Science epistemology, or beliefs about what it means to do science and how science knowledge is generated, is an integral part of authentic science inquiry. Although the development of a sophisticated science epistemology is critical for attaining science literacy, epistemology remains an elusive construct to precisely and quantitatively evaluate. Previous work has suggested that analysis of student practices in science inquiry, such as their use of language, may be reflective of their underlying epistemologies. Here we describe the usage of a learning analytics tool, TAALES, and keyness analysis to analyze the concluding statements made by students at the end of a computer-based authentic science inquiry experience. Preliminary results indicate that linguistic analysis reveals differences in domain-general lexical sophistication and in domain-specific verb usage that are consistent with the expertise level of the participant. For example, experts tend to use more hedging language such as "may" and "support" during conclusions whereas novices use stronger language such as "cause." Using these differences, a simple, rulebased prediction algorithm with LOOCV achieved prediction accuracies of greater than 80%. These data underscore the potential for the use of learning analytics in simulated authentic inquiry to provide a novel and valuable method of assessing inquiry practices and related epistemologies.
|Title of host publication||LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference|
|Subtitle of host publication||Understanding, Informing and Improving Learning with Data|
|Publisher||Association for Computing Machinery|
|Number of pages||5|
|Publication status||Published - 2017 Mar 13|
|Event||7th International Conference on Learning Analytics and Knowledge, LAK 2017 - Vancouver, Canada|
Duration: 2017 Mar 13 → 2017 Mar 17
|Name||ACM International Conference Proceeding Series|
|Conference||7th International Conference on Learning Analytics and Knowledge, LAK 2017|
|Period||17/3/13 → 17/3/17|
Bibliographical noteFunding Information:
This project was supported through a data consortium fellowship (NSF 112-1549112). We thank Mike Tissenbaum and Matthew Berland for choosing our project to receive a fellowship. We thank Don Davis and the Sona team for support and use of the Counseling and Psychological Services Research Participant System. We thank Merrin Oliver for her help with data collection.
© 2017 ACM.
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Computer Networks and Communications