Clustering of programming exercises for efficient reviewing and exploration

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http://urn.fi/URN:NBN:fi:hulib-202102021431
Title: Clustering of programming exercises for efficient reviewing and exploration
Alternative title: Ohjelmointitehtävien klusterointi tarkistuksen ja tutkimisen tehostamiseksi
Author: Koivisto, Teemu
Other contributor: Helsingin yliopisto, Matemaattis-luonnontieteellinen tiedekunta
University of Helsinki, Faculty of Science
Helsingfors universitet, Matematisk-naturvetenskapliga fakulteten
Publisher: Helsingin yliopisto
Date: 2021
Language: eng
URI: http://urn.fi/URN:NBN:fi:hulib-202102021431
http://hdl.handle.net/10138/325709
Thesis level: master's thesis
Degree program: Datatieteen maisteriohjelma
Master's Programme in Data Science
Magisterprogrammet i data science
Specialisation: ei opintosuuntaa
no specialization
ingen studieinriktning
Discipline: none
Abstract: Programming courses often receive large quantities of program code submissions to exercises which, due to their large number, are graded and students provided feedback automatically. Teachers might never review these submissions therefore losing a valuable source of insight into student programming patterns. This thesis researches how these submissions could be reviewed efficiently using a software system, and a prototype, CodeClusters, was developed as an additional contribution of this thesis. CodeClusters' design goals are to allow the exploration of the submissions and specifically finding higher-level patterns that could be used to provide feedback to students. Its main features are full-text search and n-grams similarity detection model that can be used to cluster the submissions. Design science research is applied to evaluate CodeClusters' design and to guide the next iteration of the artifact and qualitative analysis, namely thematic synthesis, to evaluate the problem context as well as the ideas of using software for reviewing and providing clustered feedback. The used study method was interviews conducted with teachers who had experience teaching programming courses. Teachers were intrigued by the ability to review submitted student code and to provide more tailored feedback to students. The system, while still a prototype, is considered worthwhile to experiment on programming courses. A tool for analyzing and exploring submissions seems important to enable teachers to better understand how students have solved the exercises. Providing additional feedback can be beneficial to students, yet the feedback should be valuable and the students incentivized to read it.
Subject: computer science education
MOOCs
clustering
similarity detection
ASTs
CodeClusters


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