Genetics and Bioinformatics Course Experience 2024-2025 PDF

### **"Genetics and Bioinformatics" Course Experience (2024-2025)** This survey is designed to gather your feedback on the *"Genetics and Bioinformatics"* course. Your responses are anonymous, and your honest feedback will help improve the course structure, content, and delivery for future students...

### **"Genetics and Bioinformatics" Course Experience (2024-2025)** This survey is designed to gather your feedback on the *"Genetics and Bioinformatics"* course. Your responses are anonymous, and your honest feedback will help improve the course structure, content, and delivery for future students. Section 1: General Information ============================== 1. **Your background knowledge before taking this course:** - No prior knowledge of genetics or bioinformatics - Basic knowledge of genetics - Basic knowledge of bioinformatics - Basic knowledge of informatics - Basic knowledge of biomedicine - Basic knowledge of data science or statistics or machine learning - Advanced knowledge of genetics - Advanced knowledge of bioinformatics - Advanced knowledge of informatics - Advanced knowledge of gbiomedicine - Advanced knowledge of data science or statistics or machine learning 2. **Did you take this course as part of a specific program?** - Bachelor - Master - Erasmus - Freelance Section 2: Course Structure and Organization ============================================ 1. **How do you feel about the current course structure with two distinct blocks (Genetics Block and Non-Genetics (Analytics-Oriented) Block)?** - 5 - Very satisfied 4 - Satisfied 3 - Neutral 2 - Dissatisfied 1 - Very dissatisfied 3. **If you could choose an alternative structure, would you prefer:** - Keeping the current structure (Genetics and Analytics blocks) - Changing to a structure organized by data types (e.g., each type with its own introduction, generation platforms, low-level analysis, and high-level analysis) - Other (please specify): \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ 2. **How well did the structure of the course support your understanding of genetics and bioinformatics?** - 5 - Extremely well 4 - Very well 3 - Moderately well 2 - Slightly well 1 - Not well at all 3. **Would you like the option to cover more advanced modules on specific topics?** - Yes, as optional homework assignments for deeper learning - Yes, covered directly in class sessions - No, I find the current level sufficient - Other (please specify): \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ 4. **How was your experience with the integration of DataCamp for learning programming and analytics?** - 5 - Extremely helpful 4 - Very helpful 3 - Moderately helpful 2 - Slightly helpful 1 - Not helpful at all 5. **Would you like to see PBL integrated more extensively into future modules?** - Yes, I found it engaging and potentially beneficial - No, I prefer traditional instruction methods - I'm undecided and would need more experience with PBL to decide - Other (please specify): \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ 6. **Would you like your efforts and performance on DataCamp assignments to be scored and count toward your final exam score?** - Yes, I think DataCamp efforts should count toward the final score. - No, I prefer that DataCamp efforts do not count toward the final score. - I'm undecided. 7. **Please share any specific feedback on the current block structure, DataCamp integration, advanced modules, or suggestions for improvement:** Section 3: Course Content {#section-3-course-content-1} ========================= 1. **Would you like to see more content focused on data platform-specific low-level analytics?**\ *Note: Low-level analytics involve data processing and quality control for specific platforms (e.g., RNA-seq, microarray). High-level analytics focus on broader, integrative analyses, such as pathway analysis, cross-platform data integration, or machine learning.* - Yes, I would like to see more on low-level analytics - No, the current focus is sufficient - Not sure 2. **Where would you prefer to have content on data platform-specific low-level analytics?** - In the Genetics Block (due to natural alignment with data types) - In the Analytics Block (to keep analytics-focused content together) - No preference - Other (please specify): \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ 3. **How would you rate the clarity and relevance of the materials provided in the Genetics Block?** - 5 - Excellent 4 - Good 3 - Fair 2 - Poor 1 - Very poor 4. **How would you rate the clarity and relevance of the materials provided in the Analytics Block?** - 5 - Excellent 4 - Good 3 - Fair 2 -- Poor 1 - Very poor 5. **Was the balance between theoretical concepts and practical applications appropriate?** - Yes, it was well balanced - No, there was too much theory - No, there was too much practical application - Other (please specify): \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ 6. **What was the most memorable part of this course?