Supporting the tutor in the design and support of adaptive e-learning

The further development and deployment of e-learning faces a number of threats. First, in order to meet the increasing demands of learners, staff have to develop and plan a wide and complex variety of learning activities that, in line with contemporary pedagogical models, adapt to the learners’ individual needs. Second, the deployment of e-learning, and therewith the freedom to design the appropriate kind of activities is bound by strict economical conditions, i.e. the amount of time available to staff to support the learning process. In this thesis two models have been developed and implemented that each address a different need. The first model covers the need to support the design task of staff, the second one the need to support the staff in supervising and giving guidance to students' learning activities. More specifically, the first model alleviates the design task by offering a set of connected design and runtime tools that facilitate adaptive e-learning. The second model alleviates the support task by invoking the knowledge and skills of fellow-students. Both models have been validated in near-real-world task settings

Introducing serious games with Wikis: empowering the teacher with simple technologies

Despite the continuous and abundant growth of the game market the uptake of games in education has been hampered by the general impression that games require complex technologies and that games are difficult to organise and to embed in education curriculums. This paper explores to what extent a simple serious game scenario that can be easily adopted and adapted by individual teachers and that only uses a common, relatively simple technology can leverage the adoption of serious games. It discusses the design of such a game, Argument, based on a Wiki and its use in a 6 weeks trial by students of a Master of Learning Sciences Programme. The results indicate that, even though a Wiki has clear limitations, it is a useful instrument to build game alike educational activities, to gain experience with and as a first step to use (more) complex serious games

Effective team formation in networked learning settings

Professional development can be achieved by interacting with the abundance of learning materials provided by Internet-based services and by collaborating with other learners. However, knowledge sources are scattered across the Internet, while suitable co-learners are hard to find. Learning professionals require strong self-direction powers to fully benefit from these resources. However, these are not readily available in all learners. Based on social-constructivist/connectivist collaborative learning theory and team formation theory, a model is presented for the effective formation of teams engaging in structured collaborative learning. The model describes the creation knowledge domain representations by centralising learning materials from various sources. It allows learners to define structured learning tasks and provides an answer to the question whether a particular learning task can be addressed sufficiently well in the knowledge domain. Based on team formation theory, it provides the means to form teams of mutual learners and peer-teachers based on bridgeable knowledge differences (an interpretation of Vygotsky's "zone of proximal development") and personality aspects. The model also allows recommending suitable learning materials to the teams. A selection of tools is presented to afford an implementation of the model. These consist of an implementation of the method of Latent Semantic Analysis, a validated learning team formation algorithm and the Big Five personality test. The model is subsequently tested. The results of this test indicate that representations of knowledge domains can be successfully created and that the fit of learning tasks to the learning materials in the domain can be assessed. An experiment with learners (n=64) shows that the implementation can successfully assess prior knowledge and that collaborations based on prior knowledge differences do lead to knowledge gains. Furthermore, learners highly appreciate the learning materials suggested. However, the evidence for a level of knowledge difference between learners at which learning becomes most effective is currently limited. The results are discussed, and conclusions and directions for future research are included

Toward Project-based Learning and Team Formation in Open Learning Environments

Open Learning Environments, MOOCs, as well as Social Learning Networks, embody a new approach to learning. Although both emphasise interactive participation, somewhat surprisingly, they do not readily support bond creating and motivating collaborative learning opportunities. Providing project-based learning and team formation services in Open Learning Environment can overcome these shortcomings. The differences between Open Learning Environments and formal learning settings, in particular with respect to scale and the amount and types of data available on the learners, suggest the development of automated services for the initiation of project-based learning and team formation. Based on current theory on project-based learning and team formation, a team formation process model is presented for the initiation of projects and team formation. The data it uses is classified into the categories “knowledge”, “personality” and “preferences”. By varying the required levels of inter-member fit on knowledge and personality, the team formation process can favour different teamwork outcomes, such as facilitating learning, creative problem solving or enhancing productivity. The approach receives support from a field survey. The survey also revealed that in every-day teaching practice in project-based learning settings team formation theory is little used and that project team formation is often left to learner self-selection. Furthermore, it shows that the data classification we present is valued differently in literature than in daily practice. The opportunity to favour different team outcomes is highly appreciated, in particular with respect to facilitating learning. The conclusions demonstrate that overall support is gained for the suggested approach to project-based learning and team formation and the development of a concomitant automated service

Project team formation support for self-directed learners in social learning networks

Spoelstra, H., Van Rosmalen, P., & Sloep, P. B. (2012). Project team formation support for self-directed learners in social learning networks. In P. Kommers, P. Isaias, & N. Bessis (Eds.), Proceedings of the IADIS International Conference on Web Based Communities and Social Media (ICWBC & SM 2012) (pp. 89-96). July, 21-23, 2012, Lisbon, Portugal.Despite their name, social learning networks often lack explicit support for collaborative learning, even though collaborative learning offers benefits over individual learning. The outcomes of collaborative, project-based learning can be optimized when team formation experts assemble the project teams. This paper addresses the question of how to provide team formation services to individual, self-directed learners in a social learning network so they can make use of and profit from project-based learning opportunities. A model of a team formation process is presented, based on current team formation theory. It is used to design an automated team formation service that can be used by self-directed learners to form teams for project-based learning. Starting from a project description situated in a knowledge domain, the model defines three categories of variables that govern the team formation process: (I) knowledge, (II) personality and (III) preferences. Learner data on these categories are combined in a measure of fit, which calculates the best team for a project. A novelty introduced is that, depending on the desired project outcomes the relative weight of the categories can be altered to optimise the project formation process. The feasibility of the approach is demonstrated in an example in which the proposed algorithm is used to determine the most productive team for a project. Finally, future work and research are indicated

Identification of critical time‐consuming student support activities in e‐learning

Higher education staff involved in e‐learning often struggle with organising their student support activities. To a large extent this is due to the high workload involved with such activities. We distinguish support related to learning content, learning processes and student products. At two different educational institutions, surveys were conducted to identify the most critical support activities, using the Nominal Group Method. The results are discussed and brought to bear on the distinction between content‐related, process‐related and product‐related support activities