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Effects of concept map extraction and a test‐based diagnostic environment on learning achievement and learners' perceptions
Author(s) -
Lin YuShih,
Chang YiChun,
Liew KengHou,
Chu ChihPing
Publication year - 2016
Publication title -
british journal of educational technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.79
H-Index - 95
eISSN - 1467-8535
pISSN - 0007-1013
DOI - 10.1111/bjet.12250
Subject(s) - remedial education , test (biology) , class (philosophy) , computer science , perception , learning environment , mathematics education , construct (python library) , artificial intelligence , machine learning , psychology , paleontology , neuroscience , biology , programming language
Computerised testing and diagnostics are critical challenges within an e‐learning environment, where the learners can assess their learning performance through tests. However, a test result based on only a single score is insufficient information to provide a full picture of learning performance. In addition, because test results implicitly include information about the underlying subject concepts and their relationships to each other, this paper proposes a more effective method for analysing test results by providing a concept map ( CM ) to facilitate learners' understanding of their learning performance. An innovative approach, not explored in previous studies, is proposed to automatically construct a personalised CM . A CM ‐smart extraction and explicit diagnosis ( CM‐SEED ) learning system has been developed to diagnose learning barriers and misconceptions and to supply relevant suggestions and guidance for remedial learning. This study examined 90 students from two classes at a university and assigned one class to be the experimental group and another class to be the control group. The results indicated that the students who used the CM‐SEED learning system had superior perceptions regarding their learning; furthermore, they accomplished superior learning achievement that displayed statistical significance. Consequently, the study concluded that CM extraction in a test‐based diagnostic environment can lead learners to enhanced learning performance.