The Advantages and Disadvantages of Using Deep Learning Technology in the Cognitive Analysis of Students
DOI:
https://doi.org/10.51278/aj.v7i1.2576Keywords:
Deep Learning, Learning Technology, Cognitive AnalysisAbstract
This study examines the advantages and disadvantages of using deep learning technology in the analysis of student cognition. This study employs a literature review method with a descriptive analytical approach, which is used to analyse the data. Deep learning technology utilises neural networks and CNNs; this technology offers several advantages, such as the ability to accurately and immediately detect students’ cognitive patterns in line with adaptive learning activities, and to reduce the risk of learning failure, thereby enabling deep learning to enhance students’ knowledge retention. The disadvantages of using deep learning include the need for large, robust, and highquality datasets and networks; high hardware and software costs; the requirement for powerful networks; a lack of transparency (‘black box’); and the risk of student data privacy breaches due to hacker attacks. This research makes a methodological contribution through an efficient descriptive analytical approach based on literature review, which is suitable for research with limited access to empirical data, as well as a risk evaluation model. In terms of policy, the findings encourage advocacy for a national digital education infrastructure, contributing to the equitable distribution of access to AI technology in regional educational institutions. This research enriches the edutech discourse with an Indonesian contextual perspective, opening up opportunities for further research such as field trials.
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