analyzing the Perceptions of Students of the College of Education, Al-Azhar University, of the Preferred Smart Learning Environment

Document Type : Original Article

Author

Department of Educational Psychology and Educational Statistics College of Education - Al-Azhar University

Abstract

The smart learning environment has become one in which teachers and students can practise rich learning experiences that have never been seen before. Therefore, many types of research have revealed the relationship between learning skills and learning outcomes and smart learning environments, and some were interested in revealing the relationship between smart learning environments and student learning skills as a flourishing learning environment in the twenty-first century. The research sample consisted of 500 students who were selected from the College of Education - Al-Azhar University in Cairo from the second year and chosen from the following departments (scientific - literary - and qualitative) and the fourth year also selected from the departments (scientific, literary and qualitative). To achieve the aim of the research, a scale was prepared that aims to measure the preferred smart learning environment, consisting of (46) phrases distributed over ten dimensions that represent the smart learning environment. The results indicated that: - Students' preferences for the smart learning environment on the dimensions of the scale were all high, and this indicates that the students prefer learning in light of a smart learning environment rich in technology in terms of its dimensions, the subject of the studied research. - There were no statistically significant differences in the responses of the research sample on the dimensions of the scale according to the study year (second - fourth), except for the dimensions of physical design and learning data. - There were statistically significant differences in the responses of the research sample on the dimensions of the scale according to the specialization variable (scientific - literary - qualitative) and these differences were in favor of the qualitative specialization, and a set of appropriate educational recommendations were produced
 

Highlights

 

 

Keywords


 

