Judging Primary School Classroom Spaces Via Artificial Neural Networks Model

Abstract

An experimental study was conducted with 2nd grade students at primary schools in Turkey as part of an attempt to describe the ideal classroom space for primary education students. In the study, photographs of 20 different primary education classrooms were evaluated by 189 students. The students evaluated the images by means of surveys in which they were asked questions on four concepts: belonging, like (partiality), learning and safety. Reliability analyses of numeric data obtained from student surveys were made and they were subjected to various statistical analyses. In the second part of the study, the students’ preferences for the classroom spaces were evaluated by means of Artificial Neural Networks (ANN) method, by using numeric data obtained from the student about concepts as well as the classroom space photos. Numeric data were treated and test procedures were performed to ensure that ANN makes decisions in the name of 2nd grade students. This is the first study in which numeric survey results and photographic characteristics have been used together.  The ANN results were very similar to the students’ evaluation of the ideal classroom space, particularly in terms of belonging and like (partiality) concepts.

 

 

                Key Words:  Spatial perception, Statistical analysis,

                                     Classroom design, Artificial Neural Networks.

 

Keywords

• Spatial perception, Statistical analysis, Classroom design, Artificial Neural Networks.

References

1. Simpson, J., Weiner, E. (eds) “Space” The Oxford English Reference Dictionary, Clarendon Press, Oxford, (1989).
2. Atman, C.J., “A comparison of freshman and senior engineering design processes”, Design Studies, 20(2): 131-152 (1999).
3. Başkaya, A., Dinç, P., Aybar, U., Karakaşlı, M., “An Experiment on Spacial Image: Gazi University Engineering-Architecture Faculty Entrance Holl of Education Block”, Journal of the Faculty of Engineering & Architecture of Gazi University, 18 (2): 79-94 (2003).
4. Dinç, P., Yüksel, E. I., “A Lens Model Approach for Analyzing the Judgment Differences of European and Turkish Architectural Students”, Arkitekt, 523, (2010).
5. Brown, G., Gifford, R., “Architects Predict Lay Evaluations Of Large Contemporary Buildings: Whose Conceptual Properties?”, Journal of Environmental Psychology, 21: 93-99 (2001).
6. Douglas, D., Gifford, R. “Evaluation of the Physical Classroom by Students and Professors: A Lens Model Approach”, Educational Research, 43(3): 295-309 (2001).
7. Gifford, R., Hine, D.W., Müler-Clemm, W., Reynolds, D. J., Shaw, K.T., “Decoding Modern Architecture: A Lens Model Approach for Understanding the Aesthetic Differences of Architects and Laypersons”, Environment and Behavior, 32:168-187 (2000).
8. Gifford, R., Hine, D.W., Clemm, W.M., Shaw, K.T. “Why Architects and Laypersons Judge Buildings Differently: Cognitive Properties and Physical Bases”, Journal of Architectureal and Planning Research, 19 (2):131-148, (2002).

