Effects of concept map extraction and a test‐based diagnostic environment on learning achievement and learners' perceptions

Abstract

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.

Knowledge Level Assessment in e-Learning Systems Using Machine Learning and User Activity Analysis

“…Concept Maps have been highlighted recently by several researchers in the domain of automatic assessment of learners' knowledge [26]- [28]. In the theme of distance learning and self assessment, Concept Maps have various advantages over traditional approaches.…”

Demystifying Learning Analytics in Personalised Learning

“…In a more advanced side of personalised learning is the PLE, or personalised learning environment. Multiple studies has been done relating to it, including the usage of Personalised Visual Narratives to enhance students' learning engagement [27]; the use of Concept Maps in PLE to subdue students' learning difficulties [28]; the use of PLE to advocate learners' empowerment towards their own learning process and progress [29] and the use of microcredentials offered in PLE for teachers' professional advancement [30]. An element that should be noted is within the technologically inclined personalised learning environment, there is a need to monitor the students' details and progress, which becomes the main function of learning analytics.…”

Computer-Based Concept Mapping as a Method for Enhancing the Effectiveness of Concept Learning in Technology-Enhanced Learning

“…Concept mapping that is made with paper and pencil is a complex task. Many students find making concept maps with paper and pencil time consuming and exhausting [31]. Since paper-based concept maps are two dimensional and have limited space, it is difficult to place all the possible relationships between concepts on them [9].…”

“…PSI is an assessment tool that is formed of 32 items and a six-point Likert scale (1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = usually, 6 = always) and the Cronbach's alpha reliability coefficient of the PSI was calculated as 0.88, while the reliability coefficient was 0.85. There are six sub-dimensions that are found in the PSI, which are: Impulsive (13,14,15,17,21,25,26,30,32), Reflective (18,20,31,33,35), Problem solving Confidence (5,23,24,27,28,34), Avoidant style (1,2,3,4,11), Monitoring (6,7,8), and Planfulness (10,12,16,19). The Cronbach's alpha coefficients of the sub-dimensions are calculated as 0.74, 0.73, 0.72, 0.66, 0.76, and 0.69, respectively.…”