2017 Analyses and Modeling Techniques of Educational Data

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Academic unit or major
Graduate major in Social and Human Sciences
Instructor(s)
Yamagishi Kimihiko  Kuriyama Naoko  Terai Asuka 
Course component(s)
Exercise
Day/Period(Room No.)
Tue1-2(W9-706)  Thr1-2(W9-706)  
Group
-
Course number
SHS.D463
Credits
2
Academic year
2017
Offered quarter
2Q
Syllabus updated
2017/4/21
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

This course covers standard statistical analysis technology dealing with educational data. We concentrate on the conduct of experimental and survey data collection, and statistical analysis and modeling afterwards. Prerequisites include Practices for Psychological and Educational Measurement A & B

Student learning outcomes

This course covers standard statistical analysis technology dealing with educational data. We concentrate on the conduct of experimental and survey data collection, and statistical analysis and modeling afterwards.

Keywords

Statistical Modeling, Survey data collection, collaborative group learning

Competencies that will be developed

Intercultural skills Communication skills Specialist skills Critical thinking skills Practical and/or problem-solving skills
- - -

Class flow

Basically we adopt standard lecture-style teaching, accompanied by occasional in-class demonstrations using statistical programs.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Orientation Mastery of Preparation for PC environment
Class 2 Planning of surveys and experimentation Can plan research objectives. Each group establishes their goal of research
Class 3 Ethical considerations on empirical research Grasp and understand necessary ethical considerations. The group discusses applicable ethical issues
Class 4 Survey methods Each group can explain survey methods and research objectives
Class 5 Observation studies Master observational data collection
Class 6 Experimental design Master factorial experimental design. Each group prepare for data collection
Class 7 Student presentations Carry out data collection
Class 8 Description of data Can characterize the obtained data
Class 9 Analyses of educational data Select the most applicable method of analysis
Class 10 Basics of Causal modeling Can explain the basics of causal model construction
Class 11 Constriction of causal models (1) Can explain causal model selection and construction
Class 12 Construction of causal models (2) Can explain data analyses generated by causal models
Class 13 Causal model analyses Can explain the meanings and implications of causal modeling
Class 14 (English) Students' presentation of model construction Prepare for the in-class presentation
Class 15 Wrap-up and Q & A The group prepares term paper

Textbook(s)

In-class handouts

Reference books, course materials, etc.

Will be introduced in class as necessary

Assessment criteria and methods

Group work and workshop: 10% Exhibition report: 90%

Related courses

  • LAT.A403 : Practices for Psychological and Educational Measurement A
  • LAT.A404 : Practices for Psychological and Educational Measurement B

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Desiderata: Concurrent registration to the following:
LAT.A403 : Practices for Psychological and Educational Measurement A
LAT.A404 : Practices for Psychological and Educational Measurement B

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