Premium
Smart computing based student performance evaluation framework for engineering education
Author(s) -
Verma Prabal,
Sood Sandeep K.,
Kalra Sheetal
Publication year - 2017
Publication title -
computer applications in engineering education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.478
H-Index - 29
eISSN - 1099-0542
pISSN - 1061-3773
DOI - 10.1002/cae.21849
Subject(s) - computer science , cloud computing , leverage (statistics) , reputation , context (archaeology) , internet of things , software engineering , data science , world wide web , artificial intelligence , paleontology , social science , sociology , biology , operating system
Internet of Things (IoT) technology has changed the educational landscape by allowing educators and administrators to turn data into actionable insight. Education organization begin to leverage solutions like cloud computing and radio frequency identification (RFID) across an IoT platform. Relative to this context, this paper proposes a five layer framework to facilitate automated student performance evaluation in engineering institutions based on smart computing concept. Student daily activity datasets are formed based on sensing capabilities of IoT nodes. Smart computing integrates hardware, software, and network technologies that provides systems with real‐time situation awareness and automated analysis. The engineering student performance per session is calculated by combining the results from sensory nodes based education data mining algorithms and student academic datasets. Moreover, based on student sessional performance score, decisions are taken by management authority to increase the reputation score of the engineering institution. The experiment comprises two sections. In first section, RFID based experimental setup is defined with objects interaction patterns. In second section, student performance score generated using proposed system is compared with manual system. The results depict that by introducing IoT in engineering education, more effective decisions can be taken to improve student learning experiences and over‐all growth of the institution.