Abstract
In order to evaluate the level of knowledge acquired by students, this study presents the contents of the students’ knowledge using a proposed novel method to monitor a knowledge map and to diagnose them. To do so, we propose a method to improve the accuracy of diagnoses by comparing the level of the surrounding knowledge related to the knowledge, rather than the level of the knowledge itself, to correct the evaluation value. Moreover, in visualizing the relationship between knowledge objects based on knowledge evaluation data using an ontological structure, the Deep Sparse Neural Network model of deep learning is applied, and the map is regarded as one neural network and is proposed to express the quantitative value using the weight instead of the qualitative concept of ontology. The proposed knowledge map visualization can monitor the relationship and relevance (weight) of the related knowledge level at a glance, so that it is possible to intuitively grasp the result with the quantitative knowledge diagnosis and to improve the efficiency of knowledge evaluation and the accurate evaluation of knowledge diagnoses.
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Acknowledgements
This research was partly supported by National Research Foundation of Korea (NRF) Grant funded by Korea Government (MSIT) (NRF-2014M3C4A7030503, Next-Generation Information Computing Development Program) and Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korea Government (MSIT) (no. 2019-0-00421, AI Graduate School Support Program).
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Choi, JY., Lee, JH., Cho, Y. et al. Visualization of knowledge map for monitoring knowledge diagnoses. J Ambient Intell Human Comput 13, 1615–1623 (2022). https://doi.org/10.1007/s12652-019-01407-x
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DOI: https://doi.org/10.1007/s12652-019-01407-x