Programming Pluralism: Using Learning Analytics to Detect Patterns in the Learning of Computer Programming

New high-frequency, automated data collection and analysis algorithms could offer new insights into complex learning processes, especially for tasks in which students have opportunities to generate unique open-ended artifacts such as computer programs. These approaches should be particularly useful...

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Vydané v:	The Journal of the learning sciences Ročník 23; číslo 4; s. 561 - 599
Hlavní autori:	Blikstein, Paulo, Worsley, Marcelo, Piech, Chris, Sahami, Mehran, Cooper, Steven, Koller, Daphne
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Philadelphia Routledge 02.10.2014 Taylor & Francis Group Taylor & Francis Ltd
Predmet:	Academic Achievement Achievement Rating Algorithms Artificial intelligence Assignments Automation Behavior Change Cluster Grouping Coding Cognitive Style Cohort Analysis Computation Computer programming Computer Science Education Computers Correlation Data Analysis Data Collection Educational evaluation Educational Experiments Graduate Students Introductory Courses Learning Learning Analytics Learning Processes Pattern Recognition Predictive Measurement Predictive Validity Programming Protocol Analysis Regression (Statistics) Student Behavior Student Centered Learning Technology education Undergraduate Students
ISSN:	1050-8406, 1532-7809
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Abstract	New high-frequency, automated data collection and analysis algorithms could offer new insights into complex learning processes, especially for tasks in which students have opportunities to generate unique open-ended artifacts such as computer programs. These approaches should be particularly useful because the need for scalable project-based and student-centered learning is growing considerably. In this article, we present studies focused on how students learn computer programming, based on data drawn from 154,000 code snapshots of computer programs under development by approximately 370 students enrolled in an introductory undergraduate programming course. We use methods from machine learning to discover patterns in the data and try to predict final exam grades. We begin with a set of exploratory experiments that use fully automated techniques to investigate how much students change their programming behavior throughout all assignments in the course. The results show that students' change in programming patterns is only weakly predictive of course performance. We subsequently hone in on 1 single assignment, trying to map students' learning process and trajectories and automatically identify productive and unproductive (sink) states within these trajectories. Results show that our process-based metric has better predictive power for final exams than the midterm grades. We conclude with recommendations about the use of such methods for assessment, real-time feedback, and course improvement.
AbstractList	New high-frequency, automated data collection and analysis algorithms could offer new insights into complex learning processes, especially for tasks in which students have opportunities to generate unique open-ended artifacts such as computer programs. These approaches should be particularly useful because the need for scalable project-based and student-centered learning is growing considerably. In this article, we present studies focused on how students learn computer programming, based on data drawn from 154,000 code snapshots of computer programs under development by approximately 370 students enrolled in an introductory undergraduate programming course. We use methods from machine learning to discover patterns in the data and try to predict final exam grades. We begin with a set of exploratory experiments that use fully automated techniques to investigate how much students change their programming behavior throughout all assignments in the course. The results show that students' change in programming patterns is only weakly predictive of course performance. We subsequently hone in on 1 single assignment, trying to map students' learning process and trajectories and automatically identify productive and unproductive (sink) states within these trajectories. Results show that our process-based metric has better predictive power for final exams than the midterm grades. We conclude with recommendations about the use of such methods for assessment, real-time feedback, and course improvement.
Audience	Higher Education Postsecondary Education
Author	Piech, Chris Sahami, Mehran Blikstein, Paulo Cooper, Steven Worsley, Marcelo Koller, Daphne
Author_xml	– sequence: 1 givenname: Paulo surname: Blikstein fullname: Blikstein, Paulo email: paulob@stanford.edu organization: School of Education and (by courtesy) Computer Science Department, Stanford University – sequence: 2 givenname: Marcelo surname: Worsley fullname: Worsley, Marcelo organization: School of Education, Stanford University – sequence: 3 givenname: Chris surname: Piech fullname: Piech, Chris organization: Computer Science Department, Stanford University – sequence: 4 givenname: Mehran surname: Sahami fullname: Sahami, Mehran organization: Computer Science Department, Stanford University – sequence: 5 givenname: Steven surname: Cooper fullname: Cooper, Steven organization: Computer Science Department, Stanford University – sequence: 6 givenname: Daphne surname: Koller fullname: Koller, Daphne organization: Computer Science Department, Stanford University
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SubjectTerms	Academic Achievement Achievement Rating Algorithms Artificial intelligence Assignments Automation Behavior Change Cluster Grouping Coding Cognitive Style Cohort Analysis Computation Computer programming Computer Science Education Computers Correlation Data Analysis Data Collection Educational evaluation Educational Experiments Graduate Students Introductory Courses Learning Learning Analytics Learning Processes Pattern Recognition Predictive Measurement Predictive Validity Programming Protocol Analysis Regression (Statistics) Student Behavior Student Centered Learning Technology education Undergraduate Students
Title	Programming Pluralism: Using Learning Analytics to Detect Patterns in the Learning of Computer Programming
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