Open Access Open Access  Restricted Access Subscription Access

Fuzzy Logic Techniques for Interpreting and Evaluating Pedagogy of Teacher Behavior and Capability to Transform Knowledge into Practice


Affiliations
1 Department of Computer Science & Applications, Apeejay College of Fine Arts Jalandhar, India
2 Department of Computer Science & Applications, PUSSGRC, Hoshiarpur, India
 

Current literature and common practices suggest that there is no consistent method available to interpret and evaluate the performance of a teacher. Due to its inherent vagueness and uncertainty, this research work aims to analyze the effectiveness of teacher depending upon various factors influencing quality of teacher using fuzzy logic. The first part studies various factors of professional behavior, interpersonal behavior and personal behavior of the teachers from student’s perspective to impart quality education. In the second part, fuzzy inference mechanism as suggested by Mamdani is designed and developed to decide the possible quality of performance of teacher from student’s point of view. The paper concludes by observing that the proposed fuzzy logic based interpretation of comprehensive quality of performance of teacher is consistent with those judged by the experts in the field and can be used to predict the possible quality of teachers.

Keywords

Teacher Behavior, Teacher Evaluation, Academic Development, Students Achievement, Fuzzy Logic, Chi-Square.
User
Notifications
Font Size

  • Akbulut, Y., & Cardak, C. S. (2012). Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011. Computers & Education, 58(2), 835-842.
  • Alderman, G. L., & Nix, M. (1997). Teachers' intervention preferences related to explanations for behavior problems, severity of the problem, and teacher experience. Behavioral Disorders, 87-95.
  • Allahverdi, N. (2002). Expert Systems. An Artificial Intelligence Application.Istanbul: Atlas, 248.
  • Amin, H. U., & Khan, A. R. (2009). Acquiring Knowledge for Evaluation of Teachers Performance in Higher Education using a Questionnaire. arXiv preprint arXiv:0906.4663.
  • Avalos, B. (2011). Teacher professional development in Teaching and Teacher Education over ten years. Teaching and teacher education, 27(1), 10-20.
  • Bai, S. M., & Chen, S. M. (2008). Automatically constructing grade membership functions of fuzzy rules for students’ evaluation. Expert Systems with Applications, 35(3), 1408-1414.
  • Bellman, R. E., & Zadeh, L. A. (1977). Local and fuzzy logics (pp. 103-165). Springer Netherlands.
  • Berliner, D. C. (2004). Describing the behavior and documenting the accomplishments of expert teachers. Bulletin of Science, Technology & Society, 24(3), 200-212.
  • Cotton, K. (2000). The Schooling Practices That Matter Most.
  • Danielson, C. (2013). The Framework for Teaching: Evaluation instrument. Princeton, NJ: The Danielson Group. Retrieved July 8, 2013.
  • Darling-Hammond, L., Chung, R., & Frelow, F. (2002). Variation in Teacher Preparation How Well Do Different Pathways Prepare Teachers to Teach?.Journal of teacher education, 53(4), 286-302.
  • Day, C. (1999). Developing teachers: The challenges of lifelong learning. Psychology Press.
  • Day, C. (2004). A passion for teaching. Routledge.
  • Dolezal, S. E., Welsh, L. M., Pressley, M., & Vincent, M. M. (2003). How nine third-grade teachers motivate student academic engagement. The Elementary School Journal, 239-267.
  • Dubois, D., Lang, J., & Prade, H. (1991). Fuzzy sets in approximate reasoning, part 2: logical approaches. Fuzzy sets and systems, 40(1), 203-244.
  • Fullan, M., & Hargreaves, A. (1996). What's Worth Fighting for in Your School? Revised Edition. Teachers College Press, 1234 Amsterdam Avenue, New York, NY 10027.
  • Grigoriadou, M., Kornilakis, H., Papanikolaou, K. A., & Magoulas, G. D. (2002). Fuzzy inference for student diagnosis in adaptive educational hypermedia. InMethods and applications of artificial intelligence (pp. 191-202). Springer Berlin Heidelberg.
  • Hanushek, E. A., & Rivkin, S. G. (2006). Teacher quality. Handbook of the Economics of Education, 2, 1051-1078.
  • Hutchings, M., Menter, I., Ross, A., Thomson, D., & Bedford, D. (2000).Teacher Supply and Retention in London 1998-99: A study of six London boroughs. London: Teacher Training Agency.
  • Ingvarson, L., & Rowe, K. (2008). Conceptualising and evaluating teacher quality: Substantive and methodological issues. Australian Journal of Education, 52(1), 5-35.
  • Kauchak, D. P., & Eggen, P. D. (1993). Learning and teaching (2nd ed.). Boston, MA: Allyn and Bacon.
  • Kington, A., Sammons, P., Day, C., & Regan, E. (2011). Stories and statistics: Describing a mixed methods study of effective classroom practice. Journal of Mixed Methods Research, 5(2), 103-125.
  • Ko, J., & Sammons, P. (2013). Effective Teaching: A Review of Research and Evidence. CfBT Education Trust. 60 Queens Road, Reading, RG1 4BS, England.
  • Kyriakides, L., Creemers, B. P., & Antoniou, P. (2009). Teacher behaviour and student outcomes: Suggestions for research on teacher training and professional development. Teaching and Teacher Education, 25(1), 12-23.
