Assessing the usability of a visual tool for the definition of e-learning processes

In this paper, we present a usability study aiming at assessing a visual language-based tool for developing adaptive e-learning processes. The tool implements the adaptive self-consistent learning object SET (ASCLO-S) visual language, a special case of flow diagrams, to be used by instructional designers to define classes of learners through stereotypes and to specify the more suited adaptive learning process for each class of learners. The usability study is based on the combined use of two techniques: a questionnaire-based survey and an empirical analysis. The survey has been used to achieve feedbacks from the subjects’ point of view. In particular, it has been useful to capture the perceived usability of the subjects. The outcomes show that both the proposed visual notation and the system prototype are suitable for instructional designers with or without experience on the computer usage and on tools for defining e-learning processes. This result is further confirmed by the empirical analysis we carried out by analysing the correlation between the effort to develop adaptive e-learning processes and some measures suitable defined for those processes. Indeed, the empirical analysis revealed that the effort required to model e-learning processes is not influenced by the experience of the instructional designer with the use of e-learning tools, but it only depends on the size of the developed process.     

Assessing e-learning 2.0 system success

Traditional e-learning systems support “one-way” communication. Teachers provide knowledge for learners, but they are unable to use a student’s learning experiences to benefit the class as a whole. To address these problems, this study explores e-learning success factors via the design and evaluation of an e-learning 2.0 system. This study develops a theoretical model to assess user satisfaction and loyalty intentions to an e-learning system using communication quality, information quality, system quality, and service quality. The empirical results show that communication quality, information quality, and service quality significantly and positively affect user satisfaction and loyalty intentions to use the e-learning system for sharing experience, communicating with others, and getting feedback.     

Assessing the determinants of flow experience in the adoption of learning management systems: the moderating role of perceived institutional support

Information technology has moved the focus of students and instructors from blackboards to online learning platforms. In the emerging e-learning revolution, the success of quality education depends on the right use of information and communication technology, including the creation of participants’ flow experience while using learning systems, which are rightly considered a backbone of contemporary education. There is a gap in the e-learning literature, reflecting a lack of understanding of students’ flow experience while using online interactive systems. This paper presents a study investigating the key factors (interpersonal factors and atmospheric cues) affecting flow experience and highlights its massive role in the use of any learning management system (LMS). Moreover, the study examines the moderating role of perceived institutional support (PIS) on student adoption of LMS. Using a survey, data were collected from university students in Pakistan and the data were analysed through structural equation modelling. We found that interpersonal factors and LMS atmospherics have significant effects on the flow experience. It was also found that PIS moderates the relationship between flow experience and the use of LMS. This study contributes to the literature by helping institutions and policy-makers know key factors of effective learning systems that students value and enjoy.     

Experience beyond knowledge: Pragmatic e-learning systems design with learning experience

With the growing demand in e-learning system, traditional e-learning systems have dramatically evolved to provide more adaptive ways of learning, in terms of learning objectives, courses, individual learning processes, and so on. This paper reports on differences in learning experience from the learner’s perspectives when using an adaptive e-learning system, where the learner’s knowledge or skill level is used to configure the learning path. Central to this study is the evaluation of a dynamic content sequencing system (DCSS), with empirical outcomes being interpreted using Csikszentmihalyi’s flow theory (i.e., Flow, Boredom, and Anxiety). A total of 80 participants carried out a one-way between-subject study controlled by the type of e-learning system (i.e., the DCSS vs. the non-DCSS). The results indicated that the lower or medium achievers gained certain benefits from the DCSS, whilst the high achievers in learning performance might suffer from boredom when using the DCSS. These contrasting findings can be suggested as a pragmatic design guideline for developing more engaging computer-based learning systems for unsupervised learning situations.     

