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Research and Practice in Technology Enhanced Learning

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

Study

What has been done

PL

Tools

Gran.

Altadmri and Brown (2015)

Used the Blackbox dataset to analyze the frequency, time-to-fix, and spread of errors among users

Java

BlueJ

C

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 techniques

Java

Test My Code

KS

Carter et al. (2015)

Proposed and derived NPSM to predict students programming performance, and compared its explanatory power against EQ and WS

C++

OSBIDE

All

Petersen et al. (2015)

Evaluated the performance of EQ in multiple contexts (different datasets, different languages, working practices, and student backgrounds)

C, Python, Java

CloudCoder, PCRS, Test My Code

S

Koprinska et al. (2015)

Attempted to predict accurately failing and passing students in the middle of the semester using a decision tree classifier

–

PASTA (auto. marking and feedback sys.)

S

Annamaa et al. (2015)

Aimed to automatically analyze students’ solving process in programming exercises using visualization

Python

Thonny

All

Leinonen et al. (2016)

Correlated keystroke latency with exam performance and programming experience

Java

Test My Code

KS

Becker (2016)

Introduced RED to quantify errors, and compared it with EQ

Java

Custom Java editor

C

  1. C compilations, S submissions, FS file saves, FM file modificiations, KS keystrokes, ME mouse events, E executions, LLE line-level edits
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