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Publisher: Tamkang University (Total: 2 journals) [Sort alphabetically]

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	J. of Educational Media & Library Sciences (Followers: 9, SJR: 0.149, CiteScore: 0)
	Tamkang J. of Mathematics (SJR: 0.334, CiteScore: 1)

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Journal of Educational Media & Library Sciences
Journal Prestige (SJR): 0.149

Number of Followers: 9

This is an Open Access Journal

Open Access journal
ISSN (Print) 1013-090X
Published by Tamkang University

[2 journals]

The Feasibility of Automated Topic Analysis: An Empirical Evaluation of
Deep Learning Techniques Applied to Skew-Distributed Chinese Text
Classification
- Abstract: Text classification (TC) is the task of assigning predefined categories (or labels) to texts for information organization, knowledge management, and many other applications. Normally the categories are topical in library science applications, although they can be any labels suitable for an application. Thus, TC often requires topical analysis which relies on human knowledge. However, in recent decades, machine learning (ML) techniques have been applied to TC for efficiency, as long as a sufficient number of training texts are available for each category. Nevertheless, in real-world cases, the number of texts (documents) for each category is often highly skewed for a certain TC task. This leads to the problem of predicting labels for small categories, which is viable for humans but challenging for machines. Deep learning (DL) is an emerging class of machine learning (ML) which was inspired by human neural networks. This study aims to evaluate whether DL techniques are feasible for the mentioned problem by comparing the performance of four off-the-shelf DL methods (CNN, RCNN, fastText, and BERT) with four traditional ML techniques on five skew-distributed datasets (four in Chinese, and one in English for comparison). Our results show that BERT is effective for moderately skewed datasets, but is still not feasible for highly skewed TC tasks. The other three DL-aware methods (CNN, RCNN, fastText) do not show any advantage in comparison with traditional methods such as SVM for the five TC tasks, although they captured extra language knowledge in the pretrained word representation. To facilitate future study, all of the Chinese datasets used in this study have been released publicly, together with all of the adapted machine learning and evaluation source codes for verification and for further study at https://github.com/SamTseng/Chinese_Skewed_TxtClf.
  PubDate: 2020-01-23
探究式資訊素養融入國中階段課程之追蹤研究
- Abstract: 本研究旨在追蹤之前已接受六年探究式資訊素養融入課程的國小學生進入國中階段後，他們應用資訊素養的經驗與表現，並瞭解在國中階段繼續實施探究式資訊素養融入課程的情形及遭遇的困難。本研究採縱貫研究法追蹤30位研究對象，自七年級下學期至八年級結束，共追蹤一年半時間。研究工具有資訊素養追蹤調查問卷(學生版和教師版)，及訪談大綱等三種。蒐集資料的方法包括深度訪談、焦點訪談、問卷及文件分析等。研究結果發現無論先前國小學業程度的高低，學生多會將習得的資訊素養持續應用在國中學習和日常生活中；探究式資訊素養融入課程在國中階段實施的情形不佳，圖書館素養、圖像媒體素養及探究式學習多未與國中課程深度結合。
  PubDate: 2020-01-23
- Abstract: 本論文探討臺灣圖書資訊學碩士班畢業生就業現況並評鑑碩士班教育價值，採問卷調查，於2018年以網路發送六所臺灣的圖書資訊學研究所2011年後碩士畢業生。問卷調查就業現況、工作知能應用、工作滿意度、碩士班滿意度與碩士班教育價值。研究顯示臺灣圖書資訊學碩士畢業生就業市場改變，圖書館工作者占50.5%，就業擴大到更多資訊機構。碩士畢業生碩士班習得知能在工作應用程度為3.87(李克特5等評量)，研究與企劃類知能最高，其次個人管理類，資訊科技類，圖書資訊學理論與服務類，行政與管理類。畢業生整體工作滿意度達中上滿意(3.66)，對碩士班評鑑整體滿意度達高度滿意(4.15)，碩士班教育價值整體達中上同意(3.89)。臺灣圖書資訊學碩士畢業生認同碩士班教育價值，與碩士班滿意度、專業知能應用及工作滿意度相關。
  PubDate: 2020-01-23
- Abstract: MARC一直是圖資界重要的資訊交換標準，由於MARC格式的過時，且不被圖資界以外所熟知與使用，反而阻礙MARC的應用。隨著語意網的推展，鏈結資料技術已被圖資界視為解構書目資訊的一項新方法。有鑑於此，重新檢視MARC採取何種方式展延至鏈結資料與其效益是值得探討的研究議題。首先，本文以鏈結資料提出的2006年為基準，分析相關MARC提案與討論文件的內容及相關的LD因應方式。再者，本文選取兩筆MARC書目記錄與一份MARC提案文件範例作為八個使用個案，採取使用個案及導入BIBFRAME與RDA等兩項書目本體，實證與解說MARC轉變為鏈結資料的方式。結果證明MARC已成功融合資源描述框架三位元結構外，也是圖資界的鏈結資料交換標準。最後，有關MARC提案文件所定義的書目實體等有關鏈結資料化的議題也一併討論。
  PubDate: 2020-01-23