The effect of the interaction between divergent thinking strategies and learning style in an adaptive learning environment on developing successful intelligence skills among female students of the Faculty of Home Economics, Al-Azhar University.

Al-Shafi’i, Amani Saeed Muhammad; Khaled, Zainab Atef Mustafa; Aboul Gheit, Iman Ali

doi:10.21608/jsrep.2023.322501

The effect of the interaction between divergent thinking strategies and learning style in an adaptive learning environment on developing successful intelligence skills among female students of the Faculty of Home Economics, Al-Azhar University.

Document Type : Original Article

Authors

قسم الاقتصاد المنزلي التربوي، كلية الاقتصاد المنزلي للبنات بطنطا، جامعة الأزهر

10.21608/jsrep.2023.322501

Abstract

The current research aimed to develop the skills of successful intelligence among first-year female students at the Faculty of Home Economics, Al-Azhar University, and to identify the impact of the interaction between neural branching thinking strategies (hypothetical thinking - similarity), and the learning style (total / sequential), in an adaptive learning environment, on the development of successful intelligence skills among the research sample. To achieve the objectives of the research, both were used: the analytical descriptive approach, and the semi-experimental approach with an experimental design known as the "2×2 factorial design" (Factoral Design), which includes four experimental groups in the pre and post measurements, and the research sample consisted of (92) female students from the first year of the Faculty of Home Economics, Al-Azhar University, by (23) students in each group, and the research used a set of tools: the learning style scale (total / sequential) prepared by Felder & Silverman (1988) translated Mr. Abu Hashem (2012), successful intelligence skills test (prepared by the researcher), and the researcher prepared an adaptive learning environment based on two neural branching thinking strategies: (the hypothetical thinking strategy - the similarity strategy), and after applying the research experience and processing the data resulting from the application of the research tools, one of the most important results of the research was the existence of an effective effect of the adaptive learning environment in improving the level of successful intelligence skills among the research sample, and the absence of a clear effect of the interaction between neural branching thinking strategies (hypothetical thinking - similarity), and the learning style (total / sequential), in developing successful intelligence skills among the research sample .

Keywords

References

Azid, N., Makhsin, M., Mohktar, A., & Hashim, R. (2015). Effectiveness of learning activities using interactive modules successful intelligence. Australian Journal of Basic and Applied Sciences, 9(32), 1-9.

Bartholomew, S. R., & Ruesch, E. Y. (2018). design fixation and divergent thinking in primary children. Technology and Engineering Teacher, 78(2), 26-31.

Boulet, L. (2007). Coping strategies and successful intelligence in adults with learning disabilities. Doctoral dissertation, Mount Saint Vincent University.

Cardellichio, T., & Field, W. (1997). Seven strategies that encourage neural branching. Educational Leadership, 54, 33-37.

El Aissaoui, O., El Madani, Y. E. A., Oughdir, L., & El Allioui, Y. (2019). A fuzzy classification approach for learning style prediction based on web mining technique in e-learning environments. Education and Information Technologies, 24(3), 1943-1959.

Esichaikul, V., Lamnoi, S., & Bechter, C. (2011). Student modelling in adaptive e-learning systems. Knowledge Management & E-Learning: An International Journal, 3(3), 342-355.

Felder, R. M., & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Engineering education, 78(7), 674-681.

Felder, R. M., & Spurlin, J. (2005). Applications, reliability, and validity of the index of learning styles. International journal of engineering education, 21(1), 103-112.

Hamada, A. K., Rashad, M. Z., & Darwesh, M. G. (2011). Behavior analysis in a learning environment to identify the suitable learning style. International Journal of Computer Science & Information Technology (IJCSIT), 3(2), 48-59.‏

Hwang, G. J., Sung, H. Y., Hung, C. M., & Huang, I. (2012). Development of A Personalized Educational Computer Game Based on Students’ Learning Styles. Educational Technology Research & Development, 60(4), 623-638.

Joughin, G. (1992). Distance learner, approaches. presented at the world conference of international council for distance education, 16th Bangkok, Thailand.

Knutov, E., De Bra, P., & Pechenizkiy, M. (2011). Generic adaptation framework: a process-oriented perspective. Journal of Digital Information, 12(1).

Natriello, G. (2013). Adaptive Educational Technologies: Tools for Learning and for Learning about Learning. National Academy of Education, Washington.

Rayneri,L. J.,Gerber,B. L.,&Wiley,L.P. (2006). The relationship between classroom environment and the learning style preferences of gifted middle school students and the impact on levels on performance. Gifted Child Quarterly, 50, 104-118.

Rinehart, A., Sharkey, J., & Kahl, C. (2015). Learning style dimensions and professional characteristics of academic librarians. College & Research Libraries, 76(4), 450- 468.

Shuib, M., & Azizan, S. N. (2015). Learning Style Preferences among Male and Female ESL Students in Universiti-Sains Malaysia. Journal of Educators Online, 12(2), 103-141.‏

Šorgo, A., Dolenc, K., Šumak, B., Podgorelec, V., Karakatič, S., & Heričko, M. (2017). Proposal for developing an autonomous intelligent and adaptive e-learning system (AIAES) for education. Central European Conference on Information and Intelligent Systems (pp. 169-175). Faculty of Organization and Informatics Varazdin.‏

Sternberg, R. (2005). The theory of successful intelligence. Interamerican Journal of Psychology, 39 (2), 189-202.

Sternberg, R. J. (2002). Raising the achievement of all students: Teaching for successful intelligence. Educational Psychology Review, 14(4), 383-393.

Sternberg, R. J. (2012). The assessment of creativity: An investment-based approach. Creativity research journal, 24(1), 3-12.

Sternberg, R. J. (2015). Successful intelligence: A model for testing intelligence beyond IQ tests. European Journal of Education and Psychology, 8(2), 76-84.

Sternberg, R. J., & Grigorenko, E. L. (2007). Teaching for successful intelligence: To increase student learning and achievement. Corwin Press.

Sternberg, R. J., Jarvin, L., Birney, D. P., Naples, A., Stemler, S. E., Newman, T., Otterbach, R., Parish, C., Randi, J., & Grigorenko, E. L. (2014). Testing the theory of successful intelligence in teaching grade 4 language arts, mathematics, and science. Journal of Educational Psychology, 106(3), 881-899.‏

Sternberg, R. J., Todhunter, R. J., Litvak, A., & Sternberg, K. (2020). The relation of scientific creativity and evaluation of scientific impact to scientific reasoning and general intelligence. Journal of Intelligence, 8(17), 1-24.

Sternberg, R. J.,& Grigorenko, E. L. (2002). The theory of successful intelligence as abasic for Gifted Education. Gifted Child Quarterly, 46 (4), 265- 277.

Wen, X. (2011). Learning styles and their implications in learning and teaching. Theory and Practice in language Studies, 1(4), 413-416.

Wolf, C. (2007). Construction of an adaptive e-learning environment to address learning styles and an investigation of the effect of media choice. Doctoral dissertation, RMIT University.

Yang, T.-C., Hwang, G.-J., & Yang, S. J.-H. (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.