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,…

Emerging applications of microcomputers and hypermedia to assessment in science education are reviewed. Although the current use of technology consists mainly of computerized administration of multiple choice tests drawn from item banks, the potential advantages are much greater. Among these advantages are immediate feedback to students, formative…