Faculty Evaluation Decision Based System with Sentiments Analysis using Naïve Bayes Algorithm
DOI:
https://doi.org/10.11594/ijmaber.07.05.18Keywords:
faculty evaluation, machine learning, Naïve Bayes Algorithm, sentiments analysisAbstract
In the educational setting, the core element of education is the quality of instruction. This is truly evident as instruction has been one separate area in terms of educational and program accreditations. The main catalyst of instruction is the performance of teachers in terms of providing quality education. To evaluate faculty members, most academic institutions are using a standard instrument. The evaluation questionnaire consists of quantitative and qualitative questions. The quantitative question is typically answered using the Likert scale model. However, open feedback, typically, is not included in the performance evaluation or appraisal due to a lack of automated text analytics methods. The creation of a Faculty Evaluation Decision Based System with Sentiments Analysis using Naïve Bayes Algorithm provides a more comprehensive understanding of the teacher’s evaluation ratings. Evolutionary Prototyping Model was used as a software methodology which provides systematic and controlled procedures for building iterative prototypes. The model consists of four phases, which include, identification, design, construction or building, and evaluation. The overall rating of the respondents using the ISO/IEC 25010, or the Software Product Quality Model Criteria is 4.82 numerical rating with an interpretation of very acceptable. As observed all criteria are rated very acceptable which indicates a high standard has been set in the development of the system. The Naïve Bayes algorithm successfully categorized comments into negative and positive classifications. Mapping Analysis based on the evaluation results of the system was successfully embedded into the System.
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Aleamoni, Lawrence. (2015). How to Develop a Fair and Valid Comprehensive Faculty Evaluation System. Psychology and Cog-nitive Sciences - Open Journal. 1. 54-56. 10.17140/PCSOJ-1-108.
Al-Khatib, A. (2014). Web-Based Teaching Evaluation Systems. International Journal of Information Technology & Manage-ment Information System (Ijitmis)
Anido, C. Online Teaching Performance Evalu-ation System: A Tool for Quality Educa-tion. Retrieved from IIS.Org: http://www.iiis.org/CDs2009/CD2009SCI/EISTA2009/PapersPdf/E573LV.pdf
Aung, Khin Zezawar; Myo, Nyein Nyein (2017). [IEEE 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) - Wuhan, China (2017.5.24-2017.5.26)] 2017 IEEE/ACIS 16th Inter-national Conference on Computer and In-formation Science (ICIS) - Sentiment analysis of students' comment using the lexicon-based approach. , (), 149–154. doi:10.1109/ICIS.2017.7959985
B. Jagtap and V. Dhotre, “SVM & HMM-based hybrid approach of sentiment analysis for teacher feedback assessment,” Interna-tional Journal of Emerging Trends & Technology in Computer Science (IJETTCS), vol. 3, no. 3, pp. 229–232, 2014.
Balahadia, Francis F.; Fernando, Ma. Corazon G.; Juanatas, Irish C. (2016). [IEEE 2016 IEEE Region 10 Symposium (TENSYMP ) - Bali, Indonesia (2016.5.9-2016.5.11)] 2016 IEEE Region 10 Symposium (TEN-SYMP) - Teacher's performance evalua-tion tool using opinion mining with sen-timent analysis. , (), 95–98. doi:10.1109/TENCONSpring.2016.7519384
Boyko, N., & Boksho, K. (2020, November). Application of the Naive Bayesian Classi-fier in Work on Sentimental Analysis of Medical Data. In IDDM (pp. 230-239).
García-Díaz, V., Espada, J. P., Crespo, R. G., Pe-layo G-Bustelo, B. C., & Cueva Lovelle, J. M. (2018). An approach to improve the accuracy of probabilistic classifiers for decision support systems in sentiment analysis. Applied Soft Computing, 67, 822–833. doi:10.1016/j.asoc.2017.05.03
Goel, Ankur; Gautam, Jyoti; Kumar, Sitesh (2016). [IEEE 2016 2nd International Conference on Next Generation Compu-ting Technologies (NGCT) - Dehradun, In-dia (2016.10.14-2016.10.16)] 2016 2nd International Conference on Next Genera-tion Computing Technologies (NGCT) – Real-time sentiment analysis of tweets using Naive Bayes. , (), 257–261. doi:10.1109/NGCT.2016.7877424
Irfan, M., Uriawan, W., Kurahman, O. T., Ramdhani, M. A., & Dahlia, I. A. (2018, November). Comparison of Naive Bayes and K-Nearest Neighbor methods to pre-dict divorce issues. In IOP Conference Se-ries: Materials Science and Engineering (Vol. 434, No. 1, p. 012047). IOP Publish-ing.
