閱讀力已成為現代公民不可或缺的關鍵能力，近年來世界各國為改善學校閱讀教學環境，積極地導入適性化閱讀平台，藉以提升學生閱讀理解能力。適性化閱讀平台必須能準確地測量學生的閱讀能力、客觀地評估圖書的文本難度，以及快速地評量學生的讀後成效。才能將學生閱讀能力與圖書文本難度進行適配，並有效地掌握學生的閱讀狀況。但在現有適性閱讀平台中，尚未能提供自動化讀後理解評量的設計來降低老師在閱讀教學上的負擔。傳統上老師會透過選擇題或撰寫摘要來進行讀後理解評量的活動，但為了滿足學生廣泛閱讀的需求，老師必須為每一本學生所閱讀的圖書設計試題或批閱摘要，對於現場教學的老師幾乎是不可能達成的任務。老師或許能集合眾人智慧或試題自動生成技術來生成試題。但無論如何都必須有效掌控試題難度，才能確保達成所要評量的目標。目前主要透過預試來取得準確的試題難度，但需耗費大量人力與時間成本，且有試題安全的疑慮。此外，目前現有摘要評分系統主要是針對單一主題與短篇文章進行設計，對於長篇且多主題的文本可能就不適用。為了解決以上所面對的困境，本研究提出基於分散式表徵的語意量測方法，利用題幹與選項之間的語意相似度做為試題難度的特徵，再透過機器學習的方法進行訓練，建立自動化試題難度的評估模型。同樣的量測方法運用在自動摘要評分，則是計算圖書中每一個句子的語意向量，再透過分群技術自動取得圖書摘要後，建立自動化讀後摘要的評分模型。實驗結果顯示本研究所提出的方法具有相當良好的效能。

Reading ability has become an indispensable ability for modern citizens, and the past few years have seen many countries striving to improve their reading education by actively introducing adaptive reading platforms to improve students' reading comprehension. Adaptive reading platforms must accurately measure students' reading ability, objectively assess the difficulty of books, and quickly assess after-reading comprehension. Only then could they accurately pair students with suitably challenging texts and grasp the dynamic reading situation of students. However, existing adaptive reading platforms have not been able to provide an automatic after-reading assessment utility to reduce the burden on reading teachers. Traditionally, teachers perform after-reading assessments by administering multiple-choice tests or by requesting their students write summaries. However, this means that teachers must design test items or grade summaries for each book that students read. It is a never-ending, arduous task for teachers. To fulfill students' needs for extensive reading, teachers may design test items through collective wisdom or automatic item generation technologies. Regardless, to ensure that the assessment objectives are achieved, it is necessary to control the difficulty of mass-generated items effectively. Currently, this is accomplished mainly by using pretests to get accurate item difficulty, but doing so is laborious, time-consuming, and leaves doubts about the safety of the items. Current summary scoring systems are mainly designed for single topics and short articles, and may not apply well to long texts. To solve these difficulties, this study proposes a semantic similarity measurement based on distributed representations and utilizes the semantic similarity between a stem, an answer, and distractors as the semantic features of item difficulty. Then, through machine learning, an estimation model can be trained that evaluates the difficulty of automatically generated test items. The same measurement method is used in automatic summary scoring: First, the semantic vector of each sentence in the text is calculated, then clustering technology is used to obtain book summaries, which are then used to establish a scoring model for after-reading summaries. Experimental results show that the method proposed in this study has reasonably good performance.

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