TY - GEN
T1 - Encouraging Grading
T2 - International Conference on Computer Supported Education, CSEDU
AU - Niemelä, Pia
AU - Hukkanen, Jenni
AU - Nurminen, Mikko
AU - Huhtamäki, Jukka
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - The surge in computer science student enrollment in Data Structures and Algorithm course necessitates flexible teaching strategies, accommodating both struggling and proficient learners. This study examines the shift from manual grading to auto-graded and peer-reviewed assessments, investigating student preferences and their impact on growth and improvement. Utilizing data from Plussa LMS and GitLab, auto-graders allow iterative submissions and quick feedback. Initially met with skepticism, peer-review gained acceptance, offering valuable exercises for reviewers and alternative solutions for reviewees. Auto-grading became the favored approach due to its swift feedback, facilitating iterative improvement. Furthermore, students expressed a preference for a substantial number of submissions, with the most frequently suggested count being 50 submissions. Manual grading, while supported due to its personal feedback, was considered impractical given the course scale. Auto-graders like unit-tests, integration tests, and perftests were well-received, with perftests and visualizations aligning with efficient code learning goals. In conclusion, used methods, such as auto-grading and peer-review, cater to diverse proficiency levels. These approaches encourage ongoing refinement, deepening engagement with challenging subjects, and fostering a growth mindset.
AB - The surge in computer science student enrollment in Data Structures and Algorithm course necessitates flexible teaching strategies, accommodating both struggling and proficient learners. This study examines the shift from manual grading to auto-graded and peer-reviewed assessments, investigating student preferences and their impact on growth and improvement. Utilizing data from Plussa LMS and GitLab, auto-graders allow iterative submissions and quick feedback. Initially met with skepticism, peer-review gained acceptance, offering valuable exercises for reviewers and alternative solutions for reviewees. Auto-grading became the favored approach due to its swift feedback, facilitating iterative improvement. Furthermore, students expressed a preference for a substantial number of submissions, with the most frequently suggested count being 50 submissions. Manual grading, while supported due to its personal feedback, was considered impractical given the course scale. Auto-graders like unit-tests, integration tests, and perftests were well-received, with perftests and visualizations aligning with efficient code learning goals. In conclusion, used methods, such as auto-grading and peer-review, cater to diverse proficiency levels. These approaches encourage ongoing refinement, deepening engagement with challenging subjects, and fostering a growth mindset.
KW - Assessment and feedback
KW - Automatic grading
KW - Flipped learning
KW - Growth mindset
KW - Leaderboards
KW - Learning analytics
KW - Learning management system
KW - Manual grading
KW - Next-generation learning environment
KW - Peer-reviews
KW - The theory of formative assessment
U2 - 10.1007/978-3-031-53656-4_2
DO - 10.1007/978-3-031-53656-4_2
M3 - Conference contribution
AN - SCOPUS:85186651157
SN - 978-3-031-53655-7
T3 - Communications in Computer and Information Science
SP - 23
EP - 46
BT - Computer Supported Education
A2 - McLaren, Bruce M.
A2 - Uhomoibhi, James
A2 - Jovanovic, Jelena
A2 - Chounta, Irene-Angelica
PB - Springer
Y2 - 21 April 2023 through 23 April 2023
ER -