Designing electronic collaborative learning environments

Electronic collaborative learning environments for learning and working are in vogue. Designers design them according to their own constructivist interpretations of what collaborative learning is and what it should achieve. Educators employ them with different educational approaches and in diverse situations to achieve different ends. Students use them, sometimes very enthusiastically, but often in a perfunctory way. Finally, researchers study them and—as is usually the case when apples and oranges are compared—find no conclusive evidence as to whether or not they work, where they do or do not work, when they do or do not work and, most importantly, why, they do or do not work. This contribution presents an affordance framework for such collaborative learning environments; an interaction design procedure for designing, developing, and implementing them; and an educational affordance approach to the use of tasks in those environments. It also presents the results of three projects dealing with these three issues

Machine and human observable differences in groups’ collaborative problem-solving behaviours

This paper contributes to our understanding of how to design learning analytics to capture and analyse collaborative problem-solving (CPS) in practice-based learning activities. Most research in learning analytics focuses on student interaction in digital learning environments, yet still most learning and teaching in schools occurs in physical environments. Investigation of student interaction in physical environments can be used to generate observable differences among students, which can then be used in the design and implementation of Learning Analytics. Here, we present several original methods for identifying such differences in groups CPS behaviours. Our data set is based on human observation, hand position (fiducial marker) and heads direction (face recognition) data from eighteen students working in six groups of three. The results show that the high competent CPS groups spend an equal distribution of time on their problem-solving and collaboration stages. Whereas, the low competent CPS groups spend most of their time in identifying knowledge and skill deficiencies only. Moreover, as machine observable data shows, high competent CPS groups present symmetrical contributions to the physical tasks and present high synchrony and individual accountability values. The findings have significant implications on the design and implementation of future learning analytics systems

E-learning: you don't always get what you hope for

Despite substantial growth in the use of information and communication technologies (ICT) throughout western societies, there is much evidence of technology-led innovations within Higher Education (HE) failing to achieve the anticipated transformations in learning and teaching. This paper reviews evidence from research and evaluation studies relating not only to e-learning, but also to wider HE practices. It argues that the use of ICT does not, in itself, result in improved educational outcomes and ways of working. It considers contextual factors that are of greater significance in determining how and why e-learning is used in HE. Students' engagement with e-learning relates to their expectations and conceptions of learning and to assessment demands. Academics need to re-assess their own beliefs and practices concerning teaching and assessment and their impact on the experience of learners. Both teachers and learners need to understand why e-learning activities are to be undertaken and the rewards expected to be derived

The Mind’s Eye on Personal Profiles: A Cognitive Perspective on Profile Elements that Inform Initial Trustworthiness Assessments in Virtual Project Teams

Rusman, E., Van Bruggen, J., Sloep, P., Valcke, M., & Koper, R. (2013). The Mind’s Eye on Personal Profiles: A Cognitive Perspective on Profile Elements that Inform Initial Trustworthiness Assessments and Social Awareness in Virtual Project Teams. Computer Supported Cooperative Work (CSCW), 22(2-3), 159-179.Collaboration in virtual project teams heavily relies on interpersonal trust, for which perceived trustworthiness is an important determinant. This study provides insight in the information that trustors value to assess a trustee’s professional trustworthiness in the initial phase of a virtual project team. We expect trustors in virtual teams to value those particular information elements that provide them with relevant cues of trust warranting properties of a trustee. We identified a list of commonly highly valued information elements to inform trustworthiness assessments (n=226). We then analysed explanations for preferences with the help of a theory-grounded coding scheme. Results show that respondents value those particular information elements that provide them with multiple cues to assess the trustworthiness of a trustee. This enables them to become aware of and assess the trustworthiness of another. Information elements that provide unique cues could not be identified. Insight in these information preferences can inform the design of artefacts, such as personal profile templates, to support acquaintanceships in the initial phase of a virtual project team

Multilevel analysis in CSCL Research

Janssen, J., Erkens, G., Kirschner, P. A., & Kanselaar, G. (2011). Multilevel analysis in CSCL research. In S. Puntambekar, G. Erkens, & C. Hmelo-Silver (Eds.), Analyzing interactions in CSCL: Methods, approaches and issues (pp. 187-205). New York: Springer. doi:10.1007/978-1-4419-7710-6_9CSCL researchers are often interested in the processes that unfold between learners in online learning environments and the outcomes that stem from these interactions. However, studying collaborative learning processes is not an easy task. Researchers have to make quite a few methodological decisions such as how to study the collaborative process itself (e.g., develop a coding scheme or a questionnaire), on the appropriate unit of analysis (e.g., the individual or the group), and which statistical technique to use (e.g., descriptive statistics, analysis of variance, correlation analysis). Recently, several researchers have turned to multilevel analysis (MLA) to answer their research questions (e.g., Cress, 2008; De Wever, Van Keer, Schellens, & Valcke, 2007; Dewiyanti, Brand-Gruwel, Jochems, & Broers, 2007; Schellens, Van Keer, & Valcke, 2005; Strijbos, Martens, Jochems, & Broers, 2004; Stylianou-Georgiou, Papanastasiou, & Puntambekar, chapter #). However, CSCL studies that apply MLA analysis still remain relatively scarce. Instead, many CSCL researchers continue to use ‘traditional’ statistical techniques (e.g., analysis of variance, regression analysis), although these techniques may not be appropriate for what is being studied. An important aim of this chapter is therefore to explain why MLA is often necessary to correctly answer the questions CSCL researchers address. Furthermore, we wish to highlight the consequences of failing to use MLA when this is called for, using data from our own studies