What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction

E-learning is emerging as the new paradigm of modern education. Worldwide, the e-learning market has a growth rate of 35.6%, but failures exist. Little is known about why many users stop their online learning after their initial experience. Previous research done under different task environments has suggested a variety of factors affecting user satisfaction with e-Learning. This study developed an integrated model with six dimensions: learners, instructors, courses, technology, design, and environment. A survey was conducted to investigate the critical factors affecting learners’ satisfaction in e-Learning. The results revealed that learner computer anxiety, instructor attitude toward e-Learning, e-Learning course flexibility, e-Learning course quality, perceived usefulness, perceived ease of use, and diversity in assessments are the critical factors affecting learners’ perceived satisfaction. The results show institutions how to improve learner satisfaction and further strengthen their e-Learning implementation.     

Multi-criteria evaluation of the web-based e-learning system: A methodology based on learner satisfaction and its applications

The web-based e-learning system (WELS) has emerged as a new means of skill training and knowledge acquisition, encouraging both academia and industry to invest resources in the adoption of this system. Traditionally, most pre- and post-adoption tasks related to evaluation are carried out from the viewpoints of technology. Since users have been widely recognized as being a key group of stakeholders in influencing the adoption of information systems, their attitudes toward this system are pivotal. Therefore, based on the theory of multi-criteria decision making and the research products of user satisfaction from the fields of human–computer interaction and information systems, this study proposed a multi-criteria methodology from the perspective of learner satisfaction to support those evaluation-based activities taking place at the pre- and post-adoption phases of the WELS life cycle. In addition, by following this methodology, this study empirically investigated learners’ perceptions of the relative importance of decision criteria. This investigation carried out a survey of college students, and the data thus obtained was then analyzed by analytic hierarchy process in order to derive an integrated preference structure of learners as a ground for evaluation. We found that learners regarded the learner interface as being the most important dimension of decision criteria. Future applications of these results are recommended and the implications are discussed.     

Developing early warning systems to predict students’ online learning performance

An early warning system can help to identify at-risk students, or predict student learning performance by analyzing learning portfolios recorded in a learning management system (LMS). Although previous studies have shown the applicability of determining learner behaviors from an LMS, most investigated datasets are not assembled from online learning courses or from whole learning activities undertaken on courses that can be analyzed to evaluate students’ academic achievement. Previous studies generally focus on the construction of predictors for learner performance evaluation after a course has ended, and neglect the practical value of an “early warning” system to predict at-risk students while a course is in progress. We collected the complete learning activities of an online undergraduate course and applied data-mining techniques to develop an early warning system. Our results showed that, time-dependent variables extracted from LMS are critical factors for online learning. After students have used an LMS for a period of time, our early warning system effectively characterizes their current learning performance. Data-mining techniques are useful in the construction of early warning systems; based on our experimental results, classification and regression tree (CART), supplemented by AdaBoost is the best classifier for the evaluation of learning performance investigated by this study.     

Multi-dimensional students’ evaluation of e-learning systems in the higher education context: An empirical investigation

There has been little research on assessment of learning management systems (LMS) within educational organizations as both a web-based learning system for e-learning and as a supportive tool for blended learning environments. This study proposes a conceptual e-learning assessment model, hexagonal e-learning assessment model (HELAM) suggesting a multi-dimensional approach for LMS evaluation via six dimensions: (1) system quality, (2) service quality, (3) content quality, (4) learner perspective, (5) instructor attitudes, and (6) supportive issues. A survey instrument based on HELAM has been developed and applied to 84 learners. This sample consists of students at both undergraduate and graduate levels who are users of a web-based learning management system, U-Link, at Brunel University, UK. The survey instrument has been tested for content validity, reliability, and criterion-based predictive validity. The analytical results strongly support the appropriateness of the proposed model in evaluating LMSs through learners’ satisfaction. The explanatory factor analysis showed that each of the six dimensions of the proposed model had a significant effect on the learners’ perceived satisfaction. Findings of this research will be valuable for both academics and practitioners of e-learning systems.     

Transactional distance revisited: Bridging face and empirical validity

Implementation of e-learning, whether in academic institutions or in the corporate world, is fast growing. While there has been a plethora of research in the field of e-learning, most empirical results remain inconsistent. One problem with such inconsistencies is the lack of clear takeaways that can guide practitioners on the best practices of e-learning. In this paper, we propose an overarching theoretical framework based on Moore’s transactional distance theory to examine e-learning. While this theory has existed for some time and has face validity, it has not received empirical support. We re-examine the core tenets of the theory, and test them in a manner that is ontologically consistent with the focus of the theory on learners’ perceptions, thereby bridging the gap between the theory’s face and empirical validity. We find strong support for the influence of transactional distance factors on our outcome of interest, i.e. individuals’ intentions to return for another e-learning experience. Our results help us arrive at contributions to research and practice, which include suggestions to enhance the success of e-learning initiatives.     

A study of student satisfaction in a blended e-learning system environment

This study proposes a research model that examines the determinants of student learning satisfaction in a blended e-learning system (BELS) environment, based on social cognitive theory. The research model is tested using a questionnaire survey of 212 participants. Confirmatory factor analysis (CFA) was performed to test the reliability and validity of the measurements. The partial least squares (PLS) method was used to validate the measurement and hypotheses. The empirical findings indicate that computer self-efficacy, performance expectations, system functionality, content feature, interaction, and learning climate are the primary determinants of student learning satisfaction with BELS. The results also show that learning climate and performance expectations significantly affect learning satisfaction. Computer self-efficacy, system functionality, content feature and interaction significantly affect performance expectations. Interaction has a significant effect on learning climate. The findings provide insight into those factors that are likely significant antecedents for planning and implementing a blended e-learning system to enhance student learning satisfaction.     

Developing an assessment-centered e-Learning system for improving student learning effectiveness

This research used Web-based two-tier diagnostic assessment and Web-based dynamic assessment to develop an assessment-centered e-Learning system, named the ‘GPAM-WATA e-Learning system.’ This system consists of two major designs: (1) personalized dynamic assessment, meaning that the system automatically generates dynamic assessment for each learner based on the results of the pre-test of the two-tier diagnostic assessment; (2) personalized e-Learning material adaptive annotation, meaning that the system annotates the e-Learning materials each learner needs to enhance learning based on the results of the pre-test of the two-tier diagnostic assessment and dynamic assessment. This research adopts a quasi-experimental design, applying GPAM-WATA e-Learning system to remedial Mathematics teaching of the ‘Speed’ unit in an elementary school Mathematics course. 107 sixth-graders from four classes in an elementary school participated in this research (55 male and 52 female). With each class as a unit, they were divided into four different e-Learning models: (1) the personalized dynamic assessment and personalized e-Learning material adaptive annotation group (n = 26); (2) the personalized dynamic assessment and non-personalized e-Learning material adaptive annotation group (n = 28); (3) the non-personalized dynamic assessment and personalized e-Learning material adaptive annotation group (n = 26); and (4) the non-personalized dynamic assessment and non-personalized e-Learning material adaptive annotation group (n = 27). Before remedial teaching, all students took the prior knowledge assessment and the pre-test of the summative assessment and two-tier diagnostic assessment. Students then received remedial teaching and completed all teaching activities. After remedial teaching, all students took the post-test of the summative assessment and two-tier diagnostic assessment. It is found that compared to the e-Learning models without personalized dynamic assessment, e-Learning models with personalized dynamic assessment are significantly more effective in facilitating student learning achievement and improvement of misconceptions, especially for students with low-level prior knowledge. This research also finds that personalized e-Learning material adaptive annotation significantly affects the percentage of reading time students spend on the e-Learning materials they need to enhance learning. However, it does not appear to predict student learning achievement and improvement of misconceptions.     

An activity-theoretical approach to investigate learners’ factors toward e-learning systems

The Internet and World Wide Web have provided opportunities of developing e-learning systems. The development of e-learning systems has started a revolution for instructional content delivering, learning activities, and social communication. Based on activity theory, the purpose of this research is to investigate learners’ attitude factors toward e-learning systems. A total 168 participants were asked to answer a questionnaire. After factor analysis, learners’ attitudes can be grouped four different factors – e-learning as a learner autonomy environment, e-learning as a problem-solving environment, e-learning as a multimedia learning environment, and teachers as assisted tutors in e-learning. In addition, this research approves that activity theory is an appropriate theory for understanding e-learning systems. Furthermore, this study also provides evidence that e-learning as a problem-solving environment can be positively influenced by three other factors.     

Agent based affective tutoring systems: A pilot study

An important trend in the development of Intelligent tutoring systems (ITSs) has been that providing the student with a more personalized and friendly environment for learning. Many researchers now feel strongly that the ITSs would significantly improve performance if they could adapt to the affective state of the learner. This idea has spawned the developing field of affective tutoring systems (ATSs): ATSs are ITSs that are able to adapt to the affective state of students. However, ATSs are not widely employed in the tutoring system market. In this paper, a survey was conducted to investigate the critical factors affecting learner’s satisfaction in ATSs based on an ATS developed by us. The results revealed that learner’s attitude toward affective computing, agent tutor’s expressiveness, emotion recognition accuracy, number of emotions recognized by agent tutor, pedagogical action and easy of the use of the system have significant influence on learner’s satisfaction. The results indicate institutions how to further strengthen the ATSs’ implementation.     

Investigating university student preferences and beliefs about learning in the web-based context

Psychological studies have shown that personal beliefs about learning and environmental preferences affect learning behaviors. However, these learner characteristics have not been widely discussed in the web-based context. By developing questionnaires, this study attempted to detect learners’ web-based learning environmental preferences (WLEP) and beliefs about web-based learning (BWL). The scope of WLEP focused on the pedagogical dimension of the web-based learning environment, while BWL concerned the attributes and control factors of the web-based learning. There were about five hundreds of Taiwan university students participating in the study. Through factor analysis, the scales discussed in the study revealed a satisfactory validity and reliability in assessing students’ preferences and beliefs. Further analyses showed that university students preferred more of individual and structured instructional configurations while expected the outward mode of interaction. In general, students held a rather contextual belief about web-based learning, which was found to be correlated with their environmental preferences.     

The contribution of learner characteristics in the development of computer-based adaptive learning environments

The development of learner models takes an active part in upcoming adaptive learning environments. The purpose of learner models is to drive personalization based on learner and learning characteristics that are considered as important for the learning process, such as cognitive, affective and behavioral variables. Despite the huge amount of theoretical propositions of learner characteristics considered as relevant for learner models, practical payoffs are rather sparse. This study aims to overview the empirical research on the mere value of learner models in the development of adaptive learning environments. The results show that a lot of high-quality studies are situated in a rather shattered research field, building few bridges from theory to practice. We conclude with the call for a theory or framework integrating current and past research results that is able to guide theory-based and systematic empirical research having concrete hypotheses on the merits of learner characteristics in adaptive learning environments.     

Learning with hypermedia: The influence of representational formats and different levels of learner control on performance and learning behavior

In this paper, two experiments on the use of hypermedia environments for learning about probability theory are reported. In Experiment 1a it was tested whether multimedia design principles (multimedia principle, modality principle, redundancy principle) are valid in hypermedia environments, despite the fact that hypermedia offers more learner control than multimedia. The results showed only little evidence for this validity, although the hypermedia environment entailed only a rather low level of learner control. In Experiment 1b it was investigated how learner control affects performance and how its possible impact is moderated by learners’ prior knowledge. A high level of learner control positively affected the effectiveness of instruction only with regard to intuitive knowledge, but was at the same time accompanied by large increases in learning time, thereby rendering the instruction inefficient. Unexpectedly, effects of learner control were not moderated by students’ prior knowledge. The results imply that the idea to use multimedia design principles for hypermedia learning is too simple and that the benefits and drawbacks of learner control depend heavily on learning objectives and time constraints.     

Factors influencing the performance of Dynamic Decision Network for INQPRO

There has been an increasing interest in employing decision-theoretic framework for learner modeling and provision of pedagogical support in Intelligent Tutoring Systems (ITSs). Much of the existing learner modeling research work focuses on identifying appropriate learner properties. Little attention, however, has been given to leverage Dynamic Decision Network (DDN) as a dynamic learner model to reason and intervene across time. Employing a DDN-based learner model in a scientific inquiry learning environment, however, remains at infant stage because there are factors contributed to the performance the learner model. Three factors have been identified to influence the matching accuracy of INQPRO’s learner model. These factors are the structure of DDN model, the variable instantiation approach, and the weights assignment method for two consecutive Decision Networks (DNs). In this research work, a two-phase empirical study involving 107 learners and six domain experts was conducted to determine the optimal conditions for the INQPRO’s dynamic learner model. The empirical results suggested each time-slice of the INQPRO’s DDN should consist of a DN, and that DN should correspond to the Graphical User Interface (GUI) accessed. In light of evidence, observable variables should be instantiated to their observed states; leaving the remaining observable nodes uninstantiated. The empirical results also indicated that varying weights between two consecutive DNs could optimize the matching accuracy of INQPRO’s dynamic learner model.     

e-Learning process management and the e-learning performance: Results of a European empirical study

The aim of this paper was to design and assess a comprehensive model for managing the e-learning process and to define the relationship between systematic implementation of the model, outcomes of certain e-learning aspects and subject of e-learning. The validation of the model was performed by using two questionnaires sent via e-mail to teachers and field experts from the chosen sample of 14 European schools participating in an EU-funded project. Research results imply the existence of a clear link between planning and controlling of the e-learning process and its learning outcomes. On the other hand, no empirical relationship between the e-learning outcomes and the subject of learning has been established. It is believed that the model and its practical implications can be used by institutions engaged in e-learning, or as a process model for introducing e-learning related activities.     