Linking employees’ e-learning system use to their overall job outcomes: An empirical study based on the IS success model

e-Learning systems are increasingly being adopted by modern enterprises, because of their cost-down effect. However, the extant literature provides little insight into their beneficial consequences. It is expected that a transfer of acquired knowledge, skills and/or work attitudes to jobs will occur through e-learning systems use. This makes e-learning systems a useful tool for improving job outcomes, although this is not well documented. The IS success model, which captures both the human and technological elements of information systems, provides a theoretical basis for linking system use to system task. The current study then examines the relation between e-learning systems use and overall job outcomes based on the model. The data collected from one hundred and ninety-three e-learning system users were analyzed with partial least square (PLS). The results indicate that e-learning systems are perceived as useful and satisfying by employees, and employees’ e-learning systems use is significantly associated with overall job outcomes. In practice, these results provide a basis for establishing a link between an organization’s investment in e-learning and human capital management. They also lend empirical support to the IS success model.     

How can one amplify the effect of e-learning? An examination of high-tech employees’ computer attitude and flow experience

While numerous studies have focused on the effectiveness and benefits of e-learning, this study elicits the determinants of the IT professionals’ e-learning outcomes by investigating the effect of IT professionals’ computer attitudes on two personal outcomes: self-perceived flow experience and learning outcomes. Data collected from 50 technological companies which located in Hsinchu Science Parks in Taiwan (N = 239) were analyzed using structural equation modeling. Results confirm that both computer attitude and flow experience generate positive and direct influence on learning outcome. Specially, the effect of personal computer attitude is amplified on learning outcome through experience of flow in an e-learning environment. Managerial implications are proposed and research limitations are discussed.     

Linking employees’ e-learning system use to their overall job outcomes: An empirical study based on the IS success model

e-Learning systems are increasingly being adopted by modern enterprises, because of their cost-down effect. However, the extant literature provides little insight into their beneficial consequences. It is expected that a transfer of acquired knowledge, skills and/or work attitudes to jobs will occur through e-learning systems use. This makes e-learning systems a useful tool for improving job outcomes, although this is not well documented. The IS success model, which captures both the human and technological elements of information systems, provides a theoretical basis for linking system use to system task. The current study then examines the relation between e-learning systems use and overall job outcomes based on the model. The data collected from one hundred and ninety-three e-learning system users were analyzed with partial least square (PLS). The results indicate that e-learning systems are perceived as useful and satisfying by employees, and employees’ e-learning systems use is significantly associated with overall job outcomes. In practice, these results provide a basis for establishing a link between an organization’s investment in e-learning and human capital management. They also lend empirical support to the IS success model.     

The effects of organizational learning environment factors on e-learning acceptance

Workplace learning is an important means of employees’ continuous learning and professional development. E-learning is being recognized as an important supportive practice for learning at work. Current research on the success factors of e-learning in the workplace has emphasized on employees’ characteristics, technological attributes, and training design elements, with little attention to workplace contextual effects. The study aims to investigate the impacts of organizational learning environment factors, including managerial support, job support, and organizational support, on employees’ motivation to use a workplace e-learning system. A model was proposed based on the expectancy theory of training motivation and the social influences and facilitating conditions in technology acceptance models. The model was tested on sample data collected from mainland China using Structural Equation Modeling and Moderated Structural Equation Modeling. The results suggested that employees’ perceived managerial support and job support had a significant impact on their perceived usefulness of the e-learning system for individual learning, and that perceived organizational support had a significant influence on the perceived usefulness of the e-learning system for social learning. Perceived usefulness for individual learning was found to completely mediate the environmental influences on individuals’ motivation to use the system, while perceived usefulness for social learning made partial mediation in the effects of the environmental factors on intention to use. In addition, perceived job support was found to have moderating effects on the relationship between employees’ perceived usefulness of the e-learning system and their intention to use the system. Consistent with previous findings, employees’ perceptions about the usefulness of the e-learning system have significant effects on their intention to use the system in the work setting.     

