Matching mental models : the starting point for authentic assessment in robotics

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Christine Edwards-Leis


This paper discusses the matches and mismatches
that occur between the mental models held by the
teacher and students undertaking a robotics activity in
an Australian school. It proposes that an
understanding of participants’ mental models of
learning and assessment plays an important role in
planning for, and reporting, on authentic assessment
of a technology-based activity.
The longitudinal project, over 20 months, was an
empirical qualitative study centred within information
processing theory and linked with the introspection
mediating process tracing paradigm. It involved
students and their teacher in a socio-economically
diverse urban Australian primary school and aimed to
establish how the identification of participants’ mental
models can assist in the authentic assessment of
learning through a richer understanding of the
cognitive development taking place in a technologybased
learning experience.
Robotics, as a component of the Queensland
Technology Years 1 to 10 Syllabus published in 2003,
provides a rich, multi-disciplinary environment in
which to engage middle years students in Australia.
The syllabus document provides guidance on
planning and assessment for design and technology
activities and provides a specific module for robotics.
However, engagement is not enough to ensure
learning. All participants, students or teachers, bring to
such activities their own mental models of robotics,
learning, and assessment. Can understanding the
matches and mismatches of such mental models
provide a greater understanding of the individual’s
learning journey and the suitable assessment
practices required to map the journey? This paper
explores the participants’ mental models at the halfway
point of the project.

Article Details

How to Cite
EDWARDS-LEIS, Christine. Matching mental models : the starting point for authentic assessment in robotics. Design and Technology Education: an International Journal, [S.l.], v. 12, n. 2, apr. 2008. ISSN 1360-1431. Available at: <>. Date accessed: 25 mar. 2023.
Mental models ; Design and technology ; Assessment ; Cognition ; Robotics ; Learning