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Purpose – The purpose of this paper is to examine factors that predict students’ continued usage intention of web-based learning management systems (LMS) in Tanzania, with a specific focus on the School of Business of Mzumbe University. Specifically, the study investigated major predictors of actual usage and continued usage intentions of e-learning system, and challenges of using the e-learning system. Design/methodology/approach – Data were collected through a questionnaire survey of 300 third year undergraduate students, with a rate of return of 77 per cent. A total of 20 faculty members were also interviewed. The unified theory of acceptance and use of technology (UTAUT) was utilized in the study. Findings – The results show that actual usage was determined by self-efficacy, while continued usage intentions of web-based learning system was predicted by performance expectancy, effort expectancy, social influence, self-efficacy, and actual usage. Challenges for using web-based LMS were related to information and communications technology (ICT) infrastructure barrier, LMS user interface was not user friendly, weak ICT policies, management and technical support, limited skills, lack of awareness, resistance to change, and lack of time to prepare e-content and use the e-learning system. Practical implications – The study findings are useful to e-learning managers and university management to identify important factors and develop appropriate policies and strategies to encourage long-term usage of e-learning systems for future studies and lifelong learning. Originality/value – By using UTAUT in the context of continued usage intentions and the integration of an additional construct (“self-efficacy”), the extended UTAUT model fits very well in the web-based learning systems in Tanzania, in particular where such studies are scant. The findings can be used in other institutions with similar conditions in investigating the continued usage intentions of e-learning systems.

The article considers a convergent approach to the synthesis of the information learning environment for higher education, which includes tools for managing educational content and learning trajectories. The process of convergence is defined as synchronization and coordination of electronic educational resources, educational programs and skill levels of specialists. The process is presented within the framework of interaction and lifecycle model synchronization for components of the information learning environment. The environment ensures the convergence of new educational models (electronic, mobile, cloud, mixed, ubiquitous) on the basis of a unified educational management system. The system includes the Alfresco educational content management subsystem, the Moodle learning management subsystem, the learning material presentation subsystem, the knowledge assessment subsystem, the learning activity management subsystem, the requirements of education standards and employers analysis subsystem.

Using traces of behaviors to predict outcomes is useful in varied contexts ranging from buyer behaviors to behaviors collected from smart-home devices. Increasingly, higher education systems have been using Learning Management System (LMS) digital data to capture and understand students’ learning and well-being. Researchers in the social sciences are increasingly interested in the potential of using digital log data to predict outcomes and design interventions. Using LMS data for predicting the likelihood of students’ success in for-credit college courses provides a useful example of how social scientists can use these techniques on a variety of data types. Here, we provide a primer on how LMS data can be feature-mapped and analyzed to accomplish these goals. We begin with a literature review summarizing current approaches to analyzing LMS data, then discuss ethical issues of privacy when using demographic data and equitable model building. In the second part of the paper, we provide an overview of popular machine learning algorithms and review analytic considerations such as feature generation, assessment of model performance, and sampling techniques. Finally, we conclude with an empirical example demonstrating the ability of LMS data to predict student success, summarizing important features and assessing model performance across different model specifications.

While research on online tests in higher education is steadily growing, there is little evidence in the literature of the use of learning management systems (LMS), such as Blackboard(TM), as rich sources of data on online tests practices. This paper reports on an investigation that used data from Blackboard(TM) LMS to gain insight into the purpose for and practice of online tests at the Charles Darwin University (CDU) Business School in Australia. Focussing on curriculum and pedagogical practices, the paper identifies indications of good practice as well as potential issues related to curriculum mapping, including possible misalignment between learning outcomes and online tests. It also affirms the versatility of using data from LMSs in the study of e-assessment in general and online tests in particular. [Author abstract]

This study examines the acceptance of technology and behavioral intention to use learning management systems (LMS). In specific, the aim of the research reported in this paper is to examine whether students ultimately accept LMSs such as eClass and the impact of behavioral intention on their decision to use them. An extended version of technology acceptance model has been proposed and used by employing one of the most reliable measures of perceived eased of use, the System Usability Scale. 345 university students participated in the study. The data analysis was based on partial least squares method. The majority of the research hypotheses were confirmed. In particular, social norm, system access and self-efficacy were found to significantly affect behavioral intention to use. As a result, it is suggested that e-learning developers and stakeholders should focus on these factors to increase acceptance and effectiveness of learning management systems.

