SMART CLASSROOM ENVIRONMENTAL PARAMETERS AS A PARAMETER OF ADAPTIVE LEARNING
DOI:
https://doi.org/10.20319/pijss.2019.52.680699Keywords:
Smart Classroom, Adaptive Computer Learning, Learning Environment, Dynamic Environmental ParameterAbstract
This paper presents results of the research aimed at establishing the possibility of using a physical environmental parameter (λ) as one of the parameters of adaptive learning in smart classrooms. In this research, the parameter quantifying physical environmental parameters of a smart classroom into a single value was introduced and the relevance of the usage of the introduced parameter as a criterion of adaptive learning in a smart classroom was evaluated. The presentation of multiple environmental parameters through one unique parameter facilitated the realization of adaptation process, especially in the case of applying several adaptation criteria. An overall of 64 third-year students of the ICT College in Belgrade participated in the research. The implemented research drew certain conclusions. The relevance of using the parameter (λ) as the criterion of adaptive learning in smart classrooms was confirmed.
References
Al-Hemoud, A., Al-Awadi, L., Al-Rashidi, M., Rahman, K., Al-Khayat, A., & Behbehani, W. (2017). Comparison of indoor air quality in schools: Urban vs. Industrial 'oil & gas' zones in Kuwait. Building And Environment, 122, 50-60. http://dx.doi.org/10.1016/j.buildenv.2017.06.001
Beldagli, B., & Adiguzel, T. (2010). Illustrating an ideal adaptive e-learning: A conceptual framework. Procedia - Social And Behavioral Sciences, 2(2), 5755-5761. http://dx.doi.org/10.1016/j.sbspro.2010.03.939
Bouzeghoub, A., Carpentie, C., Defude, B., & Duitama, F. (2003). A Model of Reusable Educational Components for the Generation of Adaptive Courses. Paris: Institut National des Télécomm.
Brahim, H., Jemaa, A., Jemni, M., & Laabidi, M. (2013). Towards the Design of Personalised Accessible E-Learning Environments. 2013 IEEE 13Th International Conference On Advanced Learning Technologies. http://dx.doi.org/10.1109/icalt.2013.128
Brusilovsky P. and Peylo C., “Adaptive and Intelligent Web-based Educational Systems” , International Journal of Artificial Intelligence in Education, 13, 2003, pp.156-169
Carbon monoxide, industry and performance. (1975). The Annals of Occupational Hygiene. http://dx.doi.org/10.1093/annhyg/18.1.1
Chorfi, H. & Mohamed, J. (2004). PERSO: Towards an Adaptive e-Learning System, Journal of Interactive Learning Research (ISSN-1093-023X), 433-447
Coley, D., Greeves, R., & Saxby, B. (2007). The Effect of Low Ventilation Rates on the Cognitive Function of a Primary School Class. International Journal of Ventilation, 6(2), 107-112. http://dx.doi.org/10.1080/14733315.2007.11683770
Crook, M., & Langdon, F. (1974). The effects of aircraft noise in schools around London airport. Journal of Sound and Vibration, 34(2), 221-232.
Deleawe, S., Kusznir, J., Lamb, B. and Cook, D. (2010). Predicting air quality in smart environments. Journal of Ambient Intelligence and Smart Environments, 2(2), pp.145-154.
Despotović-Zrakić, M., Marković, A., Bogdanović, Z., Barać, D., & Krčo, S. (2012). Providing Adaptivity in Moodle LMS Courses. Journal of Educational Technology & Society, 15(1), 326-338. http://dx.doi.org/ISSN 1436-4522 (online) and 1176-3647 (print).