  • Li, X. (2007). Intelligent Agent–Supported Online Education. Decision Sciences Journal of Innovative Education, 5(2), 311-331.
  • Mamdani, E. H. (1974, December). Application of fuzzy algorithms for control of simple dynamic plant. In Proceedings of the Institution of Electrical Engineers (Vol. 121, No. 12, pp. 1585-1588). IET Digital Library.
  • Marzano, R. J. (2003). What works in schools: Translating research into action. ASCD.
  • Maulana, R., Helms-Lorenz, M., & van de Grift, W. (2015a). Pupils’ perceptions of teaching behaviour: Evaluation of an instrument and importance for academic motivation in Indonesian secondary education. International Journal of Educational Research, 69, 98-112.
  • Maulana, R., Helms-Lorenz, M., & van de Grift, W. (2015b). A longitudinal study of induction on the acceleration of growth in teaching quality of beginning teachers through the eyes of their students. Teaching and Teacher Education,51, 225-245.
  • Maulana, R., Opdenakker, M. C., Stroet, K., & Bosker, R. (2012). Observed lesson structure during the first year of secondary education: Exploration of change and link with academic engagement. Teaching and Teacher Education,28(6), 835-850.
  • Maulana, R., Opdenakker, M. C., Stroet, K., & Bosker, R. (2013). Changes in teachers’ involvement versus rejection and links with academic motivation during the first year of secondary education: A multilevel growth curve analysis.Journal of youth and adolescence, 42(9), 1348-1371.
  • Opdenakker, M. C., & Minnaert, A. (2011). Relationship between learning environment characteristics and academic engagement 1. Psychological Reports, 109(1), 259-284.
  • Pianta, R. C., & Hamre, B. K. (2009). Conceptualization, measurement, and improvement of classroom processes: Standardized observation can leverage capacity. Educational Researcher, 38(2), 109-119.
  • Rice, J. K. (2003). Teacher quality: Understanding the effectiveness of teacher attributes. Economic Policy Institute, 1660 L Street, NW, Suite 1200, Washington, DC 20035.
  • Rivkin, S. G., Hanushek, E. A., & Kain, J. F. (2005). Teachers, schools, and academic achievement. Econometrica, 417-458.
  • Rockoff, J. E. (2004). The impact of individual teachers on student achievement: Evidence from panel data. American Economic Review, 247-252.
  • Rosenholtz, S. (1989). Teachers’ Workplace: The Social Organization of Schools (New York, Teachers College Record).
  • Ross, T. J., Booker, J. M., & Parkinson, W. J. (Eds.). (2002). Fuzzy logic and probability applications: bridging the gap (Vol. 11). SIAM.
  • Rowan, B., Correnti, R., & Miller, R. (2002). What Large-Scale Survey Research Tells Us About Teacher Effects on Student Achievement: Insights from the Prospects Study of Elementary Schools. The Teachers College Record, 104(8), 1525-1567.
  • Saaty, T. L. (2000). Fundamentals of decision making and priority theory with the analytic hierarchy process (Vol. 6). Rws Publications.
  • Saleh, I., & Kim, S. I. (2009). A fuzzy system for evaluating students’ learning achievement. Expert Systems with Applications, 36(3), 6236-6243.
  • Sanders, W. L., Wright, S. P., & Horn, S. P. (1997). Teacher and classroom context effects on student achievement: Implications for teacher evaluation. Journal of personnel evaluation in education, 11(1), 57-67.
  • Schroeder, C. M., Scott, T. P., Tolson, H., Huang, T. Y., & Lee, Y. H. (2007). A meta‐analysis of national research: Effects of teaching strategies on student achievement in science in the United States. Journal of Research in Science Teaching, 44(10), 1436-1460.
  • Shulman, L. (1987). Knowledge and teaching: Foundations of the new reform.Harvard educational review, 57(1), 1-23.
  • Siler, W., & Buckley, J. J. (2005). Fuzzy expert systems and fuzzy reasoning. John Wiley & Sons.
  • Stronge, J. H., Ward, T. J., Tucker, P. D., & Hindman, J. L. (2007). What is the relationship between teacher quality and student achievement? An exploratory study. Journal of Personnel Evaluation in Education, 20(3-4), 165-184.
  • The Mathworks, http://www.mathworks.com.
  • Troman, G., & Woods, P. (2001). Primary teachers' stress. Psychology Press.
  • Tschannen-Moran, M., Hoy, A. W., & Hoy, W. K. (1998). Teacher efficacy: Its meaning and measure. Review of educational research, 68(2), 202-248.
  • Van de Grift, W. (2007). Quality of teaching in four European countries: a review of the literature and application of an assessment instrument.Educational research, 49(2), 127-152.
  • Vescio, V., Ross, D., & Adams, A. (2008). A review of research on the impact of professional learning communities on teaching practice and student learning.Teaching and teacher education, 24(1), 80-91.
  • Wang, M. C. (1995). Serving students at the margins. Educational Leadership,52(4), 12-17.
  • Wang, X. (2000). A comparative study on effective instructional practices and ineffective instructional practices. Theory and Practice of Education, 20(9), 50-53.
  • Yair, G. (2000). Educational battlefields in America: The tug-of-war over students' engagement with instruction. Sociology of Education, 247-269.
  • Yen, J., & Langari, R. (1998). Fuzzy logic: intelligence, control, and information. Prentice-Hall, Inc.. [56] Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.