 
Aldridge, J, Dorman, B, Fraser,J, (2004).  Use of multitrait-multimethod modelling to validate actual and preferred forms of the technology-rich outcomes-focused learning environment inventory (Troflei). Aus. J. Educ. Dev. Psychol. 4, 110–125.
Alemu, B. M. (2014). Enhancing the Quality and Relevance of Higher Education Through Effective Teaching Practices and Instructors ’ Characteristics. Universal Journal of Educational Research, 2(9), 632–647.
ALizzion, K Wilson, R Simons,  (2002)  . University students’ perceptions of the learning environment and academic outcomes: Implications for theory and practice. Stud. High. Educ. 27, 27–51.
Atif, S.. Mathew, &. Lakas, A (  2015)     ‘‘Building a smart campus to support ubiquitous learning,’’ J. Ambient Intell. Humanized Comput., vol. 6, no. 2, pp. 223–238, Apr.
Brown, M. (2005). Learning spaces. In Oblinger, D. G. &Oblinger, J. L. (Eds.). Educating the net generation. EDUCAUSE. Retrieved August 18, 2012, from http://www.educause.edu/educatingthenetgen/, 2012-8-18.
Chang, C Hsiao, Y Chang, (2011 )  . Science learning outcomes in alignment with learning environment preferences. J. Sci.Educ. Technol. 20(2), 136–145.
De Corte, L Verschaffel, C Masui, )2004 (  The CLIA-model: a framework for designing powerful learning environments for thinking and problem solving. Eur. J. Psychol. Educ. 19(4), 365–384.
Dlouhá, P. Glavič, and A. Barton,  ( 2017) ‘‘Higher education in central European countries—Critical factors for sustainability transition,’’ J. Cleaner Prod., vol. 151, pp. 670–684.
 Dobrescu, T., & Grosu, E.. (2014). Aspects Regarding Classroom Management and its Part in Making the Educational Process More Effective. Procedia - Social and Behavioral Sciences, 141, 465–469.
Evans,c, ( 2008 ) ‘‘The effectiveness of m-learning in the form of podcast revision lectures in higher education,’’ Comput. Educ., vol. 50, no. 2, pp. 491–498.
Gil-Rodríguez, E.P. and Rebaque-Rivas, P. (2010). Mobile Learning and Commuting: Contextual Interview and Design of Mobile Scenarios, Springer, 6389, 266–277.–2, pp. 1–21.
Grady, DL Fisher,( 2008  ) The educology of classroom environments and the quality of student learning. Int. J. Educol. 22(1/2), 73–83.
Hsieh, W , Wu,V& . Marek,w (2017) ‘‘Using the flipped class-room to enhance EFL learning,’’ Comput. Assist. Lang. Learn., vol. 30, nos. 1
Hunter, K. (2005). Environmental psychology in classroom design. Retrieved from https://etd. ohiolink.edu/
Jairak, K., Praneetpolgrang, P., &Mekhabunchakij, K. (2009). An acceptance of mobile learning for higher education students in Thailand, The Sixth International Conference on e-learning for Knowledge-Based Society, Bangkok, Thailand. 36.1-36.8.
Karpicke, J, Blunt,. R, (2011)   Retrieval practice produces more learning than elaborative studying with concept mapping. Science 331, 772–775.
Kong, G Chen, G , ( 2014), A study on the development of the smart classroom scale, in Emerging issues in smart learning, ed. by G Chen, V Kumar, Kinshuk, RH Huang, SC Kong (Springer Berlin Heidelberg, Berlin, pp. 45–52
Koper, ( 2014)Conditions for effective smart learning environments,’’ Smart Learn. Environ., vol. 1, no. 1, pp. 1–7, Nov., doi:10.1186/s40561-014-0005-4.
Lin, YM Huang, SC Cheng, . (2011)   An automatic group composition system for composing collaborative learning groups using enhanced particle swarm optimization. Comput. Educ. 55, 1483–1493.
Liu, L Horton, J Olmanson, P Toprac, (2011) . A study of learning and motivation in a new media enriched environment for middle school science. Educ. Technol. Res. Dev. 59, 249–265
Liu, I.-F., Chen, M., Sun, Y., Wible, D., & Kuo, C.-H. (2010). Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community. Computers & Education, 54(2), 600--610. doi: 10.1016/j.compedu.2009.09.009 Google ScholarDigital Library
Lin, Y, Huang, C, Cheng, S, (2010) . An automatic group composition system for composing collaborative learning groups using enhanced particle swarm optimization. Comput. Educ. 55, 1483–1493
Meo, G. (2008). Curriculum planning for all learners: Applying universal design for learning (UDL) to a high school reading comprehension program. Preventing School Failure, 52 (2), 21–30.
Meyer, A. & Rose, D.H. (2006). The future in the margins: The role of technology and disability in educational reform. In D.H. Rose, A. Meyer & C. Hitchcock (Eds.), The universally designed classroom: Accessible curriculum and digital technologies (13–35). Cambridge, MA: Harvard Education Press.
Mikulecký,  P.  (2012).  Smart  Environments  for  Smart  Learning.  DIVAI  2012,  213–222.
Palfrey, J., and Gasser, U. (2008). Born digital: Understanding the first generation of digital natives. New York: Basic Books.
Prensky, M. (2010). Teaching Digital Natives: Partnering for Real Learning. London: Sage Publishers.
Silva, FJ Restivo, ( 2009)  . An intelligent mashup learning environment with social interaction, in Proceedings of the European Conference on e-Learning, , pp. 759–766
Sterling and W. Scott, 2008 (‘‘Higher education and ESD in England: A critical commentary on recent initiatives,’’ Environ. Educ. Res., vol. 14, no. 4, pp. 386–398.
Tapscott, D. (2009). Grown up digital: How the Net generation is changing your world. New York: McGraw-Hill.
Trilling, B., & Fadel, C. (2009). 21st century skills: Learning for life in our times. Jossey-Bass Inc Pub.
Tzuriel, D. (2000). Dynamic assessment of young children: Educational and intervention perspectives. Educational Psychology Review, 12(4), 385–435.
Yang, H. Pan, W. Zhou, and R. Huang, (    2018)   ‘‘Evaluation of smart classroom from the perspective of infusing technology into pedagogy,’’ Smart Learn. Environ., vol. 5, no. 1,             pp. 20–30.
Yang, Y, Lin,  L,   (2010)  Development and evaluation of an interactive mobile learning environment with shared display groupware. Educ Technol Soc 13(1), 195–207 .
Zhong, Guoxiang, Zhang, Xiaozhen (2006). A Building of the Current Intelligent Learning Environment Model. Computer Science, (1):170-171.