Evaluation of Human Resource Performance Using General Regression Neural Network Approach (Faculty Members)

Document Type : research article

Authors

1 PhD Student in Public Policy, Faculty of Management and Economics, Tarbiat Modares University

2 faculty member of university mazandaran

3 faculty member of university

Abstract

Assessing faculty members involves formal steps in assessing and informing faculty members about how to do the job and the responsibilities assigned to them in different dimensions. In the present study, the evaluation of faculty members has been done from two educational and research perspectives. The statistical population of the present study is 307 faculty members of one of the public universities. Educational performance information has been prepared in collaboration with the Office of Monitoring, Evaluation and Quality Assurance of the University, as well as research performance information in collaboration with the Vice Chancellor for Research. Data analysis was performed using artificial intelligence method and using MATLAB software. In analyzing the results, first, using threshold clustering, faculty members were divided into four clusters. Then, in the second step of the analysis, the potential of the general regression neural network is used. Using the general regression neural network, the degree to which individuals depend on each of the four clusters is determined. The results show that most faculty members have a good educational situation, while the research status of faculty members is not good. Also, in general, research and educational performance between faculties, Faculty of Chemistry (0.99 and 0.60), first rank and physical education faculties (0.34 and 0.99), basic sciences (0.58 and 0.37) and economic and administrative sciences ( 0.40 and 0.47) gained the next ranks. In the final section, by comparing the two methods, while examining the advantages of using the general regression neural network, the necessary suggestions are presented.

Keywords

References

  • ابطحی، ح؛ ترابیان، م.(1389).بررسی تحقق اهداف آموزش عالی بر اساس سند چشم‌انداز بیست ساله کشور با روش فرایند تحلیل سلسله مراتبی. پژوهش در نظام‌های آموزشی،4(8)،60-31.
  • آیین‌نامه ارتقای مرتبه اعضای هیات‌علمی.(1395).وزارت علوم، تحقیقات و فناوری، تهران. ایران.
  • جانعلی‌زاده، ح؛ فرزانه، س؛ غلامی، ع.(1392).همبستگی کیفیت زندگی و بهره‌وری علمی اعضای هیات‌علمی. فصلنامه مطالعات جامعه شناختی جوانان.4(12)و 31-56.
  • خورشیدی، ع؛ مهدوی، م؛ سلمانی قهبازی، ا.(1387).عوامل و شاخص‌های موثر بر بهره‌وری دانشگاه‌ها و مراکز آموزش عالی. پژوهش در نظام‌های آموزش عالی،2(5)99-75.
  • دباغ، ر؛ جواهریان، ل.(1395). بهره‌وری واحدهای آموزشی و پژوهشی در دانشگاههای جامع دولتی ایران. فصلنامه پژوهش و برنامه‌ریزی در آموزش عالی، 22(2)، 123-99.
  • سلیمی، ق؛ حیدری، ا؛ کشاورزی، ف.(1394).شایستگی‌های اعضای هیأت‌علمی جهت تحقق رسالت دانشگاهی؛ تاملی بر ادراکات و انتظارات دانشجویان دکتری. دو فصلنامه نوآوری و ارزش آفرینی،3(7)،104-85.
  • شعبانی ورکی، ب؛ حسین قلی‌زاده، ر.(1385).بررسی کیفیت تدریس در دانشگاه. پژوهش و برنامه‌ریزی در آموزش عالی.39، 22-1.
  • شیربگی, ن, اسدی, م.(1395). ارزشیابی اثربخشی عملکرد آموزشی از دیدگاه‌ اعضای هیئت علمی: مطالعه‌ای کیفی. مطالعات اندازه گیری و ارزشیابی آموزشی. 6(13)85-55.
  • صفری، ث.(1389). نقش منابع اطلاعاتی گوناگون در ارزیابی آموزشی اعضای هیات علمی. فصلنامه پژوهش و برنامه ریزی در آموزش عالی، 55، 85-69.
  • گرجی، م؛ صیامی، س.(1387). شناسایی معیارهای ارزیابی عملکرد اعضای هیات علمی دانشگاه آزاد اسلامی. فصلنامه مدیریت. شماره11،پاییز.
  • محب‌زادگان، ی؛ پرداختچی، م؛ قهرمانی، م؛ فراستخواه، م.(1392).تدوین الگویی برای بالندگی اعضای هیات‌علمی با رویکرد مبتنی بر نظریه داده بنیاد. فصلنامه پژوهش و برنامه‌ریزی در آموزش عالی،70، 25-1.
  • محمدی، ت؛ ایمانی، م؛ شیرزاد کبریا، ب؛ حسینی، ر.(1396).مقایسه فعالیتهای پژوهشی اعضای‌هیات‌علمی دانشگاه‌های دولتی و آزاد در شهر تهران به منظور ارائه راههای افزایش توانمندی اعضای هیات‌علمی. مطالعات اندازه‌گیری و ارزشیابی آموزشی.7(18)، 167-147.

