Chuang-Kai Chiou

An adaptive navigation support system for conducting context-aware ubiquitous learning in museums

In context-aware ubiquitous learning, students are guided to learn in the real world with personalized supports from the learning system. As the learning resources are realistic objects in the real world, certain physical constraints, such as the limitation of stream of people who visit the same learning object, the time for moving from one object to another, and the environmental parameters, need to be taken into account. Moreover, the values of these context-dependent parameters are likely to change swiftly during the learning process, which makes it a challenging and important issue to find a navigation support mechanism for suggesting learning paths for individual students in real time. In this paper, the navigation support problem for context-aware ubiquitous learning is formulated and two navigation support algorithms are proposed by taking learning efficacy and navigation efficiency into consideration. From the simulation results of learning in a butterfly museum setting, it is concluded that the innovative approach is helpful to the students to more effectively and efficiently utilize the learning resources and achieve better learning efficacy.

An auto-scoring mechanism for evaluating problem-solving ability in a web-based learning environment

The rapid development of computer and network technologies has attracted researchers to investigate strategies for and the effects of applying information technologies in learning activities; simultaneously, learning environments have been developed to record the learning portfolios of students seeking web information for problem-solving. Although previous research has demonstrated the benefits of applying information technologies to learning activities, the difficulties in doing so have also been revealed. One of the major difficulties is the lack of a mechanism to assist teachers in evaluating the problem-solving ability of the students, such that constructive suggestions can be given to the students, and tutoring strategies can be improved accordingly. To cope with this problem, in this paper, an auto-scoring mechanism is developed to analyze the various information searching abilities of individual students. Indicators of information searching ability (ISA) are proposed based on the famous Big6 model and are adopted in our auto-scoring mechanism. Moreover, two experiments have been conducted to demonstrate the effectiveness of this innovative approach. The experimental results show high correlation between the scores of the auto-scoring mechanism and the manual scoring. Moreover, the feedbacks from 158 teachers also show that the innovative approach is highly accepted by the teachers.

Development and Evaluation of an Active Learning Support System for Context-Aware Ubiquitous Learning

Situating students to learn from the real world has been recognized as an important and challenging issue. However, in a real-world learning environment, there are usually many physical constraints that affect the learning performance of students, such as the total learning time, the limitation of the number of students who can visit a learning target, and the time needed for moving from one learning location to another. It is essential to guide the students along an efficient learning path to maximize their learning performance according to the current situation. In this paper, an active learning support system (ALESS) for context-aware ubiquitous learning environments is designed and developed. ALESS can provide learning guidance when conducting ubiquitous learning activities. A great deal of context information is used in ALESS, including the location, the current capacity of the learning object, the time available, etc. ALESS is able to actively provide the required learning support to individual students when they approach the corresponding real-world learning targets. To evaluate the performance of ALESS, an experiment was conducted in the National Science Museum of Taiwan. The experimental results showed that, with the help of ALESS, the students learned more efficiently, and achieved better learning performance.

Design of a personalized navigation support system for context-aware ubiquitous learning environment

In context-aware ubiquitous learning environment, finding out an optimal learning path for each student in real time to maximize the learning performance is important. In addition, many studies also indicated and confirmed that personalization is effective in improving the learning efficacy of students. Although the issue of personalized navigation support has been widely discussed in e-learning, it is still under investigated in ubiquitous learning environment. This paper proposes a personalized navigation strategy based on the Learning Orientation Theory. The strategy will guide the learners in context-aware ubiquitous learning environment by their learning orientation. To fulfill the proposed strategy, three navigation modes, namely Hyper-linear Navigation, Semi-linear Navigation, and Linear Navigation are developed for Transforming, Performing and Conforming learners respectively. A personalized navigation support system that includes the three navigation modes is also implemented. A preliminary experiment is conducted and the survey shows that most of the students are satisfied by our proposed system.

An intelligent classroom management system based on wireless sensor networks

The learning efficiency in a traditional classroom is easily influenced by three factors: the learning environment, the instruction mode and the conditions of students. For improving the learning efficiency, an intelligent classroom management system with context-awareness based on the wireless sensor network technology is proposed and implemented in this research. The statuses of students and the classroom are detected by wireless sensors, and these statuses will be transferred to the management system built in a server. The management system will determine the current conditions of the students and the classroom, and give proper feedbacks to the students, the teachers, and the wireless sensor-controlled equipment deployed in the classroom. The proposed intelligent classroom management system includes three subsystems. 1) The environment management system regulates the classroom to a suitable condition for teaching and learning; 2) The instruction mode management system assists teachers to switch instruction modes quickly and smoothly to avoid wasting of time; 3) The learning behavior management system alerts students when they are inattention or fatigued. The proposed system is implemented in a classroom of Chung Hua University, Taiwan. A preliminary evaluation is also conducted to verify the correctness of the system.

