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

Does Reviewing Lead to Better Learning and Decision Making? Answers from a Randomized Stock Market Experiment

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Four patients with a history of acute exacerbations of COPD: implementing the CHEST/Canadian Thoracic Society guidelines for preventing exacerbations

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Developing argumentation skills in mathematics through computer-supported collaborative learning: the role of transactivity

Collaboration scripts and heuristic worked examples are effective means to scaffold university freshmen’s mathematical argumentation skills. Yet, which collaborative learning processes are responsible for these effects has remained unclear. Learners presumably will gain the most out of collaboration if the collaborators refer to each other’s contributions in a dialectic way (dialectic transactivity). Learners also may refer to each other’s contributions in a dialogic way (dialogic transactivity). Alternatively, learners may not refer to each other’s contributions at all, but still construct knowledge (constructive activities). This article investigates the extent to which constructive activities, dialogic transactivity, and dialectic transactivity generated by either the learner or the learning partner can explain the positive effects of collaboration scripts and heuristic worked examples on the learners’ disposition to use argumentation skills. We conducted a 2 × 2 experiment with the factors collaboration script and heuristic worked examples with N = 101 math teacher students. Results showed that the learners’ engagement in self-generated dialectic transactivity (i.e., responding to the learning partner’s contribution in an argumentative way by critiquing and/or integrating their learning partner’s contributions) mediated the effects of both scaffolds on their disposition to use argumentation skills, whereas partner-generated dialectic transactivity or any other measured collaborative learning activity did not. To support the disposition to use argumentation skills in mathematics, learning environments should thus be designed in a way to help learners display dialectic transactivity. Future research should investigate how learners might better benefit from the dialectic transactivity generated by their learning partners

Fostering teacher community development: A review of design principles and a case study of an innovative interdisciplinary team

To deal with recent reforms and the accompanying complexity of work in secondary education, ongoing collaboration between teachers has become more important. A community is seen as a promising learning environment to support and embed collaboration into the culture of the school. However, community theory for the design of teacher communities seems underdeveloped. Therefore, this study aims to formulate a set of design principles to foster the development of teacher communities in secondary education. The set of design principles is based on a review of literature, as well as on a best-practice case. The case study was used to validate design principles from the literature in the target context. The resulting design principles were based on context-intervention-mechanismoutcome logic that takes into account the context-dependency of interventions as well as the mechanisms that help with understanding of how interventions produce certain outcomes. Implications for practice relate to ownership and co-design of the arrangement. The set of design principles provides a practical basis for teachers and administrators aiming to facilitate community building in their school. Future research is recommended on testing the effectiveness of the arrangement in the target context by means of a multiple case study.NWO-pro

Augmenting Assessment with Learning Analytics

Learning analytics as currently deployed has tended to consist of large-scale analyses of available learning process data to provide descriptive or predictive insight into behaviours. What is sometimes missing in this analysis is a connection to human-interpretable, actionable, diagnostic information. To gain traction, learning analytics researchers should work within existing good practice particularly in assessment, where high quality assessments are designed to provide both student and educator with diagnostic or formative feedback. Such a model keeps the human in the analytics design and implementation loop, by supporting student, peer, tutor, and instructor sense-making of assessment data, while adding value from computational analyses