學生系所選擇與職涯規劃推薦系統

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歐安雅(Ankhtuya) 查詢紙本館藏

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論文名稱

學生系所選擇與職涯規劃推薦系統
(A Recommender for Student Major Selection and Career Planning)

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摘要(中)

人的一生中，高中或大學的階段選填志願是重要的決策。儘早發現學生適合的志願，有助於他們選擇各科系所提供專業學習方向及培養未來職場所需的技能。學生選填志願的難題是缺乏對大學科系的主修資訊及各行業別的認識。很多學生是透過學校的專業建議來選填志願。此外，學生選填志願也會受到社會環境及家人的影響。志願選填的難題可能是造成日後學生在學業成就，個人特質，興趣和能力等方面無法匹配的原因。因此，有必要建立一個行業別推薦系統（ORS），提供學生選填志願的指導與諮詢，能滿足學生選擇符合自己興趣及能力的志願。
在第一項研究中，ORS採項目為基礎協同過濾回歸方法的模型化推薦技術，基於學生的個性，學習風格和職業興趣提供合適的行業別推薦。此外，此系統提供了兩個Top-N職業列表; 一個是基於使用個人特質和學習風格的回歸模型，另一個是基於職業興趣。蒙古國的190名高中學生參加了本實驗。回歸分析的結果表明，對各興趣領域、個性和學習風格進行評估，有助於學生行業別探索。此外，也同時調查學生主要選填志願的影響因素。
在第二項研究中，採用混合推薦模型; 目的為學生提供適當的行業別諮詢、找出他們的行業別興趣及指導他們加強所需的技能。我們實現了一個混合推薦系統名為行業別推薦（OCCREC），此系統整合了內容本位及協同過濾模型。我們匯入三組數據，包括學生基本資料，行業別探索的Holland 職業代碼問卷及其行為風格。學生基本資料包含兩種類型的數據，即從Facebook檢索其背景、興趣/嗜好。在實驗中，學生來自四個國家。透過Euclidean、Intersection、Cosine、Jaccard及Pearson等五種相似度量測方法，檢驗五個系統產生的行業別結果。最後，OCCREC允許學生根據使用滿意度對結果進行評分，並在Facebook上分享使用的經驗。
最後，第三項研究是使用Google Classroom的開放式教育資源（OER）進行翻轉教室模式。這是蒙古國首次在生涯規畫課程進行翻轉教學。實驗結果顯示，學生透過ORS發現及探索行業別，與先前教師的指導和諮詢的結果相當。

摘要(英)

A major choice in high school or undergraduate stage is an important decision in the human life. To discover students’ suitable majors as early as possible can help them to choose the appropriate vocational learning direction and to build the skills and the abilities for the prospective major. The main issue of difficulty making the decisions of major choices for the students is a lack of knowledge and information about majors and occupations. A mass of students has decided their majors out of proper and professional advice from school services. On the other hand, the major choices of students are influenced by a society, an environment, and their family mostly. Those difficulties are potentially the causes of a mismatch between academic achievements, personality, interest, and abilities of students. Hence, it is necessary to build an Occupation Recommendation System (ORS) to students with a capacity to meet all the needs where it provides the orientation and the counsel to students in selecting the major that fits with their interests and skills.
In the first study, ORS driven by model-based recommendation technique using the regression approach on the item-based collaborative filtering for giving suitable occupation recommendation based on the personality, learning style, and vocational interest of students were implemented. Furthermore, the system provides the two Top-N occupation lists; one is based on the regression model using personality and learning style while the other is based on the vocational interests. Mongolian high school’s 190 students participated the experiment. The result of the regression analysis revealed that assessment of the all domains of interests, personality and learning style has several advantages for assisting the students exploring a vocational major. Moreover, the influences of the major choice of the students were investigated.
In the second study, hybrid recommendation methods were employed; it aims to counsel suitable occupation for students, to discover their occupational interests and to guide them to improve their skills. We implemented a hybrid recommendation system called Occupation Recommendation (OCCREC) that integrates content-based and collaborative filtering methods. We involved three sets of information including student’s profiles, vocational interests from the questionnaire using Holland code, and their behaviors. The student profile contains two types of data, namely, background and interest/hobby retrieved from Facebook. In the experiment, the students are from four countries. And, five occupations were shown to the students by using five similarity measures which are Euclidean, Intersection, Cosine, Jaccard, and Pearson. Finally, OCCREC allows students to rate the results accordingly based on user’s satisfied scores and to share their experiences on Facebook.
Finally, the third study employed a flipped classroom model on google classroom using the Open Educational Resources (OER). The flipped learning on career counseling course is the first time conducting in Mongolia. An experiment has conducted that students discover and explore the occupations with ORS and OCCREC as well as the guidance and counseling are being provided.

