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Table 1 Studies conducted using a process-oriented approach in the context of novice programming (Continued)

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

StudyWhat has been donePLToolsGran.
Altadmri and Brown (2015)Used the Blackbox dataset to analyze the frequency, time-to-fix, and spread of errors among usersJavaBlueJC
Ahadi et al. (2015)Detected high- and low-performing students after the very first week of the programming course to provide better support for them using EQ, WS, and standard machine learning techniquesJavaTest My CodeKS
Carter et al. (2015)Proposed and derived NPSM to predict students programming performance, and compared its explanatory power against EQ and WSC++OSBIDEAll
Petersen et al. (2015)Evaluated the performance of EQ in multiple contexts (different datasets, different languages, working practices, and student backgrounds)C, Python, JavaCloudCoder, PCRS, Test My CodeS
Koprinska et al. (2015)Attempted to predict accurately failing and passing students in the middle of the semester using a decision tree classifierPASTA (auto. marking and feedback sys.)S
Annamaa et al. (2015)Aimed to automatically analyze students’ solving process in programming exercises using visualizationPythonThonnyAll
Leinonen et al. (2016)Correlated keystroke latency with exam performance and programming experienceJavaTest My CodeKS
Becker (2016)Introduced RED to quantify errors, and compared it with EQJavaCustom Java editorC
  1. C compilations, S submissions, FS file saves, FM file modificiations, KS keystrokes, ME mouse events, E executions, LLE line-level edits