Developing argumentation processing agents for computer-supported collaborative learning

In this study, an intelligent argumentation processing agent for computer-supported cooperative learning is proposed. Learners are first assigned to heterogeneous groups based on their learning styles questionnaire given right before the beginning of learning activities on the e-learning platform. The proposed argumentation processing agent then scrutinizes each learner’s learning portfolio on e-learning platform and automatically issues feedback messages in case devious argument or abnormal behavior that is unfitted to the learners’ learning style is detected. The Moodle (http://moodle.org), an open source software e-learning platform, is used to establish the cooperative learning environment for this study. The experimental results revealed that the learners benefited by the argumentation activity with the assistance of the proposed learning style aware argumentation processing agent.     

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.     

Supporting the development of synchronous text-based computer-mediated communication with an intelligent diagnosis tool

To date, a look at the scientific literature on the construction and use of synchronous computer-mediated communication (CMC) support environments reveals that most researchers have focused either on exchanging information or on constructing and presenting posts. In this work, an intelligent collaborative synchronous CMC platform that detects whether the learners address the expected discussion issues is proposed. The concept maps related to the learning topics are first outlined by the instructor. After each learner issues a post on the synchronous CMC platform, a feature selection approach is adopted to derive the input parameters of a one-class Support Vector Machines (SVMs) classifier. The classifier then determines if the learners’ posts are related to the concept maps previously outlined by the instructor. Meanwhile, learner peers from the same group are asked to provide comments on the synchronous CMCs, and a group grading module is established in this work to evaluate the quality of the synchronous CMCs. If the evaluation results from the classifier and the group grading module are inconsistent, the instructor or the teaching assistant is consulted to verify the evaluation results. Notably, a feedback rule construction mechanism is used to issue feedback messages to learners in cases where the synchronous CMC support system detects that the learners have strayed astray from the expected learning topics in their posts. The classification rates for the one-class SVM classifier can reach up to 97.06%, and the average pre-test and post-test grades were 51.94 and 66.77, respectively, which revealed that the junior high school students participating in synchronous CMC activities related to natural science were benefited by the proposed intelligent synchronous CMC platform.     

A genetic fuzzy expert system for automatic question classification in a competitive learning environment

Intelligent tutoring systems are efficient tools to automatically adapt the learning process to the student’s progress and needs. One of the possible adaptations is to apply an adaptive question sequencing system, which matches the difficulty of the questions to the student’s knowledge level. In this context, it is important to correctly classify the questions to be presented to students according to their difficulty level. Many systems have been developed for estimating the difficulty of questions. However the variety in the application environments makes difficult to apply the existing solutions directly to other applications. Therefore, a specific solution has been designed in order to determine the difficulty level of open questions in an automatic and objective way. This solution can be applied to activities with special temporal and running features, as the contests developed through QUESTOURnament, which is a tool integrated into the e-learning platform Moodle. The proposed solution is a fuzzy expert system that uses a genetic algorithm in order to characterize each difficulty level. From the output of the algorithm, it defines the fuzzy rules that are used to classify the questions. Data registered from a competitive activity in a Telecommunications Engineering course have been used in order to validate the system against a group of experts. Results show that the system performs successfully. Therefore, it can be concluded that the system is able to do the questions classification labour in a competitive learning environment.     

An algorithm for automatic checking of exercises in a dynamic geometry system: iGeom

One of the key issues in e-learning environments is the possibility of creating and evaluating exercises. However, the lack of tools supporting the authoring and automatic checking of exercises for specifics topics (e.g., geometry) drastically reduces advantages in the use of e-learning environments on a larger scale, as usually happens in Brazil. This paper describes an algorithm, and a tool based on it, designed for the authoring and automatic checking of geometry exercises. The algorithm dynamically compares the distances between the geometric objects of the student’s solution and the template’s solution, provided by the author of the exercise. Each solution is a geometric construction which is considered a function receiving geometric objects (input) and returning other geometric objects (output). Thus, for a given problem, if we know one function (construction) that solves the problem, we can compare it to any other function to check whether they are equivalent or not. Two functions are equivalent if, and only if, they have the same output when the same input is applied. If the student’s solution is equivalent to the template’s solution, then we consider the student’s solution as a correct solution. Our software utility provides both authoring and checking tools to work directly on the Internet, together with learning management systems. These tools are implemented using the dynamic geometry software, iGeom, which has been used in a geometry course since 2004 and has a successful track record in the classroom. Empowered with these new features, iGeom simplifies teachers’ tasks, solves non-trivial problems in student solutions and helps to increase student motivation by providing feedback in real time.     

