基於文字探勘建置資訊文章檢索平台以改善學生於演算法課程的學習成效

Developing a Computer Science Article Retrieval Platform Based on Text Mining to Improve Students' Learning Performance in Algorithm Course

鄭淑真(Shu-Chen Cheng) ； 鄭育評(Yu-Ping Cheng) ； 黃悦民(Yueh-Min Huang)

《工程、技術與STEM教育研討會》 第十屆 (2021/07) Pp. 205-212

https://doi.org/10.29619/STEM.202107.0017

摘要

網際網路的蓬勃發展使學習者能更容易利用搜索引擎查詢學習資源。然而，常見的搜尋引擎卻無法篩選出與學習相關的資源，使學習者在搜尋的過程影響其學習上的效率。為了要有效解決這項問題，本研究基於文字探勘技術建置資訊文章檢索平台，通過TF-IDF演算法計算每一篇網路文章中的字詞權重，以自動化篩選出電腦科學文章。從研究結果得知，學生藉由資訊文章檢索平台查詢與閱讀電腦科學文章，可以了解演算法相關的知識與概念，並有效提升學生的學習成效。另外，學生在使用資訊文章檢索平台後也呈現高度的科技接受程度，且學生也有高度的意願於未來持續使用資訊文章檢索平台進行學習。

關鍵字

文字探勘 ； 資訊文章檢索平台 ； 學習成效 ； 整合科技接受模型

並列摘要

The rapid development of the Internet makes it easier for learners to use search engines to search for learning resources. However, general search engines cannot filter out the resources related to learning, so that the learners' search process affects their learning efficiency. In order to effectively solve this problem, this study developed a computer science article retrieval platform based on text mining and used the TF-IDF algorithm to calculate the terms weight in each web article to automatically filter out the computer science articles. According to the results, students can use the computer science article retrieval platform to query and read computer science articles to understand the knowledge and concepts related to the algorithm, and effectively improve their learning performance. In addition, after using the computer science article retrieval platform, students also show a high degree of technological acceptance and willingness to continue to use the computer science article retrieval platform for learning in the future.

並列關鍵字

Text mining ； Computer science article retrieval platform ； Learning performance ； Unified theory of acceptance and use of technology