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Olney, Andrew M. – Grantee Submission, 2022
Multi-angle question answering models have recently been proposed that promise to perform related tasks like question generation. However, performance on related tasks has not been thoroughly studied. We investigate a leading model called Macaw on the task of multiple choice question generation and evaluate its performance on three angles that…
Descriptors: Test Construction, Multiple Choice Tests, Test Items, Models
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Andrew M. Olney – Grantee Submission, 2023
Multiple choice questions are traditionally expensive to produce. Recent advances in large language models (LLMs) have led to fine-tuned LLMs that generate questions competitive with human-authored questions. However, the relative capabilities of ChatGPT-family models have not yet been established for this task. We present a carefully-controlled…
Descriptors: Test Construction, Multiple Choice Tests, Test Items, Algorithms
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
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Sami Baral; Li Lucy; Ryan Knight; Alice Ng; Luca Soldaini; Neil T. Heffernan; Kyle Lo – Grantee Submission, 2024
In real-world settings, vision language models (VLMs) should robustly handle naturalistic, noisy visual content as well as domain-specific language and concepts. For example, K-12 educators using digital learning platforms may need to examine and provide feedback across many images of students' math work. To assess the potential of VLMs to support…
Descriptors: Visual Learning, Visual Perception, Natural Language Processing, Freehand Drawing
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Lang, David; Stenhaug, Ben; Kizilcec, Rene – Grantee Submission, 2019
This research evaluates the psychometric properties of short-answer response items under a variety of grading rules in the context of a mobile learning platform in Africa. This work has three main findings. First, we introduce the concept of a differential device function (DDF), a type of differential item function that stems from the device a…
Descriptors: Foreign Countries, Psychometrics, Test Items, Test Format
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Olney, Andrew M.; Pavlik, Philip I., Jr.; Maass, Jaclyn K. – Grantee Submission, 2017
This study investigated the effect of cloze item practice on reading comprehension, where cloze items were either created by humans, by machine using natural language processing techniques, or randomly. Participants from Amazon Mechanical Turk (N = 302) took a pre-test, read a text, and took part in one of five conditions, Do-Nothing, Re-Read,…
Descriptors: Reading Improvement, Reading Comprehension, Prior Learning, Cloze Procedure