This paper will describe a project on big data analytics that was developed and introduced into Freshman Engineering Clinic, which is an introductory course for students in all engineering disciplines. Among the learning objectives for the Freshman Engineering Clinic are developing skills in data collection, analyzing data to draw sound conclusions, and writing, with visual/graphical representation of information being recognized as a critical component of effective technical writing. The NSF has awarded a grant to the authors’ University (name withheld for blind review) to support vertical integration of big data analytics throughout the engineering curriculum. This paper focuses on the Freshman Clinic project, the intent of which is to introduce students to big data analytics while also furthering the general instructional objectives of the freshman course.
The project was titled “Introduction to Big Data Analytics: Analyzing Tweets with Matlab”. The instructor provided the students with Matlab code that was designed to facilitate applying Sentiment Analysis to tweets. For example, the code can be used to (1) identify tweets that contain one or more specific keywords and (2) create a histogram of words used in these tweets, in order to identify recurring themes in tweets that mention the keyword(s). The final deliverable for the project was a report in which students detailed how they used the Matlab code to answer a number of open-ended questions, as well as an introductory section in which students discussed the importance and applications of big data analytics in general. The paper will provide a detailed description of the project and the deliverable.
For students whose sections of Freshman Clinic used the big data project, a 10-question quiz was administered at both the beginning and the end of the semester in order to assess whether students were more knowledgeable about big data analytics at the end of the project than they were prior to the start of the project. For these students there was a statistically significant improvement in the post-test results compared to the pre-test. The same quiz was also administered at the end of the course to “control” sections of Freshman Engineering Clinic; students who completed a different project unrelated to big data. The control group’s average quiz scores were lower at the end of the semester than the target group’s average scores at the beginning of the semester. This result implies that the improvement seen throughout the semester is indeed attributable to the big data project itself rather than to the Freshman Clinic experience in general. The paper will also provide the full assessment results.
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