Using a style-based ant colony system for adaptive learning

Adaptive learning provides an alternative to the traditional “one size fits all” approach and has driven the development of teaching and learning towards a dynamic learning process for learning. Therefore, exploring the adaptive paths to suit learners personalized needs is an interesting issue. This paper proposes an extended approach of ant colony optimization, which is based on a recent metaheuristic method for discovering group patterns that is designed to help learners advance their on-line learning along an adaptive learning path. The investigation emphasizes the relationship of learning content to the learning style of each participant in adaptive learning. An adaptive learning rule was developed to identify how learners of different learning styles may associate those contents which have the higher probability of being useful to form an optimal learning path. A style-based ant colony system is implemented and its algorithm parameters are optimized to conform to the actual pedagogical process. A survey was also conducted to evaluate the validity and efficiency of the system in producing adaptive paths to different learners. The results reveal that both the learners and the lecturers agree that the style-based ant colony system is able to provide useful supplementary learning paths.     

Learning with an active e‐course in the Knowledge Grid environment

An active e‐course is an open, self‐representable and self‐organizable media mechanism. Its kernel idea is to organize learning materials in a concept space rather than in a page space. The tailored content and flexible structure of the e‐courses can be dynamically formed to cater for different learners with different backgrounds, capabilities and expectations, at different times and venues. The active e‐course can also assess learners' learning performances and give appropriate suggestions to guide them in further learning. An authoring tool for constructing course ontology and a system prototype have been developed to support an active e‐course, enabling a learner‐centred, highly interactive and adaptive learning approach. The results of an empirical study show that the system can help enhance the effectiveness and efficiency of learning. Copyright © 2005 John Wiley & Sons, Ltd.     

Developing a web‐based multimedia assessment system for facilitating science laboratory instruction

This study developed a web‐based multimedia assessment system (WMA system) and applied it to science laboratory instruction. The goal was to improve students' knowledge acquisition under science laboratory instruction. The developed system enabled learners to perform self‐assessments by responding to multimedia technology test items online. The system recorded each learner's complete answer history and provided the students with personalized learning resources. This study adopted a quasi‐experimental research design. The learning content was an “experiment on separating mixtures.” Students participating in the research were divided into a typical science laboratory instruction group (TI group; n = 25) and a group that received instruction through the WMA system (WMA group; n = 26). Before instruction, all the students completed the conceptual knowledge and experimental knowledge pretests. During instruction, the TI group watched the teacher's demonstration experiment, and the students then performed the experiment in a real laboratory. In the WMA group, after learning through the WMA system, the students also performed the experiment in a real laboratory. After instruction, all the students completed the conceptual knowledge and experimental knowledge posttests. The findings indicated that the students in the WMA group showed significantly higher improvements in their scientific conceptual knowledge and experimental knowledge.     

Implement web learning environment based on data mining

The need for providing learners with web-based learning content that match their accessibility needs and preferences, as well as providing ways to match learning content to user’s devices has been identified as an important issue in accessible educational environment. For a web-based open and dynamic learning environment, personalized support for learners becomes more important. In order to achieve optimal efficiency in a learning process, individual learner’s cognitive learning style should be taken into account. Due to different types of learners using these systems, it is necessary to provide them with an individualized learning support system. However, the design and development of web-based learning environments for people with special abilities has been addressed so far by the development of hypermedia and multimedia based on educational content. In this paper a framework of individual web-based learning system is presented by focusing on learner’s cognitive learning process, learning pattern and activities, as well as the technology support needed. Based on the learner-centered mode and cognitive learning theory, we demonstrate an online course design and development that supports the students with the learning flexibility and the adaptability. The proposed framework utilizes data mining algorithm for representing and extracting a dynamic learning process and learning pattern to support students’ deep learning, efficient tutoring and collaboration in web-based learning environment. And experiments do prove that it is feasible to use the method to develop an individual web-based learning system, which is valuable for further study in more depth.     

The effects of problem-based learning on the classroom community perceptions and achievement of web-based education students

Web-based education as an alternative to face-to-face teaching is now being extended as an application to higher education. Much effort is undertaken to provide multimedia rich, attractive content to learners. While the use of multimedia technologies has a noticeable effect on students’ learning, so does the selected teaching methodology. Underpinned by the Constructivist approach, problem-based learning is one of the methods that could be applied in web-based learning environments. The current study investigates the effects of problem-based learning on students’ classroom community perceptions and their achievement. It focuses on the tertiary level course “Introduction to Computers” offered by the Department of Business Administration of the Distance Education Program at a higher education institution in Turkey. The results indicated that students who worked on problem-based projects felt much more ‘connected’ to other class members when compared to the control group. They achieved higher scores in the post-tests although their online midterm and final examination scores did not indicate any difference between the groups.     

