Hochschulschrift (Dissertation)

Open University of the Netherlands (OUNL), Heerlen, The Netherlands, 2009

ISBN: 978-90-79447-26-8

URL: http://hdl.handle.net/1820/2062

This thesis analyses the role of context for supporting self-directed and self-organised learners on the web. The goal of the related project was to develop novel approaches to provide feedback on learning actions in knowledge domains and social environments that are not pre-structured for instruction. For this purpose simple visualizations of learner activity, so called indicators, were added to web-based information systems. The research question of this thesis is whether the perception of such indicators is context related. The thesis reports on exploratory design research and consists of two parts. The first part covers the theoretical and conceptual research. The second part analyses effects of indicators that were observed in the design studies that were related to this thesis. Part 1 has three chapters: “three pillars for research”, “smart indicators to support the learning interaction cycle”, and “smart indicators of learning interactions”. Part 2 has three chapters that cover three studies analysing interaction footprints and the effects of indicators on engagement and reflection of self-directed and self-organised learners. The chapters are: “visualisation of interaction footprints for engagement in online communities” , “implications of writing, reading, and tagging on the web for reflection support of self-directed learning”, and “a tag cloud for the reflective self-directed learner” . The general discussion links the two parts of this thesis on the grounds of the three key questions that were raised earlier in the thesis. The findings indicate that it is possible to develop and provide targeted solutions for supporting selfdirected, self-organised and incidental learning. The results suggest that the data from underlying sensors themselves are not providing contextual information for a learner, but that it is a result of aggregation of the data.

This Dissertation is licensed under a Creative Commons License .