ItemConference Paper

Factors Affecting Predicting Teacher Evaluation in Higher Educational Institutions Among Faculty Members based on SA-BiLSTM

Title
Factors Affecting Predicting Teacher Evaluation in Higher Educational Institutions Among Faculty Members based on SA-BiLSTM
Creator
Ravi Kumar, P.; Acharjee, Purnendu Bikash; Kar, Rohit; Alam, Mohammad Shabbir; Santhoshkumar, M.P.; Sethumadhavan, R.
Description
To keep up with good teaching standards and pedagogical improvements, it is vital to predict assessment of higher education teachers. Based on principles of pragmatism, psychology, and pedagogy, this research should contribute to the development of diagnostic procedures and training standards that may be used in many educational settings. Because of the three-tiered speciality training architecture used to prepare teachers in master's degree programs in education in Ukraine, institutions there require a differentiated approach. Model training, feature extraction, and preprocessing are all parts of the proposed methodology. While translating, cleaning, reducing, and normalising data is part of data preparation, feature selection uses correlation and mutual information criteria to establish the significance of variables. Following feature selection using information gain, the SA-BiLSTM model begins training. Different institutional features were associated with unique approaches to teacher preparation, according to a comparison of active empathy and the growth of pedagogical reflection. The SA-BiLSTM model outperforms SA and BiLSTM when it comes to predicting teacher ratings. The results show how important it is to have different levels of individualised strategies for preparing teachers. Institutions can improve training and instruction with the support of the predictive approach, which better evaluates teachers. 2025 IEEE.
Source
2025 International Conference on Intelligent Computing and Knowledge Extraction, ICICKE 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
educational data mining (EDM); higher educational institutions (HEI); mutual information (MI); self-cure network (SCN)
Coverage
Ravi Kumar P., KPR Institute of Engineering and Technology, Department of Electrical and Electronics Engineering, Coimbatore, India; Acharjee P.B., CHRIST University, Department of Computer Science, Bangalore, India; Kar R., St. Claret College, Department of Management, Bangalore, India; Alam M.S., Jazan University, College of Computer Science and Information Technology, Department of Computer Science, Jizan, Saudi Arabia; Santhoshkumar M.P., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Computer Science, Chennai, India; Sethumadhavan R., Dayananda Sagar College of Engineering, Department of Master of Business Administration, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833153681-7;
Format
online
Language
English
Type
Conference paper

Citation

Ravi Kumar, P.; Acharjee, Purnendu Bikash; Kar, Rohit; Alam, Mohammad Shabbir; Santhoshkumar, M.P.; Sethumadhavan, R., “Factors Affecting Predicting Teacher Evaluation in Higher Educational Institutions Among Faculty Members based on SA-BiLSTM,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26012.

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