9. Çakır, H., “The Architectural Parameters Which Effect Perception of Children”, İstanbul Technical University (ITU), Institute of Natural and Applied Sciences, MSc Thesis, İstanbul (1997).
10. Gür, Ş.Ö., Özbilen, A., Ertürk, S., “Child in Enviromental Phisicology – Effect of Cognitive Environmental Evaluation of Primary School Students on Design Decision”, Karadeniz Techical University, General Paper, 148(43), (1989).
11. Koç, F., “Child and Space”, İstanbul Technical University, Institute of Natural and Applied Sciences, MSc Thesis, İstanbul (1999).
12. Sivri, H., “Physical and Spatial Environment of Child Behaviour and Attitude, Determining Design Values for Cretaed Spaces for Child”, Dokuz Eylul Univeristy, Institute of Natural and Applied Sciences, İzmir (1993).
13. Gür, Ş.Ö., Zorlu, T., “Spaces for Children”, YEM Publication, No:75, İstanbul (2002).
14. Heschong Mahone Group “Daylighting in SchoolsAn İnvestigation into the Relationship Betwen Daylighting and Human Performance”, The Pacific Gas and Electric Company on behalf of the California Board for Energy Efficiency Third Party Program (1999).
15. Mannel, E. “Color and Light Effects on Learning, Paper Presented at the Association for Childhood Education İnternational Study Conference and Exhibition”, Washington, DC, April 12-15 (1995).
16. Marx, A., Fuhrer, U., Hartig, T., “Effects of Classroom Seating Arrangements on Children’s Question-Asking”, Learning Environments Research, 2, 249-263 (2000).
17. Read, M.A., Sugawara, A.I., Brandt, J.A., “Impact of Space and Color in the Physical Environment on Preschool Children’s Cooperative Behavior”, Environment and Behavior, 31(3): 413-428(1999).
18. Cohen, S., Trostle, S.L., “Young Children’s Preferences for School –Related Physical – Environmental Setting Characteristics”, Environment and Behavior, 22(6): 753-766(1990).
19. Killen, J. P., Evans, G.W., Danko, S., “The Role of Permanent Student Artwork in Student’s Sense of Ownership in an Elementary School”, Environment and Behavior, 35(2): 250-263 (2003).
20. Demirbaş, O.O., Demirkan, H., “Privacy Dimensions: A Case Study in the Interior Architecture Design Studio”, Journal of Enviromental Psychology, 20: 53-64, (2000).
21. Upitis, R., “School Architecture and Complexity, Complicity”, An International Journal of Complexity and Education, 1 (1): 19-38 (2004).
22. Gifford, R., “Environmental Psychology: Principles and Practice”, 2nd ed., Allyn and Bacon, Boston, USA (1997).
23. Moore, G.T., Lackney, J.A., “School Design: Crisis, Educational Performance and Design Applications”, Chidren’s Environments, 10 (2): 99-112 (1993).
24. Stevens, R., Ikeda, J., Casillas, A., Palacio, J., Clyman, S., “Artificial Neural Network-Based Performance Assessments”, Computers in Human Behavior, 15(3-4): 31, 295-313 (1999).
25. Haykin, S., Neural Networks, “A Comprehensive Foundation”, New York: Macmillan, (1994).
26. Ceylan, M., Arslan, M.H., Ceylan, R., Kaltakci, M.Y., Özbay, Y. “A New Application Area of ANN and ANFIS: Determination of Earthquake Load Reduction Factor of Prefabricated Industrial Buildings”, Civil Engineering and Environmental Systems, 27(1): 53-69 (2010).
27. Ekici, B.B., Aksoy, U.T., “Prediction of Building Energy Consumption by Using Artificial Neural Networks”, Advances in Engineering Software, 40(5): 356-362 (2010).
28. Arslan, M.H., Ceylan, M, Kaltakcı, M.Y., Özbay, Y., Gülay, G., “Prediction of Force Reduction Factor R of prefabricated Industrial Buildings Using Neural Networks”, Structural Engineering and Mechanics, 27(2):117-134 (2007).
29. Rafiq, M.Y., Bugmann, G., Easterbrook, D.J., “Neural Network Design for Engineering Applications”, Computers & Structures, 79 (17):1541-1552(2001).
30. Özbay, Y., Ceylan, R., Karlik, B., “A Fuzzy Clustering Neural Network Architecture for Classification of ECG Arrhytmias”, Computers in Biology and Medicine, 36: 376-388 (2006).
31. Arslan, H.D., “Assessment of The PerceptionLinked Parameters in Primary School Classroom Design and the Recommendations on Their Design”, Selçuk University, Institute of Natural and Applied Sciences, PhD Thesis, Konya (2010).
32. Allen, D.M., “The Relationship Between Variable Selection and Data Augmentation and a Method for Prediction”, Technometrics, 16:125-127 (1974).
33. Steel, R.G.D., Torrie, J.H., “Principles and Procedures of Statistics”, New York: McGraw-Hill, 187, 287(1960).