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

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Abstract

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.

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Correspondence to Pablo Pirnay-Dummer.

Appendix A: Analysis of variance tables

Appendix A: Analysis of variance tables

The influence of the text on the text ratings (Tables 10, 11, 12, 13).

Table 10 ANOVA, simplicity
Table 11 ANOVA, length
Table 12 ANOVA, order/design
Table 13 ANOVA, motivation/stimulation

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Pirnay-Dummer, P., Ifenthaler, D. Reading guided by automated graphical representations: How model-based text visualizations facilitate learning in reading comprehension tasks. Instr Sci 39, 901–919 (2011). https://doi.org/10.1007/s11251-010-9153-2

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