Abstract
The classroom environment is a major contributor to the learning process in schools. Young students are affected by different details in their academic progress, be it their own characteristics, their teacher’s or their peers’. The combination of these factors is known to have an impact on the attainment of young students. However, what is less known are ways to accurately measure the impact of the individual variables. Moreover, in education, predicting an end-result is not enough, but understanding the process is vital. Thus, in this paper, we simulate the interactions between these factors to offer education stakeholders – administrators and teachers, in a first instance – the possibility of understanding how their activities and the way they manage the classroom can impact on students’ academic achievement and result in different learning outcomes. The simulation is based on data from Performance Indicator in Primary Schools (PIPS) monitoring system, of 65,385 records that include 3,315 classes from 2,040 schools, with an average of 26 students per class collected in 2007. The results might serve teachers in solving issues that occur in classrooms and improve their strategies based on the predicted outcome.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
RR344_-_Performance_Indicators_in_Primary_Schools.pdf (publishing.service.gov.uk).
- 2.
Please note however that PIPS data is only available for Start Math and End Math, thus only the start and end of the simulation process.
References
Agnihotri, L., et al.: Mining login data for actionable student insight. In: Proceedings 8th International Conference Educational Data Mining, pp. 472–475 (2015)
Blank, C., Shavit, Y.: The association between student reports of classmates’ disruptive behavior and student achievement. AERA Open 2(3),(2016). https://doi.org/10.1177/2332858416653921
Bourne, P.A.: A token economy: an approach used for behavior modifications among disruptive primary school children. MOJ Public Heal. 7(3) (2018). https://doi.org/10.15406/mojph.2018.07.00212
Cardoso, A.P., et al.: Personal and pedagogical interaction factors as determinants of academic achievement. Procedia Soc. Behav. Sci. 29, 1596–1605 (2011). https://doi.org/10.1016/j.sbspro.2011.11.402
Cobb, J.A.: Relationship of discrete classroom behaviors to fourth-grade academic achievement. J. Educ. Psychol. 63(1), 74–80 (1972). https://doi.org/10.1037/h0032247
Cohen, J.: Statistical Power Analysis for the Behavioral Sciences. Academic press (2013)
Finn, J.D., et al.: Disruptive and inattentive-withdrawn behavior and achievement among fourth graders. Acad. Manag. Rev. 31(2), 386–408 (2006). https://doi.org/10.1097/EDE.0b013e3181
Department for Education: Class Size and education in England evidence report. Research Report DFE-RR169, pp. 34–35 (2011)
Department for Education and Skills: Mathematical activities for the Foundation Stage Early years (2002)
Esturgó-Deu, M.E., Sala-Roca, J.: Disruptive behaviour of students in primary education and emotional intelligence. Teach. Teach. Educ. 26(4), 830–837 (2010). https://doi.org/10.1016/j.tate.2009.10.020
Finn, J.D., Pannozzo, G.M., Voelkl, K.E.: Disruptive and inattentive-withdrawn behavior and achievement among fourth graders. Elem. Sch. J. 95(5), 421–434 (1995). https://doi.org/10.1086/461853
Fitz-Gibbon, C.T.: Official indicator systems in the UK: examinations and inspections. Int. J. Educ. Res. 25(3), 239–247 (1996)
Haroun, R., O’Hanlon, C.: Teachers’ perceptions of discipline problems in a jordanian secondary school. Pastor. Care Educ. 15(2), 29–36 (1997). https://doi.org/10.1111/1468-0122.00053
Houghton, S., et al.: Classroom behaviour problems which secondary school teachers say they find most troublesome. Br. Educ. Res. J. 14(3), 297–312 (1988). https://doi.org/10.1080/0141192880140306
Ingram, F.J., Brooks, R.J.: Simulating classroom lessons: an agent-based attempt. In: Proceedings Operational Research Society Simulation Work SW 2018, pp. 230–240 (2017)
Koneya, M.: Location and interaction in row-and-column seating arrangements. Environ. Behav. 8(2), 265–281 (1976)
