Over the summer, the Journal of Educational Technology & Society published a special edition (Vol. 12, No. 3) on self-directed learning, guest edited by Marco Kalz, Rob Koper and Veronika Hornung-Prähauser. The articles are all from European-based authors.

From the guest editorial:

‘Instead of providing learners with completely pre-defined learning environments a relatively new branch of research and
technology-development in the field is focusing on the question how individuals can be supported to plan their
learning process on their own and to conduct it within networks of learners with similar competence development

Out of the ten papers on self-directed learning, I found these to be of particular interest (I have drawn from the authors’ own abstracts):

Pata, K. (2009) Modeling spaces for self-directed learning at university courses Educational Technology & Society, 12 (3), 23-43.

This paper conceptualizes a theoretical framework of modeling learning spaces for self-directed learning in university courses. It binds together two ideas: a) self-directed learners’ common learning spaces may be characterized as abstract niches, b) niche characteristics are collectively determined through individually perceived affordances. The empirical part demonstrates the learning niche formation in a masters course. The results were used to develop learning spaces that support self-directed learning with social media. The identification of learning niches demonstrated that students used different social media tools for similar types of affordances of the learning niche in action. This finding suggested that affordance-based niche descriptions would allow flexibility and learner-centredness but simultaneously might enable a common emergent learning space to be identified and to make it reusable for modeling environments for self-directed learning courses.

Väljataga, T., & Fiedler, S. (2009). Supporting students to self-direct intentional learning projects with social media. Educational Technology & Society, 12 (3), 58–69.

In addition to teaching general strategies for carrying out [self-directed learning projects in higher education], more emphasis should be put on acquiring some expertise regarding the selection and combination of a diverse set of technological means for this purpose. …The knowledge and skills needed to select, use and connect different social media in a meaningful way form is an important part of self-directed learning projects. This paper argues for a course design in which participants are not simply engaged in developing knowledge, skills and orientations in regard to curricular subject matter and the use of technology but actively involved in self-directed learning projects with the support of social media. The theoretical framework of this research is inspired by conceptual ideas developed within the European iCamp project. The argument is illustrated with some empirical data collected from a pilot course taught at Tallinn University, Estonia.

Louys, A., Hernández-Leo, D., Schoonenboom, J., Lemmers, R., & Pérez-Sanagustín, M. (2009). Self-Development of Competences for Social Inclusion Using the TENCompetence Infrastructure. Educational Technology & Society, 12 (3), 70–81.

This paper describes a pilot study of the technology-enhanced self-development of lifelong learning competences, i.e. to develop and improve competences in English language (basic and advanced levels) and ICT functional and communicative skills. The pilot study shows that the TENCompetence infrastructure…provides various kinds of benefits for adult participants with low educational profiles and who are traditionally excluded from the use of innovative learning technologies and the knowledge society. The self-organized training aims at allowing learners to create and control their own learning plans based on their interests and educational background including informal and non-formal experiences. The paper introduces the context and the pilot scenario, indicates the evaluation methodology applied and discusses the most significant findings derived from the pilot study.

Kirkham, T., Winfield, S., Smallwood, A., Coolin, K., Wood, S., & Searchwell, L. (2009). Introducing Live ePortfolios to Support Self Organised Learning. Educational Technology & Society, 12 (3), 107–114.

Stripped of its jargon, this paper is about the development of a trust-based, secure system for e-portfolio management. ‘The work is significant because it presents a new level of both distributed and live data integration into the ePortfolio domain. New data objects can be integrated into the learning process from emerging areas like social networking, giving the learning processes more depth. For the learner this approach enhances the quality and range of data that they can use in the ePortfolio, and has the potential to enhance the self-organised learning process.’

Drachsler, H., Hummel, H., van den Berg, B., Eshuis, J., Waterink, W., Nadolski, R., Berlanga, A., Boers, N., & Koper, R. (2009). Effects of the ISIS Recommender System for navigation support in self-organised Learning Networks. Educational Technology & Society, 12 (3), 115–126.

Learners in complex, self-organising Learning Networks have similar problems and need guidance to find and select most suitable learning activities, in order to attain their lifelong learning goals in the most efficient way. Several research questions regarding efficiency and effectiveness deal with adequate navigation support through recommender systems. [Note: recommender systems are online navigational aids such as those used by Amazon to direct you to related publications]. To answer some of these questions an experiment was set up within an Introduction Psychology course of the Open University of the Netherlands. Around 250 students participated in this study and were monitored over an experimental period of four months. All were provided the same course materials, but only half of them were supported with a personalised recommender system. This study examined the effects of the navigation support on the completion of learning activities (effectiveness), needed time to comply them (efficiency), actual use of and satisfaction with the system, and the variety of learning paths. The recommender system positively influenced all measures, by having significant effects on efficiency, satisfaction and variety.


It should be noted that the papers in this edition represent a very different, computer-science-based philosophy of self-directed learning from the more humanistic and socially networked approach advocated by commentators such as Stephen Downes and George Siemens, and the activities of Supercool School. Unfortunately, the papers in this special edition are often full of unnecessary jargon and painful to read, even allowing for the fact that for many of the authors, English is a second language. Nevertheless, there are some papers where it was worth the effort of trying to understand what they were talking about, and it is good to have some empirical research in this area.


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