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.     

Content-free collaborative learning modeling using data mining

Modeling user behavior (user modeling) via data mining faces a critical unresolved issue: how to build a collaboration model based on frequent analysis of students in order to ascertain whether collaboration has taken place. Numerous human-based and knowledge-based solutions to this problem have been proposed, but they are time-consuming or domain-dependent. The diversity of these solutions and their lack of common characteristics are an indication of how unresolved this issue remains. Bearing this in mind, our research has made progress on several fronts. First, we have found supportive evidence, based on a collaborative learning experience with hundreds of students over three consecutive years, that an approach using domain independent learning that is transferable to current e-learning platforms helps both students and teachers to manage student collaboration better. Second, the approach draws on a domain-independent modeling method of collaborative learning based on data mining that helps clarify which user-modeling issues are to be considered. We propose two data mining methods that were found to be useful for evaluating student collaboration, and discuss their respective advantages and disadvantages. Three data sources to generate and evaluate the collaboration model were identified. Third, the features being modeled were made accessible to students in several meta-cognitive tools. Their usage of these tools showed that the best approach to encourage student collaboration is to show only the most relevant inferred information, simply displayed. Moreover, these tools also provide teachers with valuable modeling information to improve their management of the collaboration. Fourth, an ontology, domain independent features and a process that can be applied to current e-learning platforms make the approach transferable and reusable. Fifth, several open research issues of particular interest were identified. We intend to address these open issues through research in the near future.     

Investigating users’ perspectives on e-learning: An integration of TAM and IS success model

The purpose of this paper is to examine an integrated model of TAM and D&M to explore the effects of quality features, perceived ease of use, perceived usefulness on users’ intentions and satisfaction, alongside the mediating effect of usability towards use of e-learning in Iran. Based on the e-learning user data collected through a survey, structural equations modeling (SEM) and path analysis were employed to test the research model. The results revealed that “intention” and “user satisfaction” both had positive effects on actual use of e-learning. “System quality” and “information quality” were found to be the primary factors driving users’ intentions and satisfaction towards use of e-learning. At last, “perceived usefulness” mediated the relationship between ease of use and users’ intentions. The sample consisted of e-learning users of four public universities in Iran. Past studies have seldom examined an integrated model in the context of e-learning in developing countries. Moreover, this paper tries to provide a literature review of recent published studies in the field of e-learning.     

The influence of system characteristics on e-learning use

The benefits of an e-learning system will not be maximized unless learners use the system. This study proposed and tested alternative models that seek to explain student intention to use an e-learning system when the system is used as a supplementary learning tool within a traditional class or a stand-alone distance education method. The models integrated determinants from the well-established technology acceptance model as well as system and participant characteristics cited in the research literature. Following a demonstration and use phase of the e-learning system, data were collected from 259 college students. Structural equation modeling provided better support for a model that hypothesized stronger effects of system characteristics on e-learning system use. Implications for both researchers and practitioners are discussed.     

Critical success factors for e-learning acceptance: Confirmatory factor models

E-learning, one of the tools emerged from information technology, has been integrated in many university programs. There are several factors that need to be considered while developing or implementing university curriculums that offer e-learning based courses. This paper is intended to specify e-learning critical success factors (CSFs) as perceived by university students. The published e-learning critical success factors were surveyed and grouped into 4 categories namely, instructor, student, information technology, and university support. Each category included several measures. The categorization was tested by surveying 538 university students. The results revealed 8 categories of e-learning CSFs, each included several critical e-learning acceptance and success measures. The level of criticality of each measure was represented by its validity coefficient. Confirmatory factor modeling approach was used to assess the criticality of the measures included in each CSF category.     

Student modeling approaches: A literature review for the last decade

This paper constitutes a literature review on student modeling for the last decade. The review aims at answering three basic questions on student modeling: what to model, how and why. The prevailing student modeling approaches that have been used in the past 10 years are described, the aspects of students’ characteristics that were taken into consideration are presented and how a student model can be used in order to provide adaptivity and personalisation in computer-based educational software is highlighted. This paper aims to provide important information to researchers, educators and software developers of computer-based educational software ranging from e-learning and mobile learning systems to educational games including stand alone educational applications and intelligent tutoring systems. In addition, this paper can be used as a guide for making decisions about the techniques that should be adopted when designing a student model for an adaptive tutoring system. One significant conclusion is that the most preferred technique for representing the student’s mastery of knowledge is the overlay approach. Also, stereotyping seems to be ideal for modeling students’ learning styles and preferences. Furthermore, affective student modeling has had a rapid growth over the past years, while it has been noticed an increase in the adoption of fuzzy techniques and Bayesian networks in order to deal the uncertainty of student modeling.     

