Intelligent web-based learning system with personalized learning path guidance

Personalized curriculum sequencing is an important research issue for web-based learning systems because no fixed learning paths will be appropriate for all learners. Therefore, many researchers focused on developing e-learning systems with personalized learning mechanisms to assist on-line web-based learning and adaptively provide learning paths in order to promote the learning performance of individual learners. However, most personalized e-learning systems usually neglect to consider if learner ability and the difficulty level of the recommended courseware are matched to each other while performing personalized learning services. Moreover, the problem of concept continuity of learning paths also needs to be considered while implementing personalized curriculum sequencing because smooth learning paths enhance the linked strength between learning concepts. Generally, inappropriate courseware leads to learner cognitive overload or disorientation during learning processes, thus reducing learning performance. Therefore, compared to the freely browsing learning mode without any personalized learning path guidance used in most web-based learning systems, this paper assesses whether the proposed genetic-based personalized e-learning system, which can generate appropriate learning paths according to the incorrect testing responses of an individual learner in a pre-test, provides benefits in terms of learning performance promotion while learning. Based on the results of pre-test, the proposed genetic-based personalized e-learning system can conduct personalized curriculum sequencing through simultaneously considering courseware difficulty level and the concept continuity of learning paths to support web-based learning. Experimental results indicated that applying the proposed genetic-based personalized e-learning system for web-based learning is superior to the freely browsing learning mode because of high quality and concise learning path for individual learners.     

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.     

On extracting recommendation knowledge for personalized web-based learning based on ant colony optimization with segmented-goal and meta-control strategies

Personalized web-based learning has become an important learning form in the 21st century. To recommend appropriate online materials for a certain learner, several characteristics of the learner, such as his/her learning style, learning modality, cognitive style and competency, need to be considered. An earlier research result showed that a fuzzy knowledge extraction model can be established to extract personalized recommendation knowledge by discovering effective learning paths from past learning experiences through an ant colony optimization model. Though that results revealed the theoretical potential of the proposed method in discovering effective learning paths for learners, critical limitations arose when considering its applications in real world situations, such as the requirement of a large amount of learners and a long period of training cycles in order to discover good learning paths for learners. These practical issues motivate this research. In this paper, the aim is to resolve the aforementioned issues by devising more efficient algorithms that basically run on the same ant colony model yet requiring only a reasonable number of learners and training cycles to find satisfactory good results. The key approaches to resolving the practical issues include revising the global update policy, an adaptive search policy and a segmented-goal training strategy. Based on simulation results, it is shown that these new ingredients added to the original knowledge extraction algorithm result in more efficient ones that can be applied in practical situations.     

Self-organising navigational support in lifelong learning: How predecessors can lead the way

Increased flexibility and modularisation in higher education complicates the process of learners finding their way through the offerings of higher education institutions. In lifelong learning, where learning opportunities are diverse and reach beyond institutional boundaries, it becomes even more complex to decide on a learning path. However, efficient and effective lifelong learning requires that learners can make well informed decisions. Drawing on principles of self-organisation and indirect social interaction, this article suggests solving the problem by analysing the paths followed by learners and feeding this information back as advice to learners facing navigational decisions. This article starts by introducing the principles of self-organisation and indirect social interaction. It describes how we expect the use of indirect social interaction using collaborative filtering to enhance effectiveness (completion rates and amount of progress) and efficiency (time taken to complete) in lifelong learning. The effects were tested in a controlled experiment, with the results showing effects on effectiveness though not on efficiency. The study shows that indirect feedback is a promising line of enquiry for navigational support in lifelong learning.     

Enhancement of student learning performance using personalized diagnosis and remedial learning system

Although conventional student assessments are extremely convenient for calculating student scores, they do not conceptualize how students organize their knowledge. Therefore, teachers and students rarely understand how to improve their future learning progress. The limitations of conventional testing methods indicate the importance of accurately assessing and representing student knowledge structures. The personalized diagnosis and remedial learning system (PDRLS) proposed in this study enhances the effectiveness of the Pathfinder network by providing remedial learning paths for individual learners based on their knowledge structure. The sample was 145 students enrolled in introductory JAVA programming language courses at a Central Taiwan technology university. The experimental results demonstrate that learners who received personalized remedial learning guidance via PDRLS achieved improved learning performance, self-efficacy, and PDRLS use intention. The experimental results also indicated that students with lower knowledge level gain more benefits from the PDRLS than those with higher level of knowledge and that field dependence (FD) students obtain a greater benefit from PDRLS than field independence (FI) students do.     

