A web-based e-learning system for increasing study efficiency by stimulating learner’s motivation

Due to the opportunities provided by the Internet, more and more people are taking advantage of distance learning courses and during the last few years enormous research efforts have been dedicated to the development of distance learning systems. So far, many e-learning systems are proposed and used practically. However, in these systems the e-learning completion rate is about 30%. One of the reasons is the low study desire when the learner studies the learning materials. In this research, we propose an interactive Web-based e-learning system. The purpose of our system is to increase the e-learning completion rate by stimulating learner’s motivation. The proposed system has three subsystems: the learning subsystem, learner support subsystem, and teacher support subsystem. The learning subsystem improves the learner’s study desire. The learner support subsystem supports the learner during the study, and the teacher support subsystem supports the teacher to get the learner’s study state. To evaluate the proposed system, we developed several experiments and surveys. By using new features such as: display of learner’s study history, change of interface color, encourage function, ranking function, self-determination of the study materials, and grouping of learners, the proposed system can increase the learning efficiency.

EGameFlow: A scale to measure learners’ enjoyment of e-learning games

In an effective e-learning game, the learner’s enjoyment acts as a catalyst to encourage his/her learning initiative. Therefore, the availability of a scale that effectively measures the enjoyment offered by e-learning games assist the game designer to understanding the strength and flaw of the game efficiently from the learner’s points of view. E-learning games are aimed at the achievement of learning objectives via the creation of a flow effect. Thus, this study is based on Sweetser’s & Wyeth’s framework to develop a more rigorous scale that assesses user enjoyment of e-learning games. The scale developed in the present study consists of eight dimensions: Immersion, social interaction, challenge, goal clarity, feedback, concentration, control, and knowledge improvement. Four learning games employed in a university’s online learning course “Introduction to Software Application” were used as the instruments of scale verification. Survey questionnaires were distributed to students taking the course and 166 valid samples were subsequently collected. The results showed that the validity and reliability of the scale, EGameFlow, were satisfactory. Thus, the measurement is an effective tool for evaluating the level of enjoyment provided by e-learning games to their users.     

Guiding knowledge communication in CSCL via group knowledge awareness

Computer-mediated collaboration is not in and of itself a beneficial setting for learning. Environments for computer-mediated collaboration need to trigger learning-productive interactions. In this paper, we propose to implement tools providing group knowledge awareness (GKA), i.e., information about collaborators’ knowledge. GKA is typically restricted in CSCL environments. A GKA tool visualizing self-assessed learner knowledge, their partner’s self-assessed knowledge, and thus the distribution of knowledge, was studied in a computer-mediated collaborative learning scenario. Thirty-eight dyads were randomly assigned to either the GKA condition (GKA tool) or a control condition (only learner’s own knowledge was visualized). Results show that the GKA tool guided learners in their collaboration and, more specifically, in designing their communicative acts. Depending on the self- vs. partner-oriented purpose of the communicative act, the learner’s own vs. the partner’s knowledge guided communication. Guided communication was a mechanism for perceived learning gains and perceived knowledge convergence. A knowledge test failed to reveal a significant difference between the GKA and the control condition. In this paper, we will discuss characteristics of GKA tools and their impact on collaboration.     

Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model

Although e-learning has been prompted to various education levels, the intention to continue using such systems is still very low, and the acceptance-discontinuance anomaly phenomenon (i.e., users discontinue using e-learning after initially accepting it) is a common occurrence. This paper synthesizes the expectation–confirmation model (ECM), the technology acceptance model (TAM), the theory of planned behavior (TPB), and the flow theory to hypothesize a theoretical model to explain and predict the users’ intentions to continue using e-learning. The hypothesized model is validated empirically using a sample collected from 363 learners of a Web-based learning program designed for continuing education. The results demonstrate that satisfaction has the most significant effect on users’ continuance intention, followed by perceived usefulness, attitude, concentration, subjective norm, and perceived behavior control as significant but weaker predictors. The implications of these findings for e-learning practitioners are discussed at the end of this work.     

Implementation and performance evaluation of parameter improvement mechanisms for intelligent e-learning systems

In recent years, designing useful learning diagnosis systems has become a hot research topic in the literature. In order to help teachers easily analyze students’ profiles in intelligent tutoring system, it is essential that students’ portfolios can be transformed into some useful information to reflect the extent of students’ participation in the curriculum activity. It is observed that students’ portfolios seldom reflect students’ actual studying behaviors in the learning diagnosis systems given in the literature; we thus propose three kinds of learning parameter improvement mechanisms in this research to establish effective parameters that are frequently used in the learning platforms. The proposed learning parameter improvement mechanisms can calculate the students’ effective online learning time, extract the portion of a message in discussion section which is strongly related to the learning topics, and detect plagiarism in students’ homework, respectively. The derived numeric parameters are then fed into a Support Vector Machine (SVM) classifier to predict each learner’s performance in order to verify whether they mirror the student’s studying behaviors. The experimental results show that the prediction rate for the SVM classifier can be increased up to 35.7% in average after the inputs to the classifier are “purified” by the learning parameter improvement mechanisms. This splendid achievement reveals that the proposed algorithms indeed produce the effective learning parameters for commonly used e-learning platforms in the literature.     

