TY - GEN
T1 - Knowledge acquisition for learning analytics
T2 - 14th International Workshop on Knowledge Management and Acquisition for Intelligent Systems, PKAW2016
AU - Liu, Danny Y T
AU - Richards, Deborah
AU - Dawson, Phillip
AU - Froissard, Jean Christophe
AU - Atif, Amara
PY - 2016
Y1 - 2016
N2 - One of the promises of big data in higher education (learning analytics) is being able to accurately identify and assist students who may not be engaging as expected. These expectations, distilled into parameters for learning analytics tools, can be determined by human teacher experts or by algorithms themselves. However, there has been little work done to compare the power of knowledge models acquired from teachers and from algorithms. In the context of an open source learning analytics tool, the Moodle Engagement Analytics Plugin, we examined the ability of teacher-derived models to accurately predict student engagement and performance, compared to models derived from algorithms, as well as hybrid models. Our preliminary findings, reported here, provided evidence for the fallibility and strength of teacher-and algorithm-derived models, respectively, and highlighted the benefits of a hybrid approach to model-and knowledge-generation for learning analytics. A human in the loop solution is therefore suggested as a possible optimal approach.
AB - One of the promises of big data in higher education (learning analytics) is being able to accurately identify and assist students who may not be engaging as expected. These expectations, distilled into parameters for learning analytics tools, can be determined by human teacher experts or by algorithms themselves. However, there has been little work done to compare the power of knowledge models acquired from teachers and from algorithms. In the context of an open source learning analytics tool, the Moodle Engagement Analytics Plugin, we examined the ability of teacher-derived models to accurately predict student engagement and performance, compared to models derived from algorithms, as well as hybrid models. Our preliminary findings, reported here, provided evidence for the fallibility and strength of teacher-and algorithm-derived models, respectively, and highlighted the benefits of a hybrid approach to model-and knowledge-generation for learning analytics. A human in the loop solution is therefore suggested as a possible optimal approach.
UR - http://www.scopus.com/inward/record.url?scp=84984815899&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-42706-5_14
DO - 10.1007/978-3-319-42706-5_14
M3 - Conference proceeding contribution
AN - SCOPUS:84984815899
SN - 9783319427058
VL - 9806
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 183
EP - 197
BT - Knowledge Management and Acquisition for Intelligent Systems - 14th Pacific Rim Knowledge Acquisition Workshop, PKAW 2016, Proceedings
A2 - Ohwada, Hayato
A2 - Yoshida, Kenichi
PB - Springer, Springer Nature
CY - Switzerland
Y2 - 22 August 2016 through 23 August 2016
ER -