TY - GEN
T1 - Analyzing Students’ Behavior in a MOOC Course
T2 - 22nd International Conference on Human-Computer Interaction,HCII 2020
AU - Bernal, Franklin
AU - Maldonado-Mahauad, Jorge
AU - Villalba-Condori, Klinge
AU - Zúñiga-Prieto, Miguel
AU - Veintimilla-Reyes, Jaime
AU - Mejía, Magali
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Massive Open Online Courses (MOOCs), are one of the most disruptive trends along the last 12 years. This is evidenced by the number of students enrolled since their emergence with over 101 million people taking one of the more than 11,400 MOOCs available. However, the approval rate of students in these types of courses is only about 5%. This has led to a great deal of interest among researchers in studying students’ behavior in these types of courses. The aim of this article is to explore the behavior of students in a MOOC. Specifically, to study students learning sequences and extract their behavioral patterns in the different study sessions. To reach the goal, using process mining techniques, process models of N = 1,550 students enrolled in a MOOC in Coursera were obtained. As a result, two groups of students were classified according to their study sessions, where differences were found both in the students’ interactions with the MOOC resources and in the way the lessons were approached on a weekly basis. In addition, students who passed the course repeated the assessments several times until they passed, without returning to review a video-lecture in advance. The results of this work contribute to extend the knowledge about students’ behavior in online environments.
AB - Massive Open Online Courses (MOOCs), are one of the most disruptive trends along the last 12 years. This is evidenced by the number of students enrolled since their emergence with over 101 million people taking one of the more than 11,400 MOOCs available. However, the approval rate of students in these types of courses is only about 5%. This has led to a great deal of interest among researchers in studying students’ behavior in these types of courses. The aim of this article is to explore the behavior of students in a MOOC. Specifically, to study students learning sequences and extract their behavioral patterns in the different study sessions. To reach the goal, using process mining techniques, process models of N = 1,550 students enrolled in a MOOC in Coursera were obtained. As a result, two groups of students were classified according to their study sessions, where differences were found both in the students’ interactions with the MOOC resources and in the way the lessons were approached on a weekly basis. In addition, students who passed the course repeated the assessments several times until they passed, without returning to review a video-lecture in advance. The results of this work contribute to extend the knowledge about students’ behavior in online environments.
KW - Coursera
KW - Learning analytics
KW - Learning strategies
KW - MOOC
KW - Process mining
KW - Study sessions
UR - http://www.scopus.com/inward/record.url?scp=85092911826&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-60128-7_24
DO - 10.1007/978-3-030-60128-7_24
M3 - Conference contribution
AN - SCOPUS:85092911826
SN - 9783030601270
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 307
EP - 325
BT - HCI International 2020 – Late Breaking Papers
A2 - Stephanidis, Constantine
A2 - Harris, Don
A2 - Li, Wen-Chin
A2 - Schmorrow, Dylan D.
A2 - Fidopiastis, Cali M.
A2 - Zaphiris, Panayiotis
A2 - Ioannou, Andri
A2 - Ioannou, Andri
A2 - Fang, Xiaowen
A2 - Sottilare, Robert A.
A2 - Schwarz, Jessica
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 19 July 2020 through 24 July 2020
ER -