關鍵字(中)

★ Occupation Recommendation System
★ MOOC
★ OER

關鍵字(英)

★ Occupation Recommendation system
★ MOOC
★ OER

論文目次

摘要 i
Abstract ii
Acknowledgement iv
Contents v
List of Figures viii
List of Tables xi
List of Acronyms xiii
Chapter 1. Introduction 1
1.1 Background 1
1.2 Motivation 1
1.2 Current studies 2
1.3 Thesis Structure 3
Chapter 2. Literature Review 4
2.1 Major Choice 4
2.1.1 Personality 4
2.1.2 Learning Style 5
2.1.3 Personality and Learning Style 6
2.1.4 Personal and Subject Interest 6
2.1.5 Relationships between Personality, Learning Style, and Interest 7
2.1.6 Influence of Family Members and Peers 8
2.2 Reasons of Major Selection and Restudy for Graduates 9
2.3 Recommendation Systems 10
2.3.1 Collaborative Filtering (CF) 12
2.3.2 Hybrid Recommender System 12
2.3.3 Comparisons of Job/Career/Occupation Recommendations 13
2.3.4 Occupation/Job/Major Choice in Selected Countries 17
2.4 MOOC, OER, and LMS 20
2.5 Flipped Learning 24
2.6 Career Counseling Course 26
Chapter 3. Proposed Systems 28
3.1 Occupation Recommender System among High School Students 28
3.1.1 Architecture 28
3.1.2 Occupational Ontology 30
3.1.3 Data Flow 35
3.1.4 Methods 38
3.2 Hybrid Occupation Recommendation 43
3.2.1 Architecture 43
3.2.2 Functionality 44
3.2.3 Methods 48
Chapter 4. Experiments 53
4.1 First Experiment on Occupation Recommendation System 53
4.1.1 Participant 54
4.1.2 Measure 55
4.2 Second Experiment on Hybrid Occupation Recommendation 59
4.2.1 Participant 59
4.2.2 Occupation 59
4.2.3 Behavioral data 61
4.3 Third Experiment: Flipped Learning on Career Counseling 64
4.3.1 Participants 64
4.3.2 Experimental Procedure 65
4.3.3 Teaching and Learning Activities Design 66
4.3.4 Research Tool 67
Chapter 5. Results and Discussions 72
5.1 First Experiment - Results 72
5.1.1 Descriptive Statistics 72
5.1.2 Correlation Analysis 74
5.1.3 Regression Analysis 77
5.1.4 Experimental Evaluation 78
5.2 Second Experiment - Results 80
5.2.1 Experiment 80
5.2.2 Difference between Background and Liked Occupation by 6 Interest Types 81
5.2.3 Evaluation of Recommendation System in Data Mining Metrics 82
5.2.4 Discussion 85
5.3 Third Experiment - Results 87
5.3.1 Students’ Career Planning 87
5.3.2 Skill Gaps 88
5.3.3 System Usability 89
Chapter 6. Conclusion and Future Works 91
References 94
Appendices 104
Appendix 1. Examples of BFI, LS and HC Questionnaires 104
Appendix 2. Flipped Learning – Homework Notes 106

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指導教授

施國琛、黃武元(Timothy K.Shih Wu-Yuin Hwang)

審核日期

2017-7-14

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