proceedings of Student Mobility and ICT: Can E-LEARNING

processing strategies, metacognitive regulation strategies, learning orientations, and conceptions (or mental
models) of learning. Each component is composed of five different scales, as described in the Table 1.
Table 1: Components and scales of the Inventory of Learning Styles.
Processing strategies Regulation strategies Learning orientations Learning conceptions, or
Mental models of learning
Relating and Self-regulation of Personally interested Construction of knowledge
structuring
learning processes
Critical processing Self-regulation of
learning content
Certificate directed Intake of knowledge
Memorising and External regulation of Self test directed Use of knowledge
rehearsing
learning processes
Analysing External regulation of
learning results
Vocation directed Stimulating education
Concrete processing Lack of regulation Ambivalent Co-operation
The two processing strategies ‘relating and structuring’ and ‘critical processing’ together compose
the ‘deep learning’ strategy, whereas ‘memorizing and rehearsing’, together with ‘analysing’, compose the
‘stepwise learning’ strategy.
Course performance. Multiple performance indicators are available: subtopic scores (statistics and
mathematics), and scores for different assessment instruments applied in the performance portfolio: final
written exam and quizzes. GPA is the overall measure of student performance in the first year program.
Data
Participants in this study were 1972 first year university students in two programs International
Economics and International Business Studies. In the first term of their first academic semester, these
students took two required, parallel courses: an integrated course organizational theory & marketing, two
subjects from the behavioural sciences domain, and an integrated course mathematics & statistics. The
methods course is supported by ‘practicals’. Those for statistics are based on the e-learning environment
ALEKS, and allow for the measurement of user intensity through connect hours. Doing practicals is no
requirement, and is especially beneficial for students who lack prior knowledge, and/or experience methods
courses as difficult. Therefore, data on practicals are not representative for the whole course.
During the start of the course, students filled self-report questionnaires on preferred learning
approaches, metacognitive abilities, and ex ante achievement motivations. In the last week of the term, they
filled a second questionnaire measuring ex post achievement motivations in the four subjects of the two
integrated courses: organizational theory, marketing, mathematics, and statistics.
Participants are from three consecutive cohorts. Therefore, performance measures as quizzes and
final exams are scored with equivalent, but not identical instruments.
Statistical analysis
Due to both large sample sizes and collinearity amongst most determinants of the learning
process, direct relationships between students’ preferences for e-learning and determinants or outcomes of
the learning process are not very informative. These relationships are nearly always statistically significant,
but cannot easily distinguish between direct and indirect effects. For that reason, our main analytical tool
will be to compare correlations between e-learning preferences and student background factors with
correlations between learning outcomes and the same student background factors. The latter correlations
serve as benchmarks in the assessment of the role of e-learning preferences on the learning process.
Results Metacognitive abilities reflect students’ self-perceptions of their abilities to independently
organize their studies. It will cause no surprise that the levels of these abilities correlate moderately strong
with learning performances, especially learning performances that are strongly effort based, such as quiz
scores. Learning effort in the ALEKS tutorial is above average related with two of the metacognitive
abilities: the skills and the attitudes components. See Figure 3.
Conference proceedings of Student Mobility and ICT: Can E-LEARNING overcome barriers of Life-Long learning?” 55