Factors Influencing Blended Personalized Arabic Language Learning

  • Authors

    • Rania Ahmad Said Bataineh
    • Rosseni Din
    • Nabilah Othman
    • Atef Al Mashakbh
    2018-11-26
    https://doi.org/10.14419/ijet.v7i4.21.21616
  • Personalized Learning, Blended Learning, Arabic as a Foreign Language.
  • Foreign students learning Arabic language face problems regarding writing, reading, speaking and listening.  This study uses learning modules over the Facebook social media which allow interactivity among learners and facilitators to help them improve the four skills.  In addition, learning  Arabic is also crucial to others in order to meaningfully understand Al-Quran, the Holy Book for Muslims from all over the world. The quest for effective learning strategies and instructional approach in learning Arabic as a foreign language has been a challenge for educators. Studies have shown that student centered learning must be the approach in any effective language learning to cater for each individual to achieve the learning outcome.  The main focus of this study is to identify factors influencing a personalized blended approach for learning Arabic language. A survey was administered on 157 foreign Arabic learners and SEM-PLS 3.0 software was use to identify reliability, validity and factors influencing blended personalized arabic language learning and the contribution of blended learning towards personalized learning.  The results showed (i) evidence of a five-dimension measurement model contributing to blended learning, (ii) evidence of a four-dimension measurement model contributing to personalized learning, and (iii) a relationship showing positive impact of blended  learning with significant effect on personalized learning at the (.01) level of significance (β = 0.757, t = 16.283, p < .01), and (iv) evidences of a reliable and valid model for a blended personalized learning model for Arabic Language Learning. The result also showed that personalization of Arabic as a foreign language learning supported language learning through empowering learners to build up their knowledge and enables them to think critically, work in teams and solve problems collectively. In a blended learning environments learners had the opportunity to actively interpret their experience using internal cognitive operations via the practice of reflective exercises embedded into their Facebook groups’ timeline. In this study, a blended combination of face-to-face, self-learning and computer-mediated communication was used. Blended learning indeed contributes to personalization of learning the Arabic language. Moreover, learners were in charge and in control of their learning. Learners collaborated and socially interacted with others. This enabled them to construct knowledge and gained significant learning. 

     

     

  • References

    1. [1] Glickman, C. (1991). Pretending Not to Know What We Know. Educational leadership, 48(8), 4-10.

      [2] Bataineh, R. (2017). Evaluation & modelling of blended personalized Arabic language learning for Malaysian students at Al al-BAYT University. PhD Thesis. Bangi: Universiti Kebangsaan Malaysia.

      [3] Jansen, B. A. (2014). New media’s support of knowledge building and 21st century skills development in high school curricula. Doctoral dissertation. Austin: University od Texas.

      [4] U.S. Department of Education (2010). Transforming American education—Learning powered by technology: National Education Technology Plan 2010. Washington, DC: Office of Educational Technology, U.S. Department of Education.

      [5] Din, R. (2017). Asas Pendidikan dan Kejurulatihan ICT. Bangi: Penerbit UKM.

      [6] Burgstahler, S., & Burgstahler, S. (2013, May 4). Introduction to universal design in higher education. Universal design in higher education: Promising practices. Seattle: DO-IT, University of Washington. Retrieved from https://www. washington.edu/doit/universal-design-processprinciples-and-applications.

      [7] Universiti Kebangsaan Malaysia Centre of Excellence for Learner Diversity. Laporan Tahunan Fakulti Pendidikan. Annual Report (2013). http://www.ukm.my/fpendidikan/wp-content/uploads/2016/05/Laporan-Tahunan-2013-2014-Part-1.pdf

      [8] Sivapunniam, N. (2009). Virtual Realities: A Blended Learning Approach to Bridge the Gap between Diverse ESL Learners. In Proceedings of 7th International Conference Language and Culture Creating and Fostering Global Communities SoLLsINTEC09 (pp. 283-290).

      [9] Owston, R., Wideman, H., Murphy, J., & Lupshenyuk, D. (2008). Blended teacher professional development: A synthesis of three program evaluations. The Internet and Higher Education, 11(3), 201-210.

