Supporting the Semi-automatic Feedback Provisioning on Programming Assignments

Strickroth, Sven; Holzinger, Florian

doi:10.1007/978-3-031-20617-7_3

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2023

Publication Types

Select...

Article1

Book1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, Bernius et al (2022) introduced a framework based on clustering text segments in free-form textual exercises to reduce the grading workload. Strickroth and Holzinger (2023) attempt to solve this problem specifically for programming exercises by clustering submissions based on failed tests cases and compiler error messages.…”

Section: Introductionmentioning

confidence: 99%

Pass/Fail Prediction in Programming Courses

Petegem

Deconinck

Mourisse

et al. 2022

Journal of Educational Computing Research

View full text Add to dashboard Cite

We present a privacy-friendly early-detection framework to identify students at risk of failing in introductory programming courses at university. The framework was validated for two different courses with annual editions taken by higher education students ( N = 2 080) and was found to be highly accurate and robust against variation in course structures, teaching and learning styles, programming exercises and classification algorithms. By using interpretable machine learning techniques, the framework also provides insight into what aspects of practising programming skills promote or inhibit learning or have no or minor effect on the learning process. Findings showed that the framework was capable of predicting students’ future success already early on in the semester.

show abstract

Section: Introductionmentioning

confidence: 99%