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.     

Automatic detection of learning styles for an e-learning system

A desirable characteristic for an e-learning system is to provide the learner the most appropriate information based on his requirements and preferences. This can be achieved by capturing and utilizing the learner model. Learner models can be extracted based on personality factors like learning styles, behavioral factors like user’s browsing history and knowledge factors like user’s prior knowledge. In this paper, we address the problem of extracting the learner model based on Felder–Silverman learning style model. The target learners in this problem are the ones studying basic science. Using NBTree classification algorithm in conjunction with Binary Relevance classifier, the learners are classified based on their interests. Then, learners’ learning styles are detected using these classification results. Experimental results are also conducted to evaluate the performance of the proposed automated learner modeling approach. The results show that the match ratio between the obtained learner’s learning style using the proposed learner model and those obtained by the questionnaires traditionally used for learning style assessment is consistent for most of the dimensions of Felder–Silverman learning style.     

Personalized web-based tutoring system based on fuzzy item response theory

With the rapid growth of computer and Internet technologies, e-learning has become a major trend in the computer assisted teaching and learning field. Previously, many researchers put effort into e-learning systems with personalized learning mechanism to aid on-line learning. However, most systems focus on using learner’s behaviors, interests, and habits to provide personalized e-learning services. These systems commonly neglect to consider if learner ability and the difficulty level of the recommended courseware are matched to each other. Frequently, unsuitable courseware causes learner’s cognitive overload or disorientation during learning. To promote learning effectiveness, our previous study proposed a personalized e-learning system based on Item response theory (PEL-IRT), which can consider both course material difficulty and learner ability evaluated by learner’s crisp feedback responses (i.e. completely understanding or not understanding answer) to provide personalized learning paths for individual learners. The PEL-IRT cannot estimate learner ability for personalized learning services according to learner’s non-crisp responses (i.e. uncertain/fuzzy responses). The main problem is that learner’s response is not usually belonging to completely understanding or not understanding case for the content of learned courseware. Therefore, this study developed a personalized intelligent tutoring system based on the proposed fuzzy item response theory (FIRT), which could be capable of recommending courseware with suitable difficulty levels for learners according to learner’s uncertain/fuzzy feedback responses. The proposed FIRT can correctly estimate learner ability via the fuzzy inference mechanism and revise estimating function of learner ability while the learner responds to the difficulty level and comprehension percentage for the learned courseware. Moreover, a courseware modeling process developed in this study is based on a statistical technique to establish the difficulty parameters of courseware for the proposed personalized intelligent tutoring system. Experiment results indicate that applying the proposed FIRT to web-based learning can provide better learning services for individual learners than our previous study, thus helping learners to learn more effectively.     

The technical framework of interactive functions for course-management systems: Students’ perceptions,uses, and evaluations

This study explores the interactivity of course-management systems (CMSs). First, this study reviews the concepts of interactivity, interactivity dimension, and interaction type on the basis of related theories and studies. Second, this study analyzes the interactive functions attributable to the six major CMSs in Taiwan colleges and universities, and re-constructs a technical framework containing five interaction types, nine interactivity dimensions, and 83 possible interactive functions. This study has found that a total of 21 interactive functions were featured in the six CMSs, while six functions identified from theories and research were not. In terms of interaction type, the results indicate that these six CMSs possessed the highest percentage of possible interactive functions for facilitating human interactions (e.g., learner–learner interaction and learner–instructor interaction), followed by learner–interface interaction and learner–self interaction, with the lowest percentage corresponding to learner–content interaction. In terms of interactivity dimension, these six CMSs seemed more likely to feature a learner-centered design approach than a system-centered one. Also, this study conducted user surveys on students’ perceptions, use, and evaluation of these interactive functions. A total of 491 valid sets of data were collected from six CMS user groups. The results indicate that, for their online learning, students considered the function of “Assignment handling” to be the most known, frequently used, and useful function. In addition, students were well familiar with, and made use of, any functions that would help them monitor or track their learning process. Students required more content-related interactive functions than were currently available in CMSs. Last, the regression results indicate that the more positively the students perceived the CMS interactivity, the usefulness of CMS for learning, and the interactive functions, the more positively these students perceived their CMSs.     

