Abstract
In this paper, we describe how the pathfinder algorithm converts relatedness ratings of concept pairs to concept maps; we also present how this algorithm has been used to develop the Concept Maps for Learning website (www.conceptmapsforlearning.com) based on the principles of effective formative assessment. The pathfinder networks, one of the network representation tools, claim to help more students memorize and recall the relations between concepts than spatial representation tools (such as Multi- Dimensional Scaling). Therefore, the pathfinder networks have been used in various studies on knowledge structures, including identifying students’ misconceptions. To accomplish this, each student’s knowledge map and the expert knowledge map are compared via the pathfinder software, and the differences between these maps are highlighted. After misconceptions are identified, the pathfinder software fails to provide any feedback on these misconceptions. To overcome this weakness, we have been developing a mobile-based concept mapping tool providing visual, textual and remedial feedback (ex. videos, website links and applets) on the concept relations. This information is then placed on the expert concept map, but not on the student’s concept map. Additionally, students are asked to note what they understand from given feedback, and given the opportunity to revise their knowledge maps after receiving various types of feedback.
Original language | English |
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Pages (from-to) | 297-317 |
Number of pages | 21 |
Journal | Revista Colombiana de Estadistica |
Volume | 37 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2015 |
Keywords
- Concept maps
- Effective feedback
- Pathfinder network
- Structural assessment
ASJC Scopus subject areas
- Statistics and Probability
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Dive into the research topics of 'Exploring the mobile structural assessment tool: Concept maps for learning website: Concept Maps for Learning Website'. Together they form a unique fingerprint.Student theses
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Enhancing statistics proficiency through computer-assisted feedback
Filiz, M. (Author), Thurston, A. (Supervisor) & Miller, S. (Supervisor), Dec 2019Student thesis: Doctoral Thesis › Doctor of Philosophy
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