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 familiarity with computerized statistical analysis. Courses labeled "Practices for Psychological and Educational Measurement A &B" are good examples. This course makes use of the "active learning" teaching technique, and hence sets a "minimum passenger count" of nine on the very first day of instruction. Registration deadline is at 0:00 am, on June 6th, 2022. The 2022 class meets online via Zoom (Live style).
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. We emphasize active learning method in the classroom.
Statistical Modeling, Survey data analysis, collaborative group learning, active learning
|Specialist skills||Intercultural skills||✔ Communication skills||Critical thinking skills||✔ Practical and/or problem-solving skills|
This class goes online via Zoom (Live style) in 2022. Basically we adopt both conventional lecture-style teaching as well as the "active learning", accompanied by occasional in-class demonstrations using statistical programs.
|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 through the active learning.|
|Class 3||Ethical considerations in empirical research||Grasp and understand necessary ethical considerations. The group discusses applicable ethical issues through the active learning.|
|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 through the active learning.|
|Class 7||Student presentations||Carry out data collection|
|Class 8||Description of data||Can characterize the obtained data through active learning.|
|Class 9||Analyses of educational data||Select the most applicable method of analysis through the active learning.|
|Class 10||Basics of the causal modeling||Can explain the basics of the causal model construction through the active learning.|
|Class 11||Constriction of causal models (1)||Can explain causal model selection and construction|
|Class 12||Constriction of causal models (2)||Can explain data analyses generated by causal models through the active learning.|
|Class 13||Students' presentation of model construction||Prepare for the in-class presentation through the active learning.|
|Class 14||Wrap-up and Q & A||Each active learning group prepares the term paper.|
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Will be introduced in class as necessary. The class makes use of publicly available data for analysis practices.
Contributions to the group and the active learning.: 10% Exhibition report: 90%
Desiderata: Concurrent registration to the following:
LAT.A403 ： Practices for Psychological and Educational Measurement A
LAT.A404 ： Practices for Psychological and Educational Measurement B