Personality and its effects on learning performance: Design guidelines for an adaptive e-learning system based on a user model

An increasingly widespread interest in developing fully adaptable e-learning systems (e.g., intelligent tutoring systems) has led to the development of a wide range of adaptive processes and techniques. In particular, advances in these systems are based on optimization for each user's learning style and characteristics, to enable a personalized learning experience. Current techniques are aimed at using a learner's personality traits and its effect on learning preferences to improve both the initial learning experience and the information retained (e.g., top-down or bottom-up learning organization). This study empirically tested the relationship between a learner's personality traits, analyzed the effects of these traits on learning preferences, and suggested design guidelines for adaptive learning systems. Two controlled experiments were carried out in a computer-based learning session. Our first experiment showed a significant difference in the learning performance of participants who were identified as introverts vs. those who were identified as being extroverts, according to the MBTI scale. As the distinction between extroverted personality types vs. introverted personality types showed the strongest correlation in terms of different learning styles, we used this criteria in our second experiment to determine whether design guidelines for appropriate content organization could reinforce the aforementioned correlation between personality type and learning experience.     

A learning style classification mechanism for e-learning

With the growing demand in e-learning, numerous research works have been done to enhance teaching quality in e-learning environments. Among these studies, researchers have indicated that adaptive learning is a critical requirement for promoting the learning performance of students. Adaptive learning provides adaptive learning materials, learning strategies and/or courses according to a student’s learning style. Hence, the first step for achieving adaptive learning environments is to identify students’ learning styles. This paper proposes a learning style classification mechanism to classify and then identify students’ learning styles. The proposed mechanism improves k-nearest neighbor (k-NN) classification and combines it with genetic algorithms (GA). To demonstrate the viability of the proposed mechanism, the proposed mechanism is implemented on an open-learning management system. The learning behavioral features of 117 elementary school students are collected and then classified by the proposed mechanism. The experimental results indicate that the proposed classification mechanism can effectively classify and identify students’ learning styles.     

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.     

Separating models of learning with faulty teachers

We study the power of two models of faulty teachers in Valiant’s PAC learning model and Angluin’s exact learning model. The first model we consider is learning from an incomplete membership oracle introduced by Angluin and Slonim [D. Angluin, D.K. Slonim, Randomly fallible teachers: Learning monotone DNF with an incomplete membership oracle, Machine Learning 14 (1) (1994) 7–26]. In this model, the answers to a random subset of the learner’s membership queries may be missing. The second model we consider is random persistent classification noise in membership queries introduced by Goldman, Kearns and Schapire [S. Goldman, M. Kearns, R. Schapire, Exact identification of read-once formulas using fixed points of amplification functions, SIAM Journal on Computing 22 (4) (1993) 705–726]. In this model, the answers to a random subset of the learner’s membership queries are flipped.     

Personalizing learning programs with X-Learn, an XML-based, “user-device” adaptive multi-agent system

In this paper we present X-Learn, an XML-based, multi-agent system for supporting “user-device” adaptive e-learning, i.e. e-learning activities which take into account the profile, past behaviour, preferences and needs of users, as well as the characteristics of the devices they use for these activities. X-Learn is characterized by the following features: (i) it is highly subjective, since it handles quite a rich and detailed user profile that plays a key role during the learning activities; (ii) it is dynamic and flexible, i.e. it is capable of reacting to variations in user exigencies and objectives; (iii) it is device-adaptive, since it decides the learning objects to present to the user on the basis of the device he is currently using; (iv) it is generic, i.e. it is capable of operating in a large variety of learning contexts; (v) it is XML based, since it exploits many facilities of XML technology for handling and exchanging information related to e-learning activities. The paper also reports various experimental results, as well as a comparison between X-Learn and other related e-learning management systems already presented in the literature.     

The influence of student characteristics on the use of adaptive e-learning material

Adaptive e-learning materials can help teachers to educate heterogeneous student groups. This study provides empirical data about the way academic students differ in their learning when using adaptive e-learning materials. Ninety-four students participated in the study. We determined characteristics in a heterogeneous student group by collecting demographic data and measuring motivation and prior knowledge. We also measured the learning paths students followed and learning strategies they used when working with adaptive e-learning material in a molecular biology course. We then combined these data to study if and how student characteristics relate to the learning paths and strategies they used. We observed that students did follow different learning paths. Gender did not have an effect, but (mainly Dutch) BSc students differed from (international) MSc students in the intrinsic motivation they had and the learning paths and strategies they followed when using the adaptive e-learning material.     

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.     

