Expectations of Students from Classroom Rules: A Scenario Based Bayesian Network Analysis

Year 2023, Volume: 10 Issue: 1, 424 - 442, 30.01.2023

İbrahim Demir , Ersin Şener , Hasan Aykut Karaboğa , Ahmet Başal

https://doi.org/10.17275/per.23.23.10.1

Cited By: 1

Abstract

Classroom rules are a fundamental aspect of classroom management and ensuring compliance with established rules is crucial. Previous research has shown that students often pay little attention to the development of classroom rules. This quantitative study aims to investigate the expectations that students have concerning classroom rules. To this end, a 4-point Likert scale questionnaire consisting of 30 items was administered to 356 secondary school students. The Bayesian Search method and expert opinion were used to obtain a Bayesian Network model. The findings of the study indicate that students expect rules to be determined at the beginning of the academic year, wish to be involved in the determination process, and prefer minimal changes to the rules. They also expect a limited number of rules and reinforcement from teachers for displaying desirable behavior. Additionally, the study found that students are more likely to adhere to classroom rules in a clean and uncrowded environment, and prefer that their parents are not informed about these rules. The results also suggest that increased adherence to classroom rules leads to increased class inclusion, while decreased adherence results in decreased class inclusion. Furthermore, the study found that adoption of classroom rules leads to increased in-class cohesion, while non-adoption results in decreased cohesion. These findings contribute to the existing body of knowledge concerning student expectations of classroom rules.

Keywords

Bayesian network, classroom rules, classroom management, disruptive behavior, student expectations

