Students with dyslexia & ADHD in independent and public schools

"...Data driven decision making made the scene a few years after no child left behind was introduced into public education as an accountability system. To offset and balance a schools position to making annual yearly progress a district or school would collect student benchmark achievement data through various points in the school year. So many schools adopted the practice of benchmark assessments that the benchmark assessments became rudimentary to the learning process. The benchmark assessment became known as a summative measure and was primarily used to provide interventions for students who had not mastered a particular state standard of learning. Mastery learning cut off points varied from district to district with most favoring the 80% mark as a determiner of mastery. This philosophy of test and retest for each subject consumed instructional time and was based on achievement predictability through a need for a viable curriculum. Fredric Taylor developed a similar system when organizing factory workers efficiency rate by applying stop watches to discover the best way of accomplishing work in the shortest period of time. He searched for the physical limitations of the individual and then provided interventions to offset these limitations. Taylor also advocated the aptitude test to select worker to type of work in which the worker was best suited.

 

A new type of assessment process is being explored that provides a deeper understanding of learning. This assessment process is based on an instructors ability to assess student learning as they are learning through the provision of formative assessment and mindset calculations or meta-cognition. Met-cognitive interventions play a key role within the feedback process. The feedback process defines how the teacher scaffolds needed corrective statements as learning is being monitored against attainable performance levels established within individual levels of performance. These are the calculations within learning analytics that provide the use of data and models to predict student progress and performance, and the ability to act on that information”. These are the data sets within the learning process that provides for ongoing operational interventions that keeps both the teacher and the student within a zone of proximal development.

Via Ana Cristina Pratas