 

  • Arnold, I, J. (2008). "Course Level and the Relationship Between Research Productivity and Teaching Effectiveness". Journal of Economic Education. 39 (4), 307–21.
  • Arts,J. Pedraja-Chaparro,F. Salinas-Jimne,M.(2017).Research performance and teaching quality in the Spanish higher education system: Evidence from a medium-sized university. Research Policy. 46,19-29.
  • Bendu,H. Deepak,B. Murugan,B.(2016).Application of GRNN for the prediction of performance and exhaust emissions in HCCI engine using ethanol. Energy Conversion and Management. 122 (2016) 165–173.
  • Bland,C,J. Wersal,L. VanLoy,W. Jacott,W.(2002).Evaluating faculty performance: a systematically designed and assessed Academic Medicine. 77 (1), 15-30.
  • Burke-Smalley,L. Barbara,L. Rau. Neely,A. Evans,W,R.(2017).Factors perpetuating the research-teaching gap in management: A review and propositions. The International Journal of Management Education. 15, 501-512.
  • Cadez,S. Dimovski,V. Zaman Groff,M.(2017).Research, teaching and performance evaluation in academia: the salience of quality. Studies in Higher Education. 42(8),1455-1473.
  • Chambers,D,W. Boyarsky,H. Peltier,B. Fendler,F.(2003).Development of a mission-based faculty evaluation system. Journal of Dental Education. 67 (1), 10-22.
  • Cheng, J. Xiong, Y. (2017). The Quality Evaluation of Classroom Teaching Based on FOA-GRNN. Procedia Computer Science. Volume 107, Pages 355-360.
  • Costa,C,A.Oliveira,M,D. (2012). A multicriteria decision analysis model for faculty evaluation. Omega. 40(4),424-436.
  • Ghazinoory,S. Esmail Zadeh,A. Kheirkhah,A, S.(2010).Application of fuzzy calculations for improving portfolio matrices. Information Sciences. 180, 1582-1590.
  • Ghritlahre,H. Prasad,R.(2018-A).Investigation of thermal performance of unidirectional flow porous bed solar air heater using MLP, GRNN, and RBF models of ANN technique. Thermal Science and Engineering Progress.doi: https://doi.org/10.1016/j.tsep.2018.04.006.
  • Ghritlahre,H. Prasad, R.(2018-B).Exergetic performance prediction of solar air heater using MLP, GRNN and RBF models of artificial neural network technique. Journal of Environmental Management. 223, 566-675.
  • Goos, M. Salomons, A.(2017). Measuring teaching quality in higher education: assessing selection bias in course evaluations. Res High Educ. 58:341–364.
  • Hadhinicola,G,C. Soteriou,A.(2006).Factors Affecting Research Productivity of production and operations management groups: an empirical study. Journal of applies mathematics and decision sciences.10, 1-16.
  • Jenkins,A.(2000).The relationship between Teaching and Research: where does geography stad and deliver?. Journal of Geography in Higher Education. 24(3), 325-351.
  • Karatop,B. Kubat,C. Uygun,O.(2014).Talent management in manufacturing system using fuzzy logic approach. Computers & Industrial Engineering. Computers & Industrial Engineering. http://dx.doi.org/ 10.1016/j.cie.2014.09.015.
  • Kiffer,S. Tchibozo,G.(2013).Developing the Teaching Competences of Novice Faculty Members: A Review of International Literature. Policy Futures in 11 (3): 277-289.
  • Lakin,A,L.(2016).Effective Faculty evaluation at the teaching-centered university. International Journal of Educational Management. 30(6),976-988.
  • Lukovac, V. Pamu car, D. Popovic, M. Dorovic, B. (2017). Portfolio model for analyzing human resources: an approach based on neuro-fuzzy modeling and the simulated annealing algorithm. Expert Systems with Applications. 10.1016/j.eswa.2017.08.034.
