MILA: A SCORM-Compliant Interactive Learning Analytics Tool for Moodle

被引：5

作者：

Distante, Damiano ^{[1
]}

Villa, Massimo ^{[1
]}

Sansone, Nadia ^{[1
]}

Faralli, Stefano ^{[1
]}

机构：

[1] Univ Rome, Unitelma Sapienza, Rome, Italy

来源：

2020 IEEE 20TH INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES (ICALT 2020) | 2020年

关键词：

e-learning; educational data mining; learning analytics; information visualization; learning management systems; Moodle; SCORM;

D O I：

10.1109/ICALT49669.2020.00056

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents MILA, a prototype interactive Learning Analytics tool for the Moodle learning management system that has been developed to support the analysis and improvement of the teaching and learning processes in the e-learning environment of the University of Rome Unitelma Sapienza. MILA offers a variety of interactive and real-time data visualizations that provide statistics, trends, and insight information both on the Moodle-based virtual learning environment as a whole, and on each course included in it. In addition to Moodle standard logs, MILA is able to analyze tracking data generated by SCORM-compliant learning objects and to measure the duration of the related user sessions. Although MILA has been developed as a Moodle plugin, its software architecture and analysis models can be re-used and adapted to develop a learning analytics tool for any SCORM-compliant learning management system.

引用

页码：169 / 171

页数：3

共 12 条

[1]

Atif Amara., 2013, ASCILITE-Australian Society for Computers in Learning in Tertiary Education Annual Conference, P68

[2]

Cambruzzi W, 2015, J UNIVERS COMPUT SCI, V21, P23

[3]

Charleer S., 2014, ARTEL14 P 4 WORKSH A, P85

[4]

Klasen D., 2019, UTILIZING LEARNING A, P61

[5]

Kumar Mukesh., 2017, International Journal of Education and Management Engineering, V7, P8, DOI [DOI 10.5815/IJEME.2017.02.02, 10.5815/ijeme.2017.02.02]

[6] Interactive Visualization Tools to Improve Learning and Teaching in Online Learning Environments [J].

Kuosa, Kirsi ;

Distante, Damiano ;

Tervakari, Anne ;

Cerulo, Luigi ;

Fernandez, Alejandro ;

Koro, Juho ;

Kailanto, Meri .

INTERNATIONAL JOURNAL OF DISTANCE EDUCATION TECHNOLOGIES, 2016, 14 (01) :1-21

[7]

Leitner P, 2017, STUD SYST DECIS CONT, V94, P1, DOI 10.1007/978-3-319-52977-6_1

[8] RETRACTED: Extracting Information of Urban Land Surface with High Resolution Remote Sensing Data (Retracted Article) [J].

Liu, Jiaming ;

Xian, Cuiling .

IHIP 2018: 2018 INTERNATIONAL CONFERENCE ON INFORMATION HIDING AND IMAGE PROCESSING, 2018, :62-67

[9] A Survey of Machine Learning Approaches for Student Dropout Prediction in Online Courses [J].

Prenkaj, Bardh ;

Velardi, Paola ;

Stilo, Giovanni ;

Distante, Damiano ;

Faralli, Stefano .

ACM COMPUTING SURVEYS, 2020, 53 (03)

[10] WHICH LEARNING ANALYTICS FOR A SOCIO-CONSTRUCTIVIST TEACHING AND LEARNING BLENDED EXPERIENCE [J].

Sansone, Nadia ;

Cesareni, Donatella .

JOURNAL OF E-LEARNING AND KNOWLEDGE SOCIETY, 2019, 15 (03) :319-329

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