What you see is what you learn? The role of visual model comprehension for academic success in chemistry

Autor: Maria Opfermann, Martin Lang, Stefan Rumann, Elmar Dammann, Thomas Dickmann
Rok vydání: 2019
Předmět:
Zdroj: Chemistry Education Research and Practice. 20:804-820
ISSN: 1756-1108
1109-4028
DOI: 10.1039/c9rp00016j
Popis: Visualizations and visual models are of substantial importance for science learning (Harrison and Treagust, 2000), and it seems impossible to study chemistry without visualizations. More specifically, the combination of visualizations with text is especially beneficial for learning when dual coding is fostered (Mayer, 2014). However, at the same time, comprehending the visualizations and visual models appears to be rather difficult for learners (e.g., Johnstone, 2000). This may be one reason for the difficulties students experience especially during the university entry phase, which in a worst-case-scenario can result in high university drop-out rates as they are currently found in science-related study courses (Chen, 2013). In this regard, our study investigates, how the ability to handle and learn with visualizations – which we call visual model comprehension – relates to academic success at the beginning of chemistry studies. To do so, we collected the data of 275 chemistry-freshmen during their first university year. Our results show that visual model comprehension is a key factor for students to be successful in chemistry courses. For instance, visual model comprehension is able to predict exam grades in introductory chemistry courses as well as general chemistry content knowledge. Furthermore, our analyses point out that visual model comprehension acts as a mediator for the relation between prior knowledge and (acquired) content knowledge in chemistry studies. Given this obvious importance of visual model comprehension, our findings could give valuable insights regarding approaches to foster chemistry comprehension and learning especially for students at the beginning of their academic career.
Databáze: OpenAIRE