Exploring Technology-Enhanced Learning Key Terms using TF-IDF Weighting

摘要

Technology-enhanced learning (TEL) covers a broad spectrum of discussion. Having a holistic viewpoint and use it to augment TEL is a challenge. We need extensive literature reviews requiring coverage of as many articles as possible discussing TEL. Accordingly, we may look for the key terms with discriminant power to explain this topic. This study processed 40 TEL articles, published no earlier than 2010, taken from IEEE Xplore research database. In previous work, we applied Luhn's significant words as a qualitative approach. However, the reliability of subjective justification become an issue. This study answers the issue by applying term frequency-inverse document frequency (TF-IDF) weight, to find the key terms. This research produces 23 key terms from 685 TF-IDF important words compared to 381 significant words. The finding indicates that some of the significant words also appear in the highest TF-IDF weight cluster. Further analysis could be done using other research databases for more articles.