Social Annotation (SA) tools can be used to facilitate active and collaborative learning when students have to study academic texts. However, making these tools available does not ensure students participate in argumentative discussions. Scaffolding students by means of collaborations scripts geared towards collaboration and discussion encourages students to engage in meaningful, high-quality interactions. We conducted an experiment with students (n=59) in a course running at a Dutch university, using the SA tool Perusall. A control group received normal instructions, while an experimental group received scaffolding through collaboration scripts. The results showed a significant increase in the number of responses to fellow students for the experimental group compared to the control group. The quality of the annotations, measured on levels of Bloom’s taxonomy, increased significantly for the experimental group compared to both its baseline measurement and the control group. However, when scaffolding was faded out over subsequent assignments these differences became non-significant. The experimental groups’ increased quality of annotations did not remain over time, suggesting that internalization of the scripts was not achieved.

In theory, seamless learning design and research with its focus on bridging gaps in learning across contexts can help formulate answers to educational challenges. The recent mass lockdown due to the Covid-19 pandemic causing education to urgently switch from school-based to online teaching is just one of the many examples in support of design for continuity of learning. In over twenty years of its history, seamless learning has accumulated a substantial body of knowledge of what learning across contexts entails and how bridges across boundaries can be designed. However, seamless learning principles and guidelines to design for continuity of learning with the help of e.g., ubiquitous mobile technologies still need to find their way into educational practice. The study focuses on the outcomes of a hands-on activity in designing seamless learning scenarios. This activity included getting acquainted with the basics of seamless learning and designing a seamless learning scenario. It was part of an event organized for educational practitioners interested in the topic of seamless learning. Analysis of the seamless learning scenarios collaboratively designed during this activity demonstrated that teachers build on inquiry-based learning and problem-based learning paradigms to design learning that combines in-school, out-of-school and online contexts. They were able to include location-based content in school and teacher-led scenarios, however, ideas on the use of mobile technology were still described rather vaguely. Crossing boundaries and removing seams between contexts, did not yet become apparent in these initial teachers’ designs.

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.

This thesis researches automated services for professionals aiming at starting collaborative learning projects in open learning environments, such as MOOCs. It investigates the theoretical backgrounds of team formation for collaborative learning. Based on the outcomes, a model is developed describing the process of 1) project proposal assessment for fit to learing materials, and 2) project team formation based on prior knowledge and personality. Algorithms for the formation of learning, creative, and productive teams are described. A large scale experiment demonstrates the sucessfull use of Latent Semantic Analysis for the modeling of a knowledge domain, the assessment of project fit to the learning environment, and for the assessment of learner prior knowledge. The outcomes contribute to MOOC design, team formation theory, and the LSA knowledge base.

This thesis researches automated services for professionals aiming at starting collaborative learning projects in open learning environments, such as MOOCs. It investigates the theoretical backgrounds of team formation for collaborative learning. Based on the outcomes, a model is developed describing the process of 1) project proposal assessment for fit to learing materials, and 2) project team formation based on prior knowledge and personality. Algorithms for the formation of learning, creative, and productive teams are described. A large scale experiment demonstrates the sucessfull use of Latent Semantic Analysis for the modeling of a knowledge domain, the assessment of project fit to the learning environment, and for the assessment of learner prior knowledge. The outcomes contribute to MOOC design, team formation theory, and the LSA knowledge base.

Open learning environments, such as Massive Open Online Courses (MOOCs), often lack adequate learner collaboration opportunities; they are also plagued by high levels of drop-out. Introducing project-based learning (PBL) can enhance learner collaboration and motivation, but PBL does not easily scale up into MOOCS. To support definition and staffing of projects, team formation principles and algorithms are introduced to form productive, creative, or learning teams. These use data on the project and on learner knowledge, personality and preferences. A study was carried out to validate the principles and the algorithms. Students (n = 168) and educational practitioners (n = 56) provided the data. The principles for learning teams and productive teams were accepted, while the principle for creative teams could not. The algorithms were validated using team classifying tasks and team ranking tasks. The practitioners classify and rank small productive, creative and learning teams in accordance with the algorithms, thereby validating the algorithms outcomes. When team size grows, for practitioners, forming teams quickly becomes complex, as demonstrated by the increased divergence in ranking and classifying accuracy. Discussion of the results, conclusions, and directions for future research are provided.

Current open learning environments such as Massive Open Online Courses often show a lack of learner collaboration possibilities and high levels of drop-out. Introducing project-based learning can enhance learner collaboration and motivation. Project-based learning requires extensive support from expert teachers and therefore does not easily scale up into Massive Open Online Courses. Team formation instruments are introduced, aimed at supporting teachers and learners in defining and staffing projects. These consist of team formation principles and algorithms to form productive, creative, or learning teams. They use data on the project and on learner knowledge, personality and preferences to propose teams. A study was carried out to validate the team formation principles and the results from the algorithms. The data were provided by Bachelor students Psychology and master students Learning Sciences (n=168) and processed by the algorithms. By means of a survey among human assessors (n=56), the instruments were validated. The principles for learning teams and productive teams were accepted, while the principle for creative teams was not. The algorithms were validated using team classifying tasks and team ranking tasks. Human assessors classify and rank small productive, creative and learning teams in accordance with the algorithms. This indicates that the algorithms differentiate effectively and in line with human assessors between teams with high or low fit to a team formation principle. Results also shows that forming teams quickly becomes complex when team size and the number of topics in a project increase. The article closes with a discussion of the results, conclusions, and directions for future research.

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.