A blog-based dynamic learning map

Problem-based learning is a goal directed and constructive process for learners. When meeting problems, learners usually force themselves to form work groups in order to find a solution. Currently, blogs are becoming more popular and in fact has formed a community wherein people can share their learning experiences with others. Many pedagogical applications have adopted what are posted in the community for supplementary learning. Integrating blogs in an intelligent tutoring system means that learners can better regulate and enhance their own learning. In this study, a novel learning device, a blog-based dynamic learning map, which employs both information retrieval and automated scheduling techniques, is designed to provide useful blog articles to help learning. The relevant articles in blogs are used to promote learner engagement in their interactions with the learning map and hence achieve their goals more easily. An experimental course has been implemented and the results show that learners make use of the blog-based learning aid in a very positive way and can eventually cross the specified threshold in a test. The proposed approach can encapsulate the dynamic learning principles in cohesive and supportive ways. Thus it can lead learners to gain useful supplementary materials, shorten the learning time and offering expanded alternative viewpoints to use in the solution of assigned problems. Our results show that both the learners and lectures are very positive to the design of our blog-based dynamic learning map.     

Environmental design for a structured network learning society

Social interactions profoundly impact the learning processes of learners in traditional societies. The rapid rise of the Internet using population has been the establishment of numerous different styles of network communities. Network societies form when more Internet communities are established, but the basic form of a network society, especially a network learning society, remains unclear. In 1998, a group of Taiwanese researchers created a network learning society, named “EduCities”. Based on the experience of building this network learning society, the authors found that a structured network learning society architecture helps participants to coordinate and manage interaction processes. This study describes 10 basic elements involved in establishing a structured network learning society, i.e., participants, shared visions, devices, services, rules, relations, manners, learning domains, learning goals and learning activities. A structured network learning society environment, “EduCities”, was then implemented based on the structured network learning society concept. EduCities included numerous structural designs, including EduCity, EduTown, EduSisterTown, EduVillage, EduSisterVillage, EduCitizen, and EduHome were practiced. Participants in EduCities own their roles, managing power, learning goals, and social relations. The structured network learning society concept represents a prototype of an online learning society.     

Recommending suitable learning scenarios according to learners’ preferences: An improved swarm based approach

The paper presents a new approach for recommending suitable learning paths for different learners groups. Selection of the learning path is considered as recommendations to choosing and combining the sequences of learning objects (LOs) according to learners’ preferences. Learning path can be selected by applying artificial intelligence techniques, e.g. a swarm intelligence model. If we modify and/or change some LOs in the learning path, we should rearrange the alignment of new and old LOs and reallocate pheromones to achieve effective learning recommendations. To solve this problem, a new method based on the ant colony optimisation algorithm and adaptation of the solution to the changing optimum is proposed. A simulation process with a dynamic change of learning paths when new LOs are inserted was chosen to verify the method proposed. The paper contributes with the following new developments: (1) an approach of dynamic learning paths selection based on swarm intelligence, and (2) a modified ant colony optimisation algorithm for learning paths selection. The elaborated approach effectively assist learners by helping them to reach most suitable LOs according to their preferences, and tutors – by helping them to monitor, refine, and improve e-learning modules and courses according to the learners’ behaviour.     

Evaluating peer learning and assessment in online collaborative learning environments

Transformation of learning and teaching in higher education now offers greater educational equality through enhanced access and collaboration within the framework of lifelong learning in the digital age. This study aims to evaluate online peer learning and assessment in the collaborative learning process in higher education practices. The study also investigates the impact of online peer learning on the development of skills within collaborative learning through the use of volunteered responses from learners concerning their experiences with and perceptions of online learning. Therefore, a quantitative approach is applied through the administration of a survey with 32 items that is distributed to 715 participants. According to the objective of the study, a set of inferential statistical analyses are performed. The theoretical framework of this study is the CHAT (cultural historical activity theory) which reconstructs the knowledge of learners through the application of the Adobe Connect program to demonstrate how learners can be collaborative and social with their peers in an online context. The results revealed that the collaborative online peer learning process in higher education encourages critical reflection and self-assessment. The study contributes to the understanding of the value of learner satisfaction in online collaborative learning environments through the experiences of learners.     

The design and implementation of a competency-based intelligent mobile learning system

Mobile learning provides a ubiquitous learning context for the learners to select appropriate learning paths and learning objects. Adaptive learning methods and correct learning path planning can help to achieve the goal of learning anytime and anywhere. Moreover, the display ability of mobile learning devices has become a key factor affecting the interest and acquisition time of learners. Achieving the desired functionality is currently an important topic in the field of mobile learning. This paper uses competency-based learning as the basis to evaluate the knowledge deficiency that the learner must overcome. We then use carrier selection, fuzzy interpolation computation, and ant-genetic algorithm techniques to select the appropriate learning paths and objects. Finally, we use NFC’s point-to-point technology to transfer the learning content in the learning device to a larger screen with NFC capability in the user’s environment to display the same content, thus providing a complete learning system.     