Mobile formative assessment tool based on data mining techniques for supporting web-based learning

Current trends clearly indicate that online learning has become an important learning mode. However, no effective assessment mechanism for learning performance yet exists for e-learning systems. Learning performance assessment aims to evaluate what learners learned during the learning process. Traditional summative evaluation only considers final learning outcomes, without concerning the learning processes of learners. With the evolution of learning technology, the use of learning portfolios in a web-based learning environment can be beneficially adopted to record the procedure of the learning, which evaluates the learning performances of learners and produces feedback information to learners in ways that enhance their learning. Accordingly, this study presents a mobile formative assessment tool using data mining, which involves six computational intelligence theories, i.e. statistic correlation analysis, fuzzy clustering analysis, grey relational analysis, K-means clustering, fuzzy association rule mining and fuzzy inference, in order to identify the key formative assessment rules according to the web-based learning portfolios of an individual learner for the performance promotion of web-based learning. Restated, the proposed method can help teachers to precisely assess the learning performance of individual learner utilizing only the learning portfolios in a web-based learning environment. Hence, teachers can devote themselves to teaching and designing courseware, since they save a lot of time in measuring learning performance. More importantly, teachers can understand the main factors influencing learning performance in a web-based learning environment based on the interpretable learning performance assessment rules obtained. Experimental results indicate that the evaluation results of the proposed scheme are very close to those of summative assessment results and the factor analysis provides simple and clear learning performance assessment rules. Furthermore, the proposed learning feedback with formative assessment can clearly promote the learning performances and interests of learners.     

Acceptance of competency-based workplace e-learning systems: Effects of individual and peer learning support

Current endeavors to integrate competency-based learning approaches with e-learning systems designed for delivery of training to adult learners in the workplace are growing. However, academic efforts in examining learners’ perceptions of, and reactions toward, this technology-delivered pedagogical innovation are limited. Drawing together perspectives from the literature on training and instructional design and technology acceptance, this research proposed a conceptual model to examine the influences of perceived individual and social learning support on employees’ acceptance of competency-based e-learning systems. Structural equation modeling and multi-group structural equation modeling techniques were applied to sample data collected from work settings in Mainland China. The results show the positive effects of perceived individual learning support and perceived support for promoting a norm of cooperation on employees’ intention to use the competency-based e-learning system. It was also found that perceived support for enhancing social ties had a negative effect on employees’ behavioral intention. The gender, age, prior experience, and work experience differences in the main effects were also investigated. Besides, qualitative data collected via semi-structured in-depth interview provide complementary evidence to the findings.     

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.     

TEx-Sys model for building intelligent tutoring systems

Special classes of asynchronous e-learning systems are the intelligent tutoring systems which represent an advanced learning and teaching environment adaptable to individual student’s characteristics. Authoring shells have an environment that enables development of the intelligent tutoring systems. In this paper we present, in entirety, for the first time, our approach to research, development and implementation related to intelligent tutoring systems and ITS authoring shells. Our research relies on the traditional intelligent tutoring system, the consideration that teaching is control of learning and principles of good human tutoring in order to develop the Tutor–Expert System model for building intelligent tutoring systems in freely chosen domain knowledge. In this way we can wrap up an ongoing process that has lasted for the previous fifteen years. Prototype tests with the implemented systems have been carried out with students from a primary education to an academic level. Results of those tests are advantageous, according to surveys, and the implemented and deployed software satisfies functionalities and actors’ demands.     

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.     

Extending web-based educational systems with personalised support through User Centred Designed recommendations along the e-learning life cycle

In this paper we address an open key issue during the development of web-based educational systems. In particular, we provide an educational-oriented approach for building personalised e-learning environments that focuses on putting the learners' needs in the centre of the development process. Our approach proposes user centred design methodologies involving interdisciplinary teams of software developers and domain experts. It is illustrated in an adaptive e-learning system, where a MOOC (Massive Open Online Course) was taken by nearly 400 learners. In particular, we report where user centred design methods can be applied along the e-learning life cycle to designing and evaluating personalisation support through recommendations in learning management systems.     

Unifying heterogeneous e-learning modalities in a single platform: CADI, a case study

Current e-learning forms are commonly based on improving the learning process through the enhancement of certain skills in students, such as collaborative, competitive or problem-based learning. However, it seems that there is still no e-learning formula that gathers the implementation of a number of more generic educational principles in a single e-learning system or platform. Therefore, this paper presents a web-based framework for the creation, development and implementation of heterogeneous learning environments called CADI. It offers a graphical user interface for students to interact with only the need for any generic WiFi compliant device. Its innovative design allows the instructor to select and combine the developed resources to create varied activities, providing content adapted to every single device. The usefulness of the system was tested by introducing a case study to a group of senior college students. The experience showed that the use of the system helped students to achieve better results in their evaluation. It also showed that the students had no problems interacting with it from the outset, promoting their active learning. Design, implementation and assessment results of the system are also presented and analyzed.     

