Teaching data management in a lab environment (IASSIST 2014)
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The document discusses the implementation and structure of a Data Management Lab at IUPUI, highlighting the importance of evidence-based teaching to enhance critical thinking and procedural skills. It outlines specific goals for the lab experience, such as improving project design and data management techniques, and emphasizes practical exercises and formative assessments. Additionally, it details various modules covering data management topics and best practices to ensure high standards and compliance with ethical obligations.
Teaching data management in a lab environment (IASSIST 2014)
- 1. Teaching data management in a lab environment IASSIST 2014 | Toronto, CA | Wednesday, 6/4/14 Heather Coates, Digital Scholarship & Data Management Librarian IUPUI University Library Center for Digital Scholarship
- 2. What I'm going to talk about • Background • The lab experience • Evidence-based teaching • DM Lab - The Early Days • The Future of the DM Lab
- 3. Background • IUPUI • Data Services Program • Data Management Lab • Why IASSIST, why now?
- 5. The Laboratory Experience Procedural skills Critical thinking skills Metacognitive skills
- 6. What is the lab experience?
- 7. –Hofstein & Lunetta, 2004 “...it is vital to isolate and define goals for which laboratory work could make a unique and significant contribution to the teaching and learning of science.”
- 8. Faculty identified goals for the lab experience • Critical thinking skills & experimental design • Lab skills & techniques • Engaging in science • Teamwork skills • Written communication skills • Connecting lab & lecture Bruck et al, 2010
- 9. Goals for the data management lab experience • Critical thinking skills & project design • Data management skills & techniques • Engaging in data management activities • Team science skills • Project documentation skills • Connecting data management to the research process
- 12. • Start with the end in mind • Keep it brief (15-20 minutes) • Use multiple channels for communicating the message
- 13. Effective examples • Enable learners to integrate new information into a coherent structure (e.g., mental model) • Provide worked and partially worked examples to facilitate procedural learning • Provide feedback appropriate for each learner's level of experience
- 14. Relevant exercises • Meaningful • Contextualized • Designed to teach the targeted skill NOT following instructions or getting the right answer • Provide opportunities to apply the targeted skill or procedure or strategy • Provide opportunities to practice self-regulation of learning skills
- 15. Fostering discussion • Activity-based • Encourage reflection • An important part of formative assessment • Provide opportunities to practice self-regulation of learning skills
- 16. Data Management Lab Pilot: January 2014 Modular Series: March - April 2014 Data Management Lab v2.0 materials available at: http://www.slideshare.net/goldenphizzwizards
- 17. Modules Intro to RDM DM Planning Organize Data & Files QA/QC Collection Entry & Coding Screen & Clean Automate Protection & Security Rights & Access Attribution & Citation Ethical & Legal Obligations
- 18. Measure Twice, Thrice, Many Times, Cut Once • Define expected outcomes and quality standards for data • Identify your legal obligations as they affect data management and protection and ethical obligations for ensuring data confidentiality, privacy, and security • Choose tools, formats, and standards wisely • Plan & implement a sound storage & backup plan, including use of data locks or master files • Outline planned project and data documentation to enable effective reporting • Use best practices for data collection, entry, coding • point to docs on Slideshare Ask for help www.slideshare.com/goldenphizzwizards
- 19. An actionable data management plan • Draft it during the planning phase • Update it during start-up • Correct & maintain it during the active phases • Enhance it during processing, analysis, & write-up
- 20. Defining success • If you can't measure it, you can't manage it • Anticipate problems to prevent them • Information needed to communicate the process and explain products to colleagues (e.g., thesis/dissertation, manuscripts) • Enabling extension, secondary use/reuse, and replication/ reproducibility
- 21. Linking data quality standards to process
- 22. Failing upwards • Choose best course format possible • Teach fewer topics, dive deeper • Incorporate meta cognitive skills to promote self-regulation of learning • Structure & support activities better • Formative assessment of data management plans & documentation • Evidence of behavior change, implementation
- 23. Thanks for your attention!
- 24. Images • Rocky path: https://www.flickr.com/photos/13448066@N04/3255009670/ • Hikers on rocky path: https://www.flickr.com/photos/devonaire/6071209350/ • Old Lab: https://www.flickr.com/photos/sludgeulper/3230950117/ • Enid & Betty in the lab: https://www.flickr.com/photos/28853433@N02/4679198690/ • Microarray chip: https://www.flickr.com/photos/47353092@N00/2034113679/ • Lecturer: https://www.flickr.com/photos/39213312@N07/3722413559/ • I teach: https://www.flickr.com/photos/28430474@N05/6902965047/ • Calculate SD example: http://ci.columbia.edu/ci/premba_test/c0331/s7/s7_3.html • Discussion group: https://www.flickr.com/photos/47423741@N08/8733059592/ • Success baby: https://www.flickr.com/photos/91633309@N08/8827619102/ • Lab notebooks: https://www.flickr.com/photos/89975702@N00/5878993041/ • Data steward logo: http://www.trilliumsoftware.com/images/360_01.jpg • Data quality graphic: http://library.ahima.org/xpedio/groups/public/documents/graphic/bok1_049652.jpg
- 25. Resources • Bruck, L. B., Towns, M. & Bretz, S. L. (2010). Faculty perspectives of undergraduate chemistry laboratory: Goals and obstacles to success, Journal of Chemical Education, 87(12), 1416-1424. • Clark, R. C. (2010). Evidence-based training methods: A guide for training professionals. Alexandria, Va: ASTD Press. • Heering, P., & Wittje, R. (2012). An Historical Perspective on Instruments and Experiments in Science Education. Science & Education, 21(2), 151-155. • Hofstein, A., & Lunetta, V. N. (2004). The laboratory in science education: Foundations for the twenty‐first century. Science education, 88(1), 28-54. • Nilson, L. B. (2003). Teaching at its best: A research-based resource for college instructors. Bolton, MA: Anker Publishing Co.