Engineering Students' Thinking About Technical Systems: An Ontological Categories Approach

This paper aims at identifying ontological categories as higher-order knowledge structures that underlie engineering students’ thinking about technical systems.

The Role of Spatial, Verbal, Numerical, and General Reasoning in Complex Word Problem Solving for Young Female and Male Adults

This study analyzed the relative importance of different cognitive abilities—spatial, verbal, numerical, and general reasoning—for solving Complex mathematical Word Problems among N = 1282 first-year university engineering students.

The Interplay Between the Natural Number Bias and Fraction Magnitude Processing in Low-Achieving Students

The relationship between the natural number bias and the ability to process fraction magnitudes is not well understood. We investigate this relationship by analyzing individual students’ profiles. Our results suggest that the occurrence of the natural number bias and the ability to process fraction magnitude are closely related.

The potential of digital tools to enhance mathematics and science learning in secondary schools: A context-specific meta-analysis

The comprehensive meta-analysis investigated how the use of technology can enhance learning in mathematics and science. 92 studies compared learning outcomes of students using digital tools to those of a control group taught without the use of digital tools. Overall, digital tool use had a positive effect on student learning outcomes, g = 0.65.

Learning Fractions with and without Educational Technology: What Matters for High-Achieving and Low-Achieving Students?

We developed interactive material for learning fractions providing scaffolds in an e-textbook. High-achieving students did benefit from the curriculum, regardless of whether it was presented with or without interactive scaffolds, while for low-achieving students using scaffolds was decisive.

Investigating Mental Models of Mechanical Engineering Students

This paper presents a study that investigates the mental models of mechanical engineering students of technical systems. Results indicate that mental models may depend on the presentation of a problem. A questionnaire yielded a preference for a procedural approach, while questions prompting verbal information instead yield a structural preference.

Analyzing Students' Mental Models of Technical Systems

The studies apply SA/RT and card sorting to analyze mental models and were conducted at the beginning of their academic education to understand influences on discipline-specific mental models that are observed in practice. The first study indicates that mental models of engineering students are typically based on structural properties of the machines. A second study identified nine dimensions that classify mental models of students.

Design and research potential of interactive textbooks: the case of fractions

Computer-based learning environments introduce aspects that allow further information on learning processes to be gleaned. In this study, linear mixed models revealed a negative effect of time on task on task success, which was moderated by exercise difficulty and by student competence.