School Reform: New Future-Ready Quality Outcomes and Proposed Measures - David Ng; Daryl Ku (MDPI AG, 2023)
As we increasingly emphasise the importance of developing future-ready outcomes for learners, we will need to also expand new capabilities to measure such outcomes. AI, big data, and analytics are examples of such new capabilities. Ideation is one of six habits of practice we have identified that will prepare students for the future. In this paper, we present a means to computationally appraise ideation quality as one such capability. We have developed a heuristic to appraise the ideation quality of university student essays using natural language processing, a branch of artificial intelligence concerned with the understanding of human languages. Our heuristic allows for ideation quality to be quickly quantified in the form of an ideation score. So, instead of going about the process blindly, we now have a means to provide a point of reference to allow students to give measured consideration to their ideation. Unlike a learning outcome, a future-ready habit is more of a predisposition. Consequently, it is not coherent with conventional assessments, which rather seek to evaluate than to guide. This heuristic represents an outcome of our evaluation of a new problem space in education and is, at the same time, a novel expansion into a space that exploits new capabilities.