The Use of Study Apps among the High School Students – A Data Mining Approach
Roshni Kurian1, N SaiDharshana2, Rajalakshmi V R3
1Roshni Kurian, Dept. of CS & IT, Amrita Vishwa Vidyapeetham, Kochi , (Kerala), India.
2N Sai Dharshana, Dept. of CS & IT, Amrita VishwaVidyapeetham, Kochi, (Kerala), India.
3Rajalakshmi VR, Dept. of CS & IT, Amrita Vishwa Vidyapeetham, Kochi, (Kerala), India.
Manuscript received on 18 March 2019 | Revised Manuscript received on 27 March 2019 | Manuscript published on 30 March 2019 | PP: 2023-2034 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2505037619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Learning apps are gaining huge popularity among high school students these days. It is becoming increasingly apparent that their impact and benefits benefit varies from subject to subject; broadly speaking, Learning Apps seem best suitable for subjects like Mathematics which emphasis formal and spatial learning rather than for subjects which emphasize verbal learning. This paper studies the use of learning apps among high school students and tries to quantify their impact in terms of academic benefit . It has been noted that with learning apps, users tend to look for immediate feedback on the learning process; this could result in improvements to the learning methods and efficiency. But there is increasing evidence that learning apps may not always provide the desired results and that their impact varies from subject to subject. Here we employ Data Mining Techniques to analyse students’ learning habits. Data Mining has provided elegant and efficient solutions to problems in different fields like education, medicine, business etc. The results of this study reveal and quantify the effectiveness of apps as a method of learning for a particular subject. The algorithm used for this study is Naive Bayesian algorithm and tool used for calculating the result is Weka. Naive Bayesian algorithm is probability based.
Keyword: Data Mining, Learning apps, Naive Bayesian, Weka
Scope of the Article: Data Mining