Learning outcomes are typically developed using standard group-based consensus methods. Two main constraints with standard techniques such as the Delphi method or expert working group processes are: (1) the ability to generate a comprehensive set of outcomes and (2) the capacity to reach agreement on them. We describe the first application of Group Concept Mapping (GCM) to the development of learning outcomes for an interdisciplinary module in medicine and engineering. The biomedical design module facilitates undergraduate participation in clinician-mentored team-based projects that prepare students for a multidisciplinary work environment. GCM attempts to mitigate the weaknesses of other consensus methods by excluding pre-determined classification schemes and inter-coder discussion, and by requiring just one round of data structuring. Academic members from medicine and engineering schools at three EU higher education institutions participated in this study. Data analysis, which included multidimensional scaling and hierarchical cluster analysis, identified two main categories of outcomes: technical skills (new advancement in design process with special attention to users, commercialization and standardization) and transversal skills such as working effectively in teams and creative problem solving. The study emphasizes the need to address the highest order of learning taxonomy (analysis, synthesis, problem solving, creativity) when defining learning outcomes.

Background Healthcare worldwide needs translation of basic ideas from engineering into the clinic. Consequently, there is increasing demand for graduates equipped with the knowledge and skills to apply interdisciplinary medicine/engineering approaches to the development of novel solutions for healthcare. The literature provides little guidance regarding barriers to, and facilitators of, effective interdisciplinary learning for engineering and medical students in a team-based project context. Methods A quantitative survey was distributed to engineering and medical students and staff in two universities, one in Ireland and one in Belgium, to chart knowledge and practice in interdisciplinary learning and teaching, and of the teaching of innovation. Results We report important differences for staff and students between the disciplines regarding attitudes towards, and perceptions of, the relevance of interdisciplinary learning opportunities, and the role of creativity and innovation. There was agreement across groups concerning preferred learning, instructional styles, and module content. Medical students showed greater resistance to the use of structured creativity tools and interdisciplinary teams. Conclusions The results of this international survey will help to define the optimal learning conditions under which undergraduate engineering and medicine students can learn to consider the diverse factors which determine the success or failure of a healthcare engineering solution.

Learning in the cloud can be a lonely activity for self-directing and self-organizing learners. Lack of sustained learner motivation can lead to less effective, less bond-creating learning experiences. By providing collaborative project-based learning opportunities these shortcomings can be overcome. A service design is introduced for the onset of collaborative project-based learning and team formation in the cloud, based on learning materials in the cloud, project definitions and characteristics, and learner ‘knowledge’, ‘personality’ and ‘preferences’. The article specifies how the data required by the design can be gathered. Team formations rules are deduced from existing team formation research. They steer the team formation process towards facilitating learning, creative problem solving or increased productivity outcomes. The rules are implemented in three team formation equations. Deployment of the equations on a set of test data demonstrates the effectiveness of the team formation service. Keywords-Cloud learning; project-based learning; project team formation; self-directed learning; team formation rules

The Internet affords new approaches to learning. Geographically dispersed self- directed learners can learn in computer-supported communities, forming social learning networks. However, self-directed learners can suffer from a lack of continuous motivation. And surprisingly, social learning networks do not readily support effective, coherence-creating and motivating learning settings. It is argued that providing project-based learning opportunities and team formation services can help overcome these shortcomings. A review of existing team formation tools evidences that a new design for team formation and the initiation of project- based learning is required before these can be supported in social learning networks. A design is proposed which identifies 'knowledge', 'personality' and 'preferences' as categories in which data is needed to form teams, and it specifies how the required data are gathered and assessed. The design defines rules deduced from team formation principles from prior team formation research to optimise team formations towards increased productivity, creative solutions or higher learning outcomes. The rules are implemented in three team formation expressions each calculating one of the desired team formations. The expressions are deployed on a set of test data, demonstrating the effectiveness of the team formation service design. The article includes a discussion of the results and provides indications for future research.

Learning in the cloud can be a lonely activity for self-directing and self-organizing learners. Lack of sustained learner motivation can lead to less effective, less bond-creating learning experiences. By providing collaborative project-based learning opportunities these shortcomings can be overcome. A service design is introduced for the onset of collaborative project-based learning and team formation in the cloud, based on learning materials in the cloud, project definitions and characteristics, and learner ‘knowledge’, ‘personality’ and ‘preferences’. The article specifies how the data required by the design can be gathered. Team formations rules are deduced from existing team formation research. They steer the team formation process towards facilitating learning, creative problem solving or increased productivity outcomes. The rules are implemented in three team formation equations. Deployment of the equations on a set of test data demonstrates the effectiveness of the team formation service.

Sloep, P. B., Van der Klink, M., Brouns, F., Van Bruggen, J., & Didderen, W. (Eds.) (2011). Leernetwerken; Kennisdeling, kennisontwikkeling en de leerprocessen. Houten, Nederland: Bohn, Stafleu, Van Loghum.

This paper elaborates on the design of a computer-based service that supports conceptual development. Our ambition is provide learners a way to compare their conceptual development against different reference models, so they recognize the limits of their expertise. These models are (semi) automatically generated from learning materials and learner text inputs using Latent Semantic Analysis, a technique that identifies in input text materials the concepts and their relations. The paper explains the envisioned service presenting a scenario that illustrates how it could be used in formal and informal learning context. After, the paper elaborates the theoretical background behind the design of the service and, finally, it draws conclusions and outlines future work.

Berlanga, A. J., Spoelstra, H., Rajagopal, K., Smithies, A., Braidman, I., & Wild, F. (2010). Assisting Learner’s in Monitoring their Conceptual Development. In J. Cordeiro, B. Shishkov, A. Verbraeck, & M. Helfert (Eds.), Proceedings of the International Conference on Computer Supported Education (CSEDU 2010) (pp. 294-299). April, 7-10, 2010, Valencia, Spain.