AI-Based Concept Inventories: Using Cognitive Diagnostic Computer Adaptive Testing in LASSO for Classroom Assessment

Jason W. Morphew, Amirreza Mehrabi, Ben Van Dusen, Jayson Nissen, Hua Hua Chang

Research output: Contribution to journalConference articlepeer-review

Abstract

Computerized Adaptive Testing (CAT) is a modern approach to educational technology that can transform classroom assessment and self-assessment strategies. CAT selects questions based on ability, item difficulty, and item discrimination at the moment which significantly reduces testing time. So, by considering measurement error, CAT ensures assessment accuracy, revealing a student's true ability level. The design of CAT within the Learning About STEM Student Outcomes (LASSO) platform adheres to a comprehensive spectrum of skills and attributes outlined by educators nationwide. CAT within the LASSO adeptly tailors question selection for each class, furnishing students with specialized reports grounded in distinct content. LASSO serves as a centralized platform enabling classes nationwide to access a diverse array of assessment contents and questions aligning with established educational standards, promoting frequent assessment. The amalgamation of CAT with cognitive diagnosis models within the LASSO platform empowers educators to gauge student mastery levels and confidently navigate the subsequent stages of the teaching process. Therefore, teachers can assess the effectiveness of their teaching methodologies, a vital aspect of their self-assessment.

Original languageEnglish (US)
JournalASEE Annual Conference and Exposition, Conference Proceedings
StatePublished - Jun 23 2024
Externally publishedYes
Event2024 ASEE Annual Conference and Exposition - Portland, United States
Duration: Jun 23 2024Jun 26 2024

ASJC Scopus subject areas

  • General Engineering

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