Interpretable Clustering of Students’ Solutions in Introductory
Programming

D 2021

Interpretable Clustering of Students’ Solutions in Introductory Programming

EFFENBERGER, Tomáš and Radek PELÁNEK

Basic information

Original name

Interpretable Clustering of Students’ Solutions in Introductory Programming

Authors

EFFENBERGER, Tomáš (203 Czech Republic, guarantor, belonging to the institution) and Radek PELÁNEK (203 Czech Republic, belonging to the institution)

Edition

Cham, Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science, vol 12748, p. 101-112, 12 pp. 2021

Publisher

Springer

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Switzerland

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

URL

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/21:00121982

Organization unit

Faculty of Informatics

ISBN

978-3-030-78291-7

ISSN

DOI

http://dx.doi.org/10.1007/978-3-030-78292-4_9

UT WoS

000885021300009

Keywords in English

interpretable clustering; pattern mining; introductory programming; problem solving

Abstract

V originále

In introductory programming and other problem-solving activities, students can create many variants of a solution. For teachers, content developers, or applications in student modeling, it is useful to find structure in the set of all submitted solutions. We propose a generic, modular algorithm for the construction of interpretable clustering of students’ solutions in problem-solving activities. We describe a specific realization of the algorithm for introductory Python programming and report results of the evaluation on a diverse set of problems.

Links

MUNI/A/1549/2020, interní kód MU

Name: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity 21 (Acronym: SKOMU)

Investor: Masaryk University

MUNI/A/1573/2020, interní kód MU

Name: Aplikovaný výzkum: vyhledávání, analýza a vizualizace rozsáhlých dat, zpracování přirozeného jazyka, umělá inteligence pro analýzu biomedicínských obrazů.

Investor: Masaryk University

Citovat

EFFENBERGER, Tomáš and Radek PELÁNEK. Interpretable Clustering of Students’ Solutions in Introductory Programming. Online. In Roll I., McNamara D., Sosnovsky S., Luckin R., Dimitrova V. Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science, vol 12748. Cham: Springer, 2021, p. 101-112. ISBN 978-3-030-78291-7. Available from: https://dx.doi.org/10.1007/978-3-030-78292-4_9.

@inproceedings{1783837,
   author = {Effenberger, Tomáš and Pelánek, Radek},
   address = {Cham},
   booktitle = {Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science, vol 12748},
   doi = {http://dx.doi.org/10.1007/978-3-030-78292-4_9},
   editor = {Roll I., McNamara D., Sosnovsky S., Luckin R., Dimitrova V.},
   keywords = {interpretable clustering; pattern mining; introductory programming; problem solving},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Cham},
   isbn = {978-3-030-78291-7},
   pages = {101-112},
   publisher = {Springer},
   title = {Interpretable Clustering of Students’ Solutions in Introductory Programming},
   url = {https://doi.org/10.1007/978-3-030-78292-4_9},
   year = {2021}
}

TY  - JOUR
ID  - 1783837
AU  - Effenberger, Tomáš - Pelánek, Radek
PY  - 2021
TI  - Interpretable Clustering of Students’ Solutions in Introductory Programming
PB  - Springer
CY  - Cham
SN  - 9783030782917
KW  - interpretable clustering
KW  - pattern mining
KW  - introductory programming
KW  - problem solving
UR  - https://doi.org/10.1007/978-3-030-78292-4_9
N2  - In introductory programming and other problem-solving activities, students can create many variants of a solution. For teachers, content developers, or applications in student modeling, it is useful to find structure in the set of all submitted solutions. We propose a generic, modular algorithm for the construction of interpretable clustering of students’ solutions in problem-solving activities. We describe a specific realization of the algorithm for introductory Python programming and report results of the evaluation on a diverse set of problems.
ER  -

EFFENBERGER, Tomáš and Radek PELÁNEK. Interpretable Clustering of Students’ Solutions in Introductory Programming. Online. In Roll I., McNamara D., Sosnovsky S., Luckin R., Dimitrova V. \textit{Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science, vol 12748}. Cham: Springer, 2021, p.~101-112. ISBN~978-3-030-78291-7. Available from: https://dx.doi.org/10.1007/978-3-030-78292-4\_{}9.

Detailed Information on Publication Record