Reading guided by automated graphical representations : how model-based text visualizations facilitate learning in reading comprehension tasks

Our study integrates automated natural language-oriented assessment and analysis methodologies into feasible reading comprehension tasks. With the newly developed T-MITOCAR toolset, prose text can be automatically converted into an association net which has similarities to a concept map. The "text to graph" feature of the software is based on several parsing heuristics and can be used both to assess the learner's understanding by generating graphical information from his or her text and to generate conceptual graphs from text which can be used as learning materials. In this study we investigate the effects of association nets made available to learners prior to reading. The results reveal that the automatically created graphs are highly similar to classical expert graphs. However, neither the association nets nor the expert graphs had a significant effect on learning, although the latter have been reported to have an effect in previous studies. © 2010 Springer Science+Business Media B.V

Automated knowledge visualization and assessment

In this chapter we introduce a computer-based and highly automated measurement technique which enables us to analyze even large sets of data within a few seconds. Closely linked to the demand of new approaches for designing and developing up-to-date learning environments is the necessity of enhancing the design and delivery of assessment systems and automated computer-based diagnostics. In many settings, manual and therefore labor-intensive methods have limits. Hence, following a general assessment framework design, we introduce several automated and integrated tools which have helped us in many studies so far. The technologies which we discuss in this chapter aim at the assessment, re-representation, analysis, and comparison of knowledge. The tools were developed independently and then integrated step by step. The possible applications go beyond the structural and semantic analysis and comparison of knowledge. The tools also allow the development of self-assessment technologies which can be used directly by the learners. © 2010 Springer-Verlag US

Structural understanding from note-taking within video lectures

 Note-taking during all kinds of lectures is a standard and easy technique for learners of all ages to self-document their thought during learning. Based on a metacognitive rationale, this study investigates the effect of note-taking within different kinds of video-lectures – text-driven presentations versus graphical presentations. The availability of note-taking is experimentally controlled for 54 undergraduate students, and the quality of the nodes is then projected to the learning outcome as compared to the content of the lectures. Our results indicate little direct impact of the note quality, and contra-intuitively, not taking notes helped learners with their knowledge structure. Our study helps to understand the limits of note-taking during learning and with a broader theoretical understanding of idiosyncratic externalization