ViDA: A virtual debugging advisor for supporting learning in computer programming courses

Abstract

Many students need assistance in debugging to achieve progress when they learn to write computer programs. Face-to-face interactions with individual students to give feedback on their programs, although definitely effective in facilitating their learning, are becoming difficult to achieve with ever-growing class sizes. This paper proposes a novel approach to providing practical automated debugging advice to support students' learning, based on the strong relationship observed between common wrong outputs and the corresponding common bugs in students' programs. To implement the approach, we designed a generic system architecture and process, and developed a tool called Virtual Debugging Advisor (ViDA) that was put into use in classes in a university. To evaluate the effectiveness of ViDA, a controlled experiment and a survey were conducted with first year engineering students in an introductory computer programming course. Results are encouraging, showing that (a) a higher proportion of students could correct their faulty code themselves with ViDA enabled, (b) an overwhelming majority of respondents found ViDA helpful for their learning of programming, and (c) most respondents would like to keep ViDA enabled when they practice writing programs.

AI and robotics in reshaping the dynamics of learning

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Automated Assessment of Computer Programming Practices: The 8-Years UNED Experience

“…According to [13], [14], the first AA system for computer programming assignments was developed by Hollingsworth [15] in 1960. This system evaluated assembly code written in punch cards.…”

A Survey of Automated Programming Hint Generation -- The HINTS Framework

“…Teacher feedback can be narrowed down to just the feedback relevant to the student's features. These can include output [2,3], edits that correct the program [1] or strategy [42] (See Sec. 5.3).…”

“…Specifically, automated hints can help students progress in their learning by providing instant and relevant feedback to correct their mistakes and help them advance through exercises. In recent years, numerous techniques for producing programming hints have been developed, including approaches aimed at scaling up instructor feedback [1,2,3], extracting patterns from peer data [4,5,6], identifying particular dynamic or static issues with student programs [7], automatically generating personalised paths to solutions [8,9,10,11], or combinations of these [12,13].…”

“…While this diversity of approaches offers a great range of potential options for improving feedback, it also presents a difficult challenge -namely, it is difficult for instructors and researchers to decide which techniques are most effective for different situations. To address this issue, many studies have employed a range of different evaluation methods, including user studies and surveys [14,9,6,1,2], comparisons with experts [12,10], analysis using historical data [8,5,7,13,9,4,15,11,16,3] or other technical evaluations [15]. Researchers have also conducted comparison studies on small subsets of techniques [17,12].…”