A4 Artikkeli konferenssijulkaisussa
Estimating Programming Exercise Difficulty using Performance Factors Analysis (2020)

Tirronen, V., & Tirronen, M. (2020). Estimating Programming Exercise Difficulty using Performance Factors Analysis. In FIE 2020 : Proceedings of the 50th IEEE Frontiers in Education Conference. IEEE. Conference proceedings : Frontiers in Education Conference. https://doi.org/10.1109/FIE44824.2020.9274142

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Tirronen, Ville; Tirronen, Maria

Emojulkaisu: FIE 2020 : Proceedings of the 50th IEEE Frontiers in Education Conference

Konferenssi:

Konferenssin paikka ja aika: Uppsala, Sweden, 21.-24.10.2020

ISBN: 978-1-7281-8962-8

eISBN: 9781728189611

Lehti tai sarja: Conference proceedings : Frontiers in Education Conference

ISSN: 1539-4565

eISSN: 2377-634X

Julkaisuvuosi: 2020

Kustantaja: IEEE

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

DOI: https://doi.org/10.1109/FIE44824.2020.9274142

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus: 

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/74032

Tiivistelmä

This Work in Progress Paper studies student and exercise modelling based on pass/fail log data gathered from an introductory programming course. Contemporary education capitalizes on the communications technology and remote study. This can create distance between the teacher and students and the resulting lack of awareness of the difficulties students encounter can lead to low student satisfaction, dropout and poor grades. In many cases, various technological solutions are used to collect individual exercise submissions, but there are little resources for indexing or modelling the exercises in depth. Exercise specific feedback from students may not be easily obtainable either. In the present study, we attempt to create student-exercises models solely on pass/fail log data by using statistical techniques. We conclude that such data is insufficient for student modelling, but that it can be used to credibly estimate the difficulty of programming exercises.

YSO-asiasanat: tietotekniikka; opiskelu; opiskelijat; mallintaminen; arviointi; ohjaus (neuvonta ja opastus); tyytyväisyys; opintomenestys

Vapaat asiasanat: exercise modelling; intelligent tutor; learning factors analysis; performance factors analysis

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2020

JUFO-taso: 1