Expert Consensus on the Usability of the SLC3DM Model: A Fuzzy Delphi Method Application
DOI:
https://doi.org/10.37134/mrj.vol13.1.4.2024Keywords:
School Leaders, Leadership Skills, Data-Driven Decision Making, Usability Evaluation, Fuzzy Delphi MethodAbstract
Each model should undergo a usability evaluation to ensure a model meets the users’ needs and expectations. This is a process of testing the model with real users to measure how user-friendly and effective it is. Therefore, this study aims to evaluate a model of School Leaders Competencies in Data-Driven Decision Making (SLC3DM). The model has been designed and developed using the Design and Development Research (DDR) approach, which involves three phases: the need analysis phase, the design and development phase, and the usability evaluation phase. However, the researcher only focuses on this paper in the third phase. The model was designed using the Fuzzy Delphi Method (FDM) technique, which entailed consensus from a 20-expert panel. The model usability evaluation phase discovered that all 13 components of the SLC3DM model had high expert consensus. Overall, based on the consensus of the experts, the SLC3DM model is appropriate for usage and implementation. As a result, school leaders can benefit from the SLC3DM model, an empirically grounded and tested tool, as it can boost their knowledge, skills, and attitudes related to data-driven decision-making.
Downloads
References
Abdul Muqsith Ahmad Ahmad. (2018). Pembangunan model ENi berasaskan aktiviti inkuiri bagi program latihan kemahiran kejuruteraan Institut Latihan Kemahiran Malaysia. [Unpublished doctoral dissertation]. Universiti Malaya
Adler, M., & Ziglio, E. (Eds.). (1996). Gazing into the oracle: The Delphi method and its application to social policy and public health. Jessica Kingsley Publishers.
Akal, T. D., Beshah, T., Sackmann, S., & Negash, S. (2019). Challenges of Identifying and Utilizing Big Data Analytics in a Resource-Constrained Environment: In the Case of Ethiopia. ICT Unbounded, Social Impact of Bright ICT Adoption, 234–254. doi:10.1007/978-3-030-20671-0_17.
Ashaari, M., Amran, A. & Singh, K.S.D. (2020). Big data analytics capability for improved performance of higher education institutions in the era of IR 4.0: A proposed conceptual framework. International Journal of Innovation, Creativity and Change. 13 (8). https://www.researchgate.net/publication/344334181 _Big_Data_ Analytics_Capability_for_Improved_Performance_of_Higher_Education_Institutions_in_the_Era_of_IR_40_A_Proposed_Conceptual_ Framework.
Azli Ariffin. (2018). Reka bentuk model aktiviti penggunaan lagu kanak-kanak melayu tradisional untuk peningkatan imaginasi-kreatif prasekolah. [Unpublished doctoral dissertation]. Universiti Malaya.
Bulkley, K. E., & McCotter, S. S. (2017). Learning to Lead with Data: From Espoused Theory to Theory-in-Use. Leadership and Policy in Schools, 1(27). https:/doi:10.1080/15700763.2017.1326144.
Cech, T.G., Spaulding, T.J. , & Cazier, J.A. (2018). Data competence maturity: developing data-driven decision making. Journal of Research in Innovative Teaching & Learning, 11(2), 139-158. https://doi.org/10.1108/JRIT-03-2018-0007.
Chen, Y. (2020). Data-driven decision-making literacy among rural community college leaders in Iowa: The role of leadership competencies. Community College Journal of Research and Practice, 44(1), 1-15. https://doi.org/10.1080/10668926.2019.1592032.
Coburn, C. & Turner, E. (2011). Research on Data Use: A Framework and Analysis. Measurement, 9, 173-206. https://doi.org/10.1080/15366367.2011.626729.
Darling-Hammond, L. (2007). Race, inequality and educational accountability: The irony of ‘No Child Left Behind’. Race Ethnicity and Education, 10(3), 245-260. https://doi.org/10.1080/13613320701503207.
Datnow, A., Greene, J.C. & Gannon-Slater, N. (2017). Data use for equity: implications for teaching, leadership, and policy. Journal of Educational Administration, 55(4), 354-360. https://doi.org/10.1108/JEA-04-2017-0040.
Gelderblom, G., Schildkamp, K., Pieters, J. M., & Ehren, M. C. M. (2016). Data-based decision making for instructional improvement in primary education. International Journal of Educational Research, 80, 1-14. https://doi.org/10.1016/j.ijer.2016.07.004.
Hanapiyah, Z. M., Wan Hanafi, W. N., & Daud, S. (2018). Issues, challenges and opportunities of big data management in higher education institutions in Malaysia. Indian Journal of Science and Technology, 11(4), 1-6. https://doi.org/10.17485/ijst/2018/v11i4/121088.
