Learning Analytics: Using Learning Management System Data to Evaluate an Online Curriculum Innovation of Three High-Enrollment Health Studies Courses

Learning management systems (LMS's) are a necessary tool and well suited as earning tools and activities in higher education. However, each institute has a different LMS tool that allows to users (management, instructors and students) to use it for a daily activity. This article investigates the main factors for the acceptance of LMS at Jordanian universities. Is also presents a new LMS model for Jordanian context called Learning Management System Model (JLMS). This approach is used to identify important factors that could or do affect the acceptance of using an LMS at Jordanian universities.

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A New Model for Acceptance of Analytics in Learning Management Systems at Jordanian Universities (JLMS)

Modern Technologies for Teaching and Learning in Socio-Humanitarian Disciplines - Advances in Educational Technologies and Instructional Design ◽

10.4018/978-1-5225-7841-3.ch007 ◽

2019 ◽

pp. 138-161

Author(s):

Abdeleh Bassam Al Amoush ◽

Kamaljeet Sandhu

Keyword(s):

Management System ◽

Learning Analytics ◽

Learning Management System ◽

Learning Management Systems ◽

Management Systems ◽

New Model ◽

Higher Education System ◽

Factors Influencing ◽

Learning Management ◽

Learning Issues

Learning management system (LMS) is usually used in higher education system. It has become compulsory to help the end users (instructors, students, and administrators) in their daily use, and learning analytics presents an auspicious approach. This chapter aims to investigate the acceptance of analytics and use of an LMS at Jordanian universities. It also focuses on the factors influencing acceptance of analytics in LMS at Jordanian universities. Therefore, the chapter presents a new model for acceptance of analytics in learning management systems at Jordanian universities called Jordanian Learning Management System (JLMS). This chapter is based on the most recent and related literature explaining various scenarios where LMSs address learning issues in the digital environment in a way that was not possible in the previous confines of print logic.

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Data Mining and Explorative Multivariate Data Analysis for Customer Satisfaction Study

Surveillance Technologies and Early Warning Systems ◽

10.4018/978-1-61692-865-0.ch013 ◽

2011 ◽

pp. 243-266 ◽

Cited By ~ 6

Author(s):

Rosaria Lombardo

Keyword(s):

Data Mining ◽

Data Analysis ◽

Customer Satisfaction ◽

Management System ◽

Multivariate Data Analysis ◽

Learning Management System ◽

Multivariate Data ◽

Data Sets ◽

System Data ◽

Learning Management

By the early 1990s, the term “data mining” had come to mean the process of finding information in large data sets. In the framework of the Total Quality Management, earlier studies have suggested that enterprises could harness the predictive power of Learning Management System (LMS) data to develop reporting tools that identify at-risk customers/consumers and allow for more timely interventions (Macfadyen & Dawson, 2009). The Learning Management System data and the subsequent Customer Interaction System data can help to provide “early warning system data” for risk detection in enterprises. This chapter confirms and extends this proposition by providing data from an international research project investigating on customer satisfaction in services to persons of public utility, like education, training services and health care services, by means of explorative multivariate data analysis tools as Ordered Multiple Correspondence Analysis, Boosting regression, Partial Least Squares regression and its generalizations.

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