In June of 2019, the NSF project “Building a Big Data Analytics Workforce in iSchools” held its Third and final Data Science Educators Workshop at the Penn State Center Pittsburgh. The workshop was co-chaired by Drs. Jungwoo Ryoo, project PI from Penn State, and Rachel Chung, Clinical Associate Professor of Operations and Information Systems at William and Mary. The Big Data project aims to develop a data science curriculum for college students, and the annual workshop is the main event that brings together participating educators from across the Penn State University System, and from other collaborating institutions.
Participants at third Data Science Educators Workshop
This year, the workshop focused on two main goals:
1) Presentation of the data science teaching materials developed during this project including:
- the introductory data science e-book and
- the hands-on tutorials in different tools applied to real-world case studies in cybersecurity and other advanced topics in big data mining and analytics.
2) Exploration of how and where Artificial Intelligence/Machine Learning (AI/ML) can continually assess student progress, provide timely feedback, and update data science course content to improve student success.
One of the primary outcomes of the Third Data Science Educators’ Workshop was providing access and exposure to the repository of educational resources made available through the NSF project. Additionally, a summary of innovative approaches to the use of AI/ML technologies to facilitate student feedback and success were shared.
Researchers and developers demonstrated a range of instructional technologies and tools aimed at improving the student learning experience and preparing students for the workplace of today and tomorrow. The program launched Sunday evening with a keynote presentation by Norman Bier, Director of the Open Learning Initiative and Executive Director of the Simon Initiative at Carnegie Mellon University. Norman’s talked challenged the workshop participants to consider the skills and competencies students will need to master a rapidly evolving and complex workplace.
With a focus on Big Data systems that can impact the learner experience, Stephanie Rosenthal of Carnegie Mellon discussed the challenges of teaching and assessing performance and opportunities for AI/ML to help support both students learning and faculty assessment. Umut A. Acar, also from Carnegie Mellon shared his work on Diderot. A system harnessing the power of Excel to manage student grading was featured by Raymond Frost and Vic Matta of Ohio University.
Participants engaged in the Big Data Harvest Activity–Student Feedback, brainstorming where and how Big Data and Data Analytics could improve the cycle of information from faculty to student to correct, inspire and guide students. Participants worked in small teams Unpacking the Student Feedback Cycle using the ideas harvested from the prior session and generating scenarios where information systems could improve the communication systems. Output from these small team discussions was gathered and is being formed into a white paper.
One unique aspect of this year’s workshop was the inclusion of five students from Chatham and Penn State. These students, a collection of current and recent graduates provided first-hand insight into the challenges and opportunities for Big Data/AI/ML to impact their college learning activities. These students also provided a post-reflection input sharing the impact of their workshop experience.
Overall, the Third Data Science Educators Workshop proved to be a resounding success, and the project team is organizing and disseminating the information collected via the workshop.