Reliability Investigation of Automatic Assessment of Learner-Build Concept Map with Kit-Build Method by Comparing with Manual Methods
| Autor: | Tsukasa Hirashima, Warunya Wunnasri, Yusuke Hayashi, Jaruwat Pailai |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
business.industry
Computer science Concept map 05 social sciences 050301 education 02 engineering and technology Machine learning computer.software_genre Reliability Set (abstract data type) Assessment methods Concept map assessment method Kit-Build concept map 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business 0503 education computer Reliability (statistics) |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319614243 AIED |
| ISSN: | 0302-9743 |
| Popis: | This paper describes an investigation into the reliability of an automatic assessment method of the learner-build concept map by comparing it with two well-known manual methods. We have previously proposed the Kit-Build (KB) concept map framework where a learner builds a concept map by using only a provided set of components, known as the set "kit". In this framework, instant and automatic assessment of a learner-build concept map has been realized. We call this assessment method the "Kit-Build method" (KB method). The framework and assessment method have already been practically used in classrooms in various schools. As an investigation of the reliability of this method, we have conducted an experiment to compare the assessment results of the method with the assessment results of two other manual assessment methods. In this experiment, 22 university students attended as subjects and four as raters. It was found that the scores of the KB method had a very strong correlation with the scores of the other manual methods. The results suggest that automatic assessment of the Kit-Build concept map can attain almost the same level of reliability as well-known manual assessment methods. 'Artificial Intelligence in Education' 18th International Conference, AIED 2017, Wuhan, China, June 28 – July 1, 2017, Proceedings |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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