Dwic, A. & Basuki, A. (2012). Personalized Learning Path of a Web-based Learning System. International Journal of Computer Applications, 53(7), 17-22. http://dx.doi.org/10.5120/8434-2206
ECOENERGY, R. (2018). LED Lighting Calculation- How to Calculate the requirement of LED Lights- RST LED Lights. RST Blog. Retrieved 19 October 2017, from http://rstenergy.com/blog/led-lighting-calculation-how-to-calculate-the-requirement-of-led-lights-rst-led-lights/
Egong, A.I. (2014). Reading culture and academic achievement among secondary school students. Journal of Education and Practice. 5. 132-136., ISSN 2222-288X
Fabbri, K. (2015). A Brief History of Thermal Comfort: From Effective Temperature to Adaptive Thermal Comfort. Indoor Thermal Comfort Perception, 7-23. http://dx.doi.org/10.1007/978-3-319-18651-1_2
Gómez, J., Huete, J., Hoyos, O., Perez, L., & Grigori, D. (2013). Interaction System based on Internet of Things as Support for Education. Procedia Computer Science, 21, 132-139. http://dx.doi.org/10.1016/j.procs.2013.09.019
Green, K., Pasternack, B., & Shore, R. (1982). Effects of Aircraft Noise on Reading Ability of School–Age Children. Archives of Environmental Health: An International Journal, 37(1), 141-145. http://dx.doi.org/10.1080/00039896.1982.10667528
Grossberg, S. (1999). The Link between Brain Learning, Attention, and Consciousness. Consciousness and Cognition, 8(1), 1-44. http://dx.doi.org/10.1006/ccog.1998.0372
Hamada, M. (2012). Learning Style Model for e-Learning Systems. Active Media Technology, 186-195. http://dx.doi.org/10.1007/978-3-642-35236-2_19
Hydroponics, (2018). Retrieved 19 October 2017, from (https://www.hydroponics.eu/calculating-size-capacity-air-exchange-of-your-extractor-fan_575.html
J. Sweller, Cognitive load during problem solving: Effects on learning, Cognitive Science, 12 (1988), pp. 257-285
Khenissi, M., Essalmi, F., Jemni, M., Kinshuk, Graf, S., & Chen, N. (2016). Relationship between learning styles and genres of games. Computers & Education, 101, 1-14. http://dx.doi.org/10.1016/j.compedu.2016.05.005
Kim, J., & de Dear, R. (2018). Thermal comfort expectations and adaptive behavioural characteristics of primary and secondary school students. Retrieved 10 January 2018, from
Kim, J., Lee, A., & Ryu, H. (2013). Personality and its effects on learning performance: Design guidelines for an adaptive e-learning system based on a user model. International Journal of Industrial Ergonomics, 43(5), 450-461. http://dx.doi.org/10.1016/j.ergon.2013.03.001
Klašnja-Milićević, A., Vesin, B., Ivanović, M., & Budimac, Z. (2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education, 56(3), 885-899. http://dx.doi.org/10.1016/j.compedu.2010.11.001
Küller, R., & Lindsten, C. (1992). Health and behavior of children in classrooms with and without windows. Journal of Environmental Psychology, 12(4), 305-317. http://dx.doi.org/10.1016/s0272-4944(05)80079-9
Li, B., Kong, S., & Chen, G. (2015). Development and validation of the smart classroom inventory. Smart Learning Environments, 2(1). http://dx.doi.org/10.1186/s40561-015-0012-0
Mamat, N. & Yusof, N. (2013). Learning Style in a Personalized Collaborative Learning Framework. Procedia - Social and Behavioral Sciences, 103, 586-594. http://dx.doi.org/10.1016/j.sbspro.2013.10.376
Mayer, R. The Cambridge handbook of multimedia learning.
Mekacher, D. L. (2019). Augmented Reality (AR) and Virtual Reality (VR): The Future of Interactive Vocational Education and Training for People with Handicap. PUPIL: International Journal of Teaching, Education and Learning, 3(1).
Mihai, T., & Iordache, V. (2016). Determining the Indoor Environment Quality for an Educational Building. Energy Procedia, 85, 566-574. http://dx.doi.org/10.1016/j.egypro.2015.12.246
Mihalca, L., Salden, R., Corbalan, G., Paas, F., & Miclea, M. (2011). Effectiveness of cognitive-load based adaptive instruction in genetics education. Computers in Human Behavior, 27(1), 82-88. http://dx.doi.org/10.1016/j.chb.2010.05.027
Otto, D., Hudnell, H., House, D., Mølhave, L., & Counts, W. (1992). Exposure of Humans to a Volatile Organic Mixture. I. Behavioral Assessment. Archives of Environmental Health: An International Journal, 47(1), 23-30. http://dx.doi.org/10.1080/00039896.1992.9935940
Pace, M. (2017). Adapting Literature to the Language Classroom. PUPIL: International Journal of Teaching, Education and Learning, 1(1).