Koster, A., Koch, F., Assumpção, N., Primo, T.: The role of agent-based simulation in education. In: Koch, F., Koster, A., Primo, T., Guttmann, C. (eds.) CARE/SOCIALEDU -2016. CCIS, vol. 677, pp. 156–167. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-52039-1_10
Long, R.: The School Day and Year (England). House Commons Library, Vol. 07148 (2019)
Macal, C., North, M.: Introductory tutorial: agent-based modeling and simulation, pp. 6–20 (2014)
Macal, C.M., North, M.J.: Agent-based modeling and simulation. In: Proceedings 2009 Winter Simulation Conference, pp. 86–98 (2009). https://doi.org/10.1109/WSC.2009.5429318
Masad, D., Kazil, J.: Mesa: an agent-based modeling framework. In: Proceedings 14th Python Science Conference, pp. 51–58, April 2015. https://doi.org/10.25080/majora-7b98e3ed-009
Mauricio, S., et al.: Analysing differential school effectiveness through multilevel and agent-based modelling Multilevel Modelling and School Effectiveness Research. 17, 1–13 (2014)
Merrell, C., et al.: A longitudinal study of the association between inattention, hyperactivity and impulsivity and children’s academic attainment at age 11. Learn. Individ. Differ. 53, 156–161 (2017). https://doi.org/10.1016/j.lindif.2016.04.003
Merrell, C., Tymms, P.B.: Inattention, hyperactivity and impulsiveness: their impact on academic achievement and progress. Br. J. Educ. Psychol. 71(1), 43–56 (2001)
Müller, C.M., et al.: Peer influence on disruptive classroom behavior depends on teachers’ instructional practice. J. Appl. Dev. Psychol. 56, 99–108 (2018). https://doi.org/10.1016/j.appdev.2018.04.001
Paul, R., et al.: Using agent-based modelling for EER experimental design: preliminary validation based on student cheating behaviours. In: Canadian Engineering Education Association, pp. 1–8 (2020)
Ponticorvo, M., et al.: An agent-based modelling approach to build up educational digital games for kindergarten and primary schools. Expert Syst. 34(4), 1–9 (2017). https://doi.org/10.1111/exsy.12196
Shin, H., Ryan, A.M.: Friend influence on early adolescent disruptive behavior in the classroom: teacher emotional support matters. Dev. Psychol. 53(1), 114–125 (2017). https://doi.org/10.1037/dev0000250
Smith, D.P., et al.: Who goes where? The importance of peer groups on attainment and the student use of the lecture theatre teaching space. FEBS Open Bio 8(9), 1368–1378 (2018). https://doi.org/10.1002/2211-5463.12494
Stephenson, J., et al.: Behaviours of concern to teachers in the early years of school. Int. J. Disabil. Dev. Educ. 47(3), 225–235 (2000). https://doi.org/10.1080/713671118
Swing, S.R., Peterson, P.L.: The relationship of student ability and small-group interaction to student achievement. Am. Educ. Res. J. 19(2), 259–274 (1982). https://doi.org/10.3102/00028312019002259
Tomasevic, N., et al.: An overview and comparison of supervised data mining techniques for student exam performance prediction. Comput. Educ. 143, 103676 (2020). https://doi.org/10.1016/j.compedu.2019.103676
Tymms, P.: Baseline Assessment and Monitoring in Primary Schools (1999)
Tymms, P., Albone, S.: Performance indicators in primary schools. In: School Improvement through Performance Feedback, pp. 191–218 (2002)
Tymms, P., Brown, C.: Modelling educational systems: an interactive approach to integrating theory and policy
Tymms, P., Coe, R.: Celebration of the success of distributed research with schools: the CEM centre. Durham. Br. Educ. Res. J. 29(5), 639–667 (2003)
World Health Organization: The ICD-10 classification of mental and behavioural disorders. World Heal. Organ. 55(1993), 135–139 (1993). https://doi.org/10.4103/0019
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Alharbi, K., Cristea, A.I., Shi, L., Tymms, P., Brown, C. (2021). Agent-Based Simulation of the Classroom Environment to Gauge the Effect of Inattentive or Disruptive Students. In: Cristea, A.I., Troussas, C. (eds) Intelligent Tutoring Systems. ITS 2021. Lecture Notes in Computer Science(), vol 12677. Springer, Cham. https://doi.org/10.1007/978-3-030-80421-3_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-80421-3_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80420-6
Online ISBN: 978-3-030-80421-3
eBook Packages: Computer ScienceComputer Science (R0)