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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Vydáno v:The Journal of the learning sciences Ročník 23; číslo 4; s. 561 - 599
Hlavní autoři: Blikstein, Paulo, Worsley, Marcelo, Piech, Chris, Sahami, Mehran, Cooper, Steven, Koller, Daphne
Médium: Journal Article
Jazyk:angličtina
Vydáno: Philadelphia Routledge 02.10.2014
Taylor & Francis Group
Taylor & Francis Ltd
Témata:
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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Shrnutí: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.
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content type line 14
ISSN:1050-8406
1532-7809
DOI:10.1080/10508406.2014.954750