Computer-based visualizing: learning from science texts by means of self-generated computer-based drawings

Research on the effectiveness of generating visualizations while reading and understanding a science text showed: Learning with the help of self-generated visualizations that learners generate paper-and-pencil based has a positive effect on the learning outcome (van Meter & Garner, 2005; van Meter & Firetto, 2013). Although there are several studies concerning learning with paper-and-pencil based self-generated visualizations, only one study has investigated learning with computer-based drawing by means of drag-and-drop. This study did not find distinct evidence for or against the efficacy of learning from science texts with the help of computer-based self-generated visualizations regarding learning outcome (Schwamborn, Thillmann, Opfermann & Leutner, 2011). However results did show that learners generating computer-based visualizations have less cognitive resources available to actively process the information, meaning that computer-based drawing seems to increase the cognitive load (Extraneous Cognitive Load; see Sweller, 2010). The starting point of this dissertation was the lack of evidence for the learner-generated drawing strategy (see Alesandrini, 1984, van Meter & Garner, 2005) being successful in enhancing learning outcome within computer-based learning environments. Based on two experimental studies, it was analyzed whether generative drawing by means of drag-and-drop on a computer screen can increase learning from science texts generally (Generative Drawing Principle, see Schwamborn, Mayer, et al., 2010); second which kind of effect computer-based generative drawing has on students’ cognitive load; and finally whether benefits of generative drawing are the same for paper-based and computer-based materials. Results of the first study show that students have higher learning outcome scores respectively learn more when they learn from a science text using the generative drawing strategy. In addition, the results provide strong and consistent support for the Prognostic Drawing Principle (see Schwamborn, Mayer, et al., 2010). Here, the accuracy or quality of the visualizations generated during learning correlates positively with the posttest scores. Thus, the results suggest that the generative drawing principle and the prognostic drawing principle can be extended to computer-based learning environments, when extraneous processing caused by the specific mechanics of generating computer-based drawings is reduced. Results of the second study show that students learn significantly more when they read and generate drawings on paper than on a computer screen (using drag-and-drop). Results also reveal that students reported significantly less perceived difficulty when working with a text-paragraph in the computer-based learning environment than in the paper-based learning environment. On a subsequent questionnaire, students generally reported fewer difficulties when generating drawings by drag-and-drop on the computer as well as a higher level of motivation. Additionally, the prognostic drawing principle is supported in paper-based as well as in computer-based learning environments. Finally, results of the studies are discussed with regard to their empirical, theoretical and practical contributions as well as their limitations. In addition, indications for future research are given.