Next Article in Journal
Parents’, Teachers’ and Principals’ Views on Parental Involvement in Secondary Education Schools in Greece
Next Article in Special Issue
Pre-Service Teachers’ Declarative Knowledge of Wave-Particle Dualism of Electrons and Photons: Finding Lexicons by Using Network Analysis
Previous Article in Journal
Moving Spaces: Mapping the Drama Room as Heterotopia
Previous Article in Special Issue
Network Analysis of Survey Data to Identify Non-Homogeneous Teacher Self-Efficacy Development in Using Formative Assessment Strategies
Open AccessArticle

Combining Surveys and Sensors to Explore Student Behaviour

by Inkeri Kontro 1,*,† and Mathieu Génois 2,3,†
Department of Physics, University of Helsinki, 00100 Helsinki, Finland
CNRS, CPT, Aix Marseille Univ, Université de Toulon, 19328 Marseille, France
GESIS, Leibniz Institute for the Social Sciences, 68159 Köln, Germany
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Educ. Sci. 2020, 10(3), 68;
Received: 29 January 2020 / Revised: 3 March 2020 / Accepted: 5 March 2020 / Published: 10 March 2020
(This article belongs to the Special Issue Networks Applied in Science Education Research)
Student belongingness is important for successful study paths, and group work forms an important part of modern university physics education. To study the group dynamics of introductory physics students at the University of Helsinki, we collected network data from seven laboratory course sections of approximately 20 students each for seven consecutive weeks. The data was collected via the SocioPatterns platform, and supplemented with students’ major subject, year of study and gender. We also collected the Mechanics Baseline Test to measure physics knowledge and the Colorado Learning Attitudes about Science Survey to measure attitudes. We developed metrics for studying the small networks of the laboratory sessions by using connections of the teaching assistant as a constant. In the network, we found both demographically homogeneous and heterogeneous groups that are stable. While some students are consistently loosely connected to their networks, we were not able to identify risk factors. Based on our results, the physics laboratory course is equally successful in building strongly connected groups regardless of student demographics in the sections or the formed small groups. SocioPatterns supplemented with surveys thus provides an opportunity to look into the dynamics of students’ social networks. View Full-Text
Keywords: small group; undergraduate laboratory; physics education; network; student retention small group; undergraduate laboratory; physics education; network; student retention
Show Figures

Figure 1

MDPI and ACS Style

Kontro, I.; Génois, M. Combining Surveys and Sensors to Explore Student Behaviour. Educ. Sci. 2020, 10, 68.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop