Identifying patterns in students’ scientific argumentation: content analysis through text mining using Latent Dirichlet Allocation

• Published in: Educational technology research and development 2020-10, Vol.68 (5), p.2185-2214
• Main Authors: Xing, Wanli, Lee, Hee-Sun, Shibani, Antonette
• Format: Article
• Language: English
• Subjects: Analysis; Computational linguistics; Content Analysis; Data Analysis; Data mining; Data Use; Dirichlet problem; Education; Educational Technology; Information Retrieval; Knowledge; Language processing; Learning and Instruction; Logical Thinking; Natural language interfaces; Persuasive Discourse; Probability; Research Article; Science Instruction; Scientific Concepts; Self Evaluation (Individuals); Semantics; Students; Teaching Methods
• ISSN: 1042-1629; 1556-6501
• DOI: 10.1007/s11423-020-09761-w

Constructing scientific arguments is an important practice for students because it helps them to make sense of data using scientific knowledge and within the conceptual and experimental boundaries of an investigation. In this study, we used a text mining method called Latent Dirichlet Allocation (LDA) to identify underlying patterns in students written scientific arguments about a complex scientific phenomenon called Albedo Effect. We further examined how identified patterns compare to existing frameworks related to explaining evidence to support claims and attributing sources of uncertainty. LDA was applied to electronically stored arguments written by 2472 students and concerning how decreases in sea ice affect global temperatures. The results indicated that each content topic identified in the explanations by the LDA— “data only,” “reasoning only,” “data and reasoning combined,” “wrong reasoning types,” and “restatement of the claim”—could be interpreted using the claim–evidence–reasoning framework. Similarly, each topic identified in the students’ uncertainty attributions— “self-evaluations,” “personal sources related to knowledge and experience,” and “scientific sources related to reasoning and data”—could be interpreted using the taxonomy of uncertainty attribution. These results indicate that LDA can serve as a tool for content analysis that can discover semantic patterns in students’ scientific argumentation in particular science domains and facilitate teachers’ providing help to students.