The interpretive work draws on four action research cycles conducted over two years with two student cohorts. Discussion is based on observations, staff and student focus groups and interviews examining the students’ expectations and experiences. The initial focus of the action research was on the introduction of technology into the teaching and learning experience. The great advantage of an interpretive approach, however, is to allow the findings to determine the course of the research. During the first action research cycles, the focus of the research soon changed from the use of technology in blended learning to the role of the practitioners involved. We advocate the key role of reflective practitioners in facilitating blended learning and suggest that action research is a useful framework to develop this. Keywords: Educational Technology Implementation, Electronic Learning (E-Learning), Teacher Improvement, Teacher Preparation, Action Research, Asynchronous Communications, blended learning, part-time student, reflective practice

Mining Sociotechnical Patterns of Enterprise Systems With Complex Networks

Organizations worldwide are supporting their processes and decisions with enterprise systems (ES). Large amounts of data are produced and reproduced in these increasingly complex sociotechnical systems, opening new opportunities for the adoption of self-supervised learning techniques. Complex networks are viable solutions to create models that learn from data. This chapter presents (1) a review on the possibilities of networks for self-supervised learning, (2) three cases illustrating the potential of complex networks to address the autopoietic nature of ES: adoption of enterprise resource planning, web portal development, and healthcare data analytics, and (3) a framework to mine sociotechnical patters uncovering the entanglement of human practice and information technologies. For theory, this chapter explains the potential of complex networks to assess enterprise systems dynamics. For practice, the proposed framework can assist managers in establishing a strategy to continuously learn from their data to support decision-making in self-adapting scenarios