Open Access Open Access  Restricted Access Subscription Access

Using machine learning to classify student attention in order to improve learning systems

Rameshwar .

Abstract


In the studies that have been conducted, a variety of researchers have conducted a variety of studies to classify the student's concentration. Numerous strategies are dependent on the Subjective Assessment and has many lacking Quantitative Assessment. Therefore, for the student's effective attention, the task is to bridge the gap between the two estimation methods. The K-means and SVM algorithms used in machine learning are applied in this study. All teachers, at any level, can benefit from the research's findings by incorporating them into their teaching strategies and improvising their classroom instruction. The paper talks about machine learning algorithms that teachers can use to get feedback on their teaching. Consequently, students benefit from improved learning methods. In the end, this results in students performing better in their respective classes.


Full Text:

PDF

References


Shan, C., Chen, B., Hu, C., Xue, J., & Li, N. (2014). Software defect prediction model based on LLE and SVM.

Moyo, S., & Mnkandla, E. (2020). A Novel Lightweight Solo Software Development Methodology With Optimum Security Practices. IEEE Access, 8, 33735-33747.

Siboni, S., Sachidananda, V., Meidan, Y., Bohadana, M., Mathov, Y., Bhairav, S., ... & Elovici, Y. (2019). Security testbed for internet-of-things devices. IEEE Transactions on Reliability, 68(1), 23-44.

Zhao, X., Xue, J., Hu, C., Ma, R., & Zhang, S. (2014). Research on software behavior modeling based on extended finite state automata.

Dong, H., Li, C., Li, T., Du, Y., & Xu,

G. (2014). Research on the security model of mobile application.

From a Software Engineering Viewpoint: A Systematic Mapping Study. IEEE Access, 8, 10933-10950.

Eom, T., Hong, J. B., An, S., Park, J. S., & Kim, D. S. (2019). A Systematic Approach to Threat Modeling and Security Analysis for Software Defined Networking. Ieee Access, 7, 137432-137445.

Huang, Y., Bian, Y., Li, R., Zhao, J. L., & Shi, P. (2019). Smart contract security: A software lifecycle perspective. IEEE Access, 7, 150184- 150202.

Cornish, P. L., Knowles, S. R., Marchesano, R., Tam, V., Shadowitz, S., Juurlink, D. N., & Etchells, E. E. (2005). Unintended medication discrepancies at the time of hospital admission. Archives of internal medicine, 165(4), 424-429.

T Tam, V. C., Knowles, S. R., Cornish, P. L., Fine, N., Marchesano, R., & Etchells, E. E. (2005). Frequency, type and clinical importance of medication history errors at admission to hospital: a systematic review. Cmaj, 173(5), 510- 515.


Refbacks

  • There are currently no refbacks.