Ho, Y. F., & Wang, H. L. (2008). Applying fuzzy Delphi method to select the variables of a sustainable urban system dynamics model. Paper presented at the 26th International Conference of System Dynamics Society, University of Patras, Greece.
Hsu, C. C., & Sandford, B. A. (2007). The Delphi technique: Making sense of consensus. Practical Assessment, Research & Evaluation, 12(10), 1-8.
Institut Aminuddin Baki (IAB). (2020). Standard Kompetensi Pemimpin Sekolah Malaysia 2.0 (Kompas 2.0). Bandar Enstek: Institut Aminuddin Baki.
Kaufmann, A., & Gupta, M. M. (1988). Fuzzy mathematical models in engineering and management science. Elsevier Science Publishers.
Killion, J.P. & Bellamy, G.T. (2000). On the job: Data analysts focus school improvement efforts. Journal of Staff Development, 21, 27-31.
Li, J., Long, Y., Wang, T., Shen, D., & Zhang, Z. (2020). A data-driven method for competency evaluation of personnel. ACM International Conference Proceeding Series, 1, 1-8. https://doi.org/10.1145/3414274.3414489.
MacBeath, J. (2013). Education and schooling: Myth, heresy and misconception (1st ed.). Routledge. https://doi.org/10.4324/9780203774601.
Mandinach, E. B. & Schildkamp, K. (2020). Misconceptions about data-based decision making in education: An exploration of the literature. Studies in Educational Evaluation, 69, 100842. https://doi.org/ 10.1016/j.stueduc.2020.100842.
Mandinach, E. B., Honey, M., Light, D. & Brunner, C. (2008). A conceptual framework for data-driven decision making. In E. B. Mandinach, & M. Honey (Eds.), Data-driven school improvement: Linking data and learning, 13–31. New York: Teachers College Press.
Marsh, J. A., Pane, J. F. & Hamilton, L. S. (2006). Making sense of data-driven decision making in education: Evidence from recent RAND research. RAND Education.
Mohd Ridhuan Mohd Jamil. (2016). Pembangunan model kurikulum latihan Skives bagi program pengajian kejuruteraan pembelajaran berasaskan kerja. [Unpublished doctoral dissertation]. Universiti Malaya.
Mokhtar, S., Alshboul, J. A. Q., & Shahin, G. O. A. (2019). Towards data-driven education with learning analytics for Educator 4.0. In Journal of Physics: Conference Series, 1339 (1), 012079. https://doi.org/10.1088/1742-6596/1339/1/012079.
Murray, T. J., Pipino, L. L., & van Gigch, J. P. (1985). A pilot study of fuzzy set modification of Delphi. Human Systems Management, 5(1), 76-80.
Powell, C. (2003). The Delphi technique: myths and realities. Journal of Advanced Nursing, 41(4), 376-382.
Richey, R. & Klein, J.D. (2014). Design and Development Research. Handbook of Research on Educational Communications and Technology (Fourth Edition). 141-150. New York: Springer.
Saedah Siraj, Muhammad Ridhuan Tony Lim Abdullah & Rozaini Muhamad Rozkee. (2021). Pendekatan Kajian Rekabentuk dan Pembangunan: Aplikasi kepada kajian pendidikan. Tanjung Malim: Penerbit Univerditi Pendidikan Sultan Idris.
Schildkamp, K., & Kuiper, W. (2010). Data-informed curriculum reform: Which data, what purposes, and promoting and hindering factors. Teaching and Teacher Education, 26(3), 482–496. https://doi.org/10.1016/j.tate.2009.06.001.
Schildkamp, K. (2007). The utilisation of a self-evaluation instrument for primary education [Unpublished doctoral dissertation]. University of Twente.
Schildkamp, K., Poortman, C. L., & Handelzalts, A. (2019). Data-based decision making for school improvement: Research insights and gaps. Educational Research, 61(3), 257–273. https://doi.org/10.1080/00131881.2019.1625716.
Tai, M. K., & Omar, A. K. (2019). School leaders’ competencies that make a difference in the era of education 4.0: A conceptual framework. International Journal of Academic Research in Business and Social Sciences, 9(4), 452–467. https://doi.org/10.6007/IJARBSS/v9-i4/5836.
Taylor, R. S. (1991). Information use environments. In B. Dervin & M. J. Voigt (Eds.), Progress in communication science, 10, 217–254.
Wayman, J. C., & Stringfield, S. (2006). Technology-supported involvement of entire faculties in the examination of student data for instructional improvement. American Journal of Education, 112(4), 549–571. https://doi.org/10.1086/505059.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Faiezah Shamsuddin, Ahmad Zabidi Abdul Razak
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
