Project Details
Projekt
FOR 2242: Academic Learning and Academic Success during University Entry Phase in Natural and Engineering Sciences
Subject Area
Social and Behavioural Sciences
Term
from 2014 to 2019
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 257652630
In Germany, a lack of skilled academics in the field of natural and engineering sciences is apprehended. This lack is caused by a comparatively small number of students and, in addition, a high dropout rate. Are the university courses too demanding cognitively or is there a missing of prerequisites on the part of the students? To answer this question the Research Unit is looking for consolidated findings, which take into account different perspectives of psychology, science education and engineering education.
Initially, the research will focus on the university entry phase. Key aspects will be:
(1) Resource management strategies: It will be investigated whether the extent to which learning problems at the beginning of university studies are due to deficits in the self-regulated use of resource management strategies.
(2) Mathematical competence: Mathematical modelling is used in physics for describing physics phenomena, whereas in engineering it is used for calculating technical constructions. It is aimed to model and identify basic relations between mathematics-related modelling and different other components of academic success in the two subjects physics and civil engineering, during the university entry phase.
(3) Comprehension of visual models: The comprehension of models is seen as a crucial component for the development of conceptual knowledge in chemistry as well as in engineering. This project focusses on predictors of visual model comprehension in chemistry and engineering. On the one hand, this includes the visualisations themselves (e.g., iconic versus symbolic models), and, on the other hand, individual prerequisites on the part of the learners.
(4) Subject-specific prior knowledge: Especially students subject-specific prior knowledge is a good predictor of their academic success. This is further emphasised by the predictive validity of subject-specific university admission tests. Nevertheless, there seem to be some differences concerning different types of prior knowledge.
In the second term, the focus will be on the most important correlations between predictors and academic success. On one site, the possibility of generalisation will be tested by studies at other universities. On the other side, the correlations should be proofed by longitudinal analysis. In addition, theoretical funded interventions aiming towards a decrease of dropout should be systematically evaluated in natural and engineering science study programmes.
Initially, the research will focus on the university entry phase. Key aspects will be:
(1) Resource management strategies: It will be investigated whether the extent to which learning problems at the beginning of university studies are due to deficits in the self-regulated use of resource management strategies.
(2) Mathematical competence: Mathematical modelling is used in physics for describing physics phenomena, whereas in engineering it is used for calculating technical constructions. It is aimed to model and identify basic relations between mathematics-related modelling and different other components of academic success in the two subjects physics and civil engineering, during the university entry phase.
(3) Comprehension of visual models: The comprehension of models is seen as a crucial component for the development of conceptual knowledge in chemistry as well as in engineering. This project focusses on predictors of visual model comprehension in chemistry and engineering. On the one hand, this includes the visualisations themselves (e.g., iconic versus symbolic models), and, on the other hand, individual prerequisites on the part of the learners.
(4) Subject-specific prior knowledge: Especially students subject-specific prior knowledge is a good predictor of their academic success. This is further emphasised by the predictive validity of subject-specific university admission tests. Nevertheless, there seem to be some differences concerning different types of prior knowledge.
In the second term, the focus will be on the most important correlations between predictors and academic success. On one site, the possibility of generalisation will be tested by studies at other universities. On the other side, the correlations should be proofed by longitudinal analysis. In addition, theoretical funded interventions aiming towards a decrease of dropout should be systematically evaluated in natural and engineering science study programmes.
DFG Programme
Research Units
Projects
- Academic success in physics and civil engineering with paricular regard to mathematical competence (Applicants Borowski, Andreas ; Fischer, Hans Ernst ; Lang, Martin )
- Coordinated Collection of Data and Comparative Analyses across Academic Disciplines and Study Programs (Applicants Brand, Matthias ; Leutner, Detlev ; Sumfleth, Elke )
- Influence of different types of subject-specific prior knowledge on academic success in biology and physics (Applicants Schmiemann, Philipp ; Theyßen, Heike )
- Predictors of the comprehension of visual models in chemistry and engineering sciences (Applicants Lang, Martin ; Opfermann, Maria ; Rumann, Stefan )
- Self-regulation of learning in the university entry phase: resource management strategies (Applicant Leutner, Detlev )
- study outcome, college entry phase (Applicants Leutner, Detlev ; Sumfleth, Elke )
Spokespersons
Professor Dr. Detlev Leutner; Professor Dr. Maik Walpuski, since 1/2018
