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Any educational institution has data that needs to be managed competently and used to improve the quality of education. It includes many aspects that affect the institution’s daily life, such as information about students, their results, and even learning plans. This problem marked the development of the idea of data-driven instruction. This essay will define the concept of data-driven instruction, its importance for the learning process, and provide specific examples of its use to improve education.
Data-driven instruction is one of the educational approaches based on assessment data to inform teaching practices. In other words, the method forms the basis for adjusting curricula and teaching methods by collecting information about students. At the first stage of the assessment, a database is created from the teacher’s observations, including grades and standardized test results (Custer et al., 2018). Further, a problem of transforming awareness into a practical solution occurs. During the action stage, the teacher begins to give the students the information they need based on the investigation results (Custer et al., 2018). Therefore, data-driven instruction contributes to understanding the educational needs of students and creates a platform for improving the quality of teaching.
The importance of data-driven instruction lies in the fact that this method allows teachers to understand the real demands of students in the educational process. Data-driven instruction becomes a diagnostic tool for teachers’ strategies to improve students’ academic abilities (Custer et al., 2018). Thus, it is easier for teachers to develop effective learning plans and remove the use of difficult methods for students to master. Analyzing student data to guide instruction have a significant and positive association with improvements in teaching and student achievement (Espin et al., 2021). With this approach, students are perceived more as individuals, each showing special patterns of success and needs.
There are many practical ways to use data-driven instruction, but sometimes they can seem time-consuming and tedious. In this case, the teacher should start small, determining ways to collect students’ information on a specific course. They can use oral surveys at the end of classes or even implement automated technical programs. The practical use of information collection can manifest itself in identifying gaps and opportunities. The collected data results will show which specific methods implemented in teaching practice have worked and which need improvement. The teacher can go the other way and identify high- and low-ability groups of those who have already mastered the material and those who need repetition (Faber et al., 2018). Based on this, concrete steps are being taken to improve the curriculum and eliminate academic gaps.
In order to differentiate instructions based on test data, I will resort to the method of regulating the training of the group as a whole. While collecting formative data on students’ academic performance, I will analyze the material that was insufficiently assimilated by students and think about changing the teaching method. Since students differ in their abilities, the modernization of teaching methods should be carried out regularly so that each time there are fewer students in the group to fall behind (Faber et al., 2018). It will also be necessary to adjust the pace of classes taking into account topics that require longer and more careful consideration. I will also pay attention to creating opportunities for students to learn from each other. Thus, they will be able to perceive information from different sources and improve the quality of their training.
References
Custer, S., King, E. M., Atinc, T. M., Read, L., & Sethi, T. (2018). Toward Data-Driven Education Systems: Insights into Using Information to Measure Results and Manage Change. Center for Universal Education at The Brookings Institution.
Espin, C. A., Förster, N., & Mol, S. E. (2021). International Perspectives on Understanding and Improving Teachers’ Data-Based Instruction and Decision Making: Introduction to the Special Series. Journal of Learning Disabilities, 54(4), 239-242.
Faber, J. M., Glas, C. A., & Visscher, A. J. (2018). Differentiated instruction in a data-based decision-making context. School Effectiveness and School Improvement, 29(1), 43-63.
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