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1 Teaching Geoinformatics: Computer Science Perspective Ann Quiroz Gates Professor and Chair Department of Computer Science The University of Texas at.

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Presentation on theme: "1 Teaching Geoinformatics: Computer Science Perspective Ann Quiroz Gates Professor and Chair Department of Computer Science The University of Texas at."— Presentation transcript:

1 1 Teaching Geoinformatics: Computer Science Perspective Ann Quiroz Gates Professor and Chair Department of Computer Science The University of Texas at El Paso

2 2 Organization Computer Science Perspective Geoscience Perspective Open Discussion

3 3 Overview Approaches Software Engineering: A Project-Driven Approach Example Projects Challenges

4 4 Session Goals Acquire ideas on how to integrate geoinformatics into Geoscience and Computing curricula Learn about an approach to develop cross-disciplinary software projects Have fun!

5 5 Question What approaches can be used to introduce geoinformatics into the classroom?

6 6 Suggested Approaches-1 Integrate geoinformatics components into courses –TeraGrid Education, Outreach, and Training Discover Data: Incorporate scientific data into curriculum Data Bridge: Organize data in common file formats and visualize http://www.teragrid.org/eot/resources_edu.html –Digital Library for Earth Systems Education Earth Exploration Toolkit Discover our Earth http://www.dlese.org/educators/usingdata.html –NCSA: Cybereducation http://education.ncsa.uiuc.edu/

7 7 Suggested Approaches-2 Integrate tutorials into course, e.g., Kepler Develop exercises around GEON portal and others Collaborate with faculty from Computer Science, Information Technology/Systems, or Software Engineering programs –Offer courses that are cross listed across departments –Include guest speakers in course –Become involved in project-driven courses

8 8 Questions How do you develop complex software systems?

9 9 Waterfall Model

10 10 Incremental Model

11 11 Where does the client fit in?

12 12 UTEP’s Software Engineering Course A Project-Driven Approach

13 13 Course Overview-1 Teach and apply software engineering methods, tools, and techniques Work with an actual customer to develop a product Prepare documentation in adherence to IEEE standards

14 14 Course Overview-2 Two-semester (32-week) required course Approximately 30 seniors enrolled Instructor-selected teams consisting of 5 students Students: limited background in SE principles, methods, and process

15 15 Cross-Disciplinary Features High interaction with clients (e.g., geoscientist and graduate students) to define requirements Guest speakers used to provide background Experts used to critique prototypes and validate deliverables Experts provide feedback to students at the end-of-semester presentations

16 16 Course Benefits Students learn how to interact with people from different disciplines Students develop cross-disciplinary knowledge The course results in a working prototype that can be adopted and deployed GEON Perspective –Prepare students who can work on cross-disciplinary projects in the geo-science domain –Contribute to the geo-science toolkit –Promote the field of geoinformatics

17 17 Master-Apprenticeship Learning Cycle

18 18 Master-Apprenticeship Learning Cycle

19 19 Master-Apprenticeship Learning Cycle

20 20 Deliverables: SE1 Interview report Feasibility report Software Requirements Specification Interface prototype Analysis diagrams and models –Use Case diagrams –Scenarios –Class diagrams –State transition diagrams –Dataflow diagrams

21 21 Deliverables: SE2 Configuration management plan Design document Test plans “Contracted” software Funded students complete software development after the course completes

22 22 Effective Teams Students must leave course with demonstrated abilities to work in teams. Teams must be monitored. Basis: cooperative paradigm –Positive interdependence –Promotive interaction –Individual accountability –Teach team skills –Group processing

23 23 Creating Teams Goal: heterogeneous teams Process –Submit application letter and resumé –Present in-class workshop on personality types –Create set of equally capable teams of 5 Consider position preferences and expertise Consider grades, work, and extracurricular experience Balance teams wrt diversity considering culture, gender, and personality

24 24 Team Skills Develop basic leadership skills Setting agendas Assigning roles in meetings Clarifying assignment Defining tasks and timelines Ensuring progress is made and deadlines are met Maintain meeting minutes and task assignment sheets Model and teach team skills

25 25 Individual Accountability Observe student behavior when project teams are working. Create and maintain team notebooks –Meeting records –E-mail trail –Rough drafts Submit statement of work –Document individual, subgroup, and team work –Signed by all members Question each member during presentations

26 26 Group Processing Request after each deliverable Ask questions such as: –Did you complete your task on time? –How did you encourage participation from another team member? –What is working well in your team? –What needs to be improved in your team? Consolidate and share anonymous responses Identify problems and skills that resolve them

27 27 Projects: Gravity Data Repository System (GDRP)-1 Need for GDRP –Accumulated gravity measurements stored at numerous research centers around world –Effort seeks to establish a combined database Relevance of gravity data –Important source of geophysical and geological information –Geophysical models are designed to fit gravity measurement and used to generate models of lithospheric structure

28 28 Projects: Gravity Data Repository System (GDRP)-2 Relevance con’t –Measurements used by geologists, scientists, oil and mineral exploration companies, environmental consultants Features of GDRP –Gravity data warehouse –Upload and download validated data –Collection of tools Access data Visualize data Manipulate data

29 29 Projects: Seismic Waves Rock Correlation System (SWRoCS)-1 Need for SWRoCS –Earthscope and Geoinformatics initiatives call for better access of data on physical properties of rocks and minerals –Understanding of how properties vary with temperature and pressure Relevance –Required to interpret geophysical data –Facilitates integrated analysis of different types of data

30 30 Projects: Seismic Waves Rock Correlation System (SWRoCS)-2 Features of SWRoCS –Identify rock based on mineral composition or seismic properties –Determine physical properties of named rock –Experiment with related seismic properties of rocks –Extend knowledge base –Navigate ontologies –View general information Limitations –Restricted to intrusive igneous rocks and component minerals –Properties: S-wave velocity, density, % anisotropy

31 31 Projects: Seismic Tomography Overview: –Facilitates construction and experimentation of 3-D structure of Earth –Calculates 2-D or 3-D models using travel-time tomography algorithm of Hole and first- arrival travel times and rays using Vidale’s finite difference solution

32 32 Challenges Establishing collaboration Time investment Communication gap

33 33 Contact E-mail: agates@utep.edu SE Websites: http://www.courses.utep.edu/CS4310FS

34 34 Breakout Session

35 35 Reusable Course Components What would be useful for you? –Presentation material –Pre-requisite knowledge –Learning objectives –Exercises/projects –Handouts –Exam questions –Resources for background

36 36 Exercise Brainstorm on ideas for a software project related to geoinformatics Rules –Each group member, in turn, states an idea. NO idea is criticized. –As ideas are generated, write each one on a flipchart. Don’t abbreviate or interpret. –Ideas are generated in turn until each person passes.

37 37 Uncertainty and Knowledge Representation in Geoinfomatics-1 Offered in spring 2004 by Vladik Kreinovich Attended by 13 graduate CS students Course objectives –to learn general techniques of representing and processing uncertainty and –to learn how to use these techniques in geoinformatics (using geospatial applications)

38 38 Uncertainty and Knowledge Representation in Geoinfomatics-2 Topics –Motivation for estimating and processing uncertainty –Techniques for estimating uncertainty of the results of data processing –Techniques for representing and processing expert uncertainty –Geospatial applications in uncertainty: methods for detecting outliers and duplicates –Determination of geospatial characteristics based on measurement results


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