文字歸類是用來偵測文件語料涵蓋的主題，以便於資訊瀏覽、分析、組織的資訊檢索技術。而近年教室中即時反饋系統（Instant Response System, IRS）的運用越來越普遍，以強化學生專注參與的情形，進而增進其學習成效。然而目前的 IRS較缺乏學生自由回應的簡短文字之歸類處理功能，以致無法進一步發揮其功效。本文的目的，在探討 IRS現況以及短文回應的特性與問題、回顧短文主題歸類自動處理的技術與應用情形、提出一套可行的搭配系統供 IRS整合運用，並展示我們目前實作的結果範例，以做為後續運用、研究的參考。本文比較了三種自動歸類方法，結果顯示影響歸類成效的因素眾多，理論上較好的歸類方法，不見得會有較佳的結果，還需其他因素或處理流程的配合。

Text clustering is a powerful information retrieval technique to detect topics from document corpora, so as to provide information browsing, analysis, and organization. On the other hand, the Instant Response System (IRS) has been widely used in recent years to enhance student engagement in class and thus improve their learning effectiveness. However, the lack of functions to process short text responses from the IRS prevents the further application of IRS in classes. Therefore, this article aims to discuss the current status of IRS application, illustrate the characteristics of short text responses from the IRS, review current short text processing techniques, propose a proper short text clustering module for the IRS, and demonstrate our implemented techniques through real-world examples, so as to provide experiences and insights for further study. In particular, we have compared three clustering methods and the result shows that theoretically better methods need not lead to better results, as there are various factors that may affect the final performance.