A technological acceptance of e-learning tools used in practical and laboratory teaching,according to the European higher education area 1

The application of scientific tools to analyse the use of Internet-based e-learning tools in academic settings is in general an ignored area. E-learning tools are actually an emergent topic as a result of the new ideas introduced by the European Higher Education Area. Lifelong learning, or the promotion of student initiative, is the new paradigm of a learner-centred education. In this context, e-learning tools can represent an effective way of supporting this new trend in education. Assuming the premise that successful use of these web-based tools depends primarily on a user's behaviour, the objective of this research is to examine the technology acceptance model (TAM) of web-based e-learning tools used in practical and laboratory teaching. The research hypotheses derived from this model have empirically been validated using the responses to a survey on e-learning usage among 220 users. These responses have been examined through partial least square. The obtained results strongly support the extended TAM in predicting a student's intention to use e-learning and define a set of external variables with a significant influence in the original TAM variables. Surprisingly, perceived ease of use did not posit a significant impact on student attitude or intention towards e-learning tool usage. Therefore, early evaluation of e-learning material is considered essential to providing a framework for further improvements of the tool.     

A fuzzy cognitive map model to calculate a user's desirability based on personality in e-learning environments

The recent research in artificial intelligence shows an increasing interest in the modeling of human behavior factors such as personality, mood, and emotion for developing human-friendly systems. That is why there is an interest in developing models and algorithms to determine a human's emotions while interacting with a system to improve the quality of the interaction. In this paper, we propose a computational model to calculate a user's desirability based on personality in e-learning environments. The desirability is one of the most important variables in determining a user's emotions. The model receives several e-learning environmental events and predicts the desirability of the events based on the user's personality and his/her goals. The proposed model has been evaluated in a simulated and real e-learning environment. The results show that the model formulates the relationship between personality and emotions with high accuracy.     

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.     

A method toward dynamic e-learning services modeling and the cooperative learning mechanism

The advanced information technologies have made it possible for individuals to carry out cooperative learning efficiently and effectively from anywhere and at any time. To capitalize on the individual need and address the issues associated with the late entry into the e-learning area, it has great significance to study the service mechanism of CSCL on e-learning service and e-learning service computing modeling. This paper proposes an e-learning service model supporting for the life-cycle process management. The proposed model is developed by considering the learner’s behaviours during e-learning services, the scheduling policies, and the monitoring mechanism of learning activities. Business process modeling for e-learning services can be taken according to the study ordering of the knowledge points by using workflow modeling technology and process enactment mechanism. The overall life-cycle process management of knowledge is addressed by combining knowledge product modeling, knowledge resource modeling, and credit polices for member selection in research team by considering trust value of learners, advisers and providers in e-learning services. The proposed method can be used for supporting the sustainable development of e-learning services from planning and design, organizing e-learning process, maintenance of the e-learning process, to process improvement, as well as to support learners and advisers to effectively complete innovative team study and complex computation study. Lastly, an extended topic map tool has been developed by adding a knowledge requirement level and an information extraction tool to validate the proposed methodology. These tools can used to guide learners to concentrate on the required knowledge topics and drive knowledge providers to redevelop outdated knowledge hierarchy.     

Evaluation of keyphrase extraction algorithm and tiling process for a document/resource recommender within e-learning environments

The research presented in this paper is an examination of the applicability of IUI techniques in an online e-learning environment. In particular we make use of user modeling techniques, information retrieval and extraction mechanisms and collaborative filtering methods. The domains of e-learning, web-based training and instruction and intelligent tutoring systems provide a challenging environment due to the large and diverse user population it entails. The overall system concentrates on utilizing a user modeling system to filter results as part of a collaborative document recommendation system. The goal of such a system is to actively seek out and recommend documents that will either encourage the users to expand their knowledge of a given topic or reinforce the knowledge which they already have. The system aims to recommend these documents in a non-intrusive manner with minimal user inconvenience, and attempts to do so by utilizing the Key Extraction Algorithm and automatically extracting queries, searching the web and filtering the search results. Users are encouraged to provide feedback about the resources and links they have viewed.     