References

• Aelterman, N., Vansteenkiste, M., & Haerens, L. (2019). Correlates of students’ internalization and defiance of classroom rules: A self‐determination theory perspective. British journal of educational psychology, 89(1), 22-40. doi: 10.1111/bjep.12213
• Alberto, P. A., & Troutman, A. C. (2013). Applied behavior analysis for teachers (9th ed.). Pearson.
• Algozzine, B., Wang, C., & Violette, A. S. (2011). Reexamining the relationship between academic achievement and social behavior. Journal of Positive Behavior Interventions, 13(1), 3-16. doi:10.1177/1098300709359
• Almond, R., Mislevy, R., Steinberg, L., Yan, D., & Williamson, D. (2015). Critiquing and learning model structure. In: Bayesian networks in educational assessment. Statistics for social and behavioral sciences. Springer, New York, NY. doi:10.1007/978-1-4939-2125-6_10084
• Alter, P., & Haydon, T. (2017). Characteristics of effective classroom rules: A review of the literature. Teacher Education and Special Education: The Journal of the Teacher Education Division of the Council for Exceptional Children, 40(2), 114-127. doi:10.1177/0888406417700962
• Alter, P., Walker, J., & Landers, E. (2013). Teachers’ perceptions of students’ challenging behavior and the impact of teacher demographics. Education and Treatment of Children, 36(4), 51-69. doi:10.1353/etc.2013.0040
• Billingsley, G. M., McKenzie, J. M., & Scheuermann, B. K. (2018). The effects of a structured classroom management system in secondary resource classrooms, Exceptionality, 28(5), 317-332. doi:10.1080/09362835.2018.1522257
• Brophy, J. (1999). Perspectives of classroom management: Yesterday, today, and tomorrow. In H. J. Freiberg, & J. E. Brophy (Eds.), Beyond behaviorism: Changing the class management paradigm (pp. 43-56). Boston: Allyn and Bacon.
• Browers, A., & Tomic, W. (2000). A longitudinal study of teacher burnout and perceived self‐efficacy in classroom management. Teaching and Teacher Education, 16(2), 239- 253. doi:10.1016/S0742-051X(99)00057-8
• Botsios, S., Georgiou, D. A., & Safouris, N. F. (2007). Learning style estimation using Bayesian Networks. In International Conference on Web Information Systems and Technologies, 2, 415-418.
• Carmona, C., Castillo, G., & Millán, E. (2008). Designing a dynamic bayesian network for modeling students’ learning styles. 2008 Eighth IEEE International Conference on Advanced Learning Technologies, 346-350. doi: 10.1109/ICALT.2008.116
• Cheng, J., Bell, D. A., & Liu, W. (1997). An algorithm for bayesian belief network construction from data. In Sixth International Workshop on Artificial Intelligence and Statistics, 83-90.
• Coalition for Psychology in Schools and Education. (2006, August). Report on the Teacher Needs Survey. American Psychological Association, Center for Psychology in Schools and Education.
• Conati, C., Gertner, A. S., VanLehn, K., & Druzdzel, M. J. (1997). On-line student modeling for coached problem solving using bayesian networks. In A. Jameson, C. Paris, & C. Tasso (Eds.), User Modeling (pp. 231-242). doi: 10.1007/978-3-7091-2670-7_24
• Cooper, G. F., & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9(4), 309-347. doi: 10.1007/BF00994110
• Demir, I. (2020). SPSS ile istatistik rehberi[Statistics guide with SPSS]. Efe Akademi, Istanbul.
• Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM Algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1-22. doi:10.1111/j.2517-6161.1977.tb01600.x
• Druzdzel, M. J. (1999). SMILE: Structural Modeling, Inference, and Learning Engine and GeNIe: A development environment for graphical decision-theoretic models. American Association for Artificial Intelligence, 902-903.
• Emmer, E. T., Evertson, C. M., & Anderson, L. M. (1980). Effective classroom management at the beginning of the school year. The Elementary School Journal, 80(5), 219-231. doi: 10.1086/461192
• Evertson, C., & Weinstein, C. (2006). Classroom management as a field of inquiry. In C. Evertson & C. Weinstein (Eds.), Handbook of classroom management: Research, practice and contemporary issues, 3-16. Lawrence Erlbaum Associates.
• Fernández, A., Morales, M., Rodríguez, C., & Salmerón, A. (2011). A system for relevance analysis of performance indicators in higher education using Bayesian networks. Knowledge and Information Systems, 27(3), 327-344. doi:10.1007/s10115-010-0297-9
• Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian Network Classifiers. Machine Learning, 29, 131-163. doi:10.1023/A:1007465528199
• García, P., Amandi, A., Schiaffino, S., & Campo, M. (2005). Using bayesian networks to detect students’ learning styles in a web-based education system. Proc of ASAI, Rosario, 115-126.
• García, P., Amandi, A., Schiaffino, S., & Campo, M. (2007). Evaluating Bayesian networks’ precision for detecting students’ learning styles. Computers & Education, 49(3), 794-808. doi: 10.1016/j.compedu.2005.11.017
• Gertner, A. S., Conati, C., & VanLehn, K. (1998). Procedural help in Andes: Generating hints using a bayesian network student model. AAAI-98 Proceedings, 106-111. American Association for Artificial Intelligence.
• Garwood, J., Vernon-Feagans, L., & the Family Life Project Key Investigators. (2017). Classroom management affects literacy development of students with emotional and behavioral disorders. Exceptional Children, 83, 123-142. doi:10.1177/0014402916651846
• Guney, I., Eroglu, E., & Akalin, Y. (2012). Factor analysis of the effect of class rules on the behaviors’. Energy Education Science and Technology Part B: Social and Educational Studies, 4(1), 298-301.
• Harris, A. H., & Garwood, J. D. (2015). Beginning the school year. In W.G. Scarlet (Ed.), Encyclopedia of classroom management. (pp.88-92).
• Heckerman, D., & Shachter, R. (1995). Decision-Theoretic foundations for causal reasoning. Journal of Artificial Intelligence Research, 3, 405-430. doi:10.1613/jair.202
