Tung Cheng Hsieh

A mining-based approach on discovering courses pattern for constructing suitable learning path

In recent years, browser has become one of the most popular tools for searching information on the Internet. Although a person can conveniently find and download specific learning materials to gain fragmented knowledge, most of the materials are imperfect and have no particular order in the content. Therefore, most of the self-directed learners spend most of time in surveying and choosing the right learning materials collected from the Internet. This paper develops a web-based learning support system that harnesses two approaches, the learning path constructing approach and the learning object recommending approach. With collected documents and a learning subject from a learner, the system first discovers some candidate courses by using a data mining approach based on the Apriori algorithm. Next, the leaning path constructing approach, based on the Formal Concept Analysis, builds a Concept Lattice, using keywords extracted from some selected documents, to form a relationship hierarchy of all the concepts represented by the keywords. It then uses FCA to further compute mutual relationships among documents to decide a suitable learning path. For a chosen learning path, the support system uses both the preference-based and the correlation-based algorithms for recommending the most suitable learning objects or documents for each unit of the courses in order to facilitate more efficient learning for the learner. This e-learning support system can be embedded in any information retrieval system for surfers to do more efficient learning on the Internet.

A grammar-based semantic similarity algorithm for natural language sentences.

This paper presents a grammar and semantic corpus based similarity algorithm for natural language sentences. Natural language, in opposition to “artificial language”, such as computer programming languages, is the language used by the general public for daily communication. Traditional information retrieval approaches, such as vector models, LSA, HAL, or even the ontology-based approaches that extend to include concept similarity comparison instead of cooccurrence terms/words, may not always determine the perfect matching while there is no obvious relation or concept overlap between two natural language sentences. This paper proposes a sentence similarity algorithm that takes advantage of corpus-based ontology and grammatical rules to overcome the addressed problems. Experiments on two famous benchmarks demonstrate that the proposed algorithm has a significant performance improvement in sentences/short-texts with arbitrary syntax and structure.

An Ontological Approach for Semantic Learning Objects Interoperability

This paper presents a semantic-aware classification algorithm that can leverage the interoperability among semantically heterogeneous learning object repositories using different ontologies. The proposed algorithm is to map sharable learning objects, using meanings instead of just keyword matching, from heterogeneous repositories into a local knowledge base (an e-learning ontology). Significance of this research lies in the semantic inferring rules for learning objects classification as well as the full automatic processing and self-optimizing capability. This approach is sufficiently generic to be embedded into other e-learning platforms for semantic interoperability among learning object repositories. Focused on digital learning material and contrasted to other traditional classification technologies, the proposed approach has experimentally demonstrated significantly improvement in performance.

An English Vocabulary Learning System Based on Fuzzy Theory and Memory Cycle

This paper proposes an English Vocabulary Learning System based on the Fuzzy Theory and the Memory Cycle Theory to help a learner to memorize vocabularies easily. By using fuzzy inferences and personal memory cycles, it is possible to find an article that best suits a learner. After reading an article, a quiz is provided for the learner to improve his/her memory of the vocabulary in the article. Early researches use just explicit response (ex. quiz exam) to update memory cycles of newly learned vocabulary; apart from that approach, this paper proposes a methodology that also modify implicitly the memory cycles of learned word. By intensive reading of articles recommended by our approach, a learner learns new words quickly and reviews learned words implicitly as well, and by which the vocabulary ability of the learner improves efficiently.

An approach for constructing suitable learning path for documents occasionally collected from Internet

With a faster, more accessible Internet, nowadays people tend to search and learn from Internet for some fragmented knowledge. Usually, a vast amount of documents, homepages or learning objects, will be returned by some powerful search engines with no particular order. Even if they might really be related, a user still has to move forward and backward among the material trying to figure out which page to read first because the user might has had little or no experience in the specific domain. Although a user may have some intuitions about the domain but these intuitions are yet to be connected. This paper proposes a learning path construction approach based on a modified TF-IDF, the ATF-IDF, and the well-known formal concept analysis, the FCA, algorithms. First, the approach constructs a concept lattice using keywords extracted by the ATF-IDF from collected documents to form a relationship hierarchy between all the concepts represented by the keywords. It then uses FCA to compute mutual relationships among documents to decide a suitable learning path.