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L. Hannah; E. E. Jang; M. Shah; V. Gupta – Language Assessment Quarterly, 2023
Machines have a long-demonstrated ability to find statistical relationships between qualities of texts and surface-level linguistic indicators of writing. More recently, unlocked by artificial intelligence, the potential of using machines to identify content-related writing trait criteria has been uncovered. This development is significant,…
Descriptors: Validity, Automation, Scoring, Writing Assignments
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Wilson, Joshua; Roscoe, Rod D. – Journal of Educational Computing Research, 2020
The present study extended research on the effectiveness of automated writing evaluation (AWE) systems. Sixth graders were randomly assigned by classroom to an AWE condition that used "Project Essay Grade Writing" (n = 56) or a word-processing condition that used Google Docs (n = 58). Effectiveness was evaluated using multiple metrics:…
Descriptors: Automation, Writing Evaluation, Feedback (Response), Instructional Effectiveness
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Zhang, H.; Magooda, A.; Litman, D.; Correnti, R.; Wang, E.; Matsumura, L. C.; Howe, E.; Quintana, R. – Grantee Submission, 2019
Writing a good essay typically involves students revising an initial paper draft after receiving feedback. We present eRevise, a web-based writing and revising environment that uses natural language processing features generated for rubric-based essay scoring to trigger formative feedback messages regarding students' use of evidence in…
Descriptors: Formative Evaluation, Essays, Writing (Composition), Revision (Written Composition)
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Jacovina, Matthew E.; McNamara, Danielle S. – Grantee Submission, 2017
In this chapter, we describe several intelligent tutoring systems (ITSs) designed to support student literacy through reading comprehension and writing instruction and practice. Although adaptive instruction can be a powerful tool in the literacy domain, developing these technologies poses significant challenges. For example, evaluating the…
Descriptors: Intelligent Tutoring Systems, Literacy Education, Educational Technology, Technology Uses in Education
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection