Surveying instructor and learner attitudes toward e-learning

The trend of using e-learning as a learning and/or teaching tool is now rapidly expanding into education. Although e-learning environments are popular, there is minimal research on instructors’ and learners’ attitudes toward these kinds of learning environments. The purpose of this study is to explore instructors’ and learners’ attitudes toward e-learning usage. Accordingly, 30 instructors and 168 college students are asked to answer two different questionnaires for investigating their perceptions. After statistical analysis, the results demonstrate that instructors have very positive perceptions toward using e-learning as a teaching assisted tool. Furthermore, behavioral intention to use e-learning is influenced by perceived usefulness and self-efficacy. Regarding to learners’ attitudes, self-paced, teacher-led, and multimedia instruction are major factors to affect learners’ attitudes toward e-learning as an effective learning tool. Based on the findings, this research proposes guidelines for developing e-learning environments.     

Improving the authentic learning experience by integrating robots into the mixed-reality environment

The main aim of the modern popular teaching method of authentic learning has been to provide students with everyday-life challenges that develop knowledge and skills through problem solving in different situations. Many emerging information technologies have been used to present authentic environment in pedagogical purpose. However, there are few studies that have been discussed the sense of authenticity and characters in scene and how students interact with the characters involved in the task. We designed a system, RoboStage, with authentic scenes by using mixed-reality technology and robot to investigate the difference in learning with either physical or virtual characters and learning behaviors and performance through the system. Robots were designed to play real interactive characters in the task. The experiment of the study conducted with 36 junior high students. The results indicated that RoboStage significantly improved the sense of authenticity of the task and also positively affected learning motivation. Learning performance was conditionally affected by RoboStage.     

Users’ attitudes toward Web-based collaborative learning systems for knowledge management

The Web-based technology is a potential tool for supported collaborative learning that may enrich learning performance, such as individual knowledge construction or group knowledge sharing. Thus, understanding Web-based collaborative learning for knowledge management is a critical issue. The present study is to investigate learners’ attitudes toward Web-based collaborative learning systems. Based on this research, the results of factor analysis show that five attitude factors (system functions, system satisfaction, collaborative activities, learners’ characteristics, and system acceptance) should be examined at the same time when building a Web-based collaborative learning system. The results also provide an acceptance model for understanding users’ behavioral intention of facilitating Web-based collaborative systems.     

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.     

The Influence of Relative Humidity on Nanoparticle Concentration and Particle Mass Distribution Measurements by the MOUDI

A humidity control system was operated upstream of two collocated MOUDIs (micro-orifice uniform deposit impactors) for sampling ambient aerosol particles. One MOUDI used silicone-grease-coated aluminum foils (ALs) as the impaction substrates and was considered as the reference impactor, while the other used uncoated ALs or uncoated Teflon filters (TFs) as the impaction substrates for quantifying the effect of different relative humidities (RHs) and impaction substrates on the PM_0.1 concentrations and mass distributions of ambient PMs. Test results showed that decreasing RH in general increased particle bounce from uncoated substrates with the bounce from uncoated ALs being more severe than that from uncoated TFs. Particle bounce did not influence the overall mass distribution of ambient fine particles when RH ranged between 40% and 80%, whereas it led to undersampling of particles greater than 2.5 μm in aerodynamic diameter severely. Oversampling of PM_0.1 occurred by as much as 95%–180% or 25%–55% when the MOUDI used uncoated ALs or TFs, respectively, as RH was reduced from 50% to 25%. Particle bounce was found to be negligible, and PM_0.1 and PM_2.5 could be sampled accurately with less than 5% error at the RH of 75%–80% or 65%–80% when uncoated ALs or TFs were used, respectively.     

The MSFAM: a modified fuzzy ARTMAP system

A fuzzy ARTMAP system is a system for incremental supervised learning of recognition categories and multi-dimensional maps in response to an arbitrary sequence of analog or binary input vectors. Fuzzy ARTMAP systems have been benchmarked against a variety of machine learning, neural networks, and genetic algorithms with considerable success. Owing to many appealing properties, fuzzy ARTMAP systems provide a natural basis for many researchers. Many different approaches have been proposed to modify fuzzy ARTMAP systems. In this paper, we propose a new approach to modifying a fuzzy ARTMAP system. We refer to the new system as the modified and simplified fuzzy ARTMAP (MSFAM) system. The aims of MSFAM systems are not only to reduce the architectural redundancy of the fuzzy ARTMAP system, but also to make extracted rules more comprehensible and concise. Four data sets were used for demonstrating the performance of the proposed MSFAM system.     

Making Database Schema Hierarchical for Visual Access to Databases

Making the database schema hierarchical can help a casual user retrieve information from a complex database. The hierarchical database schema provides further insight into database content and focuses on meaningful data by a top-down method. The user can proceed with a hierarchical visual query, which ultimately simplifies the query, reduces the syntax error rate and conserves the query time. In this paper, we present a hierarchical graph which makes the database schema hierarchical, naturally integrates the browsing and querying and, consequently, allows the user to proceed with an incremental query on the hierarchical database schema. Also proven herein are the existence, uniformity, and consistency of the hierarchical graph to verify that the graph can be used to query the database. This paper also discusses the semantics of high-level nodes and conducts an experiment to evaluate users’ performance. Finally, we describe how one can use the hierarchical graph to unify the tasks of making the schema hierarchical, creating concept hierarchies, and integrating the databases.     

A Neuro-Fuzzy Approach for Compensating Color Backlight Images

This paper presents a neuro-fuzzy approach for compensating exposure in the case of backlighting, regardless of the position of objects. To achieve the compensation effect, the fuzzy C-means algorithm is first used to extract features from a backlight image. Then these extracted features are presented to a trained artificial immune system based neuro-fuzzy system (AISNFS) to estimate the amount of compensation. Finally, the estimated amount of compensation incorporated with a compensation equation is used to enhance the intensity component of the backlight image to produce a compensated image. Several backlight images were used to test the performance of the algorithm.     

Collaborative storytelling experiences in social media: Influence of peer-assistance mechanisms

Collaborative storytelling activities in social media environments are generally developed in a linear way in which all participants collaborate on a shared story as it is passed from one to another in a relay form. Difficulties with this linear approach arise when collecting the contributions of participants in to a coherent story. This study proposes a hypermedia approach to enable students to integrate the episodes of others to develop different branches of stories. Since these linear and nonlinear approaches facilitate students in developing stories in quite a different manner, students’ perceptions of linear and hypermedia approaches differ in their collaboration mechanisms, which may in turn affect positive inter-dependence and ultimate success in the collaborative storytelling. The results of an empirical study show that the performance of students in the hypermedia group was superior to that of members the linear group insofar as perception of collaborative process, peer support, authorship, and collaborative result where concerned.