Yu-Lin Jeng

An intelligent human-expert forum system based on fuzzy information retrieval technique

The forum system is useful for sharing knowledge and help-seeking. However, existing forums often have the problem that some questions remain unanswered. This study proposes an intelligent human-expert forum system to perform more efficient knowledge sharing. The system uses fuzzy information retrieval techniques to discover important discussion knowledge and actively invites human-experts who might be capable of answering the question to participate in the discussion. The selection of human-experts employs the Expert-Terms Correlation Matrix, which stores the knowledge strength of human-experts. Moreover, the forgetting curve is adopted into our forum system for modeling the variations in memory strength. The experiment uses three different categories of discussions to be the testing data, and the performance study shows acceptable results on discussion searching and expert discovery.

NP-miner: a real-time recommendation algorithm by using web usage mining

Web usage mining is widely applied in various areas, and dynamic recommendation is one web usage mining application. However, most of the current recommendation mechanisms need to generate all association rules before recommendations. This takes lots of time in offline computation, and cannot provide real-time recommendations for online users. This study proposes a Navigational Pattern Tree structure for storing the web accessing information. Besides, the Navigational Pattern Tree supports incremental growth for immediately modeling web usage behavior. To provide real-time recommendations efficiently, we develop a Navigational Pattern mining (NP-miner) algorithm for discovering frequent sequential patterns on the proposed Navigational Pattern Tree. According to historical patterns, the NP-miner scans relevant sub-trees of the Navigational Pattern Tree repeatedly for generating candidate recommendations. The experiments study the performance of the NP-miner algorithm through synthetic datasets from real applications. The results show that the NP-miner algorithm can efficiently perform online dynamic recommendation in a stable manner.

Location-Based Adaptive Mobile Learning Research Framework and Topics

Location-based adaptive mobile learning utilizes a unique feature: location awareness of mobile devices in regards to other online learning to implement location-based functionalities or features to create adaptive and collaborative learning in mobile learning environment. There are many important research issues and technical challenges in the location-based adaptive mobile learning systems, including the assessment of learning in the mobile environment. In this paper, we present the research framework and topics related to the undergoing research project at Athabasca University towards the development of robust and effective location-based adaptive mobile learning systems. In addition, we also brief some the major research outcomes achieved so far within the project’s research framework.

A Collaborative Mobile Virtual Campus System Based on Location-Based Dynamic Grouping

This paper presents a collaborative mobile learning system: Mobile Virtual Campus (MVC) powered by the loction-based dynamic grouping algorithm. The MVC system has been developed to provide an innovative and interactive platform for online mobile learners by utilizing the location awareness and other built-in sensory compoments in mobile devices. On the platform, the mobile learners can learn collaboratively and interactively either at a distance or face-to-face in the mobile learning environment.

ANTS: agent-based navigational training system

There is an explosive growth of e-learning trend during the last few years. More and more learning resources are generated by different purposes in the world. How to make the learning resources to be sharable and reusable is a key factor in e-learning environment. This paper presents a system framework of Agent-based Navigational Training System (ANTS) to facilitate lecturers and learners achieving their works. Besides, we apply the model of Dynamic Fuzzy Petri Net (DFPN) and the intelligent agent into the system to assist learners. The intelligent agent can dynamically generate the learning path for each learner. Moreover, the system is compatible to Sharable Content Object Reference Model (SCORM) which is the most acceptable e-Learning system developing standard. Accordingly, the learning resources could be sharable and reusable in any platforms which are compatible to SCORM standard.

Retrieving video features for language acquisition

Using clips from contemporary films and videos is an alternative approach for students of English-as-a-Foreign-Language that can support their acquisition of the language in a real world context. Compact, attractive and easy to use, our dynamic video retrieval system (DVRS) gives students quick access to resources that can facilitate their learning. In this study, we integrate an innovative learning-assisted system, named the dynamic video retrievals system, which allows students of English-as-a-Foreign-Language to use information retrieval techniques which examine video scripts for specific word collocations and subsequently utilize a ranking-based approach to analyze the collocations discovered. Such computer-mediated interaction enables students in traditionally structured English classes to find engaging, real life examples of grammar and vocabulary in use, giving them opportunities to strengthen their language skills in a culturally relevant way.

Extended real-time learning behavior mining

Based on our previous work (Y. H. Kuo et al., 1999), learning patterns can be discovered and recommended to learners. This paper extends the proposed problem to handle the questionable mining results. According to the learning patterns discovered by using learning histories, it happened whenever the learners have ineffective learning behaviors, and we define them as questionable mining results. These ineffective behaviors may induce the bias suggestions. Therefore, we propose a candidate sequence set generation process to take care the stumble learning behavior.

Real-Time Learning Behavior Mining for e-Learning

Over the last years, we have witnessed an explosive growth of e-learning. More and more learning contents have been published and shared over the Internet. Therefore, how to progress an efficient learning process becomes a critical issue. This paper proposes a sequential mining algorithm to analyze learning behaviors for discovering frequent sequential patterns. By these patterns, we can provide suggestions for learners to select their interest learning contents. Different to other sequential mining algorithms, this study provides an incrementally method to analyze learning sequencing. More specifically, the mining algorithm in this paper can provide real-time analysis, and then report to learners for selecting learning contents more easily.

From Traditional to Digital: Factors to Integrate Traditional Game-Based Learning into Digital Game-Based Learning Environment

Traditional game-based learning is widely used as pedagogy to motivate students more and attract them in learning process. Digital game-based learning is a new trend in instruction. With high-tech equipments we are able to bring novel experience to learners. Both of game-based learning strategies are with their own advantages. However, the previous difficulty of game-based learning is to integrate these two kinds of learning strategies and strike a balance between them. Therefore, this study aims to utilize activity theory as an analytics tool to analyze the factors of each game-based learning status. After the analysis, we discussed the contradiction of each activity system. Furthermore we had also some suggestions for the instructors and learning activity designers to make game-based learning more useful and meaningful.

SNS collaborative learning design: enhancing critical thinking for human---computer interface design

In many cases, classrooms seem to be functioning as well as ever, though the challenges and expectations have changed quite dramatically. This study presents an innovative project-based cloud learning (PBCL) model which integrates various Google services in order to enhance critical thinking abilities in human–computer interface design. To investigate the effectiveness of the proposed SNS collaborative teaching model for HCI design, the one-group pretest–posttest experiment was adopted in a university HCI design course in Taiwan. Participants included 32 technical and vocational university students aged 20–22, all major in information science. The collected data is used to explore the change in students’ critical thinking skills. Additionally, this study also adopts an interview survey to collect qualitative data. There are 11 groups in this course, and the 11 respondents were randomly selected from these groups after the course. The results indicate that the practice of PBCL could help students to develop critical thinking skills rather than disposition. More specifically, the differences of low initial disposition group’s pre-test and post-test of CTD and CTS were significant. The qualitative interview results reveal that experimental subjects hold a positive view on integrating SNS and various Google services to human–computer interface course to enhance project design and critical thinking skills. It implies that the PBCL model is an innovative and practical teaching model to be beneficial to learners with low initial interest. In addition, it helps students deepen impression of learning content and inspire new ideas.