Cognitive Load Theory: Advances in Research on Worked Examples, Animations, and Cognitive Load Measurement

The contributions to this special issue document some recent advances of cognitive load theory, and are based on contributions to the Third International Cognitive Load Theory Conference (2009), Heerlen, The Netherlands. The contributions focus on developments in example-based learning, amongst others on the effects of integrating worked examples in cognitive tutoring systems; specify the effects of transience on cognitive load and why segmentation may help counteract these effects in terms of the role of time in working memory load; and discuss the possibilities offered by electroencephalography (EEG) to provide a continuous and objective measure of cognitive load. This article provides a short introduction to the contributions in this issue

A multidimensional evaluation framework for personal learning environments

Evaluating highly dynamic and heterogeneous Personal Learning Environments (PLEs) is extremely challenging. Components of PLEs are selected and configured by individual users based on their personal preferences, needs, and goals. Moreover, the systems usually evolve over time based on contextual opportunities and constraints. As such dynamic systems have no predefined configurations and user interfaces, traditional evaluation methods often fall short or are even inappropriate. Obviously, a host of factors influence the extent to which a PLE successfully supports a learner to achieve specific learning outcomes. We categorize such factors along four major dimensions: technological, organizational, psycho-pedagogical, and social. Each dimension is informed by relevant theoretical models (e.g., Information System Success Model, Community of Practice, self-regulated learning) and subsumes a set of metrics that can be assessed with a range of approaches. Among others, usability and user experience play an indispensable role in acceptance and diffusion of the innovative technologies exemplified by PLEs. Traditional quantitative and qualitative methods such as questionnaire and interview should be deployed alongside emergent ones such as learning analytics (e.g., context-aware metadata) and narrative-based methods. Crucial for maximal validity of the evaluation is the triangulation of empirical findings with multi-perspective (end-users, developers, and researchers), mixed-method (qualitative, quantitative) data sources. The framework utilizes a cyclic process to integrate findings across cases with a cross-case analysis in order to gain deeper insights into the intriguing questions of how and why PLEs work

Multi-representational solutions, an integration aid, and scaffolding self-explanation prompts all foster understanding

Berthold K, Schwonke R, Renkl A. Multi-representational solutions, an integration aid, and scaffolding self-explanation prompts all foster understanding. Presented at the Meeting of the American Educational Research Association, Chicago, USA

Learning with multi-representational worked-out examples. What eye movements can tell us (a pilot study)

Schwonke R, Renkl A, Berthold K. Learning with multi-representational worked-out examples. What eye movements can tell us (a pilot study). Presented at the Annual Meeting of the American Educational Research Association (AERA), Chicago, USA

Multiple representations and two instructional support procedures foster conceptual knowledge, but not procedural knowledge

Berthold K, Schwonke R, Renkl A. Multiple representations and two instructional support procedures foster conceptual knowledge, but not procedural knowledge. Presented at the 12th European Conference for Research on Learning and Instruction, Budapest, Hungary

How do learners actually use multiple external representations? An analysis of eye movements and learning outcomes

Schwonke R, Berthold K, Renkl A. How do learners actually use multiple external representations? An analysis of eye movements and learning outcomes. Presented at the 12th Biennial Conference of the European Association for Learning and Instruction (EARLI), Budapest, Hungary