International Journal of Man-Machine Studies
Foundations of cognitive science
Mental models: concepts for human-computer interaction research
International Journal of Man-Machine Studies
CSCL '95 The first international conference on Computer support for collaborative learning
Programming pedagogy—a psychological overview
ACM SIGCSE Bulletin
A mental model can help with learning to operate a complex device
CHI '93 INTERACT '93 and CHI '93 Conference Companion on Human Factors in Computing Systems
A low-tech, hands-on approach to teaching sorting algorithms to working students
Computers & Education
Structural Knowledge: Techniques for Representing, Conveying, and Acquiring Structural Knowledge
Structural Knowledge: Techniques for Representing, Conveying, and Acquiring Structural Knowledge
How to t(r)ap user's mental models
Selected papers of the 8th Interdisciplinary Workshop on Informatics and Psychology: Mental Models and Human-Computer Interaction 2
Sorting out sorting through concretization with robotics
Proceedings of the working conference on Advanced visual interfaces
Self-efficacy and mental models in learning to program
Proceedings of the 9th annual SIGCSE conference on Innovation and technology in computer science education
Students' Mental Models of the Internet and Their Didactical Exploitation in Informatics Education
Education and Information Technologies
Investigating the viability of mental models held by novice programmers
Proceedings of the 38th SIGCSE technical symposium on Computer science education
Modelling programming performance: Beyond the influence of learner characteristics
Computers & Education
Personalized Learning Course Planner with E-learning DSS using user profile
Expert Systems with Applications: An International Journal
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It has been advocated that pedagogical content knowledge as well as subject matter knowledge are important for improving classroom instructions. To develop pedagogical content knowledge, it is argued that understanding of students' mental representations of concepts is deemed necessary. Yet assessing and comparing mental model of each individual is very tedious and time consuming. This study attempted to use gender and learning styles to associate mental models in learning sorting algorithm. The Gregorc Style Delineator (GSD) was used to measure learning styles of the participants. Mental models were assessed using the Pathfinder Scaling Algorithm (PSA). Results indicated that females showed greater similarity in mental models than males and concrete learners also exhibited closer resemblance to the expert mental model than abstract learners. These suggest that gender and learning styles can be meaningfully used to associate mental models in order to provide a group-based instead of individual-based diagnosis and thus promote conceptual change in learning.