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Anson, Chris M. – Composition Studies, 2022
Student plagiarism has challenged educators for decades, with heightened paranoia following the advent of the Internet in the 1980's and ready access to easily copied text. But plagiarism will look like child's play next to new developments in AI-based natural-language processing (NLP) systems that increasingly appear to "write" as…
Descriptors: Plagiarism, Artificial Intelligence, Natural Language Processing, Writing Assignments
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Nuijten, Michèle B.; Polanin, Joshua R. – Research Synthesis Methods, 2020
We present the R package and web app "statcheck" to automatically detect statistical reporting inconsistencies in primary studies and meta-analyses. Previous research has shown a high prevalence of reported p-values that are inconsistent--meaning a re-calculated p-value, based on the reported test statistic and degrees of freedom, does…
Descriptors: Meta Analysis, Statistical Analysis, Reliability, Replication (Evaluation)
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Rodafinos, Angelos – Interdisciplinary Journal of e-Skills and Lifelong Learning, 2018
Aim/Purpose: This paper presents some of the issues that academia faces in both the detection of plagiarism and the aftermath. The focus is on the latter, how academics and educational institutions around the world can address the challenges that "follow" the identification of an incident. The scope is to identify the need for and…
Descriptors: Plagiarism, Intervention, Prevention, Higher Education
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Daniels, Paul – JALT CALL Journal, 2015
In this article, the author presents the history of human-to-computer interaction based upon the design of sophisticated computerized speech recognition algorithms. Advancements such as the arrival of cloud-based computing and software like Google's Web Speech API allows anyone with an Internet connection and Chrome browser to take advantage of…
Descriptors: Second Language Learning, Teaching Methods, Identification, Audio Equipment
Nugent, Rebecca; Ayers, Elizabeth; Dean, Nema – International Working Group on Educational Data Mining, 2009
In educational research, a fundamental goal is identifying which skills students have mastered, which skills they have not, and which skills they are in the process of mastering. As the number of examinees, items, and skills increases, the estimation of even simple cognitive diagnosis models becomes difficult. We adopt a faster, simpler approach:…
Descriptors: Data Analysis, Students, Skills, Cluster Grouping
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Finkelman, Matthew; Kim, Wonsuk; Roussos, Louis A. – Journal of Educational Measurement, 2009
Much recent psychometric literature has focused on cognitive diagnosis models (CDMs), a promising class of instruments used to measure the strengths and weaknesses of examinees. This article introduces a genetic algorithm to perform automated test assembly alongside CDMs. The algorithm is flexible in that it can be applied whether the goal is to…
Descriptors: Identification, Genetics, Test Construction, Mathematics