Detection of Non-Literal Errors Through the Integration of Manually and Automatically Generated Detection Rules

Thesis Abstract:
A non-literal error is defined as an error message given by the compiler that doesn’t correspond to the actual error present in the code. In these cases, debugging code can be a difficult even for seasoned programmers. Continuing recent work on most frequently committed non-literal errors, we plan to improve on the current detection system by integrating manually and automatically generated detection rules into the already existing error detection system. We believe this will enhance the capability of the detector by improving the methods for which it scans code for errors.
Members:
Joshua Bautista
Jaime Anson

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Institutional Collaborators

Penn Center for Learning Analytics
Advanced Learning Theories, Technologies, Applications, and Implementations
What-If Hypothetical Implementations Using Minecraft, University of Illinois Urbana Champaign

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Individual Collaborators

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ALLS Work Featured on Nature.com

An article produced in part by the ALLS was discussed in the Nature.com blog. The article’s statement.

“Surprisingly, in one recent experiment he found that an “intelligent tutor” – basically computer learning program with no bells or whistles – engaged math students more than a math game. But those same students registered double the “delight” when they played the game.”

Refers to results published in
Rodrigo, M.M.T., Baker, R.S.J.d. (2011) Comparing Learners’ Affect While Using an Intelligent Tutor and an Educational Game. Research and Practice in Technology Enhanced Learning, 6 (1), 43-66. [PDF]

 

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About

The Ateneo Laboratory for the Learning Sciences is a Department of Science and Technology-funded undertaking engaged in investigating learning. The goal of the lab is to create tools for educational data mining.  We are interested in quantitative analysis of student interactions with computer-based learning environments to derive new insights about how students learn best.

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