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

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.

Cognitive Mapping Tools: Review and Risk Management Needs

“…( 62 ) Computer programs for mental model diagramming based on this method are currently available, and several more are under development. ( 61 , 63–68 )…”

Development of software to support argumentative reading and writing by means of creating a graphic organizer from an electronic text

“…Because electronic documents have become increasingly popular, research is being conducted on reading strategies for online documents or digital texts depending on students' learning strategies and the strategies' suitability under particular circumstances (Hsieh and Dwyer 2009;Hermena et al 2017;Ben-Yehudah and Eshet-Alkalai 2018). Various computer software programs are being developed to guide beginner students' reading strategies by providing functions such as prompts (Britt and Aglinskas 2002;Stadtler and Bromme 2008;Park and Kim 2016), graphic organizational frameworks (Chang et al 2001;Liu et al 2010;Pirnay-Dummer and Ifenthaler 2011;Ponce et al 2012;Dwyer et al 2013;Ponce, et al 2013;Kiili et al 2016), and underlining and annotation functions (Glover et al 2007;Wolfe 2000;Chen et al 2012;van Horne et al 2016;Winne et al 2017;Yanikoglu et al 2017; for a review, see Wolfe 2002) as well as mobile reading software (e.g., Chen et al 2011;Chen and Lin 2014). Some of these functions directly liaise with digital texts and can automatically retrieve key points from the text, while others allow students to manually input answers or manipulate the texts in order to prompt their deeper thinking.…”

“…A computerized graphic organizer can help learners construct node-link assemblies such as knowledge maps and can be easily adjusted or quickly corrected; computer-assisted mapping may also have positive effects on learners' reading abilities (Liu et al 2010), especially when the software can use the correct function to review students' summaries (Chang et al 2001). Some software also provides automatic functions to support the construction of maps (e.g., Chang, et al 2001;Pirnay-Dummer and Ifenthaler 2011;Juarez Collazo et al 2015). Note that some of the above studies used premade graphic organizers to foster students' text comprehension (and even a nonautomatic premade graphic organizer; Colliot and Jamet 2018), while others had learners compare maps with their ideas or input text data in a framework proved by the software (e.g., Ponce and Mayer 2014a, b).…”

Improving summary writing through formative feedback in a technology‐enhanced learning environment

“…A written summary can serve as an externalized representation of this mental model. That is, educators and researchers can analyse the nature of a student's summary to infer the quality of the learner's mental model (Ifenthaler, 2014; K. Kim, 2017, 2018; K. Kim, Clarianay, & Kim, 2019; Pirnay‐Dummer & Ifenthaler, 2011).…”

“…A model‐based approach is an alternative means to provide a more comprehensive evaluation of the student's summary (Ifenthaler & Pirnay‐Dummer, 2014). A model‐based approach uses a concept map—a network of interrelated concepts (Axelrod, 1976; Schvaneveldt & Cohen, 2010) elicited from a summary—to generate indices in multiple dimensions of knowledge structure (Ifenthaler, 2014; M. Kim, 2012; Pirnay‐Dummer & Ifenthaler, 2011; Spector & Koszalka, 2004). Model‐based ASEs such as AKOVIA (Ifenthaler, 2014), GISK (Kim, 2018), HIMATT (Pirnay‐Dummer & Ifenthaler, 2011) and SMART (M. Kim et al, 2019) evaluate the quality of students' knowledge representation and use concept maps for feedback to help them consider appropriate content and connect important ideas together.…”