TY - GEN
T1 - Identifying Learning Activity Sequences that Are Associated with High Intention-Fulfillment in MOOCs
AU - Rabin, Eyal
AU - Silber-Varod, Vered
AU - Kalman, Yoram
AU - Kalz, Marco
N1 - Publisher Copyright:
© 2019, The Author(s).
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019
Y1 - 2019
N2 - Learners join MOOCs (Massive Open Online Courses) with a variety of intentions. The fulfillment of these initial intentions is an important success criterion in self-paced and open courses. Using post course self-reported data enabled us to divide the participants to those who fulfilled the initial intentions (high-IF) and those who did not fulfill their initial intentions (low-IF). We used methods adapted from natural language processing (NLP) to analyze the learning paths of 462 MOOC participants and to identify activities and activity sequences of participants in the two groups. Specifically, we used n-gram analysis to identify learning activity sequences and keyness analysis to identify prominent learning activities. These measures enable us to identify the differences between the two groups. Differences can be seen at the level of single activities, but major differences were found when longer n-grams were used. The high-IF group showed more consistency and less divergent learning behavior. High-IF was associated, among other things, with study patterns of sequentially watching video lectures. Theoretical and practical suggestions are introduced in order to help MOOC developers and participants to fulfill the participants’ learning intentions.
AB - Learners join MOOCs (Massive Open Online Courses) with a variety of intentions. The fulfillment of these initial intentions is an important success criterion in self-paced and open courses. Using post course self-reported data enabled us to divide the participants to those who fulfilled the initial intentions (high-IF) and those who did not fulfill their initial intentions (low-IF). We used methods adapted from natural language processing (NLP) to analyze the learning paths of 462 MOOC participants and to identify activities and activity sequences of participants in the two groups. Specifically, we used n-gram analysis to identify learning activity sequences and keyness analysis to identify prominent learning activities. These measures enable us to identify the differences between the two groups. Differences can be seen at the level of single activities, but major differences were found when longer n-grams were used. The high-IF group showed more consistency and less divergent learning behavior. High-IF was associated, among other things, with study patterns of sequentially watching video lectures. Theoretical and practical suggestions are introduced in order to help MOOC developers and participants to fulfill the participants’ learning intentions.
KW - Intention-fulfilment
KW - Keyness
KW - Learning activity sequences
KW - Massive Open Online Courses
KW - N-gram
UR - http://www.scopus.com/inward/record.url?scp=85072988460&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-29736-7_17
DO - 10.1007/978-3-030-29736-7_17
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AN - SCOPUS:85072988460
SN - 9783030297350
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 224
EP - 235
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
T2 - 14th European Conference on Technology Enhanced Learning, EC-TEL 2019
Y2 - 16 September 2019 through 19 September 2019
ER -