Maija Strautmane

Usage of graph patterns for concept map extension

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.

On Necessity of Semantic Analysis when Evaluating Concept Maps

Abstract Modern technologies have changed the landscape of teaching and learning in many ways, including introduction of computational support for knowledge assessment. Concept maps (CMs) can be used to externalise student’s internal knowledge structure in a form suitable for computer-aided assessment. Current CM-based knowledge assessment systems do not analyse semantics of CM elements. The paper represents a study designed to find out how big is the portion of a CM that is left unevaluated in default of semantic analysis.

Increasing the flexibility of automated concept map based knowledge assessment

Concept maps are an educational tool that is used both for learning and knowledge assessment. Automated knowledge assessment has a limited ability to assess non-standard constructions. and current automated concept map-based knowledge assessment systems fail to recognise the fact that learners chosen labels for conceptual relationships may differ from teachers preferred labels and still be correct. This paper conceptually introduces several mechanisms for overcoming this drawback which distinguish real misconceptions from correct statements that are expressed using different words.