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Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended Learning

Educational Technology ＆ Society, 2018-04, Vol.21 (2), p.220-232 [Peer Reviewed Journal]

COPYRIGHT 2018 International Forum of Educational Technology & Society ;COPYRIGHT 2018 International Forum of Educational Technology & Society ;2018. This work is published under https://creativecommons.org/licenses/by-nc-nd/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1176-3647 ;ISSN: 1436-4522 ;EISSN: 1436-4522

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Title:
Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended Learning
Author: Lu, Owen H. T. ; Huang, Anna Y. Q. ; Huang, Jeff C.H. ; Lin, Albert J. Q. ; Ogata, Hiroaki ; Yang, Stephen J. H.
Subjects: Academic achievement ; Academic learning ; Analysis ; Analytics ; At Risk Students ; Big data ; Blended Learning ; Calculus ; College Mathematics ; College Students ; Data Analysis ; Data Collection ; Data management ; Datasets ; Distance learning ; Educational aspects ; Educational environment ; Educational Technology ; Factor Analysis ; Foreign Countries ; Homework ; Internet resources ; Large Group Instruction ; Learning ; Massive open online courses ; Mathematical analysis ; Mathematics Achievement ; Mathematics Instruction ; Mathematics Tests ; Methods ; Online learning ; Performance prediction ; Predictor Variables ; Real variables ; Regression (Statistics) ; Regression analysis ; Scores ; Special Issue Articles ; Statistical Analysis ; Students ; Technology Uses in Education ; Tutoring ; Video Technology
Is Part Of: Educational Technology ＆ Society, 2018-04, Vol.21 (2), p.220-232
Description: Blended learning combines online digital resources with traditional classroom activities and enables students to attain higher learning performance through well-defined interactive strategies involving online and traditional learning activities. Learning analytics is a conceptual framework and as a part of our Precision education used to analyze and predict students' performance and provide timely interventions based on student learning profiles. This study applied learning analytics and educational big data approaches for the early prediction of students' final academic performance in a blended Calculus course. Real data with 21 variables were collected from the proposed course, consisting of video-viewing behaviors, out-of-class practice behaviors, homework and quiz scores, and after-school tutoring. This study applied principal component regression to predict students' final academic performance. The experimental results show that students' final academic performance could be predicted when only one-third of the semester had elapsed. In addition, we identified seven critical factors that affect students' academic performance, consisting of four online factors and three traditional factors. The results showed that the blended data set combining online and traditional critical factors had the highest predictive performance.
Publisher: Palmerston North: International Forum of Educational Technology ＆ Society
Language: English
Identifier: ISSN: 1176-3647
ISSN: 1436-4522
EISSN: 1436-4522
Source: Freely Accessible Journals
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AUTh Library subscriptions: ProQuest Central
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Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended Learning

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