Design and development of an innovative individualized adaptive and intelligent e-learning system for teaching–learning of probability unit: Details of UZWEBMAT

In this study, an innovative adaptive and intelligent web based e-learning system, UZWEBMAT (Turkish abbreviation of Adaptive and INtelligent WEB based MAThematics teaching–learning system) was designed, developed and implemented. This e-learning system was intended for learning and teaching secondary school level permutation-combination-binomial expansion and probability subjects. Content which was prepared according to Turkish curriculum for secondary school mathematics course was transformed into learning objects in three different ways in accordance with VAK (Visual–Auditory–Kinesthetic) learning styles. Primary/secondary/tertiary learning styles of learners registering the system are determined and each learner receives the content appropriate for his/her dominant learning style. Also, they can be directed to contents of other styles according to their performances thanks to an expert system. Learning objects constituting the content were prepared according to constructivist approach. An active role for the learner was the purpose. Tips and intelligent solution supports within the learning objects were presented with expert system support to the learners. With this structure, UZWEBMAT bears the characteristics of intelligent tutoring system as well as an adaptive e-learning environment. All the movements of learners studying with UZWEBMAT are recorded and the necessary information is reported to both learners and teachers in a visualized way.     

Learning and engagement in a gamified course: Investigating the effects of student characteristics

The current study investigated college students' experiences of a gamified informatics course. We surveyed 139 students aged 18–31 years (M = 20 years, SD = 1.5) enrolled in an undergraduate informatics course focused on social networking technologies. Surveys were conducted at 3 time points during the course (beginning, middle, and end). Overall, we found positive trends with respect to students' perceptions of gamification's impact on their learning, achievement, and engagement in the course material. Although students who played and identified variously with recreational games were more alike than not, we did uncover one notable difference with respect to how students' gaming frequency impacted their engagement in the course. Nongamers expressed somewhat less motivation to do well in the course than frequent gamers. For all other measures of engagement, however, nongamers appeared to be equally engaged by the gamified format of the course as gamers. There were virtually no differences between male and female students' perceptions of gamification. This study contributes new insight into the conditions under which gamification succeeds or fails in educational settings. These insights will be useful to designers and instructors of gamified learning environments as they seek to engage and support a variety of learners.     

A web-based tutoring system with styles-matching strategy for spatial geometric transformation

It has been a major objective for researchers to develop computer systems that can effectively deliver instruction to learners. Therefore, how to incorporate instructional strategies in computer-assisted learning systems in a systematic manner deserves further investigation. In this paper, a style-matching strategy that attempts to match learning materials' styles to learners' latent traits is proposed and realized in a web-based tutoring system, called CooTutor. The mechanism of adaptive material selection takes learners' different spatial ability and learning styles as an integral learning profile into account, and performs traits-based personalization of learning experience. This system is specifically designed to conquer the difficulty of tutoring the topic on fundamental spatial geometry in conventional curriculums. By conducting empirical evaluation with a small group of students, it is found that CooTutor is generally beneficial to learning the domain, but the effect of the styles-matching mechanism remains inconclusive. The work aims to contribute to the community of adaptive hypermedia in providing an explorative example adopting the concern of individual difference for personalization. The system design, a usage scenario, and an exploratory evaluation are presented in this paper as implications for further studies.     

English outside the academic sphere: A mobile-based context-aware comparison study on collaborative and individual learning

Context-aware ubiquitous learning (CAUL) technology provides language learners with interactive learning environments and has been found to increase learning effectiveness and self-efficacy due to student interaction, discussion and evaluation of the entire learning process. This study used a mobile-based ubiquitous learning system combined with a collaborative learning approach to develop Fitness-specific English listening and reading, and fitness knowledge. The researchers recruited two groups of participants, an individual learning group (N = 31) and a collaborative learning group (N = 30), and evaluated their learning performance via pre-and post-tests. In addition, a questionnaire explored the perceived usefulness, usability, follow-up intention of using the system and self-efficacy regarding fitness-specific English. The results show that both groups improved fitness-specific English in terms of listening and reading comprehension. Moreover, collaborative learning was found to facilitate fitness-specific knowledge, and those in the collaborative learning group improved their self-efficacy more than those in the individual learning group.     

Data mining for adaptive learning in a TESL-based e-learning system

This study proposes an Adaptive Learning in Teaching English as a Second Language (TESL) for e-learning system (AL-TESL-e-learning system) that considers various student characteristics. This study explores the learning performance of various students using a data mining technique, an artificial neural network (ANN), as the core of AL-TESL-e-learning system. Three different levels of teaching content for vocabulary, grammar, and reading were set for adaptive learning in the AL-TESL-e-learning system. Finally, this study explores the feasibility of the proposed AL-TESL-e-learning system by comparing the results of the regular online course control group with the AL-TESL-e-learning system adaptive learning experiment group. Statistical results show that the experiment group had better learning performance than the control group; that is, the AL-TESL-e-learning system was better than a regular online course in improving student learning performance.     

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

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

Personalized e-learning system using Item Response Theory

Personalized service is important on the Internet, especially in Web-based learning. Generally, most personalized systems consider learner preferences, interests, and browsing behaviors in providing personalized services. However, learner ability usually is neglected as an important factor in implementing personalization mechanisms. Besides, too many hyperlink structures in Web-based learning systems place a large information burden on learners. Consequently, in Web-based learning, disorientation (losing in hyperspace), cognitive overload, lack of an adaptive mechanism, and information overload are the main research issues. This study proposes a personalized e-learning system based on Item Response Theory (PEL-IRT) which considers both course material difficulty and learner ability to provide individual learning paths for learners. The item characteristic function proposed by Rasch with a single difficulty parameter is used to model the course materials. To obtain more precise estimation of learner ability, the maximum likelihood estimation (MLE) is applied to estimate learner ability based on explicit learner feedback. Moreover, to determine an appropriate level of difficulty parameter for the course materials, this study also proposes a collaborative voting approach for adjusting course material difficulty. Experiment results show that applying Item Response Theory (IRT) to Web-based learning can achieve personalized learning and help learners to learn more effectively and efficiently.     

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.     

基于大数据分析的计算机网络课程自适应学习系统构建

大数据时代数字化的学习资源将呈现爆炸式的增长,面对海量的学习资源,学习者如何选择,或者如何向学习者推送合适的学习资源将成为未来面临的主要问题。针对计算机网络课程授课内容多、知识更新快的问题,通过收集教育过程中的学习行为数据,利用大数据的学习分析技术构建了一个计算机网络自适应学习系统,该系统包含自适应、预测、干预等六大模块,可以针对性地推送学习内容,及时反馈学习者的学习效果,并推荐下一步的学习策略,从而达到因材施教和培养学生自主学习能力的效果。     

A language learning support system using course-centered ontology and its evaluation

This paper presents a course-centered ontology for assisting learning support systems to embody the relations among knowledge points and also among the learning materials for those knowledge points. An “individual-class-individual” ontology design (first an individual-class design, then an innovative design about relations among bottom individuals), was applied to the construction of a course-centered ontology for an existing Japanese grammar course. Furthermore, a customizable language learning support system was built to manipulate the course-centered ontology to provide an interface for the learning objects arrangement which displays the visual representation of knowledge points and their relations. The intention underlying the development of the system is to encourage instructors to orient their teaching materials to specific knowledge points and even directly to relations between knowledge points. With these orientations, the learning support system is able to provide an environment in which learners can readily distinguish between related knowledge points. Finally, based on the result of a preliminary evaluation, a study to explore the impact of learning styles and learning habits on learning performance was conducted to further evaluate our ontology-based learning support system. The results of the study suggest three main points: (a) the experimental students who learned with our system achieved significantly better learning achievement than those who just did self-study with textbooks after studying the same target contents for 60 mins; (b) the learning achievement of experimental group was not related to either their learning style in Sequential/Global dimension or their habit of “learning from comparison”; (c) in terms of the learning perception of experimental group, compared to “Sequential learners”, most “Global learners” had a stronger feeling that the comparison function is useful in improving their learning performance, and the learners who don't habitually “learning from comparison” were more likely to suffer from lack of the attention and feel more pressure than those who do habitually “learning from comparison”.     

