After almost 2 years of work, the NSF project “Big Data Education” held its First Data Science Educators Workshop at Penn State Altoona. The main aim of the workshop was to present progress made so far on the project to its target users – data science educators. The First Data Science Educators Workshop was very participatory, with educators working in small teams and providing feedback and ideas on the learning modules throughout the two days of the workshop.
The workshop focused on 3 main topics:
- Presenting to educators the goals and objectives of the NSF project;
- Showing the educators how to effectively use the “digital storytelling” learning module developed by the project (Module 1);
- Giving the educators insight into the upcoming learning modules to be released in the 2017/2018 academic year, and a chance for feedback on the same.
The project’s PI (Principal Investigator), Professor Jungwoo Ryoo (Penn State Altoona) gave the project overview, and introduced the education materials created over the past 2 years by his team of RAs (Research Assitants) – including Undergraduate RAs Whitney Hernandez, and Bill Aiken, managed by Graduate RA Eun-Kyeong Kim. They developed the Big Data E-Textbook [link], including the “digital storytelling” learning module [link].
The project’s Co-PI, Professor Soo-yong Byun (Penn State University Park) presented early results from the survey of how the project’s education materials did in the classroom. Results showed that the project’s education materials contribute to the students’ understanding of big data and data science concepts in a positive and statistically significant way.
A number of educators shared their early first-hand experience teaching data science. Instructor Larry Garvin (Penn State Altoona) showed how he integrated the E-Textbook and “digital storytelling” module into his own courses. Professor Jeongkyu Lee (University of Bridgeport) talked about teaching cloud services to talented high school students at Young Data Science (YDS) Summer Camp. The project’s Undergraduate RA Tyler Bohrer (Penn State Altoona) presented Weka software as a useful tool in learning about and teaching machine learning concepts. Instructor Kaitlin Farnan (Penn State Altoona) presented RapidMiner software as a great tool for introductory teaching about many data science concepts, including data mining, machine learning, etc. In fact, RapidMiner was found intuitive enough by the workshop participants that the project’s research team will incorporate RapidMiner into its upcoming learning tutorials for the 2017/2018 academic year.
Finally, all educators that came to the conference were put in an active role – to help asses the current education materials developed by the project, and provide feedback on what kind of new education materials they would like to see come out of the project. On day 1 of the workshop, all educators were put together into small teams, and given a chance to use the “digital storytelling” module to develop their own data science lesson. On day 2, all teams presented their developed lessons, showed some very interesting applications and case studies, and provided great feedback for future education materials.