  • Majumder,H. Maity, K.(2018-A).Application of GRNN and multivariate hybrid approach to predict andoptimize WEDM responses for Ni-Ti shape memory alloy. Applied Soft Computing. 70,665-679.
  • Majumder,H. Maity,K.(2018-B).Prediction and optimization of surface roughness and microhardness using grnn and MOORA-fuzzy-a MCDM approach for nitinol in WEDM. Measurement. doi: https://doi.org/10.1016/j.measurement. 2018.01.003.
  • Masikos,M. Demestichas,K. Adamopoulou,E. Theologou,M.(2015).Mesoscopic forecasting of vehicular consumption using neural networks. Soft Computing. 19(1),145-156.
  • Moya, S., D. Prior, and G. Rodriguez-Perez. 2015. “Performance-based Incentives and the Behaviour of Accounting Academics: Responding to Changes.” Accounting Education: An International Journal 24 (3): 208–32.
  • Nafukho, F, M. Caroline, S. Muyia, M. (2019). Examining research productivity of faculty in selected leading public universities in Kenya. International Journal of Educational Development. 66,44-51.
  • Ni,Y,Q. Li,M.(2016).Wind Pressure Data Reconstruction Using Neural Network Techniques: A Comparison between BPNN and GRNN. Measurement. doi: http://dx.doi.org/10.1016/j.measurement.2016.04.049.
  • Oh,J. Yang,J. Lee,S.(2012).Managing uncertainty to improve decision-making in NPD portfolio management with a fuzzy expert system. Expert Systems with Applications. 39, 9868-9885.
  • Parker, J. (2008). "Comparing Research and Teaching in University Promotion Criteria". Higher Education Quarterly. 62 (3): 237–51.
  • Ragan,L. Bigatel,P. Dillon,J.(2012). from Research to Practice: Towards an Integrated and Comprehensive Faculty Development Program. Journal of Asynchronous Learning Networks. 16(5):71-86.
  • Rahimnia,F. Kargozar,N.(2016).Objectives priority in university strategy map for resource allocation, benchmarking. An international journal, vol.23 iss 2 pp.
  • Roy, K. Michael, C. Peter, K. S. (2006). Research productivity and academic lineage in clinical psychology: who is training the faculty to do research?. Journal of Clinical Psychology, 62 (7): 893–905.
  • Sanchez, T, W. (2017). Faculty Performance Evaluation Using Citation Analysis: An Update. Journal of Planning Education and Research. 37, 83-94.
  • Sinclair, J. Barnacle, R. Cuthbert, D. (2013). How the doctorate contributes to the formation of active researchers: what the research tells us. Studies in Higher Education, 1: 1-15.
  • Stake, R . Cisneros. (2000). Situational Evaluation of Teaching on Campus. Evaluating Teaching in higher education: A vision foe the future: Jossey-Bass.
  • Walder,A,M. (2017). Pedagogical Innovation in Canadian higher education: Professors perspectives on its effects on teaching and learning. Studies in Educational Evaluation.54,71-82.
  • Williams,P.E.(2003).Roles and competencies for distance education programs in higher education institutions. American Journal of Distance Education, 17(1): 45.
  • White,Ch. James,K. Burke,L. Allen,R. (2012). "What makes a “research star”? Factors influencing the research productivity of business faculty". International Journal of Productivity and Performance Management. 61(6).584-602
  • Zhang,Y. Huang,G. Ngai,B. Chen,X.(2010).Case-based polishing process planning with fuzzy set theory. Journal of Intelligent Manufacturing. 21(6), 831–842.
Journal of Educational Planning Studies
Volume 9, Issue 17
March 2021
Pages 202-222

Files

History

  • Receive Date: 14 March 2021
  • Accept Date: 14 March 2021

Share

How to cite

Statistics