Skip to main content
Top

2021 | OriginalPaper | Chapter

9. Comparative Analysis of Educational Job Performance Parameters for Organizational Success: A Review

Authors : Sapna Arora, Manisha Agarwal, Shweta Mongia

Published in: Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences

Publisher: Springer Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference 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, Agarwal M (2018) Empowerment through big data—issues and challenges. Int J Sci Res Comput Sci Eng Inf Technol 3:423–431
2.
go back to reference Arora S (2016) A novel approach to notarize multiple datasets for medical services. Imperial J Interdisc Res 2(7):325–328 Arora S (2016) A novel approach to notarize multiple datasets for medical services. Imperial J Interdisc Res 2(7):325–328
3.
go back to reference 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 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
4.
go back to reference 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 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
5.
go back to reference 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 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
6.
go back to reference Pal A, Pal S (2013) Evaluation of teacher’s performance: a data mining approach. Int J Comput Sci Mobile Comput:359–369 Pal A, Pal S (2013) Evaluation of teacher’s performance: a data mining approach. Int J Comput Sci Mobile Comput:359–369
7.
go back to reference Mythili SM, Shanavas (2014) An analysis of student’s performance using classification techniques. IOSR J Comput Eng 16(1):63–69 Mythili SM, Shanavas (2014) An analysis of student’s performance using classification techniques. IOSR J Comput Eng 16(1):63–69
8.
go back to reference 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 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
9.
go back to reference Osmanbegovic EE, Agic H, Suljic M (2014) Prediction of student’s success by applying data mining algorithms. J Theor Appl Inf Technol:378–388 Osmanbegovic EE, Agic H, Suljic M (2014) Prediction of student’s success by applying data mining algorithms. J Theor Appl Inf Technol:378–388
10.
go back to reference Hemaid RK, Halees M (2015) Improving teacher performance using data mining. Int J Adv Res Comput Commun Eng:407–413 Hemaid RK, Halees M (2015) Improving teacher performance using data mining. Int J Adv Res Comput Commun Eng:407–413
11.
go back to reference Jindal R, Dutta BM (2015) Predictive analytics in higher education. IEEE:24–33 Jindal R, Dutta BM (2015) Predictive analytics in higher education. IEEE:24–33
12.
go back to reference 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 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
13.
go back to reference Mustafa A (2016) Predicting instructor performance using data mining techniques in higher education. IEEE:2379–2387 Mustafa A (2016) Predicting instructor performance using data mining techniques in higher education. IEEE:2379–2387
14.
go back to reference Asif R, Merceron A, Ali A, Haider GN (2017) Analysing undergraduate students’ performance using educational data mining, computers and education. Elsevier, pp 177–194 Asif R, Merceron A, Ali A, Haider GN (2017) Analysing undergraduate students’ performance using educational data mining, computers and education. Elsevier, pp 177–194
15.
go back to reference 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 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
16.
go back to reference Bhatnagar S, Saxena PS (2018) Analysis of faculty performance evaluation using classification. Int J Adv Res Comput Sci 9(1):115–121CrossRef Bhatnagar S, Saxena PS (2018) Analysis of faculty performance evaluation using classification. Int J Adv Res Comput Sci 9(1):115–121CrossRef
17.
go back to reference 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 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
18.
go back to reference Anuradha C, Velmurugan T (2015) A comparative analysis on the evaluation of classification algorithms in the prediction of students performance. Indian J Sci Technol 8:1–12CrossRef Anuradha C, Velmurugan T (2015) A comparative analysis on the evaluation of classification algorithms in the prediction of students performance. Indian J Sci Technol 8:1–12CrossRef
19.
go back to reference 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 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
21.
go back to reference Ahmad R, Bujang S (2013) Issues and challenges in the practice of performance appraisal activities in the 21st century. Int J Educ Res 4 Ahmad R, Bujang S (2013) Issues and challenges in the practice of performance appraisal activities in the 21st century. Int J Educ Res 4
22.
go back to reference Mehfooz Q, Haider S (2017) Effect of stress on academic performance of undergraduate medical students. J Commun Med Health Educ 7(6) Mehfooz Q, Haider S (2017) Effect of stress on academic performance of undergraduate medical students. J Commun Med Health Educ 7(6)
Metadata
Title
Comparative Analysis of Educational Job Performance Parameters for Organizational Success: A Review
Authors
Sapna Arora
Manisha Agarwal
Shweta Mongia
Copyright Year
2021
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-15-7533-4_9