Location: Salon 2
This is a roundtable in the Cyberlearning 2017 Roundtable session.
A Framework For Hypothesis-Driven Approaches To Support Data-Driven Learning Analytics In Measuring Computational Thinking In Block-Based Programming
K-12 classrooms use block-based programming environments (BBPEs) for teaching computer science and computational thinking (CT). To support assessment of student learning in BBPEs, we propose a learning analytics framework that combines hypothesis- and data-driven approaches to discern students’ programming strategies from BBPE log data. We use a principled approach to design assessment tasks to elicit evidence of specific CT skills. Piloting these tasks in high school classrooms enabled us to analyze student programs and video recordings of students as they built their programs. We discuss a priori patterns derived from this analysis to support data-driven analysis of log data in order to better assess understanding and use of CT in BBPEs.
Project: Homepage, NSF Award #1522990 – EXP: Understanding Computational Thinking Process and Practices in Open-Ended Programming Environments
Machine learning for data analysis
I will discuss the use of machine learning for data analysis.
Designing Learning Analytics to Support Targeted Models of Online Discussion
Learning analytics are inherently not neutral and promote certain kinds of learning activities and attitudes over other through the kinds of information they provide. We have worked to make these often hidden assumptions visible and tie creation of analytics for online discussions to particular valued modes of participation in various learning-through-discussion models. Two examples will be provided: analytics from the E-Listening project, designed to support richer attention to the ideas of others in negotiation-oriented models of discussion; and analytics from the MOOCeology project, designed to foster more effective interactions in question-answering models of discussion.