An automatic system providing constructive feedback for early programming courses

A system for constructive feedback for beginner programmers was developed which, based on an evaluation of various feedback criteria, analyses code submitted by learners and provides them with unevaluated feedback with suggestions for improvement for these submissions. Several different components were developed for this purpose, which provide feedback in different areas. For example, the quality of tests was analysed, as well as syntactic and semantic correctness. Various existing tools were used for this purpose, which were subsequently adapted to the use case and provided with predefined configurations. These configurations generally offer good default values with which very good feedback can already be created. In many cases, however, it is possible to deviate from this predefined configuration and customise it for specific tasks. Furthermore, a server and a plugin for IntelliJ were developed, with which learners can easily access the functionality from their development environment. The system was tested and analysed in several different scenarios. The use of the system has statistically significantly increased the quality awareness of learners.

Publication Details

Journal
data_UMR
Published
2025-04-23
DOI
https://doi.org/10.17192/z2025.0103
Primary Topic
Gun Ownership and Violence Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An automatic system providing constructive feedback for early programming courses

data_UMR
Gun Ownership and Violence Research
article

An automatic system providing constructive feedback for early programming courses

article en

Abstract

A system for constructive feedback for beginner programmers was developed which, based on an evaluation of various feedback criteria, analyses code submitted by learners and provides them with unevaluated feedback with suggestions for improvement for these submissions. Several different components were developed for this purpose, which provide feedback in different areas. For example, the quality of tests was analysed, as well as syntactic and semantic correctness. Various existing tools were used for this purpose, which were subsequently adapted to the use case and provided with predefined configurations. These configurations generally offer good default values with which very good feedback can already be created. In many cases, however, it is possible to deviate from this predefined configuration and customise it for specific tasks. Furthermore, a server and a plugin for IntelliJ were developed, with which learners can easily access the functionality from their development environment. The system was tested and analysed in several different scenarios. The use of the system has statistically significantly increased the quality awareness of learners.

data_UMR
Quality Education
Openalex Percentile: Top 45%
Gun Ownership and Violence Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

An automatic system providing constructive feedback for early programming courses · data_UMR (2025) | TGRS Research Map | TGRS