Designing an adaptive web-based learning system based on students’ cognitive styles identified online

This study developed an adaptive web-based learning system focusing on students’ cognitive styles. The system is composed of a student model and an adaptation model. It collected students’ browsing behaviors to update the student model for unobtrusively identifying student cognitive styles through a multi-layer feed-forward neural network (MLFF). The MLFF was adopted because of its ability on imprecise or incompletely understood data, ability to generalize and learn from specific examples, ability to be quickly updated with extra parameters, and speed in execution making them ideal for real time applications. The system then adaptively recommended learning content presented with a variety of content and interactive components through the adaptation model based on the student cognitive style identified in the student model. The adaptive web interfaces were designed by investigating the relationships between students’ cognitive styles and browsing patterns of content and interactive components. Training of the MLFF and an experiment were conducted to examine the accuracy of identifying students’ cognitive styles during browsing with the proposed MLFF and the impact of the proposed adaptive web-based system on students’ engagement in learning. The training results of the MLFF showed that the proposed system could identify students’ cognitive styles with high accuracy and the temporal effects should be considered while identifying students’ cognitive styles during browsing. Two factors, the acknowledgment of students’ cognitive styles while browsing and the existence of adaptive web interfaces, were used to assign three classes of college freshmen into three groups. The experimental results revealed that the proposed system could have significant impacts on temporal effects on students’ engagement in learning, not only for students with cognitive styles known before browsing, but also for students with cognitive styles identified during browsing. The results provide evidence of the effectiveness of the adaptive web-based learning system with students’ cognitive styles dynamically identified during browsing, thus validating the research purposes of this study.     

Performance evaluation of an online argumentation learning assistance agent

Recent research indicated that students’ ability to construct evidence-based explanations in classrooms through scientific inquiry is critical to successful science education. Structured argumentation support environments have been built and used in scientific discourse in the literature. To the best of our knowledge, no research work in the literature addressed the issue of automatically assessing the student’s argumentation quality, and the teaching load of the teacher that used the online argumentation support environments is not alleviated. In this work, an intelligent argumentation assessment system based on machine learning techniques for computer supported cooperative learning is proposed. Learners’ arguments on discussion board were examined by using argumentation element sequence to detect whether the learners address the expected discussion issues and to determine the argumentation skill level achieved by the learner. Learners are first assigned to heterogeneous groups based on their responses to the learning styles questionnaire given right before the beginning of learning activities on the e-learning platform. A feedback rule construction mechanism is used to issue feedback messages to the learners in case the argumentation assessment system detects that the learners go in a biased direction. The Moodle, an open source software e-learning platform, was used to establish the cooperative learning environment for this study. The experimental results exhibit that the proposed work is effective in classifying and improving student’s argumentation level and assisting the students in learning the core concepts taught at a natural science course on the elementary school level.     

Effects of teaching and learning styles on students’ reflection levels for ubiquitous learning

Ubiquitous learning (u-learning), in conjunction with supports from the digital world, is recognized as an effective approach for situating students in real-world learning environments. Earlier studies concerning u-learning have mainly focused on investigating the learning attitudes and learning achievements of students, while the causations such as learning style and teaching style were usually ignored. This study aims to investigate the effects of teaching styles and learning styles on reflection levels of students within the context of u-learning. In particular, we investigated the teaching styles at the dimensions of brainstorming and instruction and recall and the learning styles at the dimensions of active and reflective learning. The experiment was conducted with 39 fifth grader students at an elementary school in southern Taiwan. A u-learning environment was established at a butterfly ecology garden to conduct experiments for natural science courses. The experimental results of one-way ANCOVA show that those students who received a matching teaching–learning style presented a significant improvement in their reflection level. That is, matching the learning styles of students with the appropriate teaching styles can significantly improve students’ reflection levels in a u-learning environment.     

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.     

AH-questionnaire: An adaptive hierarchical questionnaire for learning styles

One of the main concerns when providing learning style adaptation in Adaptive Educational Hypermedia Systems is the number of questions the students have to answer. Most of the times, adaptive material available will discriminate among a few categories for each learning style dimension. Consequently, it is only needed to take into account the general tendency of the student and not the specific score obtained in each dimension. In this context, we present AH-questionnaire, a new approach to minimize the number of questions needed to classify student Learning Styles. Based on the Felder-Silverman’s Learning Style Model, it aims at classifying students into categories in spite of providing precise scores. The results obtained in a case study with 330 students are very promising. It was possible to predict students’ learning style preference with high accuracy and only a few questions.     

