Type of Credit: Elective
Credit(s)
Number of Students
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Course introduction and learner characteristics: |
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1. The course aims to cultivate students ability to analyze data from international large-scale databases, especially for educational practices. |
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2. This course is for students interested in the contents, searching for excellence, and accepting the teaching methods, including accepting that its official language is English. |
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3. The course is suitable for master or Ph.D. students with basic ability in advanced and multivariate statistics and software use for education. |
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4. Students need to bring their own laptop computers to class for hands-on activities. Major learning management systems are Facebook and Google Drive. |
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Note: This course will adjust its teaching methods based on students willingness and readiness for advanced data analysis skills. For example, R and Python will be used if students are prepared to write code for data analysis. If not, the teaching methods will change accordingly, including only reading papers and using software packages that do not need to write code (e.g. PSPP), which though cannot conduct advanced analyses. |
能力項目說明
| assessment | |||||
| Course objectives and Learning outcomes | essay | presentation/comment | interaction | ||
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1. Understand methods to access databases. |
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2. Understand papers based on secondary analysis. |
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3. Understand methods to analyze data. |
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4. Analyze data from databases. |
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5. Write a proposal or paper using secondary analysis. |
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| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
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Tentative course calendar/schedule: |
In class | Before class | After class |
assessment (score) |
Time investment (hour) | |||||
| Week | Date | Contents (papers, databases, and data analysis software packages, e.g. R, Python, and PSPP) | essay wrritten | paper brief/feedback | class Q&A; share | in class | outside class | |||
| 1 | 220 | Introduction | syllabus,Q&A,self-introduction | explore | 3 | 3 | 4.5 | |||
| 2 | 227 | data bases | lecture,hands-on,discussion (your essay) | explore | explore, essay | 3 | 3 | 4.5 | ||
| 3 | 305 | finding papers and references | same above | same above | same above | 3 | ||||
| 4 | 312 | data analysis skills and packages | same above | same above | same above | 3 | 3 | 4.5 | ||
| 5 | 319 (彈性課程) | complete (written) initial essay ideas | 7.5 | |||||||
| 6 | 326 | student ideas about their essays;Fischbach2013OutcomeSesLuxembourg_pisa06_basicStatistics_LID | each student oral 10 min for essay ideas; the paper | write essays; read the paper; one student prepars briefs | review; write essays | 2 | 5 | 3 | 3 | 4.5 |
| 7 | 402 | Marsh2013validityMathSciAffect ArabAnglo_timss07_cfa_JEP | same as above | same as above | same above | 2 | * | 3 | 3 | 4.5 |
| 8 | 409 | Chiu2023Msc3GenderMachFamilyPBS_EJPE_MCS_path | same as above | same as above | same above | 2 | * | 3 | 3 | 4.5 |
| 9 | 416 | Chiu2020ExploringModelsForIncreasing_pisa12_mediate SEM_ETRD | same as above | same as above | same above | 2 | * | 3 | 3 | 4.5 |
| 10 | 423 | Chiu2012_IE_BFLPE_combine_timss03_hlm_JEP | same as above | same as above | same above | 2 | * | 3 | 3 | 4.5 |
| 11 | 430(withdraw by) | Wang2023timss2019EastAsiaMathAch_randomForest_IJSME | same as above | same as above | same above | 2 | * | 3 | 3 | 4.5 |
| 12 | 507 | midterm essay (until Method) oral presentation and feedback | S oral present,discussion | prepare presentation | analysis, essay | 6 | 3 | 3 | 3 | 4.5 |
| 13 | 514 | case study 1 (basic statistics) | Student present,hands-on,discussion | prepare presentation | analysis, essay | 3 | 3 | 4.5 | ||
| 14 | 521 | case study 2 (cfa) | same as above | same as above | same as above | * | 3 | 4.5 | ||
| 15 | 528 | case study 3 (sem) | same as above | same as above | same as above | * | 3 | 4.5 | ||
| 16 | 604 | case study 4 (hlm) | same as above | same as above | same as above | * | 3 | 4.5 | ||
| 17 | 611 (彈性課程) | essay draft | feedback online | read peer essays | analysis, essay | 10 | 7.5 | |||
| 18 | 618(final exam) | oral presentation; final essay | complete online | write essay | essay revision | 25 | 10 | 3 | 3 | 4.5 |
| Total score | 110 | 53 | 18 | 39 | ||||||
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Assessment: essay, oral presentation, essay and weekly journal (view the assessment table for details). |
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1. For the paper-reading weeks, please ask at least one question for the assigned paper before class. We will discuss at least one of each student's questions first and go for the other questions if we have time. |
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2. GAI use regulation: Thorp, H. H. (2023). ChatGPT is fun, but not an author. Science, 379(6630), 313-313. |
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3. Essay rubric: (http://wid.ndia.org/about/Documents/WID_EssayRubric.pdf);oral presentation rubric (https://www.science.purdue.edu/Current_Students/curriculum_and_degree_requirements/oral_rubrics_gray.pdf) |
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Chiu, M.-S. (2020). Exploring models for increasing the effects of school information and communication technology use on learning outcomes through outside-school use and socioeconomic status mediation: The Ecological Techno-Process. Educational Technology Research and Development, 68, 413–436. |
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Chiu, M.-S. (2012). The internal/external frame of reference model, big-fish-little-pond effect, and combined model for mathematics and science. Journal of Educational Psychology, 104, 87-107. |
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Chiu, M.-S. (2023). Gender differences in mathematical achievement development: A family psychobiosocial model. European Journal of Psychology of Education. https://doi.org/10.1007/s10212-022-00674-1 |
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Fischbach, A., Keller, U., Preckel, F., & Brunner, M. (2013). PISA proficiency scores predict educational outcomes. Learning and Individual Differences, 24, 63-72. |
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Marsh, H. W., Abduljabbar, A. S., Abu-Hilal, M. M., Morin, A. J., Abdelfattah, F., Leung, K. C., Xu, M. K., & Nagengast, P. (2013). Factorial, convergent, and discriminant validity of TIMSS math and science motivation measures: A comparison of Arab and Anglo-Saxon countries. Journal of Educational Psychology, 105, 108-128. |
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Wang, F., King, R. B., & Leung, S. O. (2023). Why do East Asian students do so well in mathematics? A machine learning study. International Journal of Science and Mathematics Education, 21(3), 691-711. |
| 書名 Book Title | 作者 Author | 出版年 Publish Year | 出版者 Publisher | ISBN | 館藏來源* | 備註 Note |
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