• Heckerman, D. (2008). A tutorial on learning with Bayesian Networks. In D. E. Holmes & L. C. Jain (Eds.), Innovations in Bayesian Networks: Theory and Applications (pp. 33-82). doi: 10.1007/978-3-540-85066-3_3
• Hue, M. T., & Li, W. S. (2008). Classroom management: Creating a positive learning environment. Hong Kong University Press. doi:10.5790/hongkong/9789622098886.001.0001
• Ingersoll, R. M., & Smith, T. M. (2003). The wrong solution to the teacher shortage. Educational Leadership, 60(8), 30-33.
• Kerr, M. M., & Nelson, C. M. (2006). Strategies for managing behaviour problems in the classroom (4th ed.). Merrill Prentice Hall.
• Koiter, J. R. (2006). Visualizing inference in Bayesian Networks (M.Sc. Thesis). Delft University of Technology, Faculty of Electrical Engineering, Mathematics, and Computer Science, Department of Man-Machine Interaction.
• Kokkinos, C. M., Panayiotou, G., & Davazoglou, A. M. (2005). Correlates of teacher appraisals of student behaviors. Psychology in the Schools, 42(1), 79-89. doi: 10.1002/pits.20031
• Koktas, S. (2009). İlköğretim okullarında ikinci kademe öğrencilerinin sınıf kurallarını benimseme düzeyi [Level of adoption of classroom rules by second-level students in primary schools] (M. Sc. Thesis). Yeditepe University.
• Korpershoek, H., Harms, T., de Boer, H., van Kuijk, M., & Doolaard, S. (2016). A meta‐analysis of the effects of classroom management strategies and classroom management programs on students’ academic, behavioural, emotional, and motivational outcomes. Review of Educational Research, 86, 643-680. doi: 10.3102/0034654315626799
• Lauritzen, S. L. (1995). The EM algorithm for graphical association models with missing data. Computational Statistics & Data Analysis, 19(2), 191-201. doi:10.1016/0167-9473(93)E0056-A
• Lampert, M., & Graziani, F. (2009). Instructional activities as a tool for teachers' and teacher educators' learning. The Elementary School Journal, 109(5), 491-509. doi:10.1086/596998
• Madsen, C. H., Becker, W. C., & Thomas, D. R. (1968). Rules, praise, and ignoring: Elements of elementary classroom control. Journal of Applied Behavior Analysis, 1(2), 139-150. doi:10.1901/jaba.1968.1-139
• Martin, J., & VanLehn, K. (1995). Student assessment using Bayesian nets. International Journal of Human-Computer Studies, 42(6), 575-591. doi: 10.1006/ijhc.1995.1025
• Marzano, R. J., Marzano, J. S., & Pickering, D. (2003). Classroom management that works: Research-based strategies for every teacher. Alexandria, VA: Association for Supervision and Curriculum Development.
• Maxwell, L. E. (1996). Multiple effects of home and daycare crowding. Environment and Behavior, 28(4), 494-511. doi: 10.1177/0013916596284004
• Mayo, M., & Mitrovic, A. (2001). Optimising ITS behaviour with bayesian networks and decision theory. International Journal of Artificial Intelligence in Education, 12, 124-153.
• Millán, E., Loboda, T., & Pérez-de-la-Cruz, J. L. (2010). Bayesian networks for student model engineering. Computers & Education, 55(4), 1663-1683. doi: 10.1016/j.compedu.2010.07.010
• Pas, E. T., Cash, A. H., O'Brennan, L., Debnam, K. J., & Bradshaw, C. P. (2015). Profiles of classroom behavior in high schools: Associations with teacher behavior management strategies and classroom composition. Journal of School Psychology, 53(2), 137-148. doi: 10.1016/j.jsp.2014.12.005
• Pearl, J. (2008). Probabilistic reasoning in intelligent systems: Networks of plausible inference (Rev. 2. print., 12. [Dr.]). San Francisco, California, Morgan Kaufmann.
• Rose, L. C., & Gallup, A. M. (2005). Gallup poll of the public’s attitudes toward the public schools. The 37th Annual Phi Delta Kappa/Gallup Poll of the Publics Attitudes toward the Public Schools, 87, 41-57. doi:10.1177/003172170508700110
• Schwab, Y., & Elias, M. J. (2015). From compliance to responsibility: Social-emotional learning and classroom management. In E. T. Emmer, & E. J. Sabornie (Eds.), Handbook of classroom management (2nd ed., pp. 94–115). Routledge.
• Simonsen, B., Fairbanks, S., Briesch, A., Myers, D., & Sugai, G. (2008). Evidence-based practices in classroom management: Considerations for research to practice. Education and Treatment of Children, 31(3), 351-380. doi: 10.1353/etc.0.0007
• Stiggins, R. J., Arter, J. A., Chappuis, J., & Chappuis, S. (2004). Classroom assessment for student learning: Doing it right, using it well. Portland, Oregon: Assessment Training Institute.
• Sugai, G., & Horner, R. (2002). The evolution of discipline practices: School-wide positive behavior supports. Child & Family Behavior Therapy, 24(1-2), 23-50. doi:10.1300/J019v24n01_03
• Tonda, A., Lutton, E., Squillero, G., & Wuillemin, P. H. (2013). A memetic approach to bayesian network structure learning. In A. I. Esparcia-Alcázar (Ed.), Applications of Evolutionary Computation, 7835, 102-111. doi: 10.1007/978-3-642-37192-9_11
• Wang, M. C., Haertel, G. D., & Walberg, H. J. (1997). What helps students learn? Spotlight on student success, 51, 74-79.
• Wayson, W. W. (1985). Opening windows to teaching: Empowering educators to teach self‐discipline. Theory Into Practice, 24(4), 227-232. doi: 10.1080/00405848509543179
• Wei, H. (2014, April). Bayesian networks for skill diagnosis and model validation. Presented at the Annual Meeting of Council on Measurement in Education, Philadelphia, PA. Retrieved from https://www.pearson.com/content/dam/one-dot-com/one-dot-com/global/Files/efficacy-and-research/schools/030_NCME_HW.pdf
• Weinstein, C. S. (1996). Secondary classroom management: Lessons from research and practice. McGraw-Hill.
• Westling, D. L. (2010). Teachers and challenging behaviors: Knowledge, views, and practices. Remedial and Special Education, 31(1), 48-63. doi:10.1177/0741932508327466
• Xenos, M. (2004). Prediction and assessment of student behaviour in open and distance education in computers using Bayesian networks. Computers & Education, 43(4), 345-359. doi: 10.1016/j.compedu.2003.09.005