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Table 5 Comparison of the five metrics

From: A review on process-oriented approaches for analyzing novice solutions to programming problems

EQMeasures how many syntax errors a student encounters during a single programming session and how successive compilation failures in a session compare in terms of error message, location, and edit location- Proved to correlate inversely with grades, hence, can be used to predict student performance - Can be an indicator of how well or poorly a student was progressing- Dependent on parameter values - Has not explored the amount of time which a student takes to resolve an error - Assumes that students only work on a single source file, or work on multiple files in a linear manner, which is considered flawed when creating a set of consecutive compilation pairings - Vary across groups, environments, and contexts
WSUses time as a predictor to predict performance based on a how a student responds to different types of error compared to their peers- Addressed the shortcoming of EQ by constructing pairings on a per-filename basis - Can be used to predict student performance even early in the course - Outperforms EQ as a predictive measure- No measures are taken to check superficial changes made to source code can be incorrectly flagged as semantic changes
PDSMeasures probabilistic distance between an observed student solution and a correct solution using a Markov model- Can be used to determine if an edit or student path is (a) typical of students who have mastered content, and (b) productive in progressing toward a solution - Can be adapted to focus on a model state transitions that indicate misconceptions or other model-based goals of the data miner- A model of required algorithmic components must be identified first - Student evaluation is constrained based on the model used
NPSMCharacterizes students’ programming activity in terms of the dynamically changing syntactic and semantic correctness of programs- Can be used to predict student performance by considering the programming process more holistically - A substantially better predictor than EQ and WS- Provides only a rough proxy for determining semantic correctness (e.g., presence or absence of runtime exceptions in the last execution attempt)
REDQuantifies repeated errors by looking at the amount of repeated error strings a student encounters, and the length of these strings- Less context dependent - Useful for short sequences - Can be significantly reduced by an editor that has previously been shown to result in significantly fewer compiler errors- Its bounded nature brings some questions when a large number of data points are involved - Prone to outliers - Has not been validated if it correlates with student performance