Public Discussion

  • Icon for: Neil Plotnick

    Neil Plotnick

    Facilitator
    May 11, 2015 | 02:08 p.m.

    How do you balance web based and hands on activities in the classroom? Are there mechanisms in the software to allow or encourage students to collaborate in small groups while exploring the various visualizations?

  • Icon for: Dermot Donnelly

    Dermot Donnelly

    Co-Presenter
    May 11, 2015 | 05:10 p.m.

    We have data collection features within WISE. For example, in our Thermodynamics in the Kitchen unit, students use temperature probes to take the temperature of different objects and to demonstrate heating and cooling curves. Students then have opportunities to further investigate these hands-on activities through our visualizations. Both activities offer distinct affordances, and can be tailored to each classroom context.

    Students typically conduct WISE activities in pairs to support collaboration. We also have collaborative software tools. One such tool, the Idea Manager, allows students to add ideas from visualizations throughout a unit and to share these ideas with their class through WISE. Students debate and refine these ideas as a class.

  • Icon for: Jessica Hunt

    Jessica Hunt

    Facilitator
    May 11, 2015 | 02:12 p.m.

    I noticed that you say that you used the comments of expert teachers, along with research of how children develop and integrate their knowledge, to support the design of the software. I wonder if you could say more about how you identified and recruited the expert teachers that you partnered with in your study.

    I was also interested in hearing more about teachers’ perceptions of the specific affordances of the software in the classroom. In what ways might the software promote teachers’ planning of instruction that is responsive to students’ evolving understanding, both within the software as well as other activities?

  • Icon for: Dermot Donnelly

    Dermot Donnelly

    Co-Presenter
    May 11, 2015 | 05:13 p.m.

    We classify expert teachers as those who have experience using multiple WISE units and have attended multiple 1-week professional development workshops. These teachers have repeatedly analyzed their own student learning data to refine the curriculum materials, and their in-class teaching strategies.

    To answer your second question, we are currently investigating how the automated guidance can effectively support teachers. Teacher interviews and classroom observations suggest teachers believe the automated guidance helps them to identify low-scoring students, and frees them to work directly with students needing additional guidance.

  • Icon for: Jessica Hunt

    Jessica Hunt

    Facilitator
    May 12, 2015 | 04:18 p.m.

    Thanks, Dermot. I appreciate this depiction of expert teachers; I can see applications of this depiction in later work that I hope to do. I look forward to hearing more about how the automated guidance provided in your project helps support teachers- very interesting work!

  • Small default profile

    Barbara Berns

    Guest
    May 12, 2015 | 04:13 p.m.

    I’ve heard several presentations on WISE, and must say that this succinct but carefully constructed video described the project in a very interesting and informative way. And a shout out to CADRE Fellow and researcher Jonathan Vitale!

  • Icon for: Jonathan Vitale

    Jonathan Vitale

    Co-Presenter
    May 12, 2015 | 11:41 p.m.

    Thanks Barbara

  • Small default profile

    Stephanie Corliss

    Guest
    May 13, 2015 | 01:33 p.m.

    Great work (and nice to see former co-workers faces in the video)! I’d like to hear more about the automated scoring. Is it linked to each individual assessment by key words? Can you train it to work with other types of prompts? We recently tested the validity of the edX automated essay scoring tool compared to a human grader for short essay assignments in a MOOC. This technology could be really useful in higher ed as well.

  • Small default profile

    Priscilla Robinson

    Guest
    May 14, 2015 | 01:53 a.m.

    It’s exciting to see that the visualizations, grading tools and teacher support continues to be dynamic and KI focused. I’m interested to understand how much input teachers are able to contribute, considering the complexity of the technology now. As an early WISE expert teacher, I am fascinated to see how visions we had many years ago, are now becoming a reality. I wish more educators would explore this free science education platform for their classes. I loved it!

  • Icon for: Joni Falk

    Joni Falk

    Co-Director of CSR at TERC
    May 14, 2015 | 11:25 a.m.

    Hi Marcia, Great to see this video on WISE. Very rich work on how to further students conceptual understanding through visualization, graphic, reflection and revision. Great presentation. Thanks!

  • Icon for: Stephanie Teasley

    Stephanie Teasley

    Facilitator
    May 14, 2015 | 07:46 p.m.

    Really nice video, Marcia and team. You convened the project well and provided a nice overview of your system. How can I find out more about the algorithms you have built into the system?

  • Icon for: Jonathan Vitale

    Jonathan Vitale

    Co-Presenter
    May 15, 2015 | 01:07 p.m.

    Hi Stephanie, I can’t speak to the text analysis algorithms. But, regarding graphs (with a little discussion of system diagrams) you can view a new paper in JRST:

    http://onlinelibrary.wiley.com/doi/10.1002/tea....

  • Icon for: Marcia Linn

    Marcia Linn

    Presenter
    May 15, 2015 | 07:39 p.m.

    Thank you. We are using ETS c-rater algorithms.
    This involves natural language processing by matching to responses scored by experts.

  • Icon for: Kathy Perkins

    Kathy Perkins

    Director
    May 15, 2015 | 11:32 p.m.

    Nice video! It’s an impressive system of tools and teacher features you’ve built over the years – and made open for teacher use!

  • Further posting is closed as the showcase has ended.

  1. Marcia Linn
  2. http://gse.berkeley.edu/people/marcia-linn
  3. Professor of Cognition and Instruction
  4. Continuous Learning and Automated Scoring in Science (CLASS)
  5. http://wise.berkeley.edu
  6. University of California, Berkeley
  1. Dermot Donnelly
  2. http://gse.berkeley.edu/people/dermot-donnelly
  3. Post-Doctoral Researcher
  4. Continuous Learning and Automated Scoring in Science (CLASS)
  5. http://wise.berkeley.edu
  6. University of California, Berkeley
  1. Libby Gerard
  2. http://telscenter.org/about-us/participants/post-doctoral-scholars
  3. Professional Researcher
  4. Continuous Learning and Automated Scoring in Science (CLASS)
  5. http://wise.berkeley.edu
  6. University of California, Berkeley
  1. Jonathan Lim-Breitbart
  2. http://jbreitbart.com/
  3. Web Designer and Developer
  4. Continuous Learning and Automated Scoring in Science (CLASS)
  5. http://wise.berkeley.edu
  6. University of California, Berkeley
  1. Jonathan Vitale
  2. https://www.linkedin.com/profile/view?id=115215126
  3. Post-Doctoral Researcher
  4. Continuous Learning and Automated Scoring in Science (CLASS)
  5. http://wise.berkeley.edu
  6. University of California, Berkeley

Automated Guidance in the Web-based Inquiry Science Environment (WISE4)
NSF Award #: 1119670

The Web-based Inquiry Science Environment (WISE) uses new automated scoring technologies to guide middle and high school students’ science learning. Our video showcases how these technologies can guide students and support teachers in activities such as graphs, diagrams, and written explanations. In particular, WISE combines these activities with powerful visualizations that encourage students to distinguish among ideas and explain complex scientific phenomena. Check out these resources for free, courtesy of NSF funding, at wise.berkeley.edu.