A study on e-learning take-up intention from an innovation adoption perspective: A case in China

This research aims to examine, from an innovation adoption perspective, Chinese students’ intention of taking up e-learning degrees. A survey of Chinese students was conducted to reveal their perceptions concerning innovation attributes relevant to e-learning and their intentions of taking e-learning programmes provided by UK universities. Given the rapid development in e-learning and its potential impact on how learning takes place, this research argues that e-learning take-up represents adoption of an innovation in educational services, rather than just an IT technology. It therefore examined e-learning adoption using Rogers’s relational model of perceived innovation attributes. Rogers’s model was adapted to the e-learning context. A questionnaire survey was developed to collect data from a sample of Chinese students (n = 215). Prior to final analysis the dimensionality and validity of the implementation of Rogers relational model was assessed. Findings suggested that only perceived compatibility and trialability have significant influence on e-learning adoption intention.     

Personalized Learning Course Planner with E-learning DSS using user profile

Various methods of E-learning systems, based on information and communications, and geared towards improving learning effectiveness and students’ attention span, have been studied. However, most E-learning systems force students to follow the learning course or content established by a teacher. These methods are convenient, but they limit the effectiveness of E-learning.To overcome this limitation and increase effective learning, new techniques that reflect alternative learning styles, such as adaptive learning and personalized learning, have been studied. In this study, we proposed a Personalized Learning Course Planner (PLCP) that allows students to easily select the learning course they desire. User profile data was collected from the students’ initial priorities about learning contents as well as the test scores after their study. E-Learning Decision Support System (EL-DSS) in PLCP suggests an appropriate learning course organization, according to calculated results based on the user profile data.To verify the effectiveness of the proposed system, we implemented an English learning system consisting of PLCP. We conducted an experiment with 30 university students and evaluated students’ satisfaction by questionnaire analysis. The results indicate that the proposed system improved learning effectiveness and student satisfaction. Further investigation of the participants indicated that suggesting a learning course suitable for students’ previous test scores and priorities encouraged students to concentrate on the lesson.     

Students’ perceptions of collaboration, self-regulated learning, and information seeking in the context of Internet-based learning and traditional learning

This study aims to investigate students’ perceptions of three aspects of learning - collaboration, self-regulated learning (SRL), and information seeking (IS) in both Internet-based and traditional face-to-face learning contexts. A multi-dimensional questionnaire was designed to evaluate each aspect in terms of perceived capability, experience, and interest. The analyses explore (1) potential differences of students’ perceptions between Internet-based and face-to-face learning environments and (2) potential differences in the three aspects in relation to learners’ attributes and the use of the Internet and enrollment in online courses. This study surveyed students in a higher education institute who had had experiences with Internet-based and face-to-face learning. The results showed that students perceived higher levels of collaboration (capability only), SRL (capability and experience) and IS (capability, interest, and experience) in Internet-based learning than in traditional learning environments. In terms of students’ education level, graduate students perceived higher levels of capabilities and interests in some of the aspects, than undergraduate students. In addition, for Internet-based learning, significant differences in collaboration and SRL were found derived from time spent on the Internet related to learning; and students’ perceptions of collaboration, SRL, and IS were all positively correlated to students’ online course-taking experience. Implications for online learning practices and instructor’s facilitation are discussed.     

A triangle area based nearest neighbors approach to intrusion detection

Intrusion detection is a necessary step to identify unusual access or attacks to secure internal networks. In general, intrusion detection can be approached by machine learning techniques. In literature, advanced techniques by hybrid learning or ensemble methods have been considered, and related work has shown that they are superior to the models using single machine learning techniques. This paper proposes a hybrid learning model based on the triangle area based nearest neighbors (TANN) in order to detect attacks more effectively. In TANN, the k-means clustering is firstly used to obtain cluster centers corresponding to the attack classes, respectively. Then, the triangle area by two cluster centers with one data from the given dataset is calculated and formed a new feature signature of the data. Finally, the k-NN classifier is used to classify similar attacks based on the new feature represented by triangle areas. By using KDD-Cup ’99 as the simulation dataset, the experimental results show that TANN can effectively detect intrusion attacks and provide higher accuracy and detection rates, and the lower false alarm rate than three baseline models based on support vector machines, k-NN, and the hybrid centroid-based classification model by combining k-means and k-NN.     