** *(Select one)* - A specific lecture or topic - A group project or discussion - A practical assignment or research project - Other (please specify) 7. **Are there any topics you feel should be added or removed from the course content?** - \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ Section 4: Teaching and Support =============================== 1. **How would you rate the effectiveness of each tutor in explaining the course content?** - **Genetics Block Responsible Tutor:** - 5 - Excellent 4 - Good 3 - Fair 2 - Poor 1 - Very poor - **Bioinformatics Responsible Tutor:** - 5 - Excellent 4 - Good 3 - Fair 2 - Poor 1 - Very poor - **Microbiome co-tutor:** - 5 - Excellent 4 - Good 3 - Fair 2 - Poor 1 - Very poor - **RNA co-tutor:** - 5 - Excellent 4 - Good 3 - Fair 2 - Poor 1 - Very poor - **Metabolome co-tutor:** - 5 - Excellent 4 - Good 3 - Fair 2 - Poor 1 - Very poor 2. **Were the support resources (e.g., descriptions of practicals, slides) helpful for understanding the course content?** - 5 - Extremely helpful 4 - Very helpful 3 - Moderately helpful 2 - Slightly helpful 1 - Not helpful at all 3. **Any suggestions for improving the quality of teaching or support resources?** - \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ Section 5: Learning Outcomes and Skills Development =================================================== 1. **To what extent did this course improve your skills in each of the following areas?** - **Genetics Knowledge:** - 5 - Significant improvement 4 - Some improvement 3 - No improvement - **Bioinformatics Knoweldge:** - 5 - Significant improvement 4 - Some improvement 3 - No improvement - **High-Level Analyses Skills (e.g., GWAS analysis, microbiome descriptive analysis, DE analysis):** - 5 - Significant improvement 4 - Some improvement 3 - No improvement - **Low-Level Analyses Skills (e.g., data processing, platform-specific quality control):** - 5 - Significant improvement 4 - Some improvement 3 - No improvement - **Critical Thinking and Problem Solving:** - 5 - Significant improvement 4 - Some improvement 3 - No improvement 2. **What new skills or insights did you gain that you consider most valuable?** \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ Section 6: Self-reflecting about the course =========================================== Self-assessment and self-reflection involves students reviewing their work and reflecting on their learning progress. The following questions allow you to self-reflect about the course "Genetics and Bioinformatics" #### A growth mindset isn't just about effort. Perhaps the most common misconception is simply equating the growth mindset with effort. Certainly, effort is key for students' achievement, but it's not the only thing. Students need to try new strategies and seek input from others when they're stuck. They need this repertoire of approaches---not just sheer effort---to learn and improve. --Carol Dweck #### **Learning and Knowledge Gained** 1. **How would you rate your overall understanding of the subject after completing this course?** - 1 = Very poor, - 2 = Poor, - 3 = Average, - 4 = Good, - 5 = Excellent 2. **Which topics or concepts were most engaging for you?** *(Select all that apply)* - Topic Genomics - Topic Transcriptomics - Topic Microbiomics - Topic Metabolomics #### **Personal Growth and Development** 3. **How much personal growth did you experience in this course?** - 1 = None, - 2 = Slight, - 3 = Moderate, - 4 = Significant, - 5 = Transformational 4. **What personal strengths did you develop during this course?** *(Select all that apply)* - Time management - Collaboration - Problem-solving - Creativity - Resilience #### **Challenges and Areas for Improvement** 5. **What was the biggest challenge for you in this course?** *(Select one)* - Understanding content - Managing time and workload - Participating in group work - Staying motivated - Comments or other (please specify) 6. **How effectively did you overcome these challenges?** - 1 = Not at all, - 2 = Slightly, - 3 = Somewhat, - 4 = Well, - 5 = Very effectively #### **Engagement and Participation** 7. **How actively did you participate in course activities (e.g., discussions, assignments)?** - 1 = Not at all, - 2 = Rarely, - 3 = Sometimes, - 4 = Often, - 5 = Always 8. **What could you have done to engage more fully with the course?** *(Select one)* - Contributing more in discussions - Asking more questions - Managing time better - Seeking more help from the instructor - Other (please specify) #### **Future Goals and Applications** 9. **How relevant is what you learned in this course to your future goals?** - 1 = Not relevant, - 2 = Slightly relevant, - 3 = Moderately relevant, - 4 = Very relevant, - 5 = Extremely relevant 10. **What future topics or skills would you like to explore next?