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Sami Baral; Eamon Worden; Wen-Chiang Lim; Zhuang Luo; Christopher Santorelli; Ashish Gurung; Neil Heffernan – Grantee Submission, 2024
The effectiveness of feedback in enhancing learning outcomes is well documented within Educational Data Mining (EDM). Various prior research have explored methodologies to enhance the effectiveness of feedback to students in various ways. Recent developments in Large Language Models (LLMs) have extended their utility in enhancing automated…
Descriptors: Automation, Scoring, Computer Assisted Testing, Natural Language Processing
Peter Organisciak; Selcuk Acar; Denis Dumas; Kelly Berthiaume – Grantee Submission, 2023
Automated scoring for divergent thinking (DT) seeks to overcome a key obstacle to creativity measurement: the effort, cost, and reliability of scoring open-ended tests. For a common test of DT, the Alternate Uses Task (AUT), the primary automated approach casts the problem as a semantic distance between a prompt and the resulting idea in a text…
Descriptors: Automation, Computer Assisted Testing, Scoring, Creative Thinking
Botarleanu, Robert-Mihai; Dascalu, Mihai; Allen, Laura K.; Crossley, Scott Andrew; McNamara, Danielle S. – Grantee Submission, 2022
Automated scoring of student language is a complex task that requires systems to emulate complex and multi-faceted human evaluation criteria. Summary scoring brings an additional layer of complexity to automated scoring because it involves two texts of differing lengths that must be compared. In this study, we present our approach to automate…
Descriptors: Automation, Scoring, Documentation, Likert Scales
Botarleanu, Robert-Mihai; Dascalu, Mihai; Allen, Laura K.; Crossley, Scott Andrew; McNamara, Danielle S. – Grantee Submission, 2021
Text summarization is an effective reading comprehension strategy. However, summary evaluation is complex and must account for various factors including the summary and the reference text. This study examines a corpus of approximately 3,000 summaries based on 87 reference texts, with each summary being manually scored on a 4-point Likert scale.…
Descriptors: Computer Assisted Testing, Scoring, Natural Language Processing, Computer Software
Torres Irribarra, David; Diakow, Ronli; Freund, Rebecca; Wilson, Mark – Grantee Submission, 2015
This paper presents the Latent Class Level-PCM as a method for identifying and interpreting latent classes of respondents according to empirically estimated performance levels. The model, which combines elements from latent class models and reparameterized partial credit models for polytomous data, can simultaneously (a) identify empirical…
Descriptors: Item Response Theory, Test Items, Statistical Analysis, Models
Falk, Carl F.; Cai, Li – Grantee Submission, 2015
In this paper, we present a flexible full-information approach to modeling multiple userdefined response styles across multiple constructs of interest. The model is based on a novel parameterization of the multidimensional nominal response model that separates estimation of overall item slopes from the scoring functions (indicating the order of…
Descriptors: Response Style (Tests), Item Response Theory, Outcome Measures, Models
Hauk, Shandy; Matlen, Bryan; Thomas, Larry – Grantee Submission, 2017
A variety of computerized interactive learning platforms exist. Most include instructional supports in the form of problem sets. Feedback to users ranges from a single word like "Correct!" to offers of hints and partially- to fully-worked examples. Behind-the-scenes design of systems varies as well--from static dictionaries of problems…
Descriptors: Community Colleges, Algebra, Web Based Instruction, Randomized Controlled Trials
Martinková, Patrícia; Goldhaber, Dan; Erosheva, Elena – Grantee Submission, 2018
Ratings are present in many areas of assessment including peer review of research proposals and journal articles, teacher observations, university admissions and selection of new hires. One feature present in any rating process with multiple raters is that different raters often assign different scores to the same assessee, with the potential for…
Descriptors: Interrater Reliability, Public School Teachers, Job Applicants, Teacher Selection
Gorin, Joanna S.; O'Reilly, Tenaha; Sabatini, John; Song, Yi; Deane, Paul – Grantee Submission, 2014
Recent advances in cognitive science and psychometrics have expanded the possibilities for the next generation of literacy assessment as an integrated domain (Bennett, 2011a; Deane, Sabatini, & O'Reilly, 2011; Leighton & Gierl, 2011; Sabatini, Albro, & O'Reilly, 2012). In this paper, we discuss four key areas supporting innovations in…
Descriptors: Literacy Education, Evaluation Methods, Measurement Techniques, Student Evaluation
McNamara, Danielle S.; Crossley, Scott A.; Roscoe, Rod – Grantee Submission, 2013
The Writing Pal is an intelligent tutoring system that provides writing strategy training. A large part of its artificial intelligence resides in the natural language processing algorithms to assess essay quality and guide feedback to students. Because writing is often highly nuanced and subjective, the development of these algorithms must…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Writing Instruction, Feedback (Response)
Crossley, Scott; McNamara, Danielle – Grantee Submission, 2013
This study explores the potential for automated indices related to speech delivery, language use, and topic development to model human judgments of TOEFL speaking proficiency in second language (L2) speech samples. For this study, 244 transcribed TOEFL speech samples taken from 244 L2 learners were analyzed using automated indices taken from…
Descriptors: English (Second Language), Language Proficiency, Language Tests, Speech Communication