Lifelong learning requires appropriate solutions, especially for corporate training. Workers usually have difficulty combining training and their normal work. In this context, micro-learning emerges as a suitable solution, since it is based on breaking down new concepts into small fragments or pills of content, which can be consumed in short periods of time. The purpose of this paper is twofold. First, we offer an updated overview of the research on this training paradigm, as well as the different technologies leading to potential commercial solutions. Second, we introduce a proposal to add micro-learning content to more formal distance learning environments (traditional Learning Management Systems or LMS), with the aim of taking advantage of both learning philosophies. Our approach is based on a Service-Oriented Architecture (SOA) that is deployed in the cloud. In order to ensure the full integration of the micro-learning approach in traditional LMSs, we have used two well-known standards in the distance learning field: LTI (Learning Tools Interoperability) and LIS (Learning Information Service). The combination of these two technologies allows the exchange of data with the LMS to monitor the student’s activity and results. Finally, we have collected the opinion of lectures from different countries in order to know their thoughts about the potential of this new approach in higher education, obtaining positive feedback.

As a modern pedagogical philosophy, problem-based learning (PBL) is increasingly being recognized as a major research area in student learning and pedagogical innovation in health sciences education. A new area of research interest has been the role of emerging educational technologies in PBL. Although this field is growing, no systematic reviews of studies of the usage and effects of educational technologies in PBL in health sciences education have been conducted to date. The aim of this paper is to review new and emerging educational technologies in problem-based curricula, with a specific focus on 3 cognate clinical disciplines: medicine, dentistry, and speech and hearing sciences. Analysis of the studies reviewed focused on the effects of educational technologies in PBL contexts while addressing the particular issue of scaffolding of student learning. A comprehensive computerized database search of full-text articles published in English from 1996 to 2014 was carried out using 3 databases: ProQuest, Scopus, and EBSCOhost. Eligibility criteria for selection of studies for review were also determined in light of the population, intervention, comparison, and outcomes (PICO) guidelines. The population was limited to postsecondary education, specifically in dentistry, medicine, and speech and hearing sciences, in which PBL was the key educational pedagogy and curriculum design. Three types of educational technologies were identified as interventions used to support student inquiry: learning software and digital learning objects; interactive whiteboards (IWBs) and plasma screens; and learning management systems (LMSs). Of 470 studies, 28 were selected for analysis. Most studies examined the effects of learning software and digital learning objects (n=20) with integration of IWB (n=5) and LMS (n=3) for PBL receiving relatively less attention. The educational technologies examined in these studies were seen as potentially fit for problem-based health sciences education. Positive outcomes for student learning included providing rich, authentic problems and/or case contexts for learning; supporting student development of medical expertise through the accessing and structuring of expert knowledge and skills; making disciplinary thinking and strategies explicit; providing a platform to elicit articulation, collaboration, and reflection; and reducing perceived cognitive load. Limitations included cumbersome scenarios, infrastructure requirements, and the need for staff and student support in light of the technological demands of new affordances. This literature review demonstrates the generally positive effect of educational technologies in PBL. Further research into the various applications of educational technology in PBL curricula is needed to fully realize its potential to enhance problem-based approaches in health sciences education.

Purpose Student attritions in tertiary educational institutes may play a significant role to achieve core values leading towards strategic mission and financial well-being. Analysis of data generated from student interaction with learning management systems (LMSs) in blended learning (BL) environments may assist with the identification of students at risk of failing, but to what extent this may be possible is unknown. However, existing studies are limited to address the issues at a significant scale. Design/methodology/approach This study develops a new approach harnessing applications of machine learning (ML) models on a dataset, that is publicly available, relevant to student attrition to identify potential students at risk. The dataset consists of the data generated by the interaction of students with LMS for their BL environment. Findings Identifying students at risk through an innovative approach will promote timely intervention in the learning process, such as for improving student academic progress. To evaluate the performance of the proposed approach, the accuracy is compared with other representational ML methods. Originality/value The best ML algorithm random forest with 85% is selected to support educators in implementing various pedagogical practices to improve students’ learning.

The rapid development and improvement of learning management system (LMS) is driven by the rise of ubiquitous computing. As LMS design shifts from technology-centric to user-centric, developers need to prioritize user experience (UX) to improve their LMS. LMS as part of an e-learning system can benefit from UX research to measure ease of use and user satisfaction. Many academic institutions around the world prefer to use their own customized LMS, such as: B. Moodle is an open source LMS platform built and maintained by most Jordanian universities. Therefore, UX evaluation and measurement is very important for LMS. Several studies have been conducted to analyze and measure LMS products for user feedback. On the other hand, these studies only partially consider LMS products. Instead of observing the quality of LMS, they conducted a study focused on comparing LMS and gaining awareness. The purpose of this study is to collect comments and feedback and evaluate the UX when dealing with LMS. Using Moodle as a case study, this study evaluates the user experience of LMS. Data were collected from 867 participants from various Jordanian institutions using LMS. An online survey was prepared and distributed through various social media groups. This study proposes to use the UX Survey Tool (UEQ) to evaluate and quantify the user experience of LMS. This study aims to get feedback and evaluate and identify the UX while interacting with the LMS. In general, implementing a user experience questionnaire to evaluate and evaluate an LMS provides not only complete user awareness, but also the relative quality level of the LMS. The results provide feedback and support assistance in the implementation of the LMS.