Student success prediction using student exam behaviour

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F21%3A00350678" target="_blank" >RIV/68407700:21730/21:00350678 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.future.2021.07.009" target="_blank" >https://doi.org/10.1016/j.future.2021.07.009</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.future.2021.07.009" target="_blank" >10.1016/j.future.2021.07.009</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Student success prediction using student exam behaviour

Popis výsledku v původním jazyce

The Faculty of Mechanical Engineering, Czech Technical University in Prague (FME) faces a significant student drop-out in the first-year bachelor programme, which is an actual problem for many higher education institutions. Metacognitive processes play a vital role in self-regulated learning. Students become active participants in their learning, and one critical aspect of higher education studies is planning and time management. The exam taking behaviour is in the context of the FME manifestation of the time management skills of each student; thus, the exam-taking patterns may help identify at-risk students. To evaluate the importance of exam behaviour patterns, we conducted three experiments. Identification of students passing or failing the first study year has been conducted using four different machine learning models. The exam taking behaviour patterns increase the prediction F-measure significantly for the class of failing students (approximately 0.3 increase). Moreover, the approach based on student behaviour enabled us to identify the critical exam-taking patterns, which further helps the lecturers identify at-risk students and improve their time management skills and chances to pass the first academic year.

Název v anglickém jazyce

Student success prediction using student exam behaviour

Popis výsledku anglicky

The Faculty of Mechanical Engineering, Czech Technical University in Prague (FME) faces a significant student drop-out in the first-year bachelor programme, which is an actual problem for many higher education institutions. Metacognitive processes play a vital role in self-regulated learning. Students become active participants in their learning, and one critical aspect of higher education studies is planning and time management. The exam taking behaviour is in the context of the FME manifestation of the time management skills of each student; thus, the exam-taking patterns may help identify at-risk students. To evaluate the importance of exam behaviour patterns, we conducted three experiments. Identification of students passing or failing the first study year has been conducted using four different machine learning models. The exam taking behaviour patterns increase the prediction F-measure significantly for the class of failing students (approximately 0.3 increase). Moreover, the approach based on student behaviour enabled us to identify the critical exam-taking patterns, which further helps the lecturers identify at-risk students and improve their time management skills and chances to pass the first academic year.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GJ18-04150Y" target="_blank" >GJ18-04150Y: Prediktivní modelování studentova výkonu s využitím výukových zdrojů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Future Generation Computer Systems

ISSN

0167-739X

e-ISSN

1872-7115

Svazek periodika

125

Číslo periodika v rámci svazku

December

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

11

Strana od-do

661-671

Kód UT WoS článku

000687964600015

EID výsledku v databázi Scopus

2-s2.0-85111000952