How Concept Maps with and without a List of Concepts Differ

The Case of Statistics

Bibliographic Data

ID22044595
AuthorsAnastasiia Kapuza (0000-0003-4982-5663, National Research University Higher School of Economics, corresponding author)
Year2020
Volume10
Issue4
Pages91
Publication date2020-03-30
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEducation Sciences (JOURNAL)
Journal identifiersISSN: 2227-7102 • E-ISSN: 2227-7102
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/educsci10040091
OpenAlexW3012992198
LanguageEN
Citations received2
References cited43

Concept mapping is a popular tool for knowledge structure assessment. In recent years, both the amount of research about concept maps and their measurement ability have grown. It has been shown that concept maps with different types of tasks, for instance, links between concepts given or selected by a respondent, provide information about the different aspects of students’ knowledge structure. This study explores features of concept mapping with and without a list of concepts. At first, eleven masters students constructed concept maps with a topic on statistical data analysis and, after three weeks, repeated the task with the same topic and a predefined list of concepts. Both types of concept maps were evaluated using traditional scoring indicators and indicators from the network analysis. All indicators were tested for significant differences, and then the content of these maps was analysed. Results show that the list of concepts forced respondents to construct more connective maps, which is related to a more developed knowledge structure. Moreover, it is easier for them, when including even abstract concepts, to define their role in the domain. However, respondents use concepts and group them in different ways depending on the instruction. It seems that respondents feel a “list stress”, which leads to differences in the content. These findings demonstrate the possibilities of using different concept mapping tasks for learning and assessment

Concept map · Data science · Information retrieval · Natural language processing · Respondent · Advanced Text Analysis Techniques · Computer Science · Mathematics · Science Education and Pedagogy · Statistics Education and Methodologies · Artificial Intelligence

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