Applying machine learning in science assessment: a systematic review
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 …
built by 'learning'from training data rather than 'instruction', which holds great potential to …
[HTML][HTML] A systematic review of artificial intelligence techniques for collaborative learning over the past two decades
This systematic review focuses on publications related to studies of the use of artificial
intelligence (AI) for collaborative learning. The use of AI for collaborative learning is a recent …
intelligence (AI) for collaborative learning. The use of AI for collaborative learning is a recent …
AI and formative assessment: The train has left the station
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 …
artificial intelligence for formative assessment in science, we argue that artificial intelligence …
Applying machine learning to automatically assess scientific models
Involving students in scientific modeling practice is one of the most effective approaches to
achieving the next generation science education learning goals. Given the complexity and …
achieving the next generation science education learning goals. Given the complexity and …
[图书][B] Handbook of research on science education
NG Lederman, SK Abell - 2014 - api.taylorfrancis.com
Volume III of this landmark synthesis of research ofers a comprehensive, state-of-the-art
survey highlighting new and emerging research perspectives in science education. Building …
survey highlighting new and emerging research perspectives in science education. Building …
Matching exemplar as next sentence prediction (mensp): Zero-shot prompt learning for automatic scoring in science education
Developing natural language processing (NLP) models to automatically score students'
written responses to science problems is critical for science education. However, collecting …
written responses to science problems is critical for science education. However, collecting …
Comparison of machine learning performance using analytic and holistic coding approaches across constructed response assessments aligned to a science learning …
LN Jescovitch, EE Scott, JA Cerchiara, J Merrill… - Journal of Science …, 2021 - Springer
We systematically compared two coding approaches to generate training datasets for
machine learning (ML):(i) a holistic approach based on learning progression levels and (ii) a …
machine learning (ML):(i) a holistic approach based on learning progression levels and (ii) a …
On the validity of machine learning-based Next Generation Science Assessments: A validity inferential network
This study provides a solid validity inferential network to guide the development,
interpretation, and use of machine learning-based next-generation science assessments …
interpretation, and use of machine learning-based next-generation science assessments …
Machine learning-enabled automated feedback: Supporting students' revision of scientific arguments based on data drawn from simulation
A design study was conducted to test a machine learning (ML)-enabled automated feedback
system developed to support students' revision of scientific arguments using data from …
system developed to support students' revision of scientific arguments using data from …
[HTML][HTML] Enhancing teacher AI literacy and integration through different types of cases in teacher professional development
Integrating artificial intelligence (AI) into teaching practices is increasingly vital for preparing
students for a technology-centric future. This study examined the influence of a case-based …
students for a technology-centric future. This study examined the influence of a case-based …