Chun-Hsiung Lee

Bio: Chun-Hsiung Lee is an academic researcher from National Taiwan University of Science and Technology. The author has contributed to research in topics: Auditory learning & Adaptive learning. The author has an hindex of 3, co-authored 3 publications receiving 125 citations.

Application of automatically constructed concept map of learning to conceptual diagnosis of e-learning

Abstract: The concept map proposed by Novak is a good tool to portray knowledge structure and to diagnose students' misconception in education. However, most of the learning concept maps have to be constructed through the suggestions of experts or scholars in related realm. It is really a complicated and time-consuming knowledge acquisition process. The study proposed to apply the algorithm of Apriori for Concept Map to develop an intelligent concept diagnostic system (ICDS). It provides teachers with constructed concept maps of learners rapidly, and enables teachers to diagnose the learning barriers and misconception of learners instantly. The best Remedial-Instruction Path (RIP) can be reached through the algorithm of RIP suggested in this study. Furthermore, RIP can be designed to provide remedial learning to learners. Moreover, by using statistical method, the study analyzed 245 students' data to investigate whether the learning performance of learners can be significantly enhanced after they have been guided by the RIP.

Analysis on the adaptive scaffolding learning path and the learning performance of e-learning

Abstract: Existing instruction websites record learners' portfolios, they only collect the browsing time and homepage information, without directly provide teachers with more data for further analyzing learner behaviors. Consequently, this investigation uses the learners' portfolio left in the e-learning environment, and adopts "data mining" techniques to establish for each cluster of learners the most adaptive learning path pattern, which can provide a "scaffolding" to guide each cluster of learners. Using statistical methods, this investigation analyzes whether the navigational learning map of "scaffolding learning path (SLP)" can improve learning performance. This investigation discovers that among the three clusters of learners, the learners in the experimental group under the two clusters other than high-score cluster, after taking the "scaffolding learning path" as their navigational learning map, achieve more significant progress than the learners of comparative group. This implies that through the "scaffolding learning path," the learning performance of most learners can be improved.

The effectiveness of an Intelligent Annotation Sharing System on e-learning

Abstract: Reading is a very important part in learning process. When reading the teaching materials of textbooks in a traditional way, students usually underline the main points and take notes to help memorizing, thinking and understanding the contents of the teaching materials. With the progress of network technology, e-learning has gradually become a new learning trend. However, the digital e-teaching materials of e-learning are always the texts that cannot be changed by students as an easier reading format. In this paper, we propose an algorithm named Expert Keywords Annotation Alignment Algorithm (EKAAA) and based on which we have developed an Intelligent Annotation Sharing System (IASS) as an auxiliary tool for students to read the e-teaching materials. Based on the cluster to which a student belongs, the annotation sharing system adaptively provides the student a suitable sharing model. The models serve as a ''scaffolding'' to guide the students' learning, intending to achieve the purposes of auxiliary learning and knowledge sharing. Finally, we use statistics to analyze the effectiveness of the Intelligent Annotation Sharing System on e-learning.

Educational Data Mining: A Review of the State of the Art

Abstract: Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.

Educational data mining applications and tasks: A survey of the last 10 years

Abstract: Educational Data Mining (EDM) is the field of using data mining techniques in educational environments. There exist various methods and applications in EDM which can follow both applied research objectives such as improving and enhancing learning quality, as well as pure research objectives, which tend to improve our understanding of the learning process. In this study we have studied various tasks and applications existing in the field of EDM and categorized them based on their purposes. We have compared our study with other existing surveys about EDM and reported a taxonomy of task.

A survey on educational data mining and research trends

Abstract: Educational Data Mining (EDM) is an emerging field exploring data in educational context by applying different Data Mining (DM) techniques/tools. It provides intrinsic knowledge of teaching and learning process for effective education planning. In this survey work focuses on components, research trends (1998 to 2012) of EDM highlighting its related Tools, Techniques and educational Outcomes. It also highlights the Challenges EDM.

A comprehensive text analysis of lecture slides to generate concept maps

Abstract: Current instructional methods widely support verbal learning through linear and sequential teaching materials, focusing on isolated pieces of information. However, an important aspect of learning design is to facilitate students in identifying relationships between information. The transformation of linearity in teaching resources into integrated network models such as concept maps facilitates effective knowledge organisation by constructing relationships between new and existing knowledge. However, the manual construction of concept maps from teaching materials places an additional workload on the academics involved. Consequently, this research investigates the effectiveness of automated approaches in extracting concept maps from lecture slides and the suitability of auto-generated concept maps as a pedagogical tool. We develop a set of Natural Language Processing (NLP) algorithms to support concept-relation-concept triple extraction to form concept maps. Structural and graph-based features are utilised to rank the triples according to their importance. The natural layout of the lecture slides is incorporated to organise the triples in a hierarchy, facilitating highly integrated structure. Our evaluation studies identify promising results, with several case studies demonstrating a statistically significant correlation (r s > 0.455) between auto-generated concept maps and human experts' judgment. Auto-generated concept maps were rated from ‘good’ to ‘very good’ by the academics on evaluation factors such as coverage, accuracy, and suitability as a pedagogical tool. Thus, auto-generated concept maps from this research can be utilised as a positive alternative to the manual construction of expert concept maps and further, it is possible to utilise these maps for a wider range of applications including knowledge organisation and reflective visualisation of course contents. Our research contributes to bridging the gap between linearity in teaching materials and the necessity of creating integrated network models from teaching resources.