Using process mining to analyze students' quiz-taking behavior patterns in a learning management system

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23420%2F19%3A43950178" target="_blank" >RIV/49777513:23420/19:43950178 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14210/19:00108773

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0747563217306957" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0747563217306957</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.chb.2017.12.015" target="_blank" >10.1016/j.chb.2017.12.015</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Using process mining to analyze students' quiz-taking behavior patterns in a learning management system

Popis výsledku v původním jazyce

The aim of this paper is to explore students’ behavior and interaction patterns in different types of online quiz-based activities within learning management systems (LMS). Analyzing students’ behavior in online learning activities and detecting specific patterns of interaction in LMS is a topic of great interest for the educational data mining (EDM) and learning analytics (LA) research communities. Previous studies have focused primarily on frequency analysis without addressing the temporal aspects of students’ learning behavior. Therefore, we apply a process-oriented approach, investigating perspectives on using process mining methods in the context of online learning and assessment. To explore a broad range of possible student behavior patterns, we analyze students’ interactions in several online quizzes from different courses and with different settings. Using process mining methods, we identify specific types of interaction sequences that shed new light on students’ quiz-taking strategies in LMS. We believe that these findings bring important implications for researchers studying student behavior in online environments as well as practitioners using online quizzes for learning and assessment.

Název v anglickém jazyce

Using process mining to analyze students' quiz-taking behavior patterns in a learning management system

Popis výsledku anglicky

The aim of this paper is to explore students’ behavior and interaction patterns in different types of online quiz-based activities within learning management systems (LMS). Analyzing students’ behavior in online learning activities and detecting specific patterns of interaction in LMS is a topic of great interest for the educational data mining (EDM) and learning analytics (LA) research communities. Previous studies have focused primarily on frequency analysis without addressing the temporal aspects of students’ learning behavior. Therefore, we apply a process-oriented approach, investigating perspectives on using process mining methods in the context of online learning and assessment. To explore a broad range of possible student behavior patterns, we analyze students’ interactions in several online quizzes from different courses and with different settings. Using process mining methods, we identify specific types of interaction sequences that shed new light on students’ quiz-taking strategies in LMS. We believe that these findings bring important implications for researchers studying student behavior in online environments as well as practitioners using online quizzes for learning and assessment.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50301 - Education, general; including training, pedagogy, didactics [and education systems]

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Computers in Human Behavior

ISSN

0747-5632

e-ISSN

—

Svazek periodika

92

Číslo periodika v rámci svazku

March 2019

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

496-506

Kód UT WoS článku

000457504100049

EID výsledku v databázi Scopus

2-s2.0-85039452844