University of Pennsylvania Master's in Learning Analytics

• In Event: Graduate Programs in Learning Analytics Workshop: Core Competencies, Curriculum, and Instruction

Abstract

A fully-online Masters in Learning Analytics will begin at the University of Pennsylvania in Fall 2023. The core goal of the program is to prepare students to become practitioners of learning analytics in industry, government, non-profits, higher education, or K-12 school systems (typically at the school district level).
However, in doing so, our goal is to produce practitioners who use learning analytics thoughtfully and appropriately. As such, it is our goal to produce practitioners who understand the technical aspects of using learning analytics methods, but it is also our goal to produce practitioners who understand the social implications of their work. It is our goal that graduates of our program will be able to conduct analyses which are technically valid and educationally appropriate, using state of the art methods. They will understand when and why to use different methods, and will be able to choose an appropriate method for the task. They will be able to use industry-standard tools for analytics. But equally importantly, they will also understand the cultural, racial, and other biases which can negatively impact students when educational algorithms are used, and how to check for and reduce these biases. They will understand the broader educational context, and the drawbacks and limitations of conducting learning analytics work that does not properly account for educational theory and principles.
Our goal is to produce students who understand the methodological context of their work as well. As such, our graduates will understand the several methodological disciplines that come together in this interdisciplinary area (i.e. data mining/machine learning, statistics, and psychometrics), understanding the differences between areas and the strengths of each.

The program will require students to complete ten courses, which include requirements and electives such as Core Methods in Educational Data Mining, Big Data, Education, and Society, Dashboards and Analytics for Reporting, Databases and Data Management, Foundations of Teaching and Learning, Feature Engineering, Design of Learning Environments, Production Analytics Environments, Intelligent Tutoring Systems and Adaptive Learning, Deep Learning and Transformer Models, Quantitative Ethnography and Epistemic Network Analysis, Learning Sciences Past Present and Future, Collaborative Learning Online, Language Technologies in Education, and Psychometrics in Interactive Learning.
The program will culminate in a capstone project, which students will work on in the Learning Analytics Masters Capstone Seminar class. Capstone projects will involve an analysis requiring learning analytics methods and the communication of that analysis in the form of a dashboard or report. Capstone projects will involve real-world data and a problem of practical importance to the field. As mentioned above, the timing and structure of the capstone projects will be aligned so that these projects can become competitive submissions to demo and short paper categories at top international conferences in the area.

Author

• Ryan S. Baker, University of Pennsylvania
  Presenting Author