Abstract Views: 242

PDF Views: 0




  • Fuzzy Logic Techniques for Interpreting and Evaluating Pedagogy of Teacher Behavior and Capability to Transform Knowledge into Practice

Abstract Views: 242  |  PDF Views: 0

Authors

Jagmohan Mago
Department of Computer Science & Applications, Apeejay College of Fine Arts Jalandhar, India
Neeru Mago
Department of Computer Science & Applications, PUSSGRC, Hoshiarpur, India

Abstract


Current literature and common practices suggest that there is no consistent method available to interpret and evaluate the performance of a teacher. Due to its inherent vagueness and uncertainty, this research work aims to analyze the effectiveness of teacher depending upon various factors influencing quality of teacher using fuzzy logic. The first part studies various factors of professional behavior, interpersonal behavior and personal behavior of the teachers from student’s perspective to impart quality education. In the second part, fuzzy inference mechanism as suggested by Mamdani is designed and developed to decide the possible quality of performance of teacher from student’s point of view. The paper concludes by observing that the proposed fuzzy logic based interpretation of comprehensive quality of performance of teacher is consistent with those judged by the experts in the field and can be used to predict the possible quality of teachers.

Keywords


Teacher Behavior, Teacher Evaluation, Academic Development, Students Achievement, Fuzzy Logic, Chi-Square.

References