Machine learning (ML) has been increasingly employed in science assessment to facilitate automatic scoring efforts, although with varying degrees of success (i.e., magnitudes of machine-human score agreements [MHAs]). Little work has empirically examined the factors that impact MHA disparities in this growing field, thus constraining the…

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 revolutionise science assessment. This study systematically reviewed 49 articles regarding ML-based science assessment through a triangle framework with technical,…

Presents a general framework for human factors considerations in training and illustrates the relationship between human factors and computer-based training (CBT) research by descriptions of studies dealing with six major aspects of the CBT interface: training administration, management, instructional design and development, testing and…