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Zhang, Mo; Guo, Hongwen; Liu, Xiang – International Educational Data Mining Society, 2021
We present an empirical study on the use of keystroke analytics to capture and understand how writers manage their time and make inferences on how they allocate their cognitive resources during essay writing. The results suggest three distinct longitudinal patterns of writing process that describe how writers approach an essay task in a writing…
Descriptors: Keyboarding (Data Entry), Learning Analytics, Data Collection, Cognitive Processes
Sinharay, Sandip; Zhang, Mo; Deane, Paul – Applied Measurement in Education, 2019
Analysis of keystroke logging data is of increasing interest, as evident from a substantial amount of recent research on the topic. Some of the research on keystroke logging data has focused on the prediction of essay scores from keystroke logging features, but linear regression is the only prediction method that has been used in this research.…
Descriptors: Scores, Prediction, Writing Processes, Data Analysis
Choi, Ikkyu; Hao, Jiangang; Deane, Paul; Zhang, Mo – ETS Research Report Series, 2021
"Biometrics" are physical or behavioral human characteristics that can be used to identify a person. It is widely known that keystroke or typing dynamics for short, fixed texts (e.g., passwords) could serve as a behavioral biometric. In this study, we investigate whether keystroke data from essay responses can lead to a reliable…
Descriptors: Accuracy, High Stakes Tests, Writing Tests, Benchmarking
Chen, Jing; Zhang, Mo; Bejar, Isaac I. – ETS Research Report Series, 2017
Automated essay scoring (AES) generally computes essay scores as a function of macrofeatures derived from a set of microfeatures extracted from the text using natural language processing (NLP). In the "e-rater"® automated scoring engine, developed at "Educational Testing Service" (ETS) for the automated scoring of essays, each…
Descriptors: Computer Assisted Testing, Scoring, Automation, Essay Tests
Zhang, Mo; Williamson, David M.; Breyer, F. Jay; Trapani, Catherine – International Journal of Testing, 2012
This article describes two separate, related studies that provide insight into the effectiveness of "e-rater" score calibration methods based on different distributional targets. In the first study, we developed and evaluated a new type of "e-rater" scoring model that was cost-effective and applicable under conditions of absent human rating and…
Descriptors: Automation, Scoring, Models, Essay Tests
Rios, Joseph A.; Sparks, Jesse R.; Zhang, Mo; Liu, Ou Lydia – ETS Research Report Series, 2017
Proficiency with written communication (WC) is critical for success in college and careers. As a result, institutions face a growing challenge to accurately evaluate their students' writing skills to obtain data that can support demands of accreditation, accountability, or curricular improvement. Many current standardized measures, however, lack…
Descriptors: Test Construction, Test Validity, Writing Tests, College Outcomes Assessment
Zhang, Mo – ETS Research Report Series, 2013
Many testing programs use automated scoring to grade essays. One issue in automated essay scoring that has not been examined adequately is population invariance and its causes. The primary purpose of this study was to investigate the impact of sampling in model calibration on population invariance of automated scores. This study analyzed scores…
Descriptors: Automation, Scoring, Essay Tests, Sampling
Zhang, Mo; Deane, Paul – ETS Research Report Series, 2015
In educational measurement contexts, essays have been evaluated and formative feedback has been given based on the end product. In this study, we used a large sample collected from middle school students in the United States to investigate the factor structure of the writing process features gathered from keystroke logs and the association of that…
Descriptors: Middle School Students, Student Writing Models, Writing Processes, Factor Structure