We are thrilled to host our third data science educators workshop, this year at Penn State Center in Pittsburgh!
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 based in order to improve student success.
One of the primary outcomes of the Third Data Science Educators’ Workshop will be access and exposure to the repository of educational resources made available through our NSF project. Additionally, a summary of innovative approaches to the use of AI/ML technologies to facilitate student feedback and success will be provided.
- Collection of scenarios envisioning how and where AI/ML technologies may be applied in Big Data instruction to improve student feedback cycles, monitor student progress, and enhance evaluation and assessment techniques.
- Examination of affordances and limitations of described technology solutions.
Sunday, June 9, 2019
Location: Hampton Inn & Suites Pittsburgh Downtown
1247 Smallman St.
Pittsburgh, PA 15222
6:00pm Workshop Welcome and Opening Reception
7:15pm Welcoming Remarks
7:30pm Keynote: Preparing Students for Tomorrow’s Work
Norman Bier, Director of the Open Learning Initiative and Executive Director of the Simon Initiative at Carnegie Mellon University
8:15pm Workshop Program Review
Monday, June 10
Location: Penn State Center Pittsburgh, PA
10:00am Program Review
10:10am Project Overview and Status
This session will provide a detailed overview of the outcomes of Workshops 1 & 2 as well as introduce repository structure, updates, and access.
11:30am Dreaming Big: The Pitfalls and Potential of AI and ML in Big Data Analytics Education
Over the past several years, I have implemented and taught 5 different data science courses from introductory to senior capstone. I will discuss the challenges in teaching and assessing performance in these courses, and opportunities for AI/ML to help support both students learning and faculty assessment.
1:30pm Big Data Harvest Activity–Student Feedback
2:00pm Unpacking the Student Feedback Cycle
Team projects analyzing, framing, and defining the operating parameters of ideas/suggestions collected in the Big Data Collection Activity
3:15pm Diderot: Bringing together Knowledge and People
3:45pm XLGrader™: Free Form Assignment Creation and Grading in Excel
Grading should be part of a virtuous cycle that helps students learn and improve their performance. The cycle works best when students do their own work, are allowed to be creative in their solutions, and receive timely formative feedback. Ensuring these goals puts an enormous burden on the professor, particularly in large enrollment courses. Automated grading is an obvious solution but tends to work against these goals. Automated grading tends to standardize assignments and thereby facilitate cheating. Automated grading tends to favor a single path to a solution and thereby discourages creativity. And automated grading tends to provide summative rather than formative feedback. The authors have built a system which restores the virtuous cycle while automating grading.
5:15pm Workshop Adjourn
5:30pm Dinner will be provided
Tuesday, June 11
9:00am Online Learning Platform for Student Learning Research
Majd F. Sakr, Teaching Professor in the Computer Science Department within the School of Computer Science at Carnegie Mellon University
We will present our method and interoperable online learning ecosystem designed to enable effective and efficient learning through iterative and interactive learning coupled with contextualized and timely feedback, and by leveraging social interactions between students as a substantial learning resource. Our platform enables research studies on student learning and evaluates innovative approaches for incorporating social and interactive learning as a driver for developing cognitive and meta-cognitive skills and motivation.
10:15am Team Report Out
10:15am Approaches to Keeping the Big Data and Analytics Current
Table teams will explore a variety of strategies and approaches to employing AI/ML to update, manage and continually inform the curriculum and necessary competencies of students in the use of AI/ML.
11:15am Let’s Talk: Student Input Panel
Googledoc Workshop Participant Site
Discussion of STEM education experiences, expectations and potential with students from multiple institutions.
Aljabi, Youssef, Double Major in Applied Data Science and Management Information Systems, Chatham University
Balaji, Jay, Computer Science
McIntyre, Nicholas, Penn State Altoona, Security and Risk Analysis, PSU
Myzyri , Ilva, Senior, Computer Science, PSU
Rodriguez, Madeline, Sophomore, Computer Science, PSU
12:00pm Next Steps and Directions
(Box lunch provided)
A review of the culmination of the Big Data project and discussion of the potential next steps with NSF support.
1:00pm Feedback Discussion and Workshop Closing
Registration & Venue
To register for the workshop and complete a brief profile indicating your interest in attending the Workshop, please visit the Third Data Science Educators’ Workshop Form. Travel reimbursement (up to $500) and all hotel and meal expenses for the Workshop are provided. Space is limited to 35 attendees and registrations will be accepted on a rolling basis.
The Third Data Science Educators’ Workshop will be held at the:
Penn State Center Pittsburgh
1435 Bedford Avenue, Suite A
Energy Innovation Center
Pittsburgh, PA 15219
Lodging for Workshop guests will be at the
Hampton Inn & Suites Pittsburgh Downtown
1247 Smallman St.
Pittsburgh, PA 15222
Reservation Line: (412-288-4350)
The room block is now set up for the nights of June 9th and 10th. Workshop guests (once approved) may now call the hotel directly to make reservations. Please provide dates of your stay and mention that you are with the Pennsylvania State University, and a part of the Third Data Science Educators’ Workshop.Next Page: Previous Page: