Investigating acceptance toward mobile learning to assist individual knowledge management: Based on activity theory approach

Mobile devices could facilitate human interaction and access to knowledge resources anytime and anywhere. With respect to wide application possibilities of mobile learning, investigating learners’ acceptance towards it is an essential issue. Based on activity theory approach, this research explores positive factors for the acceptance of m-learning systems. In the research, we developed an m-learning system for learners’ knowledge management and invited 152 participants who knew how to use the m-learning system then report on their experience. The results show that enhancing learners’ satisfaction, encouraging learners’ autonomy, empowering system functions, and enriching interaction and communication activities have a significant positive influence on the acceptance of m-learning systems.     

Effect of interactivity on learner perceptions in Web-based instruction

The capacities of the Internet provide a flexible platform for learning that has overcome the limitations of time and space. To compensate for the lack of face-to-face communication in online education, interactivity design has become an important factor affecting online learning. This study examines how different levels of interactivity in Web-based instruction (WBI) influence learners’ perceptions of using WBI systems with a comprehensive interaction design. Three WBI systems were developed with low, medium, and high levels of interactivity, and the effect of interactivity on learners’ perceptions was investigated in a real class. The findings suggest a relationship between the interactivity level and learners’ attitudes, learning, and satisfaction, but not perceived interactivity. The results indicated that the learners required a certain amount of mental effort to access the system initially; however, repeated exposure to the WBI systems increased their ability to operate the systems, which in turn increased the similarity of the interactivity perceptions of the three groups. Although the learners felt no differences in the interactivity toward the end of the 6-week class owing to familiarity with the system, the benefits of higher interactivity remained. This indicates that learners’ interactivity perceptions may change as their experience increases, but different interactivity designs do indeed influence their performance and attitudes in learning.     

Web-based learning programs: Use by learners with various cognitive styles

To consider how Web-based learning program is utilized by learners with different cognitive styles, this study presents a Web-based learning system (WBLS) and analyzes learners’ browsing data recorded in the log file to identify how learners’ cognitive styles and learning behavior are related. In order to develop an adapted WBLS, this study also proposes a design model for system designers to tailor the preferences linked with each cognitive style. The samples comprise 105 third-grade Accounting Information System course students from a technology university in central Taiwan. Analytical results demonstrate that learners with different cognitive styles have similar but linear learning approaches, and learners with different cognitive styles adopt different navigation tools to process learning.     

An intelligent learning diagnosis system for Web-based thematic learning platform

This work proposes an intelligent learning diagnosis system that supports a Web-based thematic learning model, which aims to cultivate learners’ ability of knowledge integration by giving the learners the opportunities to select the learning topics that they are interested, and gain knowledge on the specific topics by surfing on the Internet to search related learning courseware and discussing what they have learned with their colleagues. Based on the log files that record the learners’ past online learning behavior, an intelligent diagnosis system is used to give appropriate learning guidance to assist the learners in improving their study behaviors and grade online class participation for the instructor. The achievement of the learners’ final reports can also be predicted by the diagnosis system accurately. Our experimental results reveal that the proposed learning diagnosis system can efficiently help learners to expand their knowledge while surfing in cyberspace Web-based “theme-based learning” model.     

Understanding Web-based learning continuance intention: The role of subjective task value

The success of Web-based learning depends on learner loyalty, i.e., subsequent continued usage (continuance). We extended the Unified Theory of Acceptance and Use of Technology (UTAUT) by introducing components of subjective task value into a model for studying learners’ continuance intentions in Web-based learning. Based on survey data from 286 respondents, SEM was employed to assess the model. The results indicated that performance expectancy, effort expectancy, computer self-efficacy, attainment value, utility value, and intrinsic value were significant predictors of individuals’ intentions to continue using Web-based learning, while anxiety had a significant negative effect. The results suggested the beneficial effect of positive subjective task value on stimulating learners’ intentions to continue using Web-based learning, which is as important as performance expectancy and effort expectancy. Implications and limitations of our study are discussed.     

An intelligent e-learning system based on learner profiling and learning resources adaptation

Taking advantage of the continuously improving, web-based learning systems plays an important role for self-learning, especially in the case of working people. Nevertheless, learning systems do not generally adapt to learners’ profiles. Learners have to spend a lot of time before reaching the learning goal that is compatible with their knowledge background. To overcome such difficulties, an e-learning schema is introduced that adapts to the learners’ ICT (Information and Communication Technologies) knowledge level. The IEEE Reference Model (WG 1) defined by the Learning Technology Standards Committee (LTSA) is extended and used for this purpose. The proposed approach is based on the usage of electronic questionnaires (e-questionnaires) designed by a group of experts. Through the automatic analysis of the learners’ responses to the questionnaires, all learners are assigned to different learner profiles. According to these profiles they are served with learning material that best matches their educational needs. We have implemented our approach in five European countries and the overall case study illustrates very promising results.     

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.     

Understanding moderating effects of collectivist cultural orientation on the knowledge sharing attitude by email

This paper investigates the moderating effects of collectivist cultural orientation introduced in the information systems and knowledge management (KM) literature to fully understand the important factors and relationships in knowledge sharing in the online learning environment. Social norms and collectivist cultural orientation are hypothesized as direct and moderating factors to the system users’ (or learners’) attitude toward sharing knowledge by email. An empirical test of large student samples (n = 566) with multiple cultural backgrounds in the most diverse university was implemented by PLS. The test confirmed that collectivist cultural orientation moderates the effects of social norms on the attitude toward sharing knowledge by email. Furthermore, the test results show that group norms are stronger than the instructor or classmate norms. Theoretical and practical implications of these findings for online learning and KM are discussed in the paper.     

Community-organizing agent: An artificial intelligent system for building learning communities among large numbers of learners

Web-based (or online) learning provides an unprecedented flexibility and convenience to both learners and instructors. However, large online classes relying on instructor-centered presentations could tend to isolate many learners. The size of these classes and the wide dispersion of the learners make it challenging for instructors to interact with individual learners or to facilitate learner collaborations. Since extensive literature has confirmed that the substantial impact of learner interaction on learning outcomes, it is pedagogically critical to help distributed learners engage in community-based collaborative learning and to help individual learners improve their self-regulation. The E-learning lab of Shanghai Jiaotong University created an artificial intelligence system to help guide learners with similar interests into reasonably sized learning communities. The system uses a multi-agent mechanism to organize and reorganize supportive communities based on learners’ learning interests, experiences, and behaviors. Through effective award and exchange algorithms, learners with similar interests and experiences will form a community to support each others’ learning. Simulated experimental results indicate that these algorithms can improve the speed and efficiency in identifying and grouping homogeneous learners. Here, we will describe this system in detail and present its mechanism for organizing learning communities. We will conduct human experimentations in the near future to further perfect the system.     

An empirical analysis of the antecedents of web-based learning continuance

Like any other product, service and Web-based application, the success of Web-based learning depends largely on learners’ satisfaction and other factors that will eventually increase learners’ intention to continue using it. This paper integrates the concept of subjective task value and fairness theory to construct a model for investigating the motivations behind learners’ intention to continue using Web-based learning. The model theorizes that four components of subjective task value (i.e., attainment, utility, intrinsic, and cost) and three dimensions of fairness (i.e., distributive, procedural, and interactional) affect learners’ satisfaction. We also argue that satisfaction and four distinct components of subjective task value influence learners’ intention to continue using Web-based learning. The hypothesized model is validated empirically using data collected from 202 learners of a Web-based learning program designed for continuing education. The results showed that attainment value, utility value, intrinsic value, distributive fairness, and interactional fairness exhibited significant positive effects on satisfaction. Utility value and satisfaction play significant roles in shaping learners’ intention to continue using Web-based learning.     

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 Web 2.0-based collaborative annotation system for enhancing knowledge sharing in collaborative learning environments

A limitation of current Web-based collaborative learning is the restricted ability of students to create and share individual annotations with annotated documents. Applying Web 2.0 collaborative annotation systems and analyzing students’ annotation behavior has attracted attention to improve collaborative learning. This study designed a personalized annotation management system 2.0 (PAMS 2.0) for managing, sharing, and reusing individual and collaborative annotations as well as providing a shared mechanism for discussion about shared annotations among multiple users.     

An investigation of coopetitive pedagogic design for knowledge creation in Web-based learning

Information and communication technologies (ICTs) have created a supportive environment for collaborative learning at the expense of student motivation and engagement. This study attempts to explore the development of a productive learning atmosphere in the context of Web-based learning. An experiment is conducted with university-level students having homogenous background and coursework by applying heterogeneous pedagogies that create either competitive or collaborative learning atmospheres. The differences in learning atmosphere bring about variations in social presence and enjoyment of learning. The findings show that “coopetition” (defined as collaboration within the group and competition between groups) was the best learning strategy because competition and collaboration stimulated different types of knowledge growth in the knowledge-creation spiral. Competitive learning atmospheres encourage students to develop higher analytic skills, while collaborative learning atmospheres prompt students to demonstrate higher synthetic skills. Because both atmospheres contribute to learning, this study has found that combining both pedagogies in constructing a coopetitive learning atmosphere not only contributes to analytic and synthetic skills, but also raises the overall knowledge level. The findings pinpointed the importance of creating a learning environment that integrates ICTs, learners’ backgrounds, courseware, and pedagogic considerations in the process of increasing knowledge levels.     

Learners’ navigation behavior identification based on trace analysis

Identifying learners’ behaviors and learning preferences or styles in a Web-based learning environment is crucial for organizing the tracking and specifying how and when assistance is needed. Moreover, it helps online course designers to adapt the learning material in a way that guarantees individualized learning, and helps learners to acquire meta-cognitive knowledge. The goal of this research is to identify learners’ behaviors and learning styles automatically during training sessions, based on trace analysis. In this paper, we focus on the identification of learners’ behaviors through our system: Indicators for the Deduction of Learning Styles. We shall first present our trace analysis approach. Then, we shall propose a ‘navigation type’ indicator to analyze learners’ behaviors and we shall define a method for calculating it. To this end, we shall build a decision tree based on semantic assumptions and tests. To validate our approach, and improve the proposed calculation method, we shall present and discuss the results of two experiments that we conducted.     

An activity-theoretical approach to investigate learners’ factors toward e-learning systems

The Internet and World Wide Web have provided opportunities of developing e-learning systems. The development of e-learning systems has started a revolution for instructional content delivering, learning activities, and social communication. Based on activity theory, the purpose of this research is to investigate learners’ attitude factors toward e-learning systems. A total 168 participants were asked to answer a questionnaire. After factor analysis, learners’ attitudes can be grouped four different factors – e-learning as a learner autonomy environment, e-learning as a problem-solving environment, e-learning as a multimedia learning environment, and teachers as assisted tutors in e-learning. In addition, this research approves that activity theory is an appropriate theory for understanding e-learning systems. Furthermore, this study also provides evidence that e-learning as a problem-solving environment can be positively influenced by three other factors.     

Adapting prior knowledge activation: Mobilisation, perspective taking, and learners’ prior knowledge

This study investigates the effects of two prior knowledge activation strategies, namely, mobilisation and perspective taking, on learning. It is hypothesised that the effectiveness of these strategies is influenced by learners’ prior domain knowledge. More specifically, mobilisation is expected to be the most effective activation strategy at lower levels of prior knowledge. Mobilisation is a bottom-up oriented strategy that serves a broad stage-setting function. It provides learners with a relevant context in which new information can be integrated, which might be especially beneficial for learners with lower levels of prior knowledge to help them extend their limited knowledge base. As prior knowledge increases, perspective taking is expected to become the most effective strategy for activating learners’ prior knowledge. Perspective taking is a top-down oriented strategy that results in the activation of a corresponding schema. This schema guides the selection and processing of information relevant to the schema, which might especially support learners with higher levels of prior knowledge to refine their already elaborated knowledge base. The effectiveness of the activation strategies (in terms of learning task performance) was indeed influenced by learners’ prior knowledge in the hypothesised direction.     