Determining factors of the use of e-learning environments by university teachers

E-learning environments increasingly serve as important infrastructural features of universities that enable teachers to provide students with different representations of knowledge and to enhance interaction between teachers and students and amongst students themselves. This study was designed to identify factors that can explain teachers’ use of e-learning environments in higher education. A questionnaire was completed by 178 teachers from a wide variety of departments at Wageningen University in the Netherlands. We found that 43% of the total variance in teacher use of e-learning environments could be explained by their opinions about web-based activities and their opinions about computer-assisted learning (predictors) and the perceived added value of e-learning environments (mediating variable). In other words, teachers’ use of e-learning environments can be explained to a high extent by their perceptions of the added value of these environments, which in turn are substantially influenced by their opinions about web-based activities and computer-assisted learning.     

Learning styles' recognition in e-learning environments with feed-forward neural networks

Abstract People have unique ways of learning, which may greatly affect the learning process and, therefore, its outcome. In order to be effective, e-learning systems should be capable of adapting the content of courses to the individual characteristics of students. In this regard, some educational systems have proposed the use of questionnaires for determining a student learning style; and then adapting their behaviour according to the students' styles. However, the use of questionnaires is shown to be not only a time-consuming investment but also an unreliable method for acquiring learning style characterisations. In this paper, we present an approach to recognize automatically the learning styles of individual students according to the actions that he or she has performed in an e-learning environment. This recognition technique is based upon feed-forward neural networks.     

Evaluating Bayesian networks’ precision for detecting students’ learning styles

Students are characterized by different learning styles, focusing on different types of information and processing this information in different ways. One of the desirable characteristics of a Web-based education system is that all the students can learn despite their different learning styles. To achieve this goal we have to detect how students learn: reflecting or acting; steadily or in fits and starts; intuitively or sensitively. In this work, we evaluate Bayesian networks at detecting the learning style of a student in a Web-based education system. The Bayesian network models different aspects of a student behavior while he/she works with this system. Then, it infers his/her learning styles according to the modeled behaviors. The proposed Bayesian model was evaluated in the context of an Artificial Intelligence Web-based course. The results obtained are promising as regards the detection of students’ learning styles. Different levels of precision were found for the different dimensions or aspects of a learning style.     

E-Learning personalization based on hybrid recommendation strategy and learning style identification

Personalized learning occurs when e-learning systems make deliberate efforts to design educational experiences that fit the needs, goals, talents, and interests of their learners. Researchers had recently begun to investigate various techniques to help teachers improve e-learning systems. In this paper, we describe a recommendation module of a programming tutoring system - Protus, which can automatically adapt to the interests and knowledge levels of learners. This system recognizes different patterns of learning style and learners’ habits through testing the learning styles of learners and mining their server logs. Firstly, it processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the learners through mining the frequent sequences by the AprioriAll algorithm. Finally, this system completes personalized recommendation of the learning content according to the ratings of these frequent sequences, provided by the Protus system. Some experiments were carried out with two real groups of learners: the experimental and the control group. Learners of the control group learned in a normal way and did not receive any recommendation or guidance through the course, while the students of the experimental group were required to use the Protus system. The results show suitability of using this recommendation model, in order to suggest online learning activities to learners based on their learning style, knowledge and preferences.     

Integrating learning styles and adaptive e-learning system: Current developments,problems and opportunities

Learning styles which refer to students’ preferred ways to learn can play an important role in adaptive e-learning systems. With the knowledge of different styles, the system can offer valuable advice and instructions to students and teachers to optimise students’ learning process. Moreover, e-leaning system which allows computerised and statistical algorithms opens the opportunity to overcome drawbacks of the traditional detection method that uses mainly questionnaire. These appealing reasons have led to a growing number of researches looking into the integration of learning styles and adaptive learning system. This paper, by reviewing 51 studies, delves deeply into different parts of the integration process. It captures a variety of aspects from learning styles theories selection in e-learning environment, online learning styles predictors, automatic learning styles classification to numerous learning styles applications. The results offer insights into different developments, achievements and open problems in the field. Based on these findings, the paper also provides discussion, recommendations and guidelines for future researches.     

Students’ expectations of,and experiences in e-learning: Their relation to learning achievements and course satisfaction

Within only a few years, the use of e-learning has increased rapidly in Austria. In certain subjects, up to 60% of university students report using e-learning platforms at least ‘sometimes’ or ‘frequently’ (Unger & Wroblewski, 2006). Yet, which aspects of e-learning do students consider important for their learning achievements and course satisfaction? This question was addressed by surveying 2196 students from 29 universities in Austria about their expectations of, and experiences in e-learning. Multiple regression analyses using Mplus 4.21 were carried out to investigate how different facets of students’ expectations and experiences are related to perceived learning achievements and course satisfaction.     

