Uploaded June 2015 | Updated September 2026, 4 hours ago
This talk was given by undergraduate Kevin (Kun) Wang during the 9th Annual Computer Science Undergraduate Research Symposium in 2015. Kevin‘s research was supervised by Dr. Prasun Dewan.
“Exploring Design Space of Schemes for Detecting Programming Difficulty from Interaction Logs”
This paper will utilize classifiers and surrounding toolkits to reliably mine programming activity in Eclipse to promote difficulty detection. Such program can be used effectively in various settings to promote learning and pinpoint the programming issues. We hope to achieve this by examining input vectors of user actions taken directly from Eclipse IDE. Out of the five major chosen categories of input attributes, the number of website accesses and the frequency of debugging are deemed to be the most critical to difficulty detection. The classification algorithm explored in the process are J48, Decision Stump, Adaboost.M1, and Bagging. The classification algorithms’ shortcomings and strengths are all explored with regards to the true positive, true negative, and overall prediction accuracy. J48 and tree-base algorithms excel predicting the majority class, the true negative, while Decision Stumps exceeds the capability of J48 at predicting the minority class. Finally, statistical analysis of the attribute distribution is conducted to discover any statistical anomaly.
Kevin (Kun) Wang is currently a senior undergraduate student majoring in computer science and minoring in chemistry. With a biology, chemistry, and computer science background, he is interested in statistical learning and the manner in which it can be used to solve common problems across all the aforementioned disciplines.
http://cs.unc.edu/academics/undergraduate/symposium/symposium-2015/
This talk was given by undergraduate Kevin (Kun) Wang during the 9th Annual Computer Science Undergraduate Research Symposium in 2015. Kevin‘s research was supervised by Dr. Prasun Dewan.
“Exploring Design Space of Schemes for Detecting Programming Difficulty from Interaction Logs”
This paper will utilize classifiers and surrounding toolkits to reliably mine programming activity in Eclipse to promote difficulty detection. Such program can be used effectively in various settings to promote learning and pinpoint the programming issues. We hope to achieve this by examining input vectors of user actions taken directly from Eclipse IDE. Out of the five major chosen categories of input attributes, the number of website accesses and the frequency of debugging are deemed to be the most critical to difficulty detection. The classification algorithm explored in the process are J48, Decision Stump, Adaboost.M1, and Bagging. The classification algorithms’ shortcomings and strengths are all explored with regards to the true positive, true negative, and overall prediction accuracy. J48 and tree-base algorithms excel predicting the majority class, the true negative, while Decision Stumps exceeds the capability of J48 at predicting the minority class. Finally, statistical analysis of the attribute distribution is conducted to discover any statistical anomaly.
Kevin (Kun) Wang is currently a senior undergraduate student majoring in computer science and minoring in chemistry. With a biology, chemistry, and computer science background, he is interested in statistical learning and the manner in which it can be used to solve common problems across all the aforementioned disciplines.
http://cs.unc.edu/academics/undergraduate/symposium/symposium-2015/
