Formal Data
Instructional interventions require multiple data sources to be effective and adequate for a student’s needs. The first source of data that a teacher can use is formal data. This contains any pertinent data that has been systematically gathered about children, parents, schools, administrators, teachers, and the neighborhood where the school is located (Schildkamp, 2019).
Both qualitative and quantitative methods of investigation may be used to derive these data. This essentially involves systematically analyzing the school’s available data sources, utilizing the results to inform innovative teaching methods, curricula, and student performance, and then implementing these innovations and measuring their effectiveness (Schildkamp, 2019). Analysis and organization of this data is the easiest of all types, as formal assessment data is already standardized, requiring teachers only to discern the potential areas of growth. Assessment results can help inform classroom instruction by allowing teachers to quickly modify their lesson plans to meet the needs of certain students or to accomplish particular learning objectives (Pan et al., 2021). Hence, formal data is the first data source that can be used for instructional interventions.
Informal Data
The second teacher recourse for instructional interventions is informal school data. In the course of their daily work, teachers gather data on the requirements of their students. This could be done, for instance, by having talks with and observing students while using an assessment-for-learning methodology(Schildkamp, 2019). It essentially involves students, teachers, and peers actively seeking out, considering, and acting upon information from discussions, demonstrations, and observations in ways that support ongoing learning and development.
This information is often gathered rapidly and is sometimes referred to as expert judgment or intuitive data collection (Schildkamp, 2019). This data can be organized into student portfolios to standardize and utilize in the future. Knowing this informal information will help provide lessons to students in a more approachable and tailored manner, which will enable them to grasp the material better (Pan et al., 2021). Thus, informal data from schools can serve as a valuable source of information for targeted teaching.
Research
The third and fourth data sources are practitioner and scientific research. To improve instruction, teachers can also utilize current research. This is frequently referred to as “research-informed teaching practice,” a method by which educators acquire, evaluate, and apply the findings of academic research to enhance teaching and learning in their classrooms. The research can be divided into two categories, with the first being practitioner or action research results produced by practitioners conducting research in their own schools. The second category is scientific research findings from studies in which schools did not participate (Schildkamp, 2019).
As most studies are rather large and complicated, organizing the analysis more effectively is only possible by utilizing the “Results” sections of scientific articles. Integrating both local and general expertise, insight, and experience enables teachers to transform information into knowledge that can be applied in the improvement process and inform classroom instruction (Schildkamp, 2019). Hence, both general and practitioner research can be used to inform teachers as they design a new curriculum.
Big Data
The fifth and final data source for teachers is what is referred to as “big data”. The ‘three Vs’, or volume, variety, and velocity, are what define big data. Big data refers to vast quantities of information in various formats that are rapidly added to and updated. These data can be used to track and forecast an organization’s performance.
Big data is typically derived from the combination of other data sources mentioned above (Schildkamp, 2019). As this is the most significant data type, organizing it properly requires highlighting only the information that points out the relevant problems and the prerequisites for those problems (Schildkamp, 2019). If that is done, the problems in the curriculum can be discerned and later corrected, which can be an effective way to inform classroom instruction. Hence, the final data source for teachers for instructional interventions is big data.
References
Pan, X., Zheng, M., Xu, X., & Campbell, A. G. (2021). Knowing your student: Targeted teaching decision support through asymmetric mixed reality collaborative learning. IEEE Access, 9, 164742- 164751.
Schildkamp, K. (2019). Data-based decision-making for school improvement: Research insights and gaps. Educational Research, 61(3), 257-273.