Analysing Trends in Student’s Performance Across Maharashtra Through Non-adaptive and Adaptive Online Assessments Based on the Underlying Framework of Classical Test and Item Response Theory

In today’s era with the advancement of information and communication technology, and with the rapid digitization across all arenas, it has become a necessity to identify and map the right resource to a required job profile in minimum time and cost. To identify the right resource, organizations across the world have adopted several online tools, strategies and mechanisms to assess the candidates to check on their potential and suitability. Technologies have even paved ways for creating immersive modes where candidates are required to get acclimatized and play the job role and deduce the fitment for that particular role. The changes brought about by the information and communication technology have been speedy and pervasive and so has the penetration of artificial intelligence and machine learning where human and technology are highly interconnected to achieve the desired outcome. In all segments and sectors, digital assessments (in any form) play a key role in identifying the right asset, where a lot is dependent on the impregnated intelligence and machine learning to help decision-making become easy and accurate. It is said that artificial intelligence could play a role in the growing field of assessment and learning analytics and also can be applied in evaluating the quality of curriculum content so that it can be applied to create unique pathways for individual learning. This in turn will create the desired efficacy in assessing with precision candidate/candidates for the right opportunity. In this regard, the authors in their second paper propose to first identify and extract the performance trends of candidates and item characteristics using non-adaptive and adaptive assessment techniques to create a rationale for measuring their learnability and implementation of their learned skills in specific job roles.