TY - GEN
T1 - Analyzing Learners’ Behavior Beyond the MOOC
T2 - 14th European Conference on Technology Enhanced Learning, EC-TEL 2019
AU - Pérez-Sanagustín, Mar
AU - Sharma, Kshitij
AU - Pérez-Álvarez, Ronald
AU - Maldonado-Mahauad, Jorge
AU - Broisin, Julien
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Most of literature on massive open online courses (MOOCs) have focused on describing and predicting learner’s behavior with course trace data. However, little is known on the external resources beyond the MOOC they use to shape their learning experience, and how these interactions relate with their success in the course. This paper presents the results of an exploratory study that analyzes data from 572 learners in 4 MOOCs to understand (1) what the learners’ activities beyond the MOOC are, and (2) how they relate with their course performance. We analyzed frequencies of the students’ individual activities in and beyond the MOOC, and the transitions between these activities. Then, we analyzed the time spent on outside the MOOC content as well as the nature of this content. Finally, we predict which transitions better predict final learners’ grades. The results show that we can predict accurately students’ grades of the course using only internal-course fine-grained data of student’s interactions with video-lectures and exams combined with trace data of interactions with content outside the MOOCs. Also, data shows that learners spent 75% of their time on the MOOC, but go frequently to other content, mainly social networking sites, mail boxes and search engines.
AB - Most of literature on massive open online courses (MOOCs) have focused on describing and predicting learner’s behavior with course trace data. However, little is known on the external resources beyond the MOOC they use to shape their learning experience, and how these interactions relate with their success in the course. This paper presents the results of an exploratory study that analyzes data from 572 learners in 4 MOOCs to understand (1) what the learners’ activities beyond the MOOC are, and (2) how they relate with their course performance. We analyzed frequencies of the students’ individual activities in and beyond the MOOC, and the transitions between these activities. Then, we analyzed the time spent on outside the MOOC content as well as the nature of this content. Finally, we predict which transitions better predict final learners’ grades. The results show that we can predict accurately students’ grades of the course using only internal-course fine-grained data of student’s interactions with video-lectures and exams combined with trace data of interactions with content outside the MOOCs. Also, data shows that learners spent 75% of their time on the MOOC, but go frequently to other content, mainly social networking sites, mail boxes and search engines.
KW - Exploratory study
KW - Learning Analytics
KW - Massive Open Online Courses
KW - MOOCs
UR - https://www.scopus.com/pages/publications/85072987601
U2 - 10.1007/978-3-030-29736-7_4
DO - 10.1007/978-3-030-29736-7_4
M3 - Contribución a la conferencia
AN - SCOPUS:85072987601
SN - 9783030297350
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 40
EP - 54
BT - Transforming Learning with Meaningful Technologies - 14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Proceedings
A2 - Scheffel, Maren
A2 - Broisin, Julien
A2 - Pammer-Schindler, Viktoria
A2 - Ioannou, Andri
A2 - Schneider, Jan
PB - Springer Verlag
Y2 - 16 September 2019 through 19 September 2019
ER -