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Data-driven learning

2021
Abstract This chapter investigates the effect of data-driven learning (DDL) by comparing the use of a new corpus tool, #LancsBox, with the use of a corpus-based collocations dictionary for academic collocation learning. Learners in the study improved their collocational knowledge by using #LancsBox, although not significantly.
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Towards data-driven mastery learning

Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, 2015
We have developed a novel data-driven mastery learning system to improve learning in complex procedural problem solving domains. This new system was integrated into an existing logic proof tool, and assigned as homework in a deductive logic course. Student performance and dropout were compared across three systems: The Deep Thought logic tutor, Deep ...
Behrooz Mostafavi   +2 more
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Data Driven Geometry for Learning

2015
High dimensional covariate information provides a detailed description of any individuals involved in a machine learning and classification problem. The inter-dependence patterns among these covariate vectors may be unknown to researchers. This fact is not well recognized in classic and modern machine learning literature; most model-based popular ...
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An approach to data-driven learning

1991
In the present paper a data-driven approach to learning is described. The approach is discussed in the framework of the Net-Clause Language (NCL), which is also outlined. NCL is aimed at building network models and describes distributed computational schemes. It also exhibits sound semantics as a data-driven deductive system.
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Tools for Data-Driven Learning

This entry examines the importance of corpus tools in data-driven learning (DDL) for language education, focusing on the methodological choices involved in their selection and design for pedagogical use. It emphasizes the need for tools that align with educational goals, offer user-friendly interfaces, and facilitate linguistic discovery.
Crosthwaite, Peter, Anthony, Laurence
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