| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
| The primary objective of this course is to equip students with integrated skills in agricultural genomics and bioinformatics. Students will master the complete research workflow, beginning with wet-lab techniques (DNA extraction, PCR, and ddRAD library construction) and transitioning to computational analysis using R statistics, Linux, and image phenotyping tools. Through this dual training, students will develop the ability to generate, analyze, and interpret biological data, culminating in the effective communication of scientific findings. |
| 1.Professional agricultural knowledge |
| 2.Master Thesis |
| 3.Logic/ data collection and analysis |
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| topic Discussion/Production |
| Exercises |
| Discussion |
| Practicum |
| Lecturing |
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| Oral Presentation |
| Study Outcome |
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| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Holiday |
| Week 2 |
Course Introduction:
Overview of course objectives, grading policy, and laboratory safety guidelines. |
| Week 3 |
Genomic DNA Extraction:
Principles and protocols for extracting high-quality DNA from plant tissues. |
| Week 4 |
DNA Quality Control (PCR & Gel Electrophoresis):
Assessing DNA integrity and quantity using PCR amplification and agarose gel electrophoresis. |
| Week 5 |
ddRAD Library Construction I:
Digestion & Ligation: Restriction enzyme digestion of genomic DNA and adapter ligation for reduced representation sequencing. |
| Week 6 |
Holiday |
| Week 7 |
ddRAD Library Construction II:
Amplification & Purification: PCR amplification of the library fragments and final size selection/purification for sequencing. |
| Week 8 |
Introduction to Linux:
Installation of Linux environment (e.g., WSL) and essential command-line operations for bioinformatics. |
| Week 9 |
Introduction to R & RStudio:
Setting up the R environment; learning basic syntax, data structures, and package management. |
| Week 10 |
Holiday |
| Week 11 |
Statistical Analysis in R (ANOVA):
Performing Analysis of Variance (ANOVA) to test hypotheses on biological datasets. |
| Week 12 |
Data Visualization in R:
transforming raw data into high-quality scientific figures |
| Week 13 |
Image Analysis I (Phenotyping):
Introduction to digital image processing tools for identifying plant leaf traits. |
| Week 14 |
Image Analysis II (Phenotyping):
Advanced quantification of phenotypic traits and data export for downstream analysis. |
| Week 15 |
Literature Review Presentation I:
Individual presentations on recent advancements in agricultural genomics and biotechnology. |
| Week 16 |
Literature Review Presentation II:
Individual presentations on recent advancements in agricultural genomics and biotechnology. |
self-directed learning |
   01.Participation in professional forums, lectures, and corporate sharing sessions related to industry-government-academia-research exchange activities.    02.Viewing multimedia materials related to industry and academia.    03.Preparing presentations or reports related to industry and academia.    05.Participation in various workshops organized by different departments of NCHU.
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| Evaluation |
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| Textbook & other References |
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| Teaching Aids & Teacher's Website |
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| Office Hours |
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| Sustainable Development Goals, SDGs(Link URL) |
| 03.Good Health and Well-Being   04.Quality Education   09.Industry, Innovation and Infrastructure | include experience courses:N |
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