An incremental mining algorithm for association rules based on minimal perfect hashing and pruning

In the literatures, hash-based association rule mining algorithms are more efficient than Apriori-based algorithms, since they employ hash functions to generate candidate itemsets efficiently. However, when the dataset is updated, the whole hash table needs to be reconstructed. In this paper, we propose an incremental mining algorithm based on minimal perfect hashing. In our algorithm, each candidate itemset is hashed into a hash table, and their minimum support value can be verified directly by a hash function for latter mining process. Even though new items are added, the structure of the proposed hash does not need to be reconstructed. Therefore, experimental results show that the proposed algorithm is more efficient than other hash-based association rule mining algorithms, and is also more efficient than other Apriori-based incremental mining algorithms for association rules, when the database is dynamically updated.

A Scalable Association Rules Mining Algorithm Based on Sorting, Indexing and Triming

Apriori is an influential and well-known algorithm for mining association rules. However, the main drawback of Apriori algorithm is the large amount of candidate itemsets it generates. Several hash-based algorithms, such as DHP and MPIP, were proposed to deal with the problem. DHP employs hash functions to filter out potential-less candidate itemsets. MPIP further improves DHP by employing minimal perfect hashing functions to avoid generation of candidate itemsets. Though MPIP results in a very promising mining efficiency, the memory space required in MPIP increases rapidly as the number of items grows. To obtain even better mining efficiency while reducing the memory space required, a sorting-indexing-trimming (SIT) algorithm for mining association rules is proposed in this paper. SIT uses the sorting, indexing, and trimming techniques to reduce the amount of itemsets to be considered. Then, to utilize both the advantages of Ariori and MPIP, a revised MPIP algorithm is employed to deal with 2-itemsets, and a revised apriori algorithm to deal with Mtemsets for k>2. Though the memory space required in SIT is less than MPIP, from the experiment results, SIT outperforms both Apriori and MPIP.

CoboChild: a blended mobile game-based learning service for children in museum contexts

The purpose of this paper is to develop a blended mobile game-based learning service called CoboChild Mobile Exploration Service (hereinafter CoboChild) to support children’s learning in an environment blending virtual game worlds and a museum’s physical space. The contextual model of learning (CML) was applied to consider the related influential factors affecting museum learning and to promote children’s continuous learning and revisit motivations.,CoboChild provides a thematic game-based learning environment to facilitate children’s interactions with exhibits and other visitors. A practical system has been implemented in the National Museum of Natural Science (NMNS), Taiwan. A questionnaire was used to examine whether CoboChild can effectively fulfill the CML and to evaluate the impacts on museum learning.,CoboChild effectively fulfilled the CML to facilitate children’s interactive experiences and re-visit motivations in the blended mobile game-based learning environment. Most children described the system as providing fruitful playfulness while improving their interpretations of exhibitions and learning experiences.,CoboChild considers the related contextual influences on the effective support of children’s learning in a museum, and builds a child-centered museum learning environment with highly integrated blended learning resources for children. CoboChild has been successfully operating in the NMNS since 2011.,This study developed a blended mobile game-based learning service to effectively support children’s learning in museum contexts. The related issues are shown to improve the design of blended museum learning services. This innovative approach can be applied to the design of other child-centered services for engaging children’s interactive experiences in museums.

Navigation Mechanism in Blended Context-Aware Ubiquitous Learning Environment

In recent years, navigation support problems have been discussed and investigated in ubiquitous learning environment. Several researches have proven that students can perform better when navigation supports are provided by learning systems. However, traditional ubiquitous learning environments suffer from some physical limitations. For example, the capacities of learning targets are limited and/or moving times for reaching learning targets are required. To address these limitations and create a more efficient ubiquitous learning environment, a novel learning framework, namely the blended ubiquitous learning environment, is proposed. A blended navigation algorithm, B-MONS, is also proposed for developing a navigation support mechanism which suits the new learning framework. Experimental results show that students learn in the blended navigation environment with the help of B-MONs get higher learning performance.