SOCMS : Smart online class monitoring system

Abstract

In today’s scenario, online teaching and learning platforms opened numerous opportunities and wider scope for gaining knowledge from anywhere in the world. The online method of teaching is not a novice one, but with the outbreak of the COVID 19 pandemic, a revived expression of these platforms have been introduced with better and advanced features and add-ons. With all this progression, the teaching fraternity started practicing and has got accustomed with the online teaching-learning systems. Also, the students also started exploring the optimal utilization at one end, whereas, on the other side, they have tried the novel tricks to cheat and exploit the system. One of the upcoming challenges is to monitor and keep control on students during a live online class. Here, in this paper, a Smart Online Class Monitoring System (SOCMS) has been proposed to cease student’s non-responsive behavior during an ongoing online class. The proposed system offers a trick for teachers to enhance the responsiveness and hence the attentiveness of the students. The system proposes a suggestion list for the teachers during the live online classes, with the motive of covering all the students in a random, but non-repetitive pattern using Fisher-Yates algorithm. The system’s prototype has been developed using Python Programming language. The paper includes the results obtained from its implementation on a group of Undergraduate course students. The observations were noted and analyzed using SPSS statistical tool.

Subject Classification: (2010):

• Primary 68U35
• Secondary : 68-02
• 68-04
• 68-11

Keywords:

• Online teaching-learning system
• Online class monitoring
• Fisher-yates algorithm
• Hash map
• Python
• Prototype