      [10] Pankin, J., Roberts, J., & Savio, M. (2012). Blended learning at MIT. Retrieved from http://web.mit. edu/training/trainers/resources/blended_learning_at_mit.pdf

      [11] Ahmad, W., Rusli, W., & Mat Daud, N. (2011). Developing Arabic writing skills using Facebook. In: International Language Conference (ILC) 22-24 April 2011, International Islamic University Malaysia.

      [12] Gabarre, S., Gabarre, C., Din, R., Shah, P. M., & Karim, A. A. (2013). Using mobile Facebook as an lms: Exploring impeding factors. GEMA Online Journal of Language Studies, 13(3).

      [13] Roblyer, M. D., McDaniel, M., Webb, M., Herman, J., & Witty, J. V. (2010). Findings on Facebook in higher education: A comparison of college faculty and student uses and perceptions of social networking sites. The Internet and Higher Education, 13(3), 134-140.

      [14] Din, R., (2010). Development and Validation of an integrated meaningful blended e-training (I-MeT) for computer science Theoretical-Empirical Based Design and Development approach. Unpublished PhD Thesis. Bangi: Universiti Kebangsaan Malaysia.

      [15] Din, R., Norman, H., Kamarulzaman, M. F., Shah, P. M., Karim, A., Salleh, N. S. M., Mastor, K. A. (2012). Creation of a knowledge society via the use of mobile blog: a model of integrated meaningful blended e-training. Asian Social Science, 8(16), 45.

      [16] Al Mashakbh, A. (2012). Online individualized multimedia instruction model for engineering. Unpublished PhD Thesis. Bangi: Universiti Kebangsaan Malaysia.

      [17] Hair, J. F., Ringle, C. M. & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing theory and Practice 19(2): 139-152.

      [18] Ringle, C. M., Wende, S., & Becker, J. M. (2015). SmartPLS 3. Bönningstedt: SmartPLS. GmbH, http://www. smartpls. Com.

      [19] Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the academy of marketing science, 40(3), 414-433.

      [20] Baozi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.

      [21] Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 39-50.

      [22] Hair, J., & Jnr, B. (2009). BJ, & Anderson, RE (2010). Multivariate data analysis: A global perspective. Upper Saddle River, New Jersey: Pearson Prentice Hall.

      [23] Hair, J. (2010). Multivariate data analysis. Upper Saddle River, New Jersey: Pearson Prentice Hall.

      [24] Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. & Tatham, R.L. (2006). Multivariate data analysis. 6th Ed. Upper Saddle, New Jersey: Pearson Prentice Hall.

      [25] Kline, R. B. (2015). Principles and practice of structural equation modeling. New York: USA: Guilford publications.

      [26] Nunnally, J. (1978). Psychometric methods: New York: McGraw-Hill.

      [27] Malhotra, N., Budhwar, P., & Prowse, P. (2007). Linking rewards to commitment: an empirical investigation of four UK call centers. The International Journal of Human Resource Management, 18(12), 2095-2128.

      [28] Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological bulletin, 56(2), 81.

      [29] Sin, A. B., Zailani, S., Iranmanesh, M., & Ramayah, T. (2015). Structural equation modelling on knowledge creation in Six Sigma DMAIC project and its impact on organizational performance. International Journal of Production Economics, 168, 105-117.

      [30] Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43(1), 115-135.

      [31] Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.

      [32] Smith, D., Hair, J. F., & Ferguson, K. (2014). An investigation of the effect of family influence on Commitment–Trust in retailer–vendor strategic partnerships. Journal of Family Business Strategy, 5(3), 252-263.

      [33] MacDonald, C. J., Stodel, E., Farres, L., Breithaupt, K. & Gabriel, M. A. (2001). The Demand Driven Learning Model: A framework for web-based learning. The Internet and Higher Education, 1(4): 9-30.

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    Ahmad Said Bataineh, R., Din, R., Othman, N., & Al Mashakbh, A. (2018). Factors Influencing Blended Personalized Arabic Language Learning. International Journal of Engineering & Technology, 7(4.21), 58-63. https://doi.org/10.14419/ijet.v7i4.21.21616