Student learning preferences in a blended learning environment: investigating the relationship between tool use and learning approaches

Tempelaar, Dirk T.; Rienties, Bart and Giesbers, Bas (2011). Student learning preferences in a blended learning environment: investigating the relationship between tool use and learning approaches. In: Van de Bossche, Piet; Gijselaers, Wim H. and Milter, Richard G. eds. Building Learning Experiences in a Changing World. Advanced in Business Education and Training (3). Dordrecht: Springer, pp. 195–212.

DOI: https://doi.org/10.1007/978-94-007-0802-0_12

URL: http://link.springer.com/chapter/10.1007%2F978-94-...

Abstract

Building on research into the demands on students’ self-regulated learning when learning about conceptually rich domains with computer-based learning environments (CBLEs) (Azevedo, Current perspectives on cognition, learning, and instruction, 2008; Lajoie and Azevedo, Handbook of educational psychology, 2006), our study focuses on the research question how students self-regulate their learning in a blended learning environment. In the teaching of introductory statistics to first-year students in economics and business, the Maastricht University uses a blended learning environment that allows students to individualize by attuning available learning tools to personal preferences. The blended learning environment consists of tutorials based on the problem-based learning principle and independent learning driven by learning goals produced by these tutorials; a sequence of traditional lectures, and an electronic learning environment based on the adaptive tutorial system Assessment and LEarning in Knowledge Spaces (ALEKS). Only participation in tutorial sessions is required; the usage of other components can be set according to individual preferences. The main reason to introduce the blended learning environment had been the need to accommodate a very heterogeneous inflow of students, transferring from very different secondary school systems with large differences in prior knowledge of statistics. For example, whereas a part of prospective students has had prior schooling in statistics, the majority of the inflow is educated within secondary school systems that lack coverage of statistics. The principle of repeated formative, adaptive testing that serves as the kernel of the ALEKS tool and steers all student learning and practicing makes the tool tailored to bridge short falling prior knowledge. However, on top of accommodating cognitive differences, the tool appeared to accommodate differences in learning styles.

In this study, we will focus on this last aspect, by investigating the relationship between the intensity of the use of the electronic learning environment ALEKS and student background characteristics, such as learning style preferences, achievement motivation, self-concept constructs and subject attitudes. Data of about 4,650 freshmen from six subsequent cohorts participating in this course are used. Correlational analyses suggest that especially less academically prepared students profit most from the e-learning facilities in the blended learning environment: intensity of e-learning is positively correlated with the step-wise (surface) learning style and the dependency of a stimulating learning environment, and negatively correlated with mathematical self-concept and attitudes towards the subject statistics. These findings suggest that facilitating different learning approaches in the freshman program might help in the transition of less academically adapted students.

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