Learning motivation in e-learning facilitated computer programming courses

Computer programming skills constitute one of the core competencies that graduates from many disciplines, such as engineering and computer science, are expected to possess. Developing good programming skills typically requires students to do a lot of practice, which cannot sustain unless they are adequately motivated. This paper reports a preliminary study that investigates the key motivating factors affecting learning among university undergraduate students taking computer programming courses. These courses are supported by an e-learning system – Programming Assignment aSsessment System (PASS), which aims at providing an infrastructure and facilitation to students learning computer programming. A research model is adopted linking various motivating factors, self-efficacy, as well as the effect due to the e-learning system. Some factors are found to be notably more motivating, namely, ‘individual attitude and expectation’, ‘clear direction’, and ‘reward and recognition’. The results also suggest that a well facilitated e-learning setting can enhance learning motivation and self-efficacy.     

Comparing the social knowledge construction behavioral patterns of problem-based online asynchronous discussion in e/m-learning environments

In recent years, researchers have conducted various studies on applying wireless networking technology and mobile devices in education settings. However, research on behavioral patterns in learners' online asynchronous discussions with mobile devices is limited. The purposes of this study are to develop a mobile learning system, mobile interactive teaching feedback system (MITFS), linked to both mobile devices and the internet, to support learners with online asynchronous discussion, and combine content analysis and sequential analysis to compare and contrast the social knowledge construction behavioral patterns of problem-based asynchronous discussion in e-learning and m-learning environments. This study investigated four weeks of online discussions in an “Introduction to Computer Science” course involving forty first year university students. The control group (online asynchronous discussion without mobile devices) and the experimental group (online asynchronous discussion with mobile devices) in the group discussions were explored. By using content analysis and sequential analysis for the problem-based online asynchronous discussion of the behavioral patterns and differences between students in control and experimental group, the results showed that using mobile devices in online asynchronous discussion influenced students' learning performance. Some interesting results were found. Firstly, when the students used mobile devices in discussion situations, they could more engage in reflecting thinking, sharing more information, and further facilitating social knowledge construction among group members. Secondly, the experimental group performed better than the control group in terms of participation and diversity in knowledge construction behavioral patterns. Finally, based upon the findings, some implications are proposed for further research.     

eTeacher: Providing personalized assistance to e-learning students

In this paper we present eTeacher, an intelligent agent that provides personalized assistance to e-learning students. eTeacher observes a student’s behavior while he/she is taking online courses and automatically builds the student’s profile. This profile comprises the student’s learning style and information about the student’s performance, such as exercises done, topics studied, exam results. In our approach, a student’s learning style is automatically detected from the student’s actions in an e-learning system using Bayesian networks. Then, eTeacher uses the information contained in the student profile to proactively assist the student by suggesting him/her personalized courses of action that will help him/her during the learning process. eTeacher has been evaluated when assisting System Engineering students and the results obtained thus far are promising.     

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.     

Effects of feedback in a computer-based assessment for learning

The effects of written feedback in a computer-based assessment for learning on students’ learning outcomes were investigated in an experiment at a Higher Education institute in the Netherlands. Students were randomly assigned to three groups, and were subjected to an assessment for learning with different kinds of feedback. These are immediate knowledge of correct response (KCR) + elaborated feedback (EF), delayed KCR + EF, and delayed knowledge of results (KR). A summative assessment was used as a post-test. No significant effect was found for the feedback condition on student achievement on the post-test. Results suggest that students paid more attention to immediate than to delayed feedback. Furthermore, the time spent reading feedback was positively influenced by students’ attitude and motivation. Students perceived immediate KCR + EF feedback to be more useful for learning than KR. Students also had a more positive attitude towards feedback in a CBA when they received KCR + EF rather than KR only.     

Investigating students’ perceived satisfaction,behavioral intention,and effectiveness of e-learning: A case study of the Blackboard system

Although the benefits of e-learning have been discussed in various previous studies; it is a critical issue of better understanding the reasons why some learners are dissatisfied with the e-learning experience. Therefore, this research investigates learners’ satisfaction, behavioral intentions, and the effectiveness of the Blackboard e-learning system. A total of 424 university students were surveyed using a standard questionnaire. The results showed that perceived self-efficacy is a critical factor that influences learners’ satisfaction with the Blackboard e-learning system. Perceived usefulness and perceived satisfaction both contribute to the learners’ behavioral intention to use the e-learning system. Furthermore, e-learning effectiveness can be influenced by multimedia instruction, interactive learning activities, and e-learning system quality. This research proposes a conceptual model for understanding learners’ satisfaction, behavioral intention, and effectiveness of using the e-learning system.