e‐Learning recommender system for a group of learners based on the unified learner profile approach

In the age of information explosion, e‐learning recommender systems (eL_RSs) have emerged as effective information filtering techniques that attempt to provide the most appropriate learning resources for learners while using e‐learning systems. These learners are differentiated on the basis of their learning styles, goals, knowledge levels and others. Several attempts have been made in the past to design eL_RSs to recommend resources to individuals; however, an investigation of recommendations to a group of learners in e‐learning is still in its infancy. In this paper, we focus on the problem of recommending resources to a group of learners rather than to an individual. The major challenge in group recommendation is how to merge the individual preferences of different learners that form a group and extract a pseudo unified learner profile (ULP) that closely reflects the preferences of all learners. Firstly, we propose a profile merging scheme for the ULP by utilizing learning styles, knowledge levels and ratings of learners in a group. Thereafter, a collaborative approach is proposed based on the ULP for effective group recommendations. Experimental results are presented to demonstrate the effectiveness of the proposed group recommendation strategy for e‐learning.     

Towards new forms of knowledge communication: the adaptive dimension of a web-based learning environment

Adaptive Educational Hypermedia Systems aim to increase the functionality of hypermedia by making it personalised to individual learners. The adaptive dimension of these systems mainly supports knowledge communication between the system and the learner by adapting the content or the appearance of hypermedia to the knowledge level, goals and other characteristics of each learner. The main objectives are to protect learners from cognitive overload and disorientation by supporting them to find the most relevant content and path in the hyperspace. In the approach presented in this paper, learners' knowledge level and individual traits are used as valuable information to represent learners' current state and personalise the educational system accordingly, in order to facilitate learners to achieve their personal learning goals and objectives. Learners' knowledge level is approached through a qualitative model of the level of performance that learners exhibit with respect to the concepts they study and is used to adapt the lesson contents and the navigation support. Learners' individual traits and especially their learning style represent the way learners perceive and process information, and are exploited to adapt the presentation of the educational material of a lesson. The proposed approach has been implemented through various adaptation technologies and incorporated into a prototype hypermedia system. Finally, a pilot study has been conducted to investigate system's educational effectiveness.     

Using eye movement modelling examples to guide visual attention and foster cognitive performance: A meta-analysis

Eye movement modelling examples (EMME) are computer-based videos displaying the visualized eye gaze behaviour of a domain expert person (model) while carefully executing the learning or problem-solving task. The role of EMME in promoting cognitive performance (i.e., final scores of learning outcome or problem solving) has been questioned due to the mixed findings from empirical studies. This study tested the effects of EMME on attention guidance and cognitive performance by means of meta-analytic procedures. Data for both experimental and control groups and both posttest and pretest were extracted to calculate the effect sizes. The EMME group was treated as the experimental group and the non-EMME group was treated as the control group. Twenty-five independent articles were included. The overall analysis showed a significant effect of EMME on time to first fixation (d = −0.83), fixation duration (d = 0.74), as well as cognitive performance (d = 0.43), but not on fixation count, indicating that using EMME not only helped learners attend faster and longer to the task-relevant elements, but also fostered their final cognitive performance. Interestingly, task type significantly moderated the effect of EMME on cognitive performance. Moderation analyses showed that EMME was beneficial to learners' performance when non-procedural tasks (rather than procedural tasks) were used. These findings show contributions for future research as well as practical application in the field of computers and learning regarding videos displaying a model's visualized eye gaze behaviour.     

Exploring the learning effectiveness of financial literacy from the microworld perspective: Evidence from the simulated transactional interactive concurrent system

Because the microworld can create a learning environment that allows learners to interact with the real world, the view of the microworld has been widely used in education and training. In this study, the simulated transactional interactive concurrent system (STICS) was used to provide an environment that simulates the stock market trading of the Taiwan Stock Exchange; the goal is to explore the effectiveness of using STICS in enhancing the financial literacy of students. This study adopts a quasi-experimental design approach and divides students into two groups. The intervention group was made up of 42 students who learned financial knowledge aided by STICS, with a comparison group consisting of 43 students who learned with traditional lecturing and dictating methods. The study found that learners who used the STICS as an aid to learn financial knowledge had significantly better learning effectiveness than those who did not use STICS, in terms of motivation, attitude, and achievement. The intervention group also made significant improvements in average scores in learning motivation and achievement after 8 weeks of experimental teaching and learning.