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Download full textHaberman, Shelby J. – ETS Research Report Series, 2020
Best linear prediction (BLP) and penalized best linear prediction (PBLP) are techniques for combining sources of information to produce task scores, section scores, and composite test scores. The report examines issues to consider in operational implementation of BLP and PBLP in testing programs administered by ETS [Educational Testing Service].
Descriptors: Prediction, Scores, Tests, Testing Programs
Direct linkBrunfaut, Tineke; Kormos, Judit; Michel, Marije; Ratajczak, Michael – Language Testing, 2021
Extensive research has demonstrated the impact of working memory (WM) on first language (L1) reading comprehension across age groups (Peng et al., 2018), and on foreign language (FL) reading comprehension of adults and older adolescents (Linck et al., 2014). Comparatively little is known about the effect of WM on young FL readers' comprehension,…
Descriptors: Second Language Learning, Second Language Instruction, Reading Comprehension, Accuracy
Yao, Lili; Haberman, Shelby J.; Zhang, Mo – ETS Research Report Series, 2019
Many assessments of writing proficiency that aid in making high-stakes decisions consist of several essay tasks evaluated by a combination of human holistic scores and computer-generated scores for essay features such as the rate of grammatical errors per word. Under typical conditions, a summary writing score is provided by a linear combination…
Descriptors: Prediction, True Scores, Computer Assisted Testing, Scoring
Plakans, Lia; Gebril, Atta; Bilki, Zeynep – Language Testing, 2019
The present study investigates integrated writing assessment performances with regard to the linguistic features of complexity, accuracy, and fluency (CAF). Given the increasing presence of integrated tasks in large-scale and classroom assessments, validity evidence is needed for the claim that their scores reflect targeted language abilities.…
Descriptors: Accuracy, Language Tests, Scores, Writing Evaluation
Crossley, Scott A.; Kyle, Kristopher; Allen, Laura K.; Guo, Liang; McNamara, Danielle S. – Grantee Submission, 2014
This study investigates the potential for linguistic microfeatures related to length, complexity, cohesion, relevance, topic, and rhetorical style to predict L2 writing proficiency. Computational indices were calculated by two automated text analysis tools (Coh- Metrix and the Writing Assessment Tool) and used to predict human essay ratings in a…
Descriptors: Computational Linguistics, Essays, Scoring, Writing Evaluation