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.     

Collaborative e-learning systems using semantic data interoperability

In a collaborative e-learning content management environment, the heterogeneity of data in different learning management systems presents many difficulties for data sharing; some of these difficulties are how to integrate data, produce results for user queries, and find the correct data from heterogeneous learning management systems. Over the last few years, numerous e-learning system architectures have been proposed; however, issues related to sharing and integrating data from different e-learning systems have been given less attention. Considering this need, this paper presents solutions for semantic data interoperability, distributed metadata management, and an agent-based query processing approach for supporting the exchange of learning content from different e-learning systems. The paper presents an empirical evaluation of the user acceptability of the proposed solutions to find qualitative measures of the users' acceptability and satisfaction; our proposed solutions resulted in high user satisfaction.     

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.     

PeRSIVA: An empirical evaluation method of a student model of an intelligent e-learning environment for computer programming

In this paper an evaluation method for assessing the effectiveness, accuracy and validity of a student model was presented. Our method is called PeRSIVA and is a combination of the well-known evaluation method of Kirkpatrick and the layered evaluation framework. These well-known and commonly used evaluation techniques have been selected in order to design an accurate and correct evaluation methodology, since there are no clear guidelines in the literature for the evaluation of the student model of an adaptive tutoring system. Furthermore, PeRSIVA method was used to evaluate the hybrid student model, which combines an overlay model with stereotypes and fuzzy logic techniques, of an e-learning system. Particularly, PeRSIVA assesses the results of student modeling in students' satisfaction, performance, progress, behavior and state, as well as the validity of the conclusions drawn by the student model and the validity of the adaptation decision making. The e-learning system was used by the students of a postgraduate program in the field of informatics in the University of Piraeus and the evaluation results demonstrated learning improvements in students and adaptation success to students' needs.     

Natural Language Processing and User Modeling: Synergies and Limitations

The fields of user modeling and natural language processing have been closely linked since the early days of user modeling. Natural language systems consult user models in order to improve their understanding of users' requirements and to generate appropriate and relevant responses. At the same time, the information natural language systems obtain from their users is expected to increase the accuracy of their user models. In this paper, we review natural language systems for generation, understanding and dialogue, focusing on the requirements and limitations these systems and user models place on each other. We then propose avenues for future research.     

Performance measurement of e-learning using student satisfaction analysis

The purpose of this paper is to analyze e-learning system quality through the analysis of student satisfaction and the usage of learning materials. This analysis takes into consideration both online and traditional students who are using the same e-learning system. The goal of the analysis is to identify factors which influence student satisfaction and to address heterogeneous styles and needs of both groups of students, so that future pedagogical and motivational methods in teaching and learning can be appropriately selected, developed and implemented. It was of particular interest to explore student satisfaction with quality of an e-learning system and online study approach. Reasons that may influence opinions of online and traditional students are examined and presented. The results are used to give recommendations for e-learning improvements and to propose the model with 4 groups of dimensions for performance measurement each of which best represents satisfaction of both groups of students.     

内容适配中基于本体的传输上下文模型*

在内容适配的研究中,传输上下文的描述语言得到了广泛的关注,然而现有的描述语言在语义表达能力和用户偏好描述方面存在不足。为此提出了一种基于本体的传输上下文模型,通过将本体引入建模过程,提高了模型的语义表达能力和用户偏好的描述能力,较好地满足了e learning背景下的内容适配的需求。     

Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies

With the rapid change in all types of working environment, there is a need to implement electronic learning (e-learning) systems to train people in new technologies, products, and services. However, the large investment in e-learning has made user acceptance an increasingly critical issue for technology implementation and management. Although user acceptance received fairly extensive attention in prior research, efforts were needed to examine or validate previous results, especially in different technologies, user populations, and/or organizational contexts. We therefore proposed a new construct, perceived credibility, to examine the applicability of the technology acceptance model (TAM) in explaining engineers’ decisions to accept e-learning, and address a pragmatic technology management issue. Based on a sample of 140 engineers taken from six international companies, the results strongly support the extended TAM in predicting engineers’ intention to use e-learning.