Designing learning objects that afford learners the experience of important variations in Chinese characters

Effective teaching should focus the attention of learners to its essential aspects. It follows that instructional software can be designed in such a way that allows learners to experience the important variations in the critical aspects of the content to be learned. This paper reports on the experience of designing such special kinds of instructional learning objects for the learning of Chinese characters. The design of these learning objects takes into consideration not only what Chinese characters are all about but also how learners commonly make errors while they learn to write the characters. Out of the analysis of these learners' errors, variations in the structural features of Chinese characters were pulled out and embodied in the design of the learning objects. Learners tinkering with the learning objects can thus implicitly develop a sense of the structural features or regularity of Chinese characters, which most importantly should prepare the learners to learn more new characters in the future. The main proposal of this paper is the notion of this variation‐affording instructional software that allows learners to attend to the essential aspects of what is to be learned. Furthermore, the idea of the learning object also differs from other instructional software in its small, self‐contained and reusable nature, such that teachers can flexibly embed the learning objects into their own teaching materials.     

形式背景下的学习路径与技能评估

在认知学习过程中,学习者可能学习并掌握某些技能,但知识状态却无法发生改变.在此情形下,根据学习者知识状态的改变不足以对其技能进行准确评估,因此,文中基于技能函数,运用形式概念分析的方法寻找学习路径并进行技能评估.首先,介绍后继状态、有效技能和良好技能函数的概念.然后,基于形式背景,在两种情形下讨论技能函数满足良好性的条件,得到满足良好性条件下可进行逐步有效学习和有效评估的结果,并设计获取良好技能背景、良好技能函数及寻找学习路径的算法.最后,在两个数据集上进行实验分析,验证文中算法的有效性,并且得出如下结论:基于良好技能函数得到的学习路径图,不仅可有效指导学习者进行学习,还可根据学习者知识状态的变化评估其是否掌握相应的有效技能.     

Personalized E-learning system with self-regulated learning assisted mechanisms for promoting learning performance

With the rapid development of Internet technologies, the conventional computer-assisted learning (CAL) is gradually moving toward to web-based learning. Additionally, instructors typically base their teaching methods to simultaneously interact with all learners in a class based on their professional disciplines in the traditional classroom learning. However, the requirements of individual learners are frequently ignored in the traditional classroom learning. Compared to the conventional classroom learning, individual learners are the focus in web-based learning environments and many web-based learning systems provide personalized learning mechanisms for individual learners. One key problem is that learners have to frequently interact with web-based learning systems even though they lack instructors to monitor their learning attitudes and behavior during learning processes. Hence, a learner’s ability to self-regulated learning is clearly an important factor affecting learning performance in a web-based learning environment. Self-regulated learning is a goal-oriented learning strategy that is very suited to self-managed learning to promote learning performance of individual learners in a web-based learning environment. However, how to assist learners in cultivating self-regulated learning abilities efficiently is an important research issue in the self-regulated learning field. This study presents a novel personalized e-learning system with self-regulated learning assisted mechanisms that help learners enhance their self-regulated learning abilities. The proposed self-regulated learning mechanisms assist learners in becoming lifelong learners who have autonomous self-regulated learning abilities. Additionally, four self-regulated learning types, based on a self-regulated learning competence index and self-regulated learning performance index, are also proposed. Experimental results demonstrate that the proposed self-regulated learning assisted mechanisms aid learners by speeding up their acquisition of self-regulated learning abilities in a personalized e-learning system, and help their learning performance.     

A study of learning time patterns in asynchronous learning environments

Abstract This research makes use of learning time intensity, burst evaluating equations, and state denotation approaches to evaluate the learning time characteristics of students. Through comparing learning time intensity, six burst styles and three diligence styles are categorized. From the statistical results and interaction content analysis, some pedagogical phenomena were found. The first finding is that the more diligent learners were, the higher the quality and quantity of their interaction. The second is that learners whose learning time intensity was mainly located in the early period of the course and whose interaction content included many complaints were suspected to be possible dropouts. The third finding is that learners whose learning time intensity was mainly located in the later period had achievements that were significantly different from those of the regular periodical reading learners whose learning time intensity was distributed in all periods of the course. The above findings raise some issues and suggestions for those concerned with proposing asynchronous courses. As students can pace their own learning in an asynchronous learning environment, it is hard to avoid getting used to intermittent intensive reading. Instructors should consider seriously how to guide students to learn in a proper sequence through a well‐scheduled instructional programme. It is necessary to encourage students to exercise self‐discipline in regular on‐line reading for better learning outcomes.     