Designing a semantic bliki system to support different types of knowledge and adaptive learning

Though blogs and wikis have been used to support knowledge management and e-learning, existing blogs and wikis cannot support different types of knowledge and adaptive learning. A case in point, types of knowledge vary greatly in category and viewpoints. Additionally, adaptive learning is crucial to improving one’s learning performance. This study aims to design a semantic bliki system to tackle such issues. To support various types of knowledge, this study has developed a new social software called “bliki” that combines the advantages of blogs and wikis. This bliki system also applies Semantic Web technology to organize an ontology and a variety of knowledge types. To aid adaptive learning, a function called “Book” is provided to enable learners to arrange personalized learning goals and paths. The learning contents and their sequences and difficulty levels can be specified according to learners’ metacognitive knowledge and collaborative activities. An experiment is conducted to evaluate this system and the experimental results show that this system is able to comprehend various types of knowledge and to improve learners’ learning performance.     

Can instruction as such affect learning? The case of learner control

This study examined the role of learners’ perceptions in a learner-controlled computer-based learning environment. Computer-based learning environments that offer learner control (LC) to the learners are assumed to enhance motivation and learning outcomes. Recently, the focus of LC research has shifted from measuring the direct effect of LC on learning towards focusing on the underlying mechanisms of effective LC and determining under which conditions LC is most effective (Corbalan, Kester, & van Merriënboer, 2009). There is considerable agreement that learners’ skills, perceptions and the experienced cognitive load interactively affect LC as instructional strategy. For this study, 165 first-year university students participated in an on-line English learning course on verb conjugation. We investigated the effect of learners’ perceptions of LC by comparing the learning outcomes and motivation of learners that received additional instructions on learner control with a group of learners that did not receive additional instruction. Learner characteristics, such as prior knowledge, working memory capacity, self-efficacy and cognitive load, were taken into account. The results indicate that it is not instruction as such, but rather satisfaction with the degree of control that affects learning outcomes and motivation. We suggest that instruction as such does not suffice to enhance perception of control, and that learners’ perceptions play a mere role in the effectiveness of instructional strategies such as learner control.     

Collaborative information environments to support knowledge construction by communities

Computer-based design environments for skilled domain workers have recently graduated from research prototypes to commercial products, supporting the learning of individual designers. Such systems do not, however, adequately support the collaborative nature of work or the evolution of knowledge within communities of practice. If innovation is to be supported within collaborative efforts, thesedomain-oriented design environments (DODEs) must be extended to becomecollaborative information environments (CIEs), capable of providing effective community memories for managing information and learning within constantly evolving collaborative contexts. In particular, CIEs must provide functionality that facilitates the construction of new knowledge and the shared understanding necessary to use this knowledge effectively within communities of practice.This paper reviews three stages of work on artificial (computer-based and Web-based) systems that augment the intelligence of people and organisations. NetSuite illustrates the DODE approach to supporting the work of individual designers with learning-on-demand. WebNet extends this model to CIEs that support collaborative learning by groups of designers. Finally, WebGuide shows how a computational perspectives mechanism for CIEs can support the construction of knowledge and of shared understanding within groups. According to recent theories of cognition, human intelligence is the product of tool use and of social mediations as well as of biological development; CIEs are designed to enhance this intelligence by providing computationally powerful tools that are supportive of social relations.     

Ubiquitous learning website: Scaffold learners by mobile devices with information-aware techniques

The portability and immediate communication properties of mobile devices influence the learning processes in interacting with peers, accessing resources and transferring data. For example, the short message and browsing functions in a cell phone provide users with timely and adaptive information access. Although many studies of mobile learning indicate the pedagogical potential of mobile devices, the screen size, computational power, battery capacity, input interfaces, and network bandwidth are too restricted to develop acceptable functionality for the entire learning processes in a handheld device. Therefore, mobile devices can be adopted to fill the gap between Web-based learning and ubiquitous mobile learning. This study first creates a website, providing functions enabling learning to take place anytime and anywhere with any available learning device, for ubiquitous learning according to various properties of mobile devices. Nowadays, learners’ behaviors on a website can be recorded as learning portfolios and analyzed for behavioral diagnosis or instructional planning. A student model is then built according to the analytical results of learning portfolios and a concept map of the learning domain. Based on the student model and learners’ available learning devices, three modules are developed to build a ubiquitous learning environment to enhance learning performance via learning status awareness, schedule reminders and mentor recommendation. Finally, an experiment is conducted with 54 college students after implementation of the ubiquitous learning website. Experimental results indicate that the proposed system can enhance three learning performance indicators, namely academic performance, task accomplishment rates, and learning goals achievement rates.