Factors of learner–instructor interaction which predict perceived learning outcomes in online learning environment

Interaction in the online learning environment has been regarded as one of the most critical elements that affect learning outcomes. This study examined what factors in learner–instructor interaction can predict the learner's outcomes in the online learning environment. Learners in K Online University participated by answering the survey, and data from 654 respondents were analysed for this study. Results showed that factors related to instructional interaction predicted perceived learning achievement and satisfaction better than factors related to social interaction. However, it was revealed that social interaction such as social intimacy could negatively affect perceived learning achievement and satisfaction. This study has value because it found factors under learner–instructor interaction which predict perceived learning achievement and satisfaction with empirical evidence.     

多目标的学习者模型研究

建立学习者模型几乎是所有ITS系统的普遍任务,当前的软件系统趋向于转入分布式和多代理系统,学习者模型转向零散的、由各种软件代理在特定环境下产生的模型。在“个性化课件生成子系统”中运用了这一思想,建立了基于多目标的、运行于分布式与多代理系统之上的学习者模型,该文将主要介绍这一系统的基本构成与运行结构、系统中目标的定义与组织结构,并对目标的解释、目标的生存周期等问题做了进一步的探讨。     

The impact of learning design on student behaviour,satisfaction and performance: A cross-institutional comparison across 151 modules

Pedagogically informed designs of learning are increasingly of interest to researchers in blended and online learning, as learning design is shown to have an impact on student behaviour and outcomes. Although learning design is widely studied, often these studies are individual courses or programmes and few empirical studies have connected learning designs of a substantial number of courses with learning behaviour. In this study we linked 151 modules and 111.256 students with students' behaviour (<400 million minutes of online behaviour), satisfaction and performance at the Open University UK using multiple regression models. Our findings strongly indicate the importance of learning design in predicting and understanding Virtual Learning Environment behaviour and performance of students in blended and online environments. In line with proponents of social learning theories, our primary predictor for academic retention was the time learners spent on communication activities, controlling for various institutional and disciplinary factors. Where possible, appropriate and well designed communication tasks that align with the learning objectives of the course may be a way forward to enhance academic retention.     

Attentiveness assessment in learning based on fuzzy logic analysis

Learner attention affects learning efficiency. However, in many classes, teachers cannot assess the degree of attention of every learner. When a teacher is capable of addressing inattentive learners immediately, he can avoid situations in which learners are inattentive. Many studies have analyzed driver attentiveness by the applying of image detection technologies. If this mechanism can be applied to in-class learning, it will help teachers keep learners attentive, and reduce teacher load during class. This study mainly applies fuzzy logic analysis of learner facial images when participating in class. Two fuzzy logic algorithms are proposed to determine the level of inattention by measuring the leaving, drowsiness, head turning and no motion. Applying fuzzy logic can prevent erroneous judgments associated with a single term, and help teachers deal with learner attentiveness. The simulation works are carried to evaluate the effect of the proposed system under various conditions. The simulation results indicated that the proposed system is effective for detecting of learner attentiveness in class.     

Community-organizing agent: An artificial intelligent system for building learning communities among large numbers of learners

Web-based (or online) learning provides an unprecedented flexibility and convenience to both learners and instructors. However, large online classes relying on instructor-centered presentations could tend to isolate many learners. The size of these classes and the wide dispersion of the learners make it challenging for instructors to interact with individual learners or to facilitate learner collaborations. Since extensive literature has confirmed that the substantial impact of learner interaction on learning outcomes, it is pedagogically critical to help distributed learners engage in community-based collaborative learning and to help individual learners improve their self-regulation. The E-learning lab of Shanghai Jiaotong University created an artificial intelligence system to help guide learners with similar interests into reasonably sized learning communities. The system uses a multi-agent mechanism to organize and reorganize supportive communities based on learners’ learning interests, experiences, and behaviors. Through effective award and exchange algorithms, learners with similar interests and experiences will form a community to support each others’ learning. Simulated experimental results indicate that these algorithms can improve the speed and efficiency in identifying and grouping homogeneous learners. Here, we will describe this system in detail and present its mechanism for organizing learning communities. We will conduct human experimentations in the near future to further perfect the system.     

基于本体的e-Learning环境个性化服务处理方法*

为了向e-Learning环境中的学习者提供符合其个性化需求的学习服务,结合本体论具有概念和关系定义明确的特性,提出了e-Learning环境中学习者的个性化情形本体模型和相应的学习者个性化服务处理方法,该方法综合考虑了学习者的认知状态和学习偏好,进行个性化的答疑和进一步学习的内容推荐。采用该方法实现的原型系统实验表明,可使学习者的学习更有针对性,可更及时有效地消解疑惑,从而提高了学习者的学习效果和效率。