Measuring e-learning systems success in an organizational context: Scale development and validation

Electronic learning (e-learning) has been widely adopted as a promising solution by many companies to offer learning-on-demand opportunities to individual employees in order to reduce training time and cost. While information systems (IS) success models have received much attention among researchers, little research has been conducted to assess the success and/or effectiveness of e-learning systems in an organizational context. Whether traditional IS success models can be extended to investigate e-learning systems success has been scarcely addressed. Based on previous IS success literature, this study developed and validated a multi-dimensional model for assessing e-learning systems success (ELSS) from the perspective of the employee (e-learner). The procedures used in conceptualizing an ELSS construct, generating items, collecting data, and validating a multiple-item scale for measuring ELSS are described. This paper presents evidence of the scale’s factor structure, reliability, content validity, criterion-related validity, convergent validity, and discriminant validity on the basis of analyzing data from a sample of 206 respondents. Theoretical and managerial implications of our results are discussed. This empirically validated instrument will be useful to researchers in developing and testing e-learning systems theories, as well as to organizations in implementing successful e-learning systems.     

Developing a model to assess the success of e-learning systems: evidence from a manufacturing company in transitional economy

This paper examines the success of an e-learning system in a company from the perspective of employees by using a multimethod approach. For this purpose Moodle learning management system was used. The success of e-learning as an information system was evaluated using four constructs of the updated DeLone and McLean IS success model—system quality, use, user satisfaction and net benefits, and adding one more construct—user performance. In this research a combination of observation and survey as two different research methods was used, which allowed the new measure to be incorporated into the model. Empirical assessment was carried out by exploratory factor analysis, confirmatory factor analysis and structural equation modeling. The research model was found to be valid and reliable. The results provide an expanded understanding of the constructs that measure the success of an e-learning system, helping to more deeply understand the key success dimensions and their interrelationships. The implications of our work were discussed. The DeLone and McLean IS success model applied equally well. However, the use of observation as a method of data collection revealed the weaknesses of the original model.     

Design of a performance-oriented workplace e-learning system using ontology

E-learning is emerging as a popular learning approach utilized by many organizations. Despite the ever increasing practices of e-learning in the workplace, most e-learning applications fail to meet learners’ needs or serve organization’s quests for success. Significant gaps exist between organizational interests and individual needs when they come to e-learning, which make e-learning applications less goal-effective. To solve this problem, a performance-oriented approach is presented in this study. Key performance indicators (KPIs) are set up to clarify organizational training needs, and help learners establish rational learning objectives. In addition, ontology is used for constructing formal and machine-understandable conceptualization of the performance-oriented learning environment. Using this approach, a KPI-oriented learning ontology and prototype system have been developed and evaluated to demonstrate the effectiveness of the approach.     

User sensitive research in e-learning: exploring the role of individual user characteristics

The increasing need for active and accessible learning in the inclusive knowledge society drives the demand for e-learning that engages users much more effectively than ever before. In this context, it is crucial to conduct research that embraces innovation in user sensitive design, or else influential individual user differences may be overlooked. The objective of this paper is to explore the creation of successful e-learning systems that are able to increase users’ learning performance and enhance their personal learning experiences. The paper reports two converging and complimentary approaches, namely case studies and experimentation. First, case studies are used to explore the extent to which effective e-learning systems comply with eight specific factors. Of the eight, accessibility, individual differences and student modeling turn out to be the weakest points in current practice. Second, an empirical study investigates the influences of user individual user differences on users’ learning outcomes in an e-learning environment. The experiment found that individual differences in motivation to learn and expectations about e-learning significantly impacted users’ learning achievements. Third, based on these studies, improvements in research methodology are identified towards greater consideration of user sensitive research issues, thus enabling us to outline improved experimental procedures. Further experiment results should provide us with better insights into the arguments needed to carefully assess benefits of developing and involving a user model in an e-learning application. Consequently, evaluation and justification could now encompass both system performance as well as user performance.     

