The proposal is under consideration and this cadre ensures a collaboration between the various state boards in the country
The government plans to form a cadre of dedicated professionals in each state of the country. These professionals will be drawn from the existing pool of teachers who are engaged with schools and academic activities with the SCERTs. The main objective of this step is to set question papers for the boards and establish standard marking schemes to set up a bar of equivalence in assessment, which ensures inter rating relativity.
The cadre will play a pivotal role in addressing the differences in the assessments. These differences result in the disparities scores of board exams, including CBSE. It departs from the existing model of using only teachers with some experience for the purpose.
The latest body to be set up, PARAKH (Performance, Assessment, Review, and Analysis of Knowledge for the Holistic Development) is under the administrative control of NCERT. This step will eliminate the possibility of two students writing similar answers and receiving different marks. This happens as some teachers are lenient while some of them are strict in correction. With this initiative, such differences will be eliminated.
With the intention of developing standard rubric for marking, the cadre will also be trained to develop higher order thinking skill questions. This will differ from the current system of experienced teachers who ought to have the ability to create questions. PARAKH attempts to create a collaboration among the state school boards according to CBSE.
PARAKH has held several regional workshops in some parts of the country to analyse the different question papers of the boards. Three such meetings have taken place at Ajmer (northern and western region), Mysore (southern region), and Jammu and Kashmir (Eastern and North-eastern region boards).
PARAKH has been testing the differences between these question papers and the prime example is the testing the number of questions in a paper, which tests rote learning and the number of questions are application based. The basic idea behind this is to reduce the dependency on rote learning and focus more on application based learning and testing.
