Abstract
The purpose of this paper is to examine the promising contributions of the Concept Maps for Learning (CMfL) website to assessment for learning practices. The CMfL website generates concept maps from relatedness degree of concepts pairs through the Pathfinder Scaling Algorithm. This website also confirms the established principles of effective assessment for learning, for it is capable of automatically assessing students' higher order knowledge, simultaneously identifying strengths and weaknesses, immediately providing useful feedback and being user-friendly. According to the default assessment plan, students first create concept maps on a particular subject and then they are given individualized visual feedback followed by associated instructional material (e.g., videos, website links, examples, problems, etc.) based on a comparison of their concept map and a subject matter expert's map. After studying the feedback and instructional material, teachers can monitor their students' progress by having them create revised concept maps. Therefore, we claim that the CMfL website may reduce the workload of teachers as well as provide immediate and delayed feedback on the weaknesses of students in different forms such as graphical and multimedia. For the following study, we will examine whether these promising contributions to assessment for learning are valid in a variety of subjects.
Original language | English |
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Pages (from-to) | 134-148 |
Number of pages | 15 |
Journal | Knowledge Management and E-Learning |
Volume | 7 |
Issue number | 1 |
Publication status | Published - 01 Mar 2015 |
Keywords
- Assessment for learning
- Concept mapping
- Digital knowledge maps
- Structural assessment of knowledge
ASJC Scopus subject areas
- Education
- Management of Technology and Innovation
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Dive into the research topics of 'The potential contributions of concept maps for learning website to assessment for learning practices'. 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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