** *(Select all that apply)* - Advanced topics in this subject - Interdisciplinary applications - Practical skills (e.g., coding, writing) - Other (please specify) #### **Course Structure and Teaching** 11. **How effective were the teaching methods in supporting your learning?** - 1 = Not effective, - 2 = Slightly effective, - 3 = Moderately effective, - 4 = Very effective, - 5 = Extremely effective 12. **Which aspects of the course were most helpful?** *(Select all that apply)* - Lectures - In-class assignments (group projects) - Feedback from the instructors (including during oroutside class) - Other (please specify) Section 7: Future Opportunitiess ================================ #### **Community Service Learning (beyond internships)** Incorporating community service learning (CSL) into a bioinformatics curriculum enhances students' technical skills while promoting a civic mindset, showing them how data science can directly impact community health and well-being. This approach prepares students for the dual role of being skilled scientists and responsible, engaged citizens. In particular, we are exploring opportunties to integrate community service into your curriculum through bioinformatics, machine learning, and data science projects. Your input will help us tailor these activities to your interests and strengths. 1. **Do you think this proposal could be of interest for yourself or other student?** - Yes - No - No opinion/ I cannot decide #### **Continued Education** As this course is an introductory course, we understand that several topics may require further exploration. How would you feel about a master-level program with a provisional title **"X-Omics for Health"**. Such a program could involve: *Year 1:* Focused learning on multiple omics types (e.g., genomics, proteomics, holo-omics), with modules organized per data type and covering essential lab experiments, data generation, and data pre-processing. *Year 2:* A "do-thesis" requiring hands-on data handling. This can be experimental or analytical in nature and would involve high-level analytic techniques aimed at solving a particular real-life problem. 2. **Do you thing that this proposal could be interesting for yourself or future students:** - Yes - No - No opinion/ I cannot decide For the following questions, assume you have enrolled in "X-omics for Health", which contains a CSL part. What is your opinion? ### **General Preferences** 1. **Which type of community engagement appeals to you the most? (Select all that apply):** - Teaching or mentoring others - Developing technical tools (e.g., dashboards, models) - Hands-on collaboration with organizations - Research and data analysis - Public presentations or consultations 2. **What kind of outcomes would you like to achieve through a CSL project? (Select up to 3):** - Improve community health or awareness - Gain technical skills (e.g., coding, data analysis) - Enhance communication and teaching skills - Build a portfolio of real-world projects - Learn about ethical data practices - Create lasting community impact 3. **Which of the following CSL activities would you be most interested in participating in? (Select up to 3):** - Data Literacy Workshops for Health Organizations - Open-Source Data Projects for Public Health Awareness - Community Health Prediction Models - Educational Programs for High School Students on Bioinformatics and Machine Learning - Collaborative Data Analysis for Environmental Health Projects - Predictive Models for Food Insecurity and Nutritional Needs - Crowdsourced Health Data Projects with Privacy Preservation - Patient-Centric Data Analysis for Local Clinics - Public Data Science Consultations ### **Skills and Development** 5. **What skills do you hope to develop through a CSL project? (Select all that apply):** - Coding and data visualization - Machine learning and predictive modeling - Bioinformatics principles - Teaching and mentorship skills - Ethical data usage and privacy practices - Communication and consulting skills - Reproducibility 6. **Do you have prior experience with any of the following? (Select all that apply):** - Coding (e.g., Python, R) - Data visualization tools (e.g., Tableau, Power BI) - Bioinformatics software (e.g., BLAST, Bioconductor) - Machine learning frameworks (e.g., TensorFlow, scikit-learn) - Public speaking or teaching - Community service or nonprofit work ### **Logistical Preferences** 7. **How much time per week would you be willing to dedicate to a CSL project?** - 1-3 hours - 4-6 hours - 7-10 hours - More than 10 hours 8. **What type of project structure do you prefer?** - Short-term projects (1-3 months) - Long-term projects (4-12 months) - Ongoing projects with flexible participation 9. **Do you prefer to work: (Select one):** - Individually - In small teams (2-5 people) - In larger groups (6+ people) 10. **Are there any specific constraints or preferences (e.g., remote work, location, timing) we should consider?** - Remote work - Location - Timing - Other (please specify)