An exploration of learning tool log data in CS1: how to better understand student behaviour and learning
Date
2017-02-02
Authors
Estey, Anthony
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Abstract
The overall goal of this work is to support student success in computer science. First, I introduce BitFit, an ungraded practice programming tool built to provide students with a pressure-free environment to practice and build confidence working through weekly course material. BitFit was used in an introductory programming course (CSC 110) at the University of Victoria for 5 semesters in 2015 and 2016.
The contributions of this work are a number of studies done analyzing the log data collected by BitFit over those years. First, I explore whether patterns can be identified in log data to differentiate successful from unsuccessful students, with a specific focus on identifying students at-risk of failure within the first few weeks of the semester. Next, I separate out only those students who struggle early in the semester, and examine their changes in programming behaviour over time. The goal behind the second study is to differentiate between transient and sustained struggling, in an attempt better understand the reasons successful students are able to overcome early struggles. Finally, I combine survey data with log data to explore whether students understand whether their study habits are likely to lead to success.
Overall, this work provides insight into the factors contributing to behavioural change in an introductory programming course. I hope this information can aid educators in providing supportive intervention aimed at guiding struggling students towards more productive learning strategies.
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Keywords
CS1, programming practice tool, student study behaviour, educational data mining, predictors of success, student experience, automated classification techiques