PC<Superscript>2</Superscript>PSO: personalized e-course composition based on Particle Swarm Optimization

This paper proposes a Personalized e-Course Composition approach based on Particle Swarm Optimization (PSO) algorithm, called PC²PSO, to compose appropriate e-learning materials into personalized e-courses for individual learners. The PC²PSO composes a personalized e-course according to (1) whether or not the covered learning concepts of the personalized e-course meets the expected learning target of a learner, (2) whether or not the difficulty of the e-learning material matches a learner’s ability, (3) the limitation of learning time for individual learners, and (4) the balance of the weight of learning concepts that are covered in a personalized e-course. PC²PSO can provide a truly personalized learning environment when used in conjunction with an Intelligent Tutoring System (ITS). When an e-course authoring tool is based on the proposed approach, the PC²PSO can facilitate instructors in selecting appropriate e-learning materials from a mass of candidate e-learning materials, and then saves time and effort in the e-course editing process.     

Affective impact of navigational and signaling aids to e-learning

College students had 30 min to study a 17-frame online lesson on distance learning that included navigational aids (for showing the learner’s location in the lesson), signaling aids (for highlighting the important content), both aids, or no aids. On a 30-item usability survey consisting of 8 usability scales, students who received navigational aids produced significantly higher mean ratings on each of the 8 usability scales—ease of use, satisfaction of use, awareness of lesson structure, awareness of lesson length, awareness of location, ease of navigation, lesson comprehension, and lesson learning—with effect sizes ranging from d = 0.50 to d = 1.35. Students who received signaling aids produced significantly higher ratings on 4 of the 8 usability scales—ease of use, satisfaction of use, lesson comprehension, and lesson learning with effect sizes ranging from d = 0.39 to d = 2.15. Results help to clarify the mechanism underlying previous findings showing that students learned more from e-lessons that contained navigational aids. In the present study, there was a significant positive correlation between usability rating and recall test score for 5 of the 8 usability scales (particularly for ease of use), indicating partial support for the prediction that learners’ satisfaction with an e-learning system is related to their learning outcome. Results support the predictions of the emotional design hypothesis and have implications for the design of e-learning interfaces.     

ERP training with a web-based electronic learning system: The flow theory perspective

Whilst the importance of end-user training is recognized as a factor in the success of information systems, companies have suffered from relatively low information system training budgets and an insufficient number of trainers. However, technological innovations in computers, telecommunications and the Internet, e-learning has made it possible to overcome many constraints. In this study, we suggest an e-learning success model based on flow theory. A questionnaire-based empirical study was used to test the model. It used data from e-learners who participated in a program on Enterprise Resource Planning training with a web-based e-learning system supported by the Korea Ministry of Information and Communication. Results confirm the significant interdependent relationships between the characteristics of e-learning, flow experience, learners’ attitude towards e-learning, and the resulting learning outcomes. In particular, it was revealed that flow experience plays a critical role as a central part of our research model, having direct and indirect impact on learning outcomes (i.e., the technology self-efficacy in ERP system usage in this study). This study should be of relevance to both researchers and practitioners alike, as a step towards a better understanding of e-learning, especially in the context of information system training.     

A new e-learning achievement evaluation model based on RBF-NN and similarity filter

As Internet rises fast in recent decades, teaching and learning tools based on Internet technology are rapidly applied in education. Learning through Internet can make learners absorb knowledge without the limitations on learning time and distance. Therefore, in academy, e-learning is one of the popular learning assistant instruments. Recently, “student-centered” instruction has become one of the primary approaches in education, and the e-learning system, which can provide the learning environment of personalization and adaptability, is more and more popular. By using e-learning system, teachers can adjust the learning schedule instantly for learners according to their learning achievements, and build more adaptive learning environments. However, in some cases, bias assessments are given for student achievements under specific uncontrollable conditions (i.e. tiredness, preference). In dire need of overcoming this predicament, a new model based on radial basis function neural networks (RBF-NN) and similarity filter to evaluate learning achievements is proposed. The proposed model includes three phases to reduce bias assessments: (1) preprocess: select important features (attributes) to enhance classification performance by feature selection methods and utilize minimal entropy principle approach (MEPA) to fuzzify the quantitative data, (2) similarity filter: select linguistic values for each feature and delete inconsistent data by the similarity threshold (similarity filter) and (3) construct classification model and accuracy evaluation: build the proposed model based on RBF-NN and evaluate model performance. To verify the proposed model, a practical achievement dataset, collected from e-learning online examination system in a university of Taiwan, is used as experiment dataset, and the performance of the proposed model is compared with the listing models in this paper. From the empirical study, it is shown that the proposed model provided more proper achievement evaluations than the listing models.