Investigating e-learning system usage outcomes in the university context

This paper investigates the outcomes of e-learning systems adoption and use by conceptualizing three e-learning systems adoption outcome constructs namely perceived learning assistance, perceived community building assistance and perceived academic performance. Utilizing these constructs, the paper proposes a research model for assessing the possible outcomes of e-learning systems adoption and use. The study collected longitudinal survey data from 249 university students participating in hybrid courses using a popular learning management system, Moodle. Partial least squares (PLS) approach was then used to test the research model.The findings suggest that beliefs about perceived usefulness and perceived ease of use, and how an e-learning system is used influence students' perceived learning assistance and perceived community building assistance. In turn, perceived learning assistance and perceived community building assistance influence the students' perceived academic performance.     

Clarifying the empirical connection of new entrants’ e-learning systems use to their job adaptation and their use patterns under the collective-individual training environment

In recent years, with the development of e-learning, it is feasible for enterprises to adopt information systems to enhance organizations’ human capital and knowledge renewal for competition. e-Learning systems designed for new entrants training aim to facilitate new entrants’ job adaptation; however, the empirical link between their system use and job adaptation lacks. In addition, the influence of environmental variables on new entrants’ e-learning training still needs clarification. Thus, based on the theoretical framework of the IS (information system) success model, this study is motivated to make an empirical connection of new entrants’ e-learning systems use to their job adaptation, including organizational socialization and overall job-adaptation outcome, and also to clarify their system use patterns under different collective-individual socialization training environments. Data from one hundred and eighty-six valid respondents who entered their organizations within a year were gathered and analyzed with PLS (partial least square). The results suggested the valid connection of new entrants’ e-learning systems use to their organizational socialization and overall job-adaptation outcome. New entrants also presented partially different system use patterns for adaptation under the different human interaction environments. The findings facilitated the design of training programs for new entrants at the e-learning environment.     

Behavioral intention,use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning

Widespread implementation of e-learning systems – learning management systems, virtual learning environments – across higher education institutions has aroused great interest on the study of e-learning acceptance. Acceptance studies focus on the predictors of system adoption and use, with behavioral intention to use the system as a proxy for actual use. This study proposes a TAM3-based model – with the inclusion of two additional variables: personal innovativeness in the domain of information technology and perceived interaction – to study the factors influencing the acceptance of e-learning systems. Attention is also brought towards the role of behavioral intention, especially in its relation with use behavior. In order to do so, two different settings were considered: higher education and lifelong learning; data was gathered from a survey administrated to Spanish graduate and lifelong learning students, and partial least squares analysis was used to test the research model. Results supported TAM relations, except for the intention-behavior linkage, and unveiled a dual nature of perceived usefulness – with one component related to efficiency and performance, and another component related to flexibility. The adequacy of applying TAM3-based models in educational contexts and suitability of actual system usage measures are also discussed.     

Student Behavior Modeling for an E-Learning System Offering Personalized Learning Experiences

With the advent of computing and communication technologies, it has become possible for a learner to expand his or her knowledge irrespective of the place and time. Web-based learning promotes active and independent learning. Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process. This digital learning improves the quality of teaching and also promotes educational equity. However, the challenges in e-learning platforms include dissimilarities in learner’s ability and needs, lack of student motivation towards learning activities and provision for adaptive learning environment. The quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional strategy. It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course. It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest level. In this research work, a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective attainment. Catering to the demands of e-learner, an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm. An adaptive e-learning system suits every category of learner, improves the learner’s performance and paves way for offering personalized learning experiences.     

Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning

Recommender systems in e-learning domain play an important role in assisting the learners to find useful and relevant learning materials that meet their learning needs. Personalized intelligent agents and recommender systems have been widely accepted as solutions towards overcoming information retrieval challenges by learners arising from information overload. Use of ontology for knowledge representation in knowledge-based recommender systems for e-learning has become an interesting research area. In knowledge-based recommendation for e-learning resources, ontology is used to represent knowledge about the learner and learning resources. Although a number of review studies have been carried out in the area of recommender systems, there are still gaps and deficiencies in the comprehensive literature review and survey in the specific area of ontology-based recommendation for e-learning. In this paper, we present a review of literature on ontology-based recommenders for e-learning. First, we analyze and classify the journal papers that were published from 2005 to 2014 in the field of ontology-based recommendation for e-learning. Secondly, we categorize the different recommendation techniques used by ontology-based e-learning recommenders. Thirdly, we categorize the knowledge representation technique, ontology type and ontology representation language used by ontology-based recommender systems, as well as types of learning resources recommended by e-learning recommenders. Lastly, we discuss the future trends of this recommendation approach in the context of e-learning. This study shows that use of ontology for knowledge representation in e-learning recommender systems can improve the quality of recommendations. It was also evident that hybridization of knowledge-based recommendation with other recommendation techniques can enhance the effectiveness of e-learning recommenders.     

Structural equation model (SEM)-neural network (NN) model for predicting quality determinants of e-learning management systems

The success of e-learning management systems (e-LMSs) such as MOODLE depends on the usage of students as well as instructor acceptance in a virtual learning environment. E-Learning enables instructors to access educational resources to support traditional classroom teaching. This paper attempts to develop a model to understand and predict the effect of individual characteristics (technology experience [TE] and personal innovativeness [PI]) and e-LMS quality determinants (system quality [SYS-Q], information quality, and service quality) on the continuous use of e-LMS by instructors, which is critical to its success. A total of 219 instructors using MOODLE responded to the survey. The structural equation model (SEM) was employed to test the proposed research model. The SEM results showed that SYS-Q, PI, service quality, and TE have a statistically significant influence on continuous usage of e-LMS by instructors. Furthermore, all determinants of the research model were given as input to an NN model to overcome the simplistic nature of the SEM model. The NN model results showed that service quality is the most important predictor of e-learning acceptance followed by SYS-Q, PI, information quality, and TE. This paper attempts to develop a causal and predictive statistical model for predicting instructor e-LMS acceptance.     

Faculty of medicine students' attitudes towards electronic learning and their opinion for an example of distance learning application

The impact of technology on every aspect of life has resulted in a change in the curricula of many Faculties of Medicine all around the world. The longstanding classical sense of education in Faculties of Medicine has given way to integrated and interactive applications, and more active applications, e.g. electronic learning (e-learning), have been distinguished among others over the last decade with the help of ever-developing technology. The first stage of this research involved assessing attitudes of 414 first-, second- and third-year students at the Hacettepe University Faculty of Medicine towards e-learning. The second stage, involving a session of “Ethics and Professionalism” within good medical practice (GMP), was synchronously conducted as a distance learning application with the participation of 17 students which was followed by receiving students' opinions. This study revealed that students' attitudes toward e-learning were neutral. With regards to the variables of gender, classroom level and academic success, faculty of medicine students' attitudes towards e-learning significantly differed in the variables of gender and academic success whereas it did not differ in the variable of classroom level. On the other hand, the students expressed that the distance learning application they had was very beneficial in terms of time. However, the students stated that the session of distance learning had limits for communication.     

User Profile Modeling in the context of web-based learning management systems

Over the past two decades, great research efforts have been made towards the personalization of e-learning platforms. This feature increases remarkably the quality of the provided learning services, since the users’ special needs and capabilities are respected. The idea of predicting the users’ preferences and adapting the e-learning platform accordingly is the focal point of this paper. In particular, this paper starts with the main requirements of an advanced e-learning system, explains the way a user navigates in such a system, presents the architecture of a corresponding e-learning system and describes its main components. Research is focused on the User Model component, its role in the e-learning system and the parameters that comprise it. In this context, Bayesian Networks are used as a tool for the encoding, learning and reasoning of probabilistic relationships, with the aim to effectively predict user preferences. In support of this vision, four different scenarios are presented, in order to test the way Bayesian Networks apply in the e-learning field.