Abstract
Job performance in educational institutions is a major parameter to decide its success. Numerous parameters such as teaching methods, family background, student’s interest, student–teacher interaction, etc., are responsible to support the decision-making process in organizational success. Job performance is mainly related to student’s and educationist’s performance. Thus, there is a need to keep an eye on parameters associated with both student’s performance and educationist’s performance. This paper aims to provide a comparative analysis of tools, techniques, parameters, and algorithms along with different challenges, associated with monitoring performance of students and educationists. Various educational organizations apply data mining tools to analyze the performance of students and educationists. There are various versatile data mining algorithms available to serve the purpose. Thus, it becomes important to select the appropriate algorithm in an appropriate situation. The literature so far focused on student performance for analyzing the organizational success. In this paper, both the entities; i.e., student and educationist are being considered. The work presented highlights the possible benefits to the students, educationists, and management.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arora S, Agarwal M (2018) Empowerment through big data—issues and challenges. Int J Sci Res Comput Sci Eng Inf Technol 3:423–431
Arora S (2016) A novel approach to notarize multiple datasets for medical services. Imperial J Interdisc Res 2(7):325–328
Quan P, Liu Y, Zing T, Wen Y (2018) A novel data mining approach towards human resource performance appraisal. Springer International Publishing AG, pp 476–488
Radaideh A, Qasem N, Eman A (2012) Using data mining techniques to build a classification model for predicting employees performance. Int J Adv Comput Sci Appl 3(2):144–151
Waqas M, Qureshi T, Anwar F, Haroon S (2012) Job satisfaction of educationists: an important antecedent for enhancing service quality in the education sector of Pakistan. Arabian J Bus Manage Rev 2(2):33–49
Pal A, Pal S (2013) Evaluation of teacher’s performance: a data mining approach. Int J Comput Sci Mobile Comput:359–369
Mythili SM, Shanavas (2014) An analysis of student’s performance using classification techniques. IOSR J Comput Eng 16(1):63–69
Mishra T, Kumar D, Gupta S (2014) Mining Students’ data for performance prediction. In: International conference on advanced computing and communication technologies, pp 255–262
Osmanbegovic EE, Agic H, Suljic M (2014) Prediction of student’s success by applying data mining algorithms. J Theor Appl Inf Technol:378–388
Hemaid RK, Halees M (2015) Improving teacher performance using data mining. Int J Adv Res Comput Commun Eng:407–413
Jindal R, Dutta BM (2015) Predictive analytics in higher education. IEEE:24–33
Pruthi K, Bhatia P (IEEE) Application of data mining in predicting placement of students. In: International conference on green computing and Internet of things, pp 528–533
Mustafa A (2016) Predicting instructor performance using data mining techniques in higher education. IEEE:2379–2387
Asif R, Merceron A, Ali A, Haider GN (2017) Analysing undergraduate students’ performance using educational data mining, computers and education. Elsevier, pp 177–194
Pal S, Chaurasia V (2017) Is alcohol affect higher education students performance: searching and predicting pattern using data mining algorithms. Int J Innov Adv Comput Sci:8–17
Bhatnagar S, Saxena PS (2018) Analysis of faculty performance evaluation using classification. Int J Adv Res Comput Sci 9(1):115–121
Adekitan AI, Salau O (2019) The impact of engineering students’ performance in the first three years on their graduation result using educational data mining. Heliyon, Elsevier, pp 1–21
Anuradha C, Velmurugan T (2015) A comparative analysis on the evaluation of classification algorithms in the prediction of students performance. Indian J Sci Technol 8:1–12
Agarwal S, Pandey GN, Tiwari MD (2012) Data mining in education: data classification and decision tree approach. Int J e-Educ e-bus e-manage e-learn 2(2):140–144
Shah D (2017) Towards data science. Comprehensive list of data mining tools [Online], Retrieved from towardsdatascience.com. https://towardsdatascience.com/data-mining-tools-f701645e0f4c, 16 Nov 2017
Ahmad R, Bujang S (2013) Issues and challenges in the practice of performance appraisal activities in the 21st century. Int J Educ Res 4
Mehfooz Q, Haider S (2017) Effect of stress on academic performance of undergraduate medical students. J Commun Med Health Educ 7(6)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Arora, S., Agarwal, M., Mongia, S. (2021). Comparative Analysis of Educational Job Performance Parameters for Organizational Success: A Review. In: Dave, M., Garg, R., Dua, M., Hussien, J. (eds) Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-7533-4_9
Download citation
DOI: https://doi.org/10.1007/978-981-15-7533-4_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7532-7
Online ISBN: 978-981-15-7533-4
eBook Packages: EngineeringEngineering (R0)