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Abbas, Mohsin; van Rosmalen, Peter; Kalz, Marco – IEEE Transactions on Learning Technologies, 2023
For predicting and improving the quality of essays, text analytic metrics (surface, syntactic, morphological, and semantic features) can be used to provide formative feedback to the students in higher education. In this study, the goal was to identify a sufficient number of features that exhibit a fair proxy of the scores given by the human raters…
Descriptors: Feedback (Response), Automation, Essays, Scoring
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Uto, Masaki; Aomi, Itsuki; Tsutsumi, Emiko; Ueno, Maomi – IEEE Transactions on Learning Technologies, 2023
In automated essay scoring (AES), essays are automatically graded without human raters. Many AES models based on various manually designed features or various architectures of deep neural networks (DNNs) have been proposed over the past few decades. Each AES model has unique advantages and characteristics. Therefore, rather than using a single-AES…
Descriptors: Prediction, Scores, Computer Assisted Testing, Scoring
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Liu, Ming; Li, Yi; Xu, Weiwei; Liu, Li – IEEE Transactions on Learning Technologies, 2017
Writing an essay is a very important skill for students to master, but a difficult task for them to overcome. It is particularly true for English as Second Language (ESL) students in China. It would be very useful if students could receive timely and effective feedback about their writing. Automatic essay feedback generation is a challenging task,…
Descriptors: Foreign Countries, College Students, Second Language Learning, English (Second Language)