Ashley Anderson
Pronouns: She/Her/Hers
UROP Fellowship: Intel Semiconductor Research Corporation
Research Mentor(s): Barbara Ericson, PhD
School of Information
Presentation Date: Tuesday, July 28, 2020 | Session 1 | Presenter: 4
Authors: Ashley Anderson, Barbara Ericson
Abstract
With the increasing use of electronic textbooks and online courses, there is a wealth of student interaction data that can be analyzed. Interactive ebooks include a variety of practice problems including typical practice types like multiple-choice, fill in the blank, matching, and short answer questions. Ebooks for programming also include the ability to write, modify, and execute code. They also include newer practice types like mixed-up code problems, which are also known as Parsons problems. In a Parsons problem the correct code is broken into blocks and mixed up. The user must place the blocks in the correct order. Previous research from randomized controlled studies has provided evidence that users can solve Parsons problems significantly faster than writing the equivalent code, but with the same learning gains from pre to post. In this research we are analyzing interaction data from an interactive ebook for a School of Information course SI 206: Data Oriented Programming in Python. We are comparing the time to complete and self-reported cognitive load of answering Parsons problems versus code writing problems. We are also using the interaction data in the log file to understand how students are solving these problems, identify potential user interface issues, uncover student misconceptions, and suggest improvements.
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Research Disciplines
Engineering
