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研究生:彭成葳
研究生(外文):PENG, CHENG-WEI
論文名稱:基於深度學習的課輔機器人之研製
論文名稱(外文):Development of a Tutor-bot based on Deep Learning
指導教授:曾秋蓉曾秋蓉引用關係
指導教授(外文):Judy C. R. Tseng
口試委員:鄭麗珍張欽智曾秋蓉
口試委員(外文):CHENG, LI-CHENCHANG, CHIN-CHIHJudy C. R. Tseng
口試日期:2021-01-30
學位類別:碩士
校院名稱:中華大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:67
中文關鍵詞:人工智慧教育機器人問答匹配系統深度學習
外文關鍵詞:Artificial intelligenceSelf-directed learningEducational robotDeep learningQA matching
相關次數:
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隨著人工智慧的相關技術越來越成熟,將機器人應用於教育上,已成為相當具有潛力的研究議題。許多學術文獻也指出,教育機器人可有效提升學生學習動機和學習成效。隨著機器人技術的進步,教育機器人從扮演教學輔具進一步擴展到扮演學習對象、導師、助教、秘書、顧問…等等角色。其中,由於助教機器人可減輕教師的教學負擔,讓教師可以將有限的時間投入到更加有意義、有思想的工作中,因此目前也有越多越多的學者投入助教機器人的研究中。
另一方面,培養學生的自主學習能力在教育界也越來越受到重視。自主學習能力可以幫助學生更能夠適應知識快速更新的需求,也更容易將知識轉移到新的領域學習。然而學生在自主學習的過程中,常因遇到問題沒有辦法及時解決造成學習中斷,從而降低其自主學習意願與學習成效。由於教師無法隨時陪伴在學生身旁,及時為學生解答問題,若能由助教機器人來代理教師的角色,在學生自主學習的過程中,與學生對話,及時幫助學生解決心中疑惑,相信能讓學生自主學習的過程更加順暢,並提升其自主學習的成效。
為了讓助教機器人具備解答學生問題的能力,需要蒐集學生常見的問題與解答,並利用問答匹配機制來找出學生問題的答案。但若問答匹配的精準度不足,常常發生答非所問的狀況,反而會降低學生的學習意願。傳統的問答匹配機制多半利用特徵向量的相似度計算來找出與學生問題相似的常問問題,方法過於簡單,導致精準度尚有改進的空間。
有鑑於此,本論文研發一套具有解答學生課業問題能力的助教機器人,簡稱課輔機器人。本研究以Zenbo機器人為載具,透過語音以及自然語言與學生對話,並且運用深度學習技術來剖析自然語言,建立基於深度學習的問答匹配模型,藉以提升解答學生問題的精準度。預期將此課輔機器人應用於學生的自主學習過程中,可有效減輕教師回答學生問題的負擔,並提升學生自主學習的意願。

As the related technologies of artificial intelligence become more and more mature, the application of robots in education has become a potential research topic. Many literatures have pointed out that educational robots can effectively improve students' learning motivation and learning effectiveness. Educational robots can play the roles of learning objects, mentors, tutors, secretaries, consultants, etc. Among them, tutor robots gain more attentions from researchers because they can reduce the teaching burden of teachers.
On the other hand, cultivating students’ self-directed learning ability also gain more attentions in education. Self-directed learning ability can help students better adapt to the rapid update of knowledge. However, during self-directed learning, if students encounter problems which are not able to be solved in time, their motivation and effectiveness of learning will be reduced. Since teachers cannot accompany students at any time to answer questions for students in a timely manner, it is meaningful to develop a tutor robot to solve student’s problems in time.
To develop a tutor robot which can answer students’ questions, it is necessary to collect students' common questions and answers, then use a QA matching mechanism to find out the answers for students’ questions. In this way, the accuracy of QA matching mechanisms play an important role for the success of the tutor robot. If the tutor robot often fails to provide correct answers to students’ questions, the students’ learning motivation will be affected.
In this thesis, a tutor robot with the ability to answer students' academic problems is developed. The tutor robot uses the ASUS Zenbo robot as a vehicle to communicate with students through voice and natural language. Moreover, the tutor robot employs deep learning to establish a QA matching mechanism to improve the accuracy of answering student questions. It is expected that the tutor robot will promote the students’ learning efficacy in their self-directed learning.

目錄
摘要 i
Abstract ii
致謝 iii
目錄 1
表目錄 4
圖目錄 5
第一章 緒論 6
1.1 研究背景 6
1.2 研究動機 7
1.3 研究目的 8
1.4 論文架構 9
第二章 相關研究 10
2.1 自主學習 10
2.2 教育機器人 11
2.3 聊天機器人 13
2.4 問答匹配系統 14
2.5 機器學習 16
2.6 深度學習 17
第三章 問答匹配機制 19
3.1 傳統問答匹配機制 19
3.2 基於LSTM之深度學習問答匹配機制 23
3.3 基於BERT之深度學習問答匹配機制 32
第四章 系統開發 39
4.1 系統架構 39
4.2 功能模組 41
4.3 使用案例 46
第五章 系統評估 49
5.1 實驗設計 49
5.2 精確率 51
5.3 召回率 52
5.4 F1 measure 53
5.5 執行時間 55
第六章 結論與未來展望 58
6.1 結論 58
6.2 未來展望 59
參考文獻 60


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