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.
Background: This article addresses the question of which physical-technical prior competencies students in Germany start their engineering studies with. Furthermore, it analyzes the influence of e.g. formal qualifications, curricular weights in school, or participation in preparatory courses on these prior competencies. Methods: Using a sample of 2345 students, we modelled the structure of competencies and conducted proficiency scaling. Furthermore, we computed t-tests and analyses of variance in order to analyze the physical-technical prior competencies' dependency on education biographies, gender, participation in propaedeutic courses etc. Results: Our results reveal a three-dimensional structure for the physical-technical prior competencies as most suitable. Additionally, we find a big variance in the physicaltechnical prior competencies. Students with a general entrance qualification, male students, students attending universities, students having had many physics lessons in school and students having participated in preparatory courses in physics achieve better results. Conclusions: Summing up, the results of our survey reveal a big variance in the physical-technical prior competencies. Hence, we find a substantial proportion of freshmen with significant competency deficits. We assume that these competency deficits constitute a factor which makes (the beginning of) engineering studies more difficult.
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