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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
Powell, Sarah R.; Hebert, Michael A. – Elementary School Journal, 2016
Mathematics standards expect students to communicate about mathematics using oral and written methods, and some high-stakes assessments ask students to answer mathematics questions by writing. Assumptions about mathematics communication via writing include (a) students possess writing skill, (b) students can transfer this writing skill to…
Descriptors: Mathematics Instruction, Mathematics Skills, Transfer of Training, Correlation
Wilson, Joshua; Olinghouse, Natalie G.; Andrada, Gilbert N. – Learning Disabilities: A Contemporary Journal, 2014
The current study examines data from students in grades 4-8 who participated in a statewide computer-based benchmark writing assessment that featured automated essay scoring and automated feedback. We examined whether the use of automated feedback was associated with gains in writing quality across revisions to an essay, and with transfer effects…
Descriptors: Writing Skills, Writing Ability, Feedback (Response), Computer Mediated Communication

Wright, Raymond E.; Rosenberg, Sheldon – Journal of Educational Psychology, 1993
A study with 28 fourth graders, 30 eighth graders, and 30 college students demonstrates that the relationship between knowledge of the requirements of a globally coherent essay and the ability to produce such an essay generally remains statistically significant when the effect of grade level is removed. (SLD)
Descriptors: Age Differences, Coherence, College Students, Comparative Analysis