Ti Kai Chiu

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.

Using Comprehensive Memory Cycle Updating in Extensive Reading

English is a global language and thus learning it is important in many contexts. One way to approach this learning task is to undertake extensive reading of English texts. However, if students have an inadequate vocabulary, it is difficult for them to select appropriate articles to read. To address this problem, a number of studies have applied the theory of the memory cycle to help learners memorize words more efficiently. However, the method is inefficient when it just uses to update the memory cycle of the target words directly. In this work we propose a new framework, comprehensive memory cycle updating, which can not only update the memory cycle of the word directly, but also can update the memory cycle indirectly via learner response. This framework can reduce the number of times a learner needs to review a word in order to memorize it. In addition, by adopting the concept of the memory cycle, this framework can find articles, which contain words that the learners have already learned, as well as those they have almost forgotten.

Using memory cycle in video-based interactive learning

Nowadays, learning English is necessary in most non-English speaking countries. One of the interesting ways to learn languages is learning by watching videos. By doing so, people can learn not only learn foreign languages but also understand foreign cultures and idioms. However, such a practice would lead language learners to rely on subtitles because most videos are embedded with subtitles. Even if they are familiar with the target language, they try understanding videos through subtitles. In addition, memorizing words in visual is not an effective way to learn the languages of the alphabet system, because one of the features of the system is that the text represents the pronunciation, not the meaning. To help learners learn and memorize vocabulary efficiently in video watching activities as well as improving their listening ability, this study proposes a video-based interactive learning system. Appropriate videos with adapted subtitles are recommended to learners according to their ability and their memory cycles of vocabulary. When videos are playing, subtitles will be shown when a passage of subtitles contains unfamiliar words and new words; otherwise, they will be hidden. During the watching activity, learners can look up the words or read the translation if they have trouble in understanding videos. Based on the actions that learners do during watching videos, the system would accordingly update their memory cycles of vocabulary, which are used to choose appropriate videos subsequently.