Informal learning recognition through a cloud ecosystem

Learning and teaching processes, like all human activities, can be mediated through the use of tools. Information and communication technologies are now widespread within education. Their use in the daily life of teachers and learners affords engagement with educational activities at any place and time and not necessarily linked to an institution or a certificate. In the absence of formal certification, learning under these circumstances is known as informal learning. Despite the lack of certification, learning with technology in this way presents opportunities to gather information about and present new ways of exploiting an individual’s learning. Cloud technologies provide ways to achieve this through new architectures, methodologies, and workflows that facilitate semantic tagging, recognition, and acknowledgment of informal learning activities. The transparency and accessibility of cloud services mean that institutions and learners can exploit existing knowledge to their mutual benefit. The TRAILER project facilitates this aim by providing a technological framework using cloud services, a workflow, and a methodology. The services facilitate the exchange of information and knowledge associated with informal learning activities ranging from the use of social software through widgets, computer gaming, and remote laboratory experiments. Data from these activities are shared among institutions, learners, and workers. The project demonstrates the possibility of gathering information related to informal learning activities independently of the context or tools used to carry them out.     

Journeys up the mountain: Different paths to learning object-oriented programming

Among the challenges facing companies transitioning from structured to object-oriented (OO) programming is how (and whether) to retrain existing procedural programmers. Common wisdom has it that old-time programmers can be retrained in object technology only with great difficulty, but new programmers lack experience building large systems and the knowledge of the business. This paper describes a study of students learning OO programming where the participants ranged in experience from a single semester of programming to over 10 years of professional programming. The purpose of this study was to explore how students learn OO programming by observing them between their first exposure to OO programming and the time they finally “get it.” We identified five categories of learners who each took a different path to learning OO programming, encountered different obstacles, and adopted different learning strategies. We describe some factors that may play a part in helping and/or hindering a student's progress and that may be used to predict a student's learning category. We conclude with suggestions for alternative training program strategies that may be appropriate for each category and with directions for future research.     

Mobile learning: Two case studies of supporting inquiry learning in informal and semiformal settings

Mobile technologies can support learning across different contexts as their portability enables them to be used by the learner in whichever context she or he is in. They can be particularly beneficial in informal and semiformal contexts where learners have more control over their learning goals and where motivation is often high. Inquiries in informal contexts are likely to be personally relevant in terms of topics of interest and capitalise on learners' location as learners decide what, where, when and whether to learn. There is considerable interest in how such benefits can be harnessed for more formal learning and one challenge is how to make inquiries personally relevant in such contexts. However, there is little literature that considers the structure needed to support informal and semiformal inquiry learning. This paper contributes to that literature by examining dimensions for researchers and designers to consider investigating or developing support for inquiries in informal or semiformal settings.The paper examines two case studies of inquiry learning in contrasting settings in order to understand more about learner control and how technology can support learners' inquiries. Case study one considers the use of web based software to support science inquiry learning by 14–15 year olds in a semiformal context, whilst the second case study reports on informal adult learners using their own mobile technologies to learn about landscape. These case studies are compared and contrasted in terms of the dimensions of learner control, location of learning, and the different support mechanisms for inquiry learning and a framework is proposed for considering these dimensions.     

Epistemic activities and collaborative learning: towards an analytical model for studying knowledge construction in networked learning settings

The aim of this paper is to provide an analytical model for studying knowledge creation among geographically dispersed students in an online postgraduate programme by using the concept of epistemic activity. Rooted in qualitative methodologies, it takes a case study approach which strengthens the grounding of theory on social learning by using triangulation of evidence from online discussions and seven semi‐structured interviews. The analytical model that emerges, distinguishes a set of moves that serve understanding negotiation of meaning and co‐construction of knowledge in online discussion. The findings of the study suggest that knowledge advancement processes can be understood as part of identifying key strategies in the form of online epistemic activities for postgraduate learners in a networked learning context. The paper offers a conceptual account for researching knowledge construction in higher education.     

A Heuristic Algorithm for planning personalized learning paths for context-aware ubiquitous learning

In a context-aware ubiquitous learning environment, learning systems can detect students’ learning behaviors in the real-world with the help of context-aware (sensor) technology; that is, students can be guided to observe or operate real-world objects with personalized support from the digital world. In this study, an optimization problem that models the objectives and criteria for determining personalized context-aware ubiquitous learning paths to maximize the learning efficacy for individual students is formulated by taking the meaningfulness of the learning paths and the number of simultaneous visitors to each learning object into account. Moreover, a Heuristic Algorithm is proposed to find a quality solution. Experimental results from the learning activities conducted in a natural science butterfly-ecology course of an elementary school are also given to depict the benefits of the innovative approach.