Engineering Education is a developing field, with new research and ideas constantly emerging and contributing to the ever-evolving nature of this discipline. Textual data (such as publications, open-ended questions on student assignments, and interview transcripts) form an important means of dialogue between the various stakeholders of the engineering community. Analysis of textual data demands consumption of a lot of time and resources. As a result, researchers end up spending a lot of time and effort in analyzing such text repositories. While there is a lot to be gained through in-depth research analysis of text data, some educators or administrators could benefit from an automated system which could reveal trends and present broader overviews for given datasets in more time and resource efficient ways. Analyzing datasets using Natural Language Processing is one solution to this problem.
The purpose of my doctoral research was two-pronged: first, to describe the current state of use of Natural Language Processing as it applies to the broader field of Education, and second, to demonstrate the use of Natural Language Processing techniques for two Engineering Education specific contexts of instruction and research respectively. Specifically, my research includes three manuscripts: (1) systematic review of existing publications on the use of Natural Language Processing in education research, (2) automated classification system for open-ended student responses to gauge metacognition levels in engineering classrooms, and (3) using insights from Natural Language Processing techniques to facilitate exploratory analysis of a large interview dataset led by a novice researcher.
A common theme across the three tasks was to explore the use of Natural Language Processing techniques to enable the computer to extract meaningful information from textual data for Engineering Education related contexts. Results from my first manuscript suggested that researchers in the broader fields of Education used Natural Language Processing for a wide range of tasks, primarily serving to automate instruction in terms of creating content for examinations, automated grading or intelligent tutoring purposes. In manuscripts two and three I implemented some of the Natural Language Processing techniques such as Part-of-Speech tagging and tf-idf (text frequency-inverse document frequency) that were found (through my systematic review) to be used by researchers, to (a) develop an automated classification system for student responses to gauge their metacognitive levels and (b) conduct an exploratory novice led analysis of excerpts from interviews of students on career preparedness, respectively. Overall results of my research studies indicate that although the use of Natural Language Processing techniques in Engineering Education is not widespread, although such research endeavors could facilitate research and practice in our field. Particularly, this type of approach to textual data could be of use to practitioners in large engineering classrooms who are unable to devote large amounts of time to data analysis but would benefit from algorithmic systems that could quickly present a summary based on information processed from available text data. / Ph. D. / Textual data (such as publications, open-ended questions on student assignments, and interview transcripts) form an important means of dialogue between the various stakeholders of the engineering community. However, analyzing these datasets can be time consuming as well as resource-intensive. Natural Language Processing techniques exploit the machine’s ability to process and handle data in time-efficient ways. In my doctoral research I demonstrate how Natural Language Processing techniques can be used in the classrooms and in education research. Specifically, I began my research by systematically reviewing current studies describing the use of Natural Language Processing for education related contexts. I then used this understanding to inform use of Natural Language Processing techniques to two Engineering Education specific contexts: one in the classroom to automatically classify students’ responses to open-ended questions to understand the metacognitive levels, and the second context of informing analysis of a large dataset comprising excerpts from interview transcripts of engineering students describing career preparedness.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/82202 |
| Date | 19 February 2018 |
| Creators | Bhaduri, Sreyoshi |
| Contributors | Engineering Education, Matusovich, Holly M., McNair, Elizabeth D., Knight, David B., Scales, Glenda R. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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