TY - GEN
T1 - Personalising Learning with Learning Analytics
T2 - 13th International Conference on Blended Learning, ICBL 2020
AU - Li, Kam Cheong
AU - Wong, Billy Tak Ming
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Advances have been made in personalised learning by the developments in learning analytics, where useful information can be extracted from educational data and analysed to devise personalised learning solutions. This paper presents a review of the literature in this area, covering a total of 144 relevant empirical articles published between 2012 and 2019 collected from Scopus. It identifies the patterns in the use of learning analytics to personalise learning in terms of the environments (what), stakeholders (who), objectives (why) and methods (how). The results show a clear growth in the number of practices, and diversity in terms of the learning contexts where learning analytics was implemented; the types of data collected; the groups of target stakeholders; the objectives of learning analytics practices; the personalised learning goals; and the analytics methods. The findings also reveal the emergence of practices related to the teacher perspective and some areas which have not been fully addressed, such as personalised intervention for future work.
AB - Advances have been made in personalised learning by the developments in learning analytics, where useful information can be extracted from educational data and analysed to devise personalised learning solutions. This paper presents a review of the literature in this area, covering a total of 144 relevant empirical articles published between 2012 and 2019 collected from Scopus. It identifies the patterns in the use of learning analytics to personalise learning in terms of the environments (what), stakeholders (who), objectives (why) and methods (how). The results show a clear growth in the number of practices, and diversity in terms of the learning contexts where learning analytics was implemented; the types of data collected; the groups of target stakeholders; the objectives of learning analytics practices; the personalised learning goals; and the analytics methods. The findings also reveal the emergence of practices related to the teacher perspective and some areas which have not been fully addressed, such as personalised intervention for future work.
KW - Adaptive learning
KW - Learning analytics
KW - Personalisation
KW - Personalised learning
UR - http://www.scopus.com/inward/record.url?scp=85089217198&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-51968-1_4
DO - 10.1007/978-3-030-51968-1_4
M3 - Conference contribution
AN - SCOPUS:85089217198
SN - 9783030519674
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 39
EP - 48
BT - Blended Learning. Education in a Smart Learning Environment - 13th International Conference, ICBL 2020, Proceedings
A2 - Cheung, Simon K.S.
A2 - Li, Richard
A2 - Phusavat, Kongkiti
A2 - Paoprasert, Naraphorn
A2 - Kwok, Lam-For
Y2 - 24 August 2020 through 27 August 2020
ER -