Data Driven Instruction: Education Term Paper

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Driven Instruction

Definition and Meaning

Data-driven instruction is a type of instruction where the teacher uses student performance as the benchmark for planning their instruction. Student performance in this case is defined in terms of their result or scores in various assessments. The teacher uses these results to identify those areas that students were able to grasp effectively and those in which they are still encountering difficulty; and then structures their subsequent instruction in such a way that it focuses more on the latter (Thompson, 2008). Data-driven instruction, therefore, basically has three fundamental elements -- baseline data (where the students are currently), clear goals (where they ought to be in a pre-determined period), and regular assessments (a mode of benchmarking their performance in relation to the set goals over time) (Thompson, 2008). Research has shown data-based instruction to be an effective way of improving student performance (Thompson 2008).

What I personally learnt from the Use of data-supported instruction

I used data-supported instruction multiple times during my practicum and, well, I learnt a variety of things from my use of the same. The most important of these perhaps is how to be effective in the use of data-driven techniques. The process of data-driven instruction is a three-stage process that involves targeting, focusing stage, and finally, planning. In the targeting stage, the teacher administers an assessment on a particular content with the aim of identifying i) the strengths -- areas in which the class performed generally well; ii) the challenges -- areas where the overall performance of the class in relation to the tested content is fair; and iii) critical needs -- areas in which the class put up a poor performance, say a majority of the students failed the tested content (Thompson, 2008). Once this data has been obtained, there is need then to inject meaning into it. This takes place in the focus/analysis stage, where the teacher subjects the 'critical needs' identified earlier to intense scrutiny with the aim of identifying the specific concept(s) that students did not grasp.

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For instance, the target phase may reveal that students are weak in relation to the use of adjectives. This identifies 'the use of adjectives' as a critical need, but the teacher then needs to conduct an analysis to determine exactly where the problem lies -- are students having problems identifying adjectives, or using the same to improve their own writing?

The results of this evaluation then serve as the basis for the final (planning) stage, where the teacher acts on the information obtained from the analysis by structuring their lesson or instruction plans to address the specific need that has been identified (Thompson, 2008). How effective one is in using data-based instruction to improve student performance will depend, partly, on how effective they are in identifying critical needs, analyzing them, and structuring their programs to internalize them.

I also learnt that besides these, the success of data-driven instruction also depends on the school's organizational culture, which is manifested in the code of conduct and student aspirations. Data-driven instruction would only be effective if the school culture promotes positive learning behaviors and encourages the use of DDI for school-wide improvement (Fenton & Murphy, 2015).

Benefits of Data-Supported Instructions in my Institutional Practice

The use of data-supported instruction has contributed to the betterment of my institutional practices in a variety of ways. First, it has sharpened my back-mapping skills. Back-mapping is the process of analyzing a particular grade level expectation with the aim of determining what students at that grade level are expected to know in relation to a particular study topic (Thompson, 2008). Back-mapping is crucial to the teaching process because it is only in understanding the specific concepts that students at….....

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