Machine learning (ML) is an emergent computerised technology that relies on algorithms
built by 'learning'from training data rather than 'instruction', which holds great potential to …

Educational data mining is used to discover significant phenomena and resolve educational
issues occurring in the context of teaching and learning. This study provides a systematic …

Abstract In response to Li, Reigh, He, and Miller's commentary, Can we and should we use
artificial intelligence for formative assessment in science, we argue that artificial intelligence …

Application of new automated scoring technologies, such as natural language processing
and machine learning, makes it possible to provide automated feedback on students' short …

This study develops a framework to conceptualize the use and evolution of machine
learning (ML) in science assessment. We systematically reviewed 47 studies that applied …

Introduction Science educators use writing assignments to assess competencies and
facilitate learning processes such as conceptual understanding or reflective thinking. Writing …

In the push to achieve exascale performance, systems will grow to over 100,000 sockets, as
growing cores-per-socket and improved single-core performance provide only part of the …

Constructed response items can both measure the coherence of student ideas and serve as
reflective experiences to strengthen instruction. We report on new automated scoring …

To improve assessments of academic achievement, test developers have been urged to use
an “assessment triangle” that starts with research‐based models of cognition and learning …

Computer-based analysis of preservice teachers' written reflections could enable
educational scholars to design personalized and scalable intervention measures to support …