Quantum computing inspired framework of student performance assessment in smart classroom

Abstract

SummaryInternet of Things (IoT) technology, merged with fog‐cloud computing, has provided efficient services in the education industry. Inspired by this aspect, this paper presents an effective framework of the smart classroom for optimal evaluation of the performance of students and teachers based on quantum game‐theoretic (QGT) decision making. Specifically, IoT technology is incorporated inside the classroom to acquire data about students and teachers for determining the academic enhancement of students. The acquired data is evaluated over the fog‐cloud computing platform to estimate a quantifiable measure of probability of performance using the Bayesian modeling technique. Moreover, to analyze the student and teacher performance over the temporal scale, student performance indicator and teacher performance indicator are calculated. Finally, student‐teacher performance is assessed using the effective two‐player quantum gametic decision‐making. The experimental simulation on four challenging datasets namely student performance (7029 instances), student academic performance (3585 instances), student performance prediction (2120 instances), and student performance analysis (6507 instances) shows the effectiveness of the proposed framework. The simulation results are contrasted with various state‐of‐the‐art techniques to estimate the overall efficiency of the proposed system. Moreover, mathematical analysis has been carried out to depict the performance of the proposed framework.