Comparing dropouts and persistence in e-learning courses

Several studies have been conducted related to dropouts from on-campus and distance education courses. However, no clear definition of dropout from academic courses was provided. Consequently, this study proposes a clear and precise definition of dropout from academic courses in the context of e-learning courses. Additionally, it is documented in literature that students attending e-learning courses dropout at substantially higher rates than their counterparts in on-campus courses. Little attention has been given to the key factors associated with such substantial difference. This study explores two main constructs: (1) academic locus of control; and, (2) students’ satisfaction with e-learning. Results show that students’ satisfaction with e-learning is a key indicator in students’ decision to dropout from e-learning courses. Moreover, dropout students (non-completers) reported to have significantly lower satisfaction with e-learning than students who successfully completed (completers or persistent students) the same e-learning courses. Additionally, results of this study show that the academic locus of control appears to have no impact on students’ decision to drop from e-learning courses.     

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.     

An attribute-based ant colony system for adaptive learning object recommendation

Teachers usually have a personal understanding of what “good teaching” means, and as a result of their experience and educationally related domain knowledge, many of them create learning objects (LO) and put them on the web for study use. In fact, most students cannot find the most suitable LO (e.g. learning materials, learning assets, or learning packages) from webs. Consequently, many researchers have focused on developing e-learning systems with personalized learning mechanisms to assist on-line web-based learning and to adaptively provide learning paths. However, although most personalized learning mechanism systems neglect to consider the relationship between learner attributes (e.g. learning style, domain knowledge) and LO’s attributes. Thus, it is not easy for a learner to find an adaptive learning object that reflects his own attributes in relationship to learning object attributes. Therefore, in this paper, based on an ant colony optimization (ACO) algorithm, we proposed an attributes-based ant colony system (AACS) to help learners find an adaptive learning object more effectively. Our paper makes three critical contributions: (1) It presents an attribute-based search mechanism to find adaptive learning objects effectively; (2) An attributes-ant algorithm was proposed; (3) An adaptive learning rule was developed to identify how learners with different attributes may locate learning objects which have a higher probability of being useful and suitable; (4) A web-based learning portal was created for learners to find the learning objects more effectively.     

The effect of an information ethics course on the information ethics values of students - A Chinese guanxi culture perspective

The development of information technology has a significant influence on social structure and norms, and also impacts upon human behavior. In order to achieve stability and social harmony, people need to respect various norms, and have their rights protected. Students’ information ethics values are of critical and radical importance in achieving this goal. Using qualitative approach, the present study utilizes Kohlberg’s CMD model to measure improvement in students’ “information ethics values” through “technology mediated learning (TML)” models, and to assess the extent to which it is influenced by gender and Chinese guanxi culture. We find that while e-learning improves female students’ “respect rules,” “privacy,” “accessibility” and “intellectual property” values more than male students, the percentages relating to “intellectual property” for females in the higher stages remain lower than for males. Moreover, these results are interpreted from a Chinese guanxi culture perspective. In light of these results, educators should take account of such improvements when designing effective teaching methods and incentives.     

A distributed system for learning programming on-line

Several Web-based on-line judges or on-line programming trainers have been developed in order to allow students to train their programming skills. However, their pedagogical functionalities in the learning of programming have not been clearly defined. EduJudge is a project which aims to integrate the “UVA On-line Judge”, an existing on-line programming trainer with an important number of problems and users, into an effective educational environment consisting of the e-learning platform Moodle and the competitive learning tool QUESTOURnament. The result is the EduJudge system which allows teachers to apply different pedagogical approaches using a proven e-learning platform, makes problems easy to search through an effective search engine, and provides an automated evaluation of the solutions submitted to these problems. The final objective is to provide new learning strategies to motivate students and present programming as an easy and attractive challenge. EduJudge has been tried and tested in three algorithms and programming courses in three different Engineering degrees. The students’ motivation and satisfaction levels were analysed alongside the effects of the EduJudge system on students’ academic outcomes. Results indicate that both students and teachers found that among other multiple benefits the EduJudge system facilitates the learning process. Furthermore, the experiment also showed an improvement in students’ academic outcomes. It must be noted that the students’ level of satisfaction did not depend on their computer skills or their gender.