理學院

Permanent URI for this communityhttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/3

學院概況

理學院設有數學系、物理學系、化學系、生命科學系、地球科學系、資訊工程學系6個系(均含學士、碩士及博士課程),及科學教育研究所、環境教育研究所、光電科技研究所及海洋環境科技就所4個獨立研究所,另設有生物多樣性國際研究生博士學位學程。全學院專任教師約180人,陣容十分堅強,無論師資、學術長現、社會貢獻與影響力均居全國之首。

特色

理學院位在國立臺灣師範大學分部校區內,座落於臺北市公館,佔地約10公頃,是個小而美的校園,內含國際會議廳、圖書館、實驗室、天文臺等完善設施。

理學院創院已逾六十年,在此堅固基礎上,理學院不僅在基礎科學上有豐碩的表現,更在臺灣許多研究中獨占鰲頭,曾孕育出五位中研院院士。近年來,更致力於跨領域研究,並在應用科技上加強與業界合作,院內教師每年均取得多項專利,所開發之商品廣泛應用於醫、藥、化妝品、食品加工業、農業、環保、資訊、教育產業及日常生活中。

在科學教育研究上,臺灣師大理學院之排名更高居世界第一,此外更有獨步全臺的科學教育中心,該中心就中學科學課程、科學教與學等方面從事研究與推廣服務;是全國人力最充足,設備最完善,具有良好服務品質的中心。

在理學院紮實、多元的研究基礎下,學生可依其性向、興趣做出寬廣之選擇,無論對其未來進入學術研究領域、教育界或工業界工作,均是絕佳選擇。

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Now showing 1 - 4 of 4
  • Item
    Are you SLiM? – Developing an instrument for civic scientific literacy measurement (SLiM) based on media coverage
    (SAGE Publications, 2012-08-01) Rundgren, C. J.; Chang Rundgren, S. N.; Tseng, Y. H.; Lin Pei-Ling; Chang, C. Y.
    The purpose of this study is to develop an instrument to assess civic scientific literacy measurement (SLiM), based on media coverage. A total of 50 multiple-choice items were developed based on the most common scientific terms appearing in media within Taiwan. These questions covered the subjects of biology (45.26%, 22 items), earth science (37.90%, 19 items), physics (11.58%, 6 items) and chemistry (5.26%, 3 items). A total of 1034 students from three distinct groups (7th graders, 10th graders, and undergraduates) were invited to participate in this study. The reliability of this instrument was 0.86 (KR 20). The average difficulty of the SLiM ranged from 0.19 to 0.91, and the discrimination power was 0.1 to 0.59. According to participants’ performances on SLiM, it was revealed that 10th graders (Mean = 37.34±0.23) performed better than both undergraduates (Mean = 33.00±0.33) and 7th graders (Mean = 26.73±0.45) with significant differences in their SLiM.
  • Item
    Mining concept maps from news stories for measuring civic scientific literacy in media
    (Elsevier, 2010-08-01) Tseng, Y. H.; Chang, C. Y.; Chang Rundgren, S. N.; Rundgren, C. J.
    Motivated by a long-term goal in education for measuring Taiwanese civic scientific literacy in media (SLiM), this work reports the detailed techniques to efficiently mine a concept map from 2 years of Chinese news articles (901,446 in total) for SLiM instrument development. From the Chinese news stories, key terms (important words or phrases), known or new to existing lexicons, were first extracted by a simple, yet effective, rule-based algorithm. They were subjected to an association analysis based on their co-occurrence in sentences to reveal their term-to-term relationship. A given list of 3657 index terms from science textbooks were then matched against the term association network. The resulting term network (including 95 scientific terms) was visualized in a concept map to scaffold the instrument developers. When developing an item, the linked term pair not only suggests the topic for the item due to the clear context being mutually reinforced by each other, but also the content itself because of the rich background provided by the recurrent snippets in which they co-occur. In this way, the resulting instrument (comprised of 50 items) reflect the scientific knowledge revealed in the daily news stories, meeting the goal for measuring civic scientific literacy in media. In addition, the concept map mined from the texts served as a convenient tool for item classification, developer collaboration, and expert review and discussion.