The research on the assessment of collaborative problem solving (ColPS), as one crucial 21st Century Skill, is still in its beginnings. Using Tangible User Interfaces (TUI) for this purpose has only been marginally investigated in technology-based assessment. Our first empirical studies focused on light-weight performance measurements, usability,…

Problem solving is often challenging for students because they do not understand the problem-solving process (PSP). This study presents a three-stage, context-based, problem-solving, learning activity that involves watching detective films, constructing a context-simulation activity, and introducing a project design to enable students to construct…

In contexts other than immersive virtual environments, theoretical and empirical work has identified flow experience as a major factor in learning and human-computer interaction. Flow is defined as a "holistic sensation that people feel when they act with total involvement". We applied the concept of flow to modeling the experience of…

The relationship between working memory, intelligence and problem-solving is explored. Wittmann and Suss [Wittmann, W.W., & Suss, H.M. (1999). Investigating the paths between working memory, intelligence, knowledge, and complex problem-solving performances via Brunswik symmetry. In P.L. Ackerman, R.D. Roberts (Ed.), "Learning and individual…

Exploratory factor analysis (EFA) is a frequently used multivariate analysis technique in statistics. Jennrich and Sampson (1966) solved a significant EFA factor loading matrix rotation problem by deriving the direct Quartimin rotation. Jennrich was also the first to develop standard errors for rotated solutions, although these have still not made…

This article discusses 5 approaches that secondary researchers might use to obtain robust estimates in structural equation modeling analyses when using data that come from large survey programs. These survey programs usually collect data using complex sampling designs and estimates obtained from conventional analyses that ignore the dependencies…

Finite mixture models are well known to have poorly behaved likelihood functions featuring singularities and multiple optima. Growth mixture models may suffer from fewer of these problems, potentially benefiting from the structure imposed on the estimated class means and covariances by the specified growth model. As demonstrated here, however,…