Francis Brouns

A Learner Support Model Based on Peer Tutor Selection

Van Rosmalen, P., Sloep, P., Kester, L., Brouns, F., De Croock, M., Pannekeet, K., et al. (2008). A learner support model based on peer tutor selection. Journal of Computer Assisted Learning, 24(1), 74-86.

A Design Model for Lifelong Learning Networks

The provision of lifelong learning facilities is considered to be a major new direction for higher and distance teaching educational institutes catering for the demands of industry and society. ICT networks will in future support seamless, ubiquitous access to lifelong learning facilities at home, at work, in schools and universities. This implies the development of new ways of organizing learning delivery that goes beyond course and programme-centric models. It envisions a learner-centred, learner-controlled model of distributed lifelong learning. We present a conceptual model for the support of lifelong learning which is based on notions from self-organization theory, learning communities, agent technologies and learning technology specifications such as IMS Learning Design. An exploratory implementation has been developed and used in practice. We reflect on the findings and future directions.

Encouraging contributions in learning networks using incentive mechanisms

We investigate incentive mechanisms to increase active participation in Learning Networks (LNs). The LN under study is LN4LD, an LN for the exchange of information about the IMS Learning Design specification. We examine how to encourage learners in LN4LD to contribute their knowledge, and whether incentive mechanisms can increase the level of active participation. We describe an incentive mechanism based on constructivist principles and Social Exchange Theory, and experimentation using the mechanism designed to increase the level of active participation. The incentive mechanism allows individual learners to gain personal access to additional information through the accumulation of points earned by making contributions. Repeated measurements according to a simple interrupted time series with removal design show that the level of participation was indeed increased by the introduction of the reward system. It can therefore be considered worthwhile to use incentive mechanisms in LNs.

Latent semantic analysis as a tool for learner positioning in learning networks for lifelong learning

When referring to this paper please use the original source: British Journal of Educational Technology, 2004, 35 nr 6, pp. 729 - 738

Facilitating community building in learning networks through peer tutoring in ad hoc transient communities

Learning in a so-called Learning Network is particularly attractive to self-directed learners, who themselves decide on their learning programme as well as the timing, pace and place of their studies. However, such learners may easily become isolated, which is detrimental to their studies. Furthermore, supporting them in their studies rapidly leads to staff overload. This paper discusses ad hoc transient communities as a means of tackling both problems. Such communities are well poised to enhance the sociability of a Learning Network and increase learning effectiveness.

Knowledge matchmaking in Learning Networks: Alleviating the tutor load by mutually connecting Learning Network users

Tutors have only limited time to support the learning process. In this paper, we introduced a model that helps answer the questions of students. The model invoked the knowledge and skills of fellow students, who jointly formed an ad hoc, transient community. The paper situated the model within the context of a Learning Network, a self-organised, distributed system, designed to facilitate lifelong learning in a particular knowledge domain. We discussed the design of the model and explained how we selected and supported capable peers. Finally, we examined the calibration of the model and a simulation, which was intended to verify if the model is fit for use in experiments with students. The results indicate that, indeed, it is possible to identify and support capable peers efficiently and effectively.

A Model for Online Learner Support Based on Selecting Appropriate Peer Tutors

The definitive version is available at www.blackwell-synergy.com Please cite this as: Van Rosmalen, P., Sloep, P.B, Brouns, F., Kester, L., Berlanga, A., Bitter, M., Koper, R. (2008). A model for online learner support based on selecting appropriate peer tutors. Journal of Computer Assisted Learning 24, 483–493. Available online at doi: 10.1111/j.1365-2729.2008.00283.x

Matchmaking in learning networks: Bringing learners together for knowledge sharing

In this article we describe a system that matches learners with complementary content expertise in reaction to a learner-request for knowledge sharing. It works through the formation of ad hoc, transient communities, that exist for a limited period of time and stimulate learners socially to interact. The matchmaking system consists of a request module, a population module and a community module, all supported by a database that contains learning content, learner information and output of the system. The request module allows the learner to type in a request, the time span in which an answer should be provided and the content it is related to. The population module selects suitable learners to populate the community by determining their content competence, sharing competence, eligibility and availability. Modular Object-orientated Dynamic Learning Environment (MOODLE) is used to host the community. A first experiment is briefly described that shows that content competence can be successfully determined using our method. Future experiments are discussed that aim at establishing the feasibility of the overall design.

Towards an open framework for adaptive, agent-supported e-learning

Refer to: Van Rosmalen, P., Brouns, F., Tattersall, C.,Vogten, H. Van Bruggen, J, Sloep, P., & Koper, E.J.R. (in press). Towards an Open Framework for Adaptive, Agent-supported e-learning. International Journal of Continuing Engineering Education.

Swarm-based sequencing recommendations in e-learning

Open and distance learning (ODL) gives learners freedom of time, place and pace of study, putting learner self-direction centre-stage. However, increased responsibility should not come at the price of over-burdening or abandonment of learners as they progress along their learning journey. This paper introduces an approach to recommending the sequencing of e-learning modules for distance learners based on self-organization theory. It describes an architecture which supports the recording, processing and presentation of collective learner behavior designed to create a feedback loop informing learners of successful paths towards the attainment of learning goals. The article includes initial results from a large-scale experiment designed to validate the approach.