Evaluating Students’ Concept Maps in the Concept Map Based Intelligent Knowledge Assessment System

Author(s):  
Alla Anohina-Naumeca ◽  
Janis Grundspenkis
Author(s):  
Janis Grundspenkis ◽  
Alla Anohina

Evolution of the Concept Map Based Adaptive Knowledge Assessment System: Implementation and Evaluation ResultsThe paper represents the concept map based adaptive knowledge assessment system. Advantages of concept maps are analyzed emphasizing that the approach offers a reasonable balance between requirements to assess higher levels of knowledge according to Bloom's taxonomy and complexity of a system. Concept maps allow revealing of student's knowledge structure, promote system thinking and support process oriented learning where a study course is divided into stages in each of which knowledge assessment is carried out. The developed knowledge assessment system consists from a teacher's, learner's and administrator's modules and is implemented as a multiagent system. Four prototypes of the system developed within four projects are described. The first prototype supports only fill-in-the-map tasks where a learner must put given concepts in correct places. The second prototype provides changing the degree of task difficulty, thus, performing adaptation to a learner's knowledge level. The set of tasks are also extended by construct-the-map tasks. Improvements implemented in the third prototype allow using of directed arcs and standard relationships in concept maps. The three-tier architecture used in the fourth prototype is chosen to rise the security level of the system. Besides that learner's support is considerably expanded giving help and tutoring to a learner. Results of evaluation of the developed system's prototypes in different study courses are presented. The paper concludes with the comparison of all four prototypes using all main characteristics of the developed knowledge assessment system.


2012 ◽  
Vol 13 (1) ◽  
pp. 29-36 ◽  
Author(s):  
Vita Graudina ◽  
Janis Grundspenkis ◽  
Sigita Milasevica

Abstract - This paper proposes the approach to a concept map merging using methods and tools developed for the same task in the domain of ontologies. The developed method is based on ideas that concept maps and ontologies have structural similarities, and mutual transformations between them are possible therefore tools and methods suitable for ontologies can be applied to concept maps. Concept map merging is necessary to extend the functionality of intelligent concept map-based knowledge assessment system IKAS for reuse of captured concept maps.


Author(s):  
Alla Anohina ◽  
Marks Vilkelis ◽  
Romans Lukasenko

The paper is devoted to the knowledge assessment system that has been developed at the Department of Systems Theory and Design of Riga Technical University for the last four years. The system is based on concept maps that allow displaying the knowledge structure of a particular learner in the form of a graph. Teacher’s created concept maps serve as a standard against which learner’s concept maps are compared. However, it is not correct to compare teacher’s and learners’ concept maps by examining the exact equivalence of relationships in both maps, because people construct knowledge in different ways. Thus, an appropriate mechanism is needed for the flexible evaluation of learners’ concept maps. The paper describes the algorithm implemented in the concept map based knowledge assessment system and its evolution through four prototypes of the system.


2012 ◽  
Vol 13 (1) ◽  
pp. 37-43 ◽  
Author(s):  
Maija Strautmane

Abstract In concept map-based assessment an expert’s concept map can be expanded using graph patterns to add hidden and inverse relations. This helps to avoid forcing a learner to use certain structures and names. Graph patterns are subgraphs that describe combinations of concept map elements, from which extra relations can be inferred. In this paper an enriched set of graph patterns is described along with their respective IF...THEN rules which can be used for automated knowledge assessment. Some of them are already implemented in the intelligent and adaptive knowledge assessment system IKAS.


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