An Empirical Study of Developing Automated Scoring Engine Using Supervised Latent Dirichlet Allocation

Autor:	Allan S. Cohen, Juyeon Lee, Jordan M. Wheeler, Jiawei Xiong, Hye-Jeong Choi
Rok vydání:	2021
Předmět:	Topic model Language arts Educational measurement Topic structure Computer science business.industry computer.software_genre Latent Dirichlet allocation Test (assessment) symbols.namesake Empirical research Scoring Engine symbols Artificial intelligence business computer Natural language processing
Zdroj:	Springer Proceedings in Mathematics & Statistics ISBN: 9783030747718
DOI:	10.1007/978-3-030-74772-5_38
Popis:	The use of constructed-response and performance-oriented items is becoming increasingly more common in educational measurement. These items may be in the form of written essays or short answers and may appear in both high- and low-stakes assessments. Constructed responses may be scored by human raters or through an automated scoring engine. Topic modeling provides a tool for mining textual data in an effort to detect the latent semantic structures. The supervised Latent Dirichlet Allocation model (sLDA) is widely used in text analysis. In this study, we examine and compare the utility of different sLDA models for detecting the latent topic structure and scoring on a test of English and language arts.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::16733ec2aba350d429bed774ee142870 https://doi.org/10.1007/978-3-030-74772-5_38 Zobrazit plný text záznamu