Educational data mining and learning analytics for 21st century higher education: A review and synthesis

被引：248

作者：

Aldowah, Hanan ^{[1
]}

Al-Samarraie, Hosam ^{[1
]}

Fauzy, Wan Mohamad ^{[2
]}

机构：

[1] Univ Sains Malaysia, Ctr Instruct Technol & Multimedia, George Town, Malaysia

[2] Sultan Qaboos Univ, Instruct Learning Technol Dept, Muscat, Oman

来源：

TELEMATICS AND INFORMATICS | 2019年 / 37卷

关键词：

Dta analytics; Educational data mining; Learning analytics; Higher education; DROPOUT PREDICTION; CLUSTER-ANALYSIS; INTERACTION PATTERNS; STUDENT-ACHIEVEMENT; ASYNCHRONOUS ONLINE; KNOWLEDGE DISCOVERY; SUPPORTING TEACHERS; ASSOCIATION RULES; CONCEPT MAP; PERFORMANCE;

D O I：

10.1016/j.tele.2019.01.007

中图分类号：

G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];

学科分类号：

1205 ; 120501 ;

摘要：

The potential influence of data mining analytics on the students' learning processes and outcomes has been realized in higher education. Hence, a comprehensive review of educational data mining (EDM) and learning analytics (LA) in higher education was conducted. This review covered the most relevant studies related to four main dimensions: computer-supported learning analytics (CSLA), computer-supported predictive analytics (CSPA), computer-supported behavioral analytics (CSBA), and computer-supported visualization analytics (CSVA) from 2000 till 2017. The relevant EDM and LA techniques were identified and compared across these dimensions. Based on the results of 402 studies, it was found that specific EDM and LA techniques could offer the best means of solving certain learning problems. Applying EDM and LA in higher education can be useful in developing a student-focused strategy and providing the required tools that institutions will be able to use for the purposes of continuous improvement.

引用

页码：13 / 49

页数：37

共 379 条

[1]

Acharya T, 2003, DATA MINING MULTIMED

[2]

Agapito J. B., 2009, ED DAT MIN

[3] Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning [J].

Agudo-Peregrina, Angel F. ;

Iglesias-Pradas, Santiago ;

Angel Conde-Gonzalez, Miguel ;

Hernandez-Garcia, Angel .

COMPUTERS IN HUMAN BEHAVIOR, 2014, 31 :542-550

[4] Design Adaptive Learning System Using Metacognitive Strategy Path for Learning in Classroom and Intelligent Tutoring Systems [J].

Agustianto, Khafidurrohman ;

Permanasari, Adhistya Erna ;

Kusumawardani, Sri Suning ;

Hidayah, Indriana .

ADVANCES OF SCIENCE AND TECHNOLOGY FOR SOCIETY, 2016, 1755

[5] Combination of machine learning algorithms for recommendation of courses in E-Learning System based on historical data [J].

Aher, Sunita B. ;

Lobo, L. M. R. J. .

KNOWLEDGE-BASED SYSTEMS, 2013, 51 :1-14

[6]

Al-Radaideh Q, 2006, INT AR C INF TECHN A

[7]

Aleven Vincent, 2006, Int. J. Artif. Intell. Educ., V16, P101

[8] An approach for automatic generation of adaptive hypermedia in education with multilingual knowledge discovery techniques [J].

Alfonseca, Enrique ;

Rodriguez, Pilar ;

Perez, Diana .

COMPUTERS & EDUCATION, 2007, 49 (02) :495-513

[9] Factors influencing beliefs for adoption of a learning analytics tool: An empirical study [J].

Ali, Liaqat ;

Asadi, Mohsen ;

Gasevic, Dragan ;

Jovanovic, Jelena ;

Hatala, Marek .

COMPUTERS & EDUCATION, 2013, 62 :130-148

[10]

Altun A., 2014, ADV LEARN TECHN ICAL

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