Peng, J., Zhang, H., & Wang, D. (2017). Measurement and analysis of teaching and background noise level in classrooms of Chinese elementary schools. Applied Acoustics, 131, 1-4. http://dx.doi.org/10.1016/j.apacoust.2017.10.012
Play.google.com. (2018). [online] Available at: https://play.google.com/store/apps/details?id=com.gamebasic.decibel&hl=en_US [Accessed 27 Sep. 2018].
Ricciardi, P., & Buratti, C. (2018). Environmental quality of university classrooms: Subjective and objective evaluation of the thermal, acoustic, and lighting comfort conditions. Retrieved 8 January 2018, from
Sala, E., & Rantala, L. (2016). Acoustics and activity noise in school classrooms in Finland. Applied Acoustics, 114, 252-259. http://dx.doi.org/10.1016/j.apacoust.2016.08.009
Sancho, P., Matrinez, I., & Fernandez-Munjon, B. (2005). Semantic Web Technologies Applied to e-learning Personalization in . Journal Of Universal Computer Science, 11(9), 1470-1481. http://dx.doi.org/10.3217/jucs-011-09-1470
Singh, M., Kumar, S., Ooka, R., Rijal, H., Gupta, G., & Kumar, A. (2018). Status of thermal comfort in naturally ventilated classrooms during the summer season in the composite climate of India. Building And Environment, 128, 287-304. http://dx.doi.org/10.1016/j.buildenv.2017.11.031
Sweller, J., van Merrienboer, J., & Paas, F. (1998). Educational Psychology Review, 10(3), 251-296. http://dx.doi.org/10.1023/a:1022193728205
Uzelac, A., Gligorić, N., & Krčo, S. (2018). System for recognizing lecture quality based on analysis of physical parameters. Retrieved 8 January 2018, from
Vilčeková, S., Kapalo, P., Mečiarová, Ľ., Burdová, E., & Imreczeová, V. (2017). Investigation of Indoor Environment Quality in Classroom - Case Study. Procedia Engineering, 190, 496-503. http://dx.doi.org/10.1016/j.proeng.2017.05.369
Walberg, H. (1982). Improving educational standards and productivity. Berkeley (California): McCutchan.
Wang, F. & Hannafin, M. (2005). Design-based research and technology-enhanced learning environments. ETR&D, 53(4), 5-23. http://dx.doi.org/10.1007/bf02504682
Wu, H., Lee, S., Chang, H., & Liang, J. (2013). Current status, opportunities and challenges of augmented reality in education. Computers & Education, 62, 41-49. http://dx.doi.org/10.1016/j.compedu.2012.10.024
Wurtman, R. (1975). The Effects of Light on the Human Body. Scientific American, 233(1), 68-77. http://dx.doi.org/10.1038/scientificamerican0775-68
Yang, J., & Huang, R. (2015). Development and validation of a scale for evaluating technology-rich classroom environment. Journal of Computers in Education, 2(2), 145-162. http://dx.doi.org/10.1007/s40692-015-0029-y
Yang, T., Hwang, G., & Yang, S. (2013). Development of an Adaptive Learning System with Multiple Perspectives based on Students ’ Learning Styles and Cognitive Styles. Educational Technology & Society, 16(4), 185-200. http://dx.doi.org/ISSN 1436 -4522 (online) and 1176- 3647 (print)
Zaki, S., Damiati, S., Rijal, H., Hagishima, A. and Abd Razak, A. (2017). Adaptive thermal comfort in university classrooms in Malaysia and Japan. Building and Environment, 122, pp.294-306
Zhu, Z., Yu, M., & Riezebos, P. (2016). A research framework of smart education. Smart Learning Environments, 3(1). http://dx.doi.org/10.1186/s40561-016-0026-2
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2019 Authors
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.