近年來MOOC課程提供了靈活的學習環境，然而也導致了以下問題:
第一，MOOC學習者是多樣化的，並非所有人都有足夠的背景相關知識。第二，許多學生在學習中面臨認知負擔過重及容易迷失學習方向。 第三，課程設計的考試可能無法對學生的表現進行清晰客觀的評估。
第四，MOOC課程缺乏有效的方式來獲得有效的學習反饋。

本篇研究藉助於大數據和人工智慧技術在MOOCs中的應用，有助於洞察及理解教育數據，達到提高教學效果，並有助於學生和教師之間的互動
透過收集學生在MOOCs平台的學習資料，搭配上試題分析，利用深度學習的方式，設計出四種應用模型。
1.提供學生對個知識節點的熟悉程度並加以量化。生成每位學生專屬的學習知識地圖。
2.預測學生是否會答對試題
3.預測學生成績表現模組
4.預測學生滿意度模組

本篇提出的四個模組，使教師可以從各個向度評估學生以及對學習行為追蹤，得知每位學生的學習狀況，並給予適時的幫助。

Online platform providing Massive Open Online Courses (MOOCs) are valuable because they offer efficient and flexible learning. However, MOOCs have fundamental problems.

First, MOOC learners are diverse, and thus it is possible that not all of them have insufficient background knowledge when registering some courses.
Second, a significant disorientation is observed when a person attempts to acquire knowledge because of the overwhelming massive amount of learning material presented to users
Third, examinations designed by lecturers may not be reliable enough to give clear and objective evaluations of a students' performance. Lacking a efficient way to obtain effective learning feedback is another disadvantage of MOOCs.
Course designers have attempted to evaluate the experiences of MOOC participants and found out that it is difficult to track and analyze the online actions and interactions of students due to the large class sizes.

The application of big data and artificial intelligence (AI) in MOOCs help comprehend raw educational data and enrich the learning process for students and instructors. The student data generated on MOOCs can be used to substantially improve teaching and learning effectiveness.

This study proposes the HSNL AI Tutor platform based on Deep Neural Networks(DNN) method.
The HSNL AI tutor now provide four Predictive Learning Analytics modules to give more accurate output when facing different scenarios.
First, the tutor has a concept assessment system integrated with knowledge maps to determine the students' familiarity and mastery of the content of a given course. The knowledge map approach is a suitable tool for evaluating and presenting students' learning performance at information education.
Second, a Correct on Attempt(COA) prediction system is added to the AI tutor. The system uses a innovative exercise-based model that predicts the outcome of whether a student will correctly answer examination questions which are relevant to the presented course materials.
Third, it features a Prediction of Learning Performance System, predicting learning outcomes on the basis of learning behaviors resulted from students watching class videos.
Lastly, the Evaluation Courses System incorporated into the platform explores the use of deep learning techniques to assess MOOC student experiences.

The data used by this thesis was collected from National Tsing Hua University’s MOOCs platform and ShareCourse.The results indicate that these approaches yield reliable predictions. Furthermore, teachers and teaching assistants can know students' performance and weaknesses in order to offer them more support promptly. By using data mining in education and learning analytics techniques, both teachers and online learners could benefited from the HSNL AI Tutor platform. The purpose of this study is to propose a system providing a more customized and intelligent online learning environment for students to learn in a more efficient and flexible way.

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