Advancing Formative Assessment: Using Natural Language Processing Within a Sociocultural Context to Measure Multilingual Student Science Word Knowledge

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Publicat a:Education Sciences vol. 15, no. 12 (2025), p. 1668-1687
Autor principal: Kowalkowski, Holland P
Altres autors: Palma, Jose, Herrera, Cinthia B, Baker, Doris Luft, Wu Zhongdi, Larson, Eric C
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100 1 |a Kowalkowski, Holland P  |u Meadows Center for Preventing Educational Risk, College of Education, The University of Texas at Austin, Austin, TX 78712, USA; ciherrera@utexas.edu (C.B.H.); doris.baker@austin.utexas.edu (D.L.B.) 
245 1 |a Advancing Formative Assessment: Using Natural Language Processing Within a Sociocultural Context to Measure Multilingual Student Science Word Knowledge 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a This study investigates how natural language processing (NLP) can support the assessment and learning of science vocabulary among multilingual and multicultural learners, drawing on data from two federally funded studies in the United States. Students define and use target vocabulary in a sentence, with responses transcribed and scored using NLP tools. Employing a mixed-methods design and guided by established socioecological theoretical frameworks, we examine how students’ sociocultural contexts and background knowledge influence their understanding of science word knowledge and applicability. Our findings highlight both the potential and challenges of using AI tools in equitable and culturally responsive ways, offering insights to improve NPL-based assessment tools that support literacy teaching and learning in diverse student populations. 
653 |a Teaching 
653 |a Students 
653 |a Politics 
653 |a Cognitive development 
653 |a Influence 
653 |a Teachers 
653 |a Cognition & reasoning 
653 |a System theory 
653 |a Science education 
653 |a Cultural factors 
653 |a Knowledge 
653 |a Vocabulary development 
653 |a Linguistics 
653 |a Learning 
653 |a Language 
653 |a Literacy 
653 |a Sociocultural factors 
653 |a Formative evaluation 
653 |a Automation 
653 |a Ecosystems 
653 |a Artificial intelligence 
653 |a Natural language processing 
653 |a Integrated approach 
653 |a Multilingualism 
653 |a Social factors 
653 |a Multiculturalism & pluralism 
653 |a Mixed methods research 
653 |a Socioeconomic factors 
653 |a Oral Language 
653 |a Educational Research 
653 |a Academic Achievement 
653 |a School Administration 
653 |a Language Patterns 
653 |a Interpersonal Relationship 
653 |a Educational Assessment 
653 |a Language Processing 
653 |a Learner Engagement 
653 |a Language Usage 
653 |a Influence of Technology 
653 |a Learning Strategies 
653 |a Cultural Background 
653 |a Learning Theories 
653 |a Middle Schools 
653 |a Feedback (Response) 
653 |a Data Analysis 
653 |a Learning Readiness 
653 |a Academic Standards 
700 1 |a Palma, Jose  |u Department of Teaching, Learning & Culture, College of Education & Human Development, Texas A&M University, College Station, TX 77840, USA; jpalma@tamu.edu 
700 1 |a Herrera, Cinthia B  |u Meadows Center for Preventing Educational Risk, College of Education, The University of Texas at Austin, Austin, TX 78712, USA; ciherrera@utexas.edu (C.B.H.); doris.baker@austin.utexas.edu (D.L.B.) 
700 1 |a Baker, Doris Luft  |u Meadows Center for Preventing Educational Risk, College of Education, The University of Texas at Austin, Austin, TX 78712, USA; ciherrera@utexas.edu (C.B.H.); doris.baker@austin.utexas.edu (D.L.B.) 
700 1 |a Wu Zhongdi  |u Computer Science Department, Lyle School of Engineering, Southern Methodist University, Dallas, TX 75205, USA; zhongdiw@mail.smu.edu (Z.W.); eclarson@smu.edu (E.C.L.) 
700 1 |a Larson, Eric C  |u Computer Science Department, Lyle School of Engineering, Southern Methodist University, Dallas, TX 75205, USA; zhongdiw@mail.smu.edu (Z.W.); eclarson@smu.edu (E.C.L.) 
773 0 |t Education Sciences  |g vol. 15, no. 12 (2025), p. 1668-1687 
786 0 |d ProQuest  |t Education Database 
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