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Visualization of the Relationship Between Lectures and Laboratories Using SSNMF

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Visualization of the Relationship Between Lectures and Laboratories Using SSNMF

  1. 1. Visualization of the Relationship Between Lectures and Laboratories Using SSNMF Kansai university Kyoka Yamamoto Ryosuke Yamanishi Mitsunori Matsushita Which lectures should I take to learn for that laboratory? Which laboratory should be good for using the knowledge I have learned in the lectures? 1
  2. 2. Background [How students should behave] l Select a laboratory to specialize knowledge and skills in the lectures they have taken l Select lectures to acquire the knowledge and skills necessary for the laboratory they wish to join [How students actually behave] l Lectures and laboratories are carelessly selected In daily circumstances ・what to wear ・what to eat 2
  3. 3. Problems Attending lectures based on their own biases They donʼt know what they will need in the laboratory at the time when they have joined Chose a laboratory without knowing their potential options Not being able to do the research they really were interested in [Why these problems happen] Difficulty in understanding relationships between objects Quantity problem: Difficulty in covering all lectures and labs Knowledge problem: lacking knowledge of options 3
  4. 4. The Feature of Selecting Lecture and Labs The relationships between lectures and laboratories are fluid. lThe candidates of the laboratory where they offer to join should differ lThe set of laboratories in the department might change Based on aggregate information of alternatives rather than one-to-one comparisons Necessity of understanding many-to-many relationships Student A Student B The relationships differ depending on the situation.
  5. 5. Goals and Approach [Goals] l Helping students think exploratively about lecture and laboratory selection l Providing evidence for choosing lectures and labs [Approach] lFocusing on the relationship between elements beyond sets lQuantitatively evaluating potential relationships based on the characteristics of each set 5
  6. 6. A method to visualize the relationships between elements [Procedures] STEP1: Data collection and normalization STEP2: Multidimensional vector representation STEP3: Application of SSNMF Representing the knowledge covered in lectures and labs as multidimensional vectors Semi-supervised non-negative matrix factorization (SSNMF) is used for visualizing the relationship between them. 6
  7. 7. Basic concept of the method Y H + FG 〜 〜 Proposed method: Application of SSNMF Y H U a set of laboratories a set of lectures SSNMF A method for feature analysis which is commonly used in the field of signal processing (E.g.) sound source separation Audio data composed of mixed sound sources: Y How active a certain sound source feature: H is for Y can be shown as a matrix U. By decomposing the matrix Y representing laboratories by the matrix H concerning the lectures, we expect to obtain an activation matrix U showing the relationship between laboratories and knowledge handled in the lectures. U 7
  8. 8. STEP1:Data collection and normalization [Target data] Specialty: Graduation Thesis Defining one laboratory as one field of specialization The text excluding the name of the supervisor, student ID number, authorʼs name, and references Lecture : Lecture syllabus Course outline, plan, and achievement objectives indicating course content [Normalization] l Normalization of one-byte alphanumeric characters and symbols l Remove line breaks and spaces l Morphological analysis to extract only nouns; the text is separated into part-of-speech in Japanese STEP1: Data collection and normalization STEP2: Multidimensional vector representation STEP3: Application of SSNMF 8
  9. 9. STEP2: Multidimensional vector representation [bag of words method] Labs and Lectures. Numerical representation by frequency Lectures Labs words [NMF] Dimensional compression Sparsity resolution (Labs + Lectures) words ( Labs + Lectures) Dimensional variance STEP1:データ収集・正規化 STEP2:研究室と講義を多次元ベクトルで表現 STEP3:半教師あり⾮負値⾏列因⼦分(SSNMF)の適⽤ Dimensional variance 500-dimensional vector for each object [Splitting] Splitting the matrix into two vector sets representing the matrix concerning lectures and laboratories 9 Lectures Labs STEP1: Data collection and normalization STEP2: Multidimensional vector representation STEP3: Application of SSNMF
  10. 10. Y 〜 HU + FG 〜 Matrix representation of what kind and how much knowledge is covered in each lab/lecture Y : Observed information of lectures H : A template indicating how each type of knowledge is used/applied/adjusted in a laboratory U : Activation matrix showing which and how much knowledge in the lecture is referenced in the laboratory FG : knowledge unrelated with the target (e.g., basic knowledge commonly needed in all labs) The Relationship between Lectures and Laboratories representing which combination of lectures constitutes a specialized field STEP3: Application of SSNMF Output:The Relationship of Objects STEP1:データ収集・正規化 STEP2:講義と研究室を多次元ベクトルで表現 STEP3:半教師あり⾮負値⾏列因⼦分(SSNMF)の適⽤ 10 STEP1: Data collection and normalization STEP2: Multidimensional vector representation STEP3: Application of SSNMF
  11. 11. To Analyze The Relationship between Lectures and Laboratories, Which Has A Fluid Relationship Target Faculty The lectures are labeled C, M, and S in a Faculty of Informatics to identify the lectures' specialties. ・programming and algorithms such as basic theories of informatics: C course ・processing in media and communication: M course ・information processing in various fields including management, economics, psychology, and politics: S course Resource Labs:the graduation thesis outlines from 43 labs (459students) which is collected from the SJ undergraduate thesis outline collection in 2019 Lectures:192 lectures in total the syllabus of the SJ faculty for the year 2020 which is collected from the university website. 11
  12. 12. To Analyze The Relationship between Lectures and Laboratories, Which Has A Fluid Relationship [The task] _To analyze the relationship between lectures and laboratories, which has a fluid relationship [Comparison] ・The proposed method is applied to the following sets of lectures. ・Comparison of results between sets of lectures c-series: 31 lectures, m-series: 25 lectures, s-series: 28 lectures, all lectures: 84 Lectures ●A compilation of lectures on estimation accuracy in a laboratory specializing in human media communication desig Decomposed in S lectures Decomposed in all lectures Focusing on “Environmental economics” When C- and M-series lectures are added to the choices, the activation trend value and the rank changed Therefore, we confirmed that the proposed method captured the many-to-many relationships complying with the input set 12
  13. 13. Summary 13 Background Selecting a laboratory to specialize knowledge and skills in the lectures they have taken Problems Attending lectures based on their own biases Chose a laboratory without knowing their potential options Purpose Representing the knowledge covered in lectures and labs as multidimensional vectors Method Focusing on the relationship between elements beyond sets Quantitatively evaluating potential relationships based on the characteristics of each set
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