J. Sang, H. Naeem (2015) . A Generic Feedback System for Better Evaluation of Teacher Performance.
J.Ren, S.D. Lee, X.Chen, B.Kao, R.Cheng, D.Cheung, “Naive Bayes Classification of Uncertain Data", Ninth IEEE International Conference on Data Mining, 2009. ICDM ’09, pp. 944 – 949
Janpla, S., & Wanapiron, P. (2018). System framework for an intelligent question bank and examination system, 8, 488-494 [PDF file]. Retrieved from http://www.ijmlc.org/vol8/734-L0156.pdf?fbclid=IwAR0j1DZpL0EBmItjdZW-q9Ey_mMLmLj_IlpSXchGxPHpVhF7hpdGUGpwC3E
Kumar, Alok & Jain, Renu. (2018). Faculty Evaluation System. Procedia Computer Science. 125. 533-541. 10.1016/j.procs.2017.12.069.
Laguador J., Deigero, C. (2015). Students’ Evaluation On The Teaching Performance Of Tourism And Hospitality Management Faculty Members. Asian Journal of Educa-tional Research Vol. 3, No. 3, 2015 ISSN 2311-6080
Malyal, M., Sudheshna, & Soniya, D. (2015). E-Learning: An approach to evaluate sub-jective questions for online examination system using data similarity: Research paper, 8(11), 1-5 [PDF File].
Marucci-Wellman, H. R., Lehto, M. R., & Corns, H. L. (2015). A practical tool for public health surveillance: semi-automated cod-ing of short injury narratives from large administrative databases using Naïve Bayes algorithms. Accident Analysis & Prevention, 84, 165-176.
Mauricio, J.Q., Cagayan, K.B., Serrano, J.R., Bal-ahadia, F.F. (2017). Centralized learning and assessment tool for the department of education – Division of Laguna’s Ara-ling Panlipunan Subjects, 1, 21-35 [PDF file].
Nandini, V., & Maheswari, P.U. (2018). Auto-matic assessment of descriptive answers in an online examination system using semantic relational features. Retrieved from https://link.springer.com/article/10.1007/s11227-018-2381-y?fbclid=IwAR2lDpqAhKNDYdh6TzsuxP9rKsrjkGVrMb-Av7OZVejuMBNSDI-s4oqL-FA
Pattekari, A. Parveen, a Prediction system for heart disease using Naïve Bayes, Int. J. Adv. Comput. Math. Sci. 3 (3) (2012) 290–294.
Rahman, C. M., Farid, D. M., & Rahman, M. Z. (2011). Adaptive intrusion detection based on boosting and naïve Bayesian classifier.
Rajput, Quratulain; Haider, Sajjad; Ghani, Sayeed (2016). Lexicon-Based Sentiment Analysis of Teachers’ Evaluation. Applied Computational Intelligence and Soft Computing, 2016(), 1–12. doi:10.1155/2016/2385429
Reyes, Vicente. (2017). Faculty Evaluation by Faculty. 10.13140/RG.2.2.30947.14882.
Shaikh, Naim & Kumari, Sneha & Kasat, Kishori. (2018). Exploring E-Governance of Faculty Evaluation System: Using a To-tal Interpretive Structural Modeling Ap-proach. Journal of Cases on Information Technology. 20. 36-47. 10.4018/JCIT.2018070103.
Triayudi, A., & Widyarto, W. O. (2021, June). Comparison J48 and Naïve Bayes Meth-ods in Educational Analysis. In Journal of Physics: Conference Series (Vol. 1933, No. 1, p. 012062). IOP Publishing.
W. Medhat, A. Hassan, and H. Korashy, “Senti-ment analysis algorithms and applica-tions: a survey,” Ain Shams Engineering Journal, vol. 5, no. 4, pp. 1093–1113, 2014.View at: Publisher Site | Google Scholar.
Xia, X., & Yan, J. (2021). Construction of music teaching evaluation model based on weighted Naïve Bayes. Scientific Pro-gramming, 2021
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