Assessment for Learning

For several decades now, assessment has become an increasingly pressing educational priority. Teacher and school accountability systems have come to be based on analysis of large-scale, standardized summative assessments. As a consequence, assessment now dominates most conversations about reform, particularly as a measure of teacher and school accountability for learner performance. Behind the often heated and at times ideologically gridlocked debate is a genuine challenge to address gaps in achievement between different demographically identifiable groups of students. There is an urgent need to lift whole communities and cohorts of students out of cycles of underachievement. For better or for worse, testing and public reporting of achievement is seen to be one of the few tools capable of clearly informing public policy makers and communities alike about how their resources are being used to expand the life opportunities for their children. This course is an overview of current debates about testing, and analyses the strengths and weaknesses of a variety of approaches to assessment. The course also focuses on the use of assessment technologies in learning. It will explore recent advances in computer adaptive and diagnostic testing, the use of natural language processing technologies in assessments, and embedded formative assessments in digital and online curricula. Other topics include the use of data mining, learning analytics and artificial intelligence in learning management systems and educational technology platforms. Participants will be required to consider issues of data access, privacy and the challenges raised by ‘big data’ including data persistency and student profiling. -------------------------------- Recommended Background -------------------------------- This course is designed for people interested in the future of education and the "learning society," including people who may wish to join education as a profession, practicing teachers interested