Abstract
Reducing instructors workload in online and large-scale learning environments could be one of the most important factors in educational systems. To address this challenge, techniques such as Artificial Intelligence has been considered in tutoring systems and automatic essay scoring tasks. In this paper, we construct a novel model to enable learning distributed representations of assessments namely Assessment2Vec and mark assessments automatically with Supervised Contrastive Learning loss which will effectively reduce instructors’ workload in marking large number of assessments. The experimental results based on the real-world datasets show the effectiveness of the proposed approach.
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We Acknowledge the AI-enabled Processes (AIP) Research Centre and ITIC Pty Ltd for funding this research.
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Wang, S. et al. (2021). Assessment2Vec: Learning Distributed Representations of Assessments to Reduce Marking Workload. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham. https://doi.org/10.1007/978-3-030-78270-2_68
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DOI: https://doi.org/10.1007/978-3-030-78270-2_68
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