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Using Machine Learning to Model How Students Learn to Program  

Prof.  Mehran Sahami

Associate Professor of Computer Science,               
Stanford University, Stanford, CA, USA

 
Abstract .Gaining insight into how students learn to program is a critical factor in improving software engineering education. Despite the potential wealth of educational indicators expressed in students' approaches to completing programming assignments, how students arrive at their final solution is largely overlooked in courses--only their final program submission is evaluated as an indicator of their understanding of how to solve a particular programming problem. In this talk, we present a methodology which uses machine learning techniques to autonomously create a graphical model of how students in an introductory programming course progress through a programming assignment. We subsequently show that this model is predictive of which students will struggle with material presented later in the class. Our eventual goal is to be able better understand students' learning and the conceptual difficulties they may encounter as novice programmers so as to be able to provide better and more personalized guidance to them during their learning process, and ultimately improve education in software engineering.
 
Keywords : Machine Learning; Modelling; Learn; software engineering education
 URL: http://dx.doi.org/10.7321/jscse.v3.n3.3  
 
 

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