*Recommendations from Advisory  Board*

Most helpful in Jan. proposal:

*Data from real students and real teachers – implications on whom we select for pilot teachers, degree of commitment to 2-3 week unit using the stuff

Include in proposal:
*student engagement
*teacher fidelity (maybe robust, so differences in use are still effective)
*Get teachers to commit to getting to mouse data
*Observation and feedback from kids and teachers – look at classes and also interview, get feeling of using stuff and perception; also feedback on software side from kids
*Anything in print for teachers during training so they can figure out what to do quickly
*Recording student logging of use – what analysis can we do between that and proposal to make recommendations

Curriculum design:
*Details about idea that pilot – keep small, to be able to do well. Intro w/dragons/drakes and mouse data. Keep simple enough to all students,; can project student work for advanced.
*Keep learning goal clear to let material desgn meet these goals – backward design. Only 3 or 4 learning goals per section. Small and focused,  to target pilot. [Watch feature creep]. Only need to prove something about design of lessongs, not tool (?).
*Do a better job with less; design small units that pilot teachers will agree to teach. Try to be in classrooms during pilot, since things will happen that you would never believe unless you see. Pre-post test (funding?). Observation form to collect similar types of data across classrooms.

*Kept separate big goals in creating and testing tool vs. learning genetics — Remember that we're testing the tool, not the genetics understanding. In fall, probably better to do this with advanced students. If students definitely have genetics background, then we don't need to worry about that.

FE: Sample size?

*Just enough of a hint that something might work – don't have time to do more than a few. Nice to have 3 or 4 teachers. Number not as important as would be in the research project. This is a proof of concept. PH: Little kids can learn big ideas. If not all same type of teacher, would be good. Only 15 kids doesn't do the trick for evidence. Each time, ideally would be in the classroom frequently or every day. Not a normal classroom; this is exploratory. Need to be "in their face" to learn this.

*Not there as teachers; we're there to learn from them, not teach them. 

*Pre-test and post-test, plus performance application, but now that they have learned this and gone through.

*Can probably design lessons that are a bit more traditional, knowing that we want to develop more later – real goal is to test the tool. Tool is non-traditional…

SB: If elements of perf. assessment needed for larger proposal, need to include these in the front end, in order to include this in the proposal.

*Perf assess: challenge to create strains, maybe. How do they know they have done it? When do they stop? What determines that?

PH:

*NSF RFP – Frontier challenge: R&D requiring a vision of schools – cyber-enabled, new ways by expansion of classroom boundaries…Groundbreaking ideas, innovative research, development and use of measurement and methodological tech., high-risk, high-gain.

*Challenges – closest is: Impact of K-12 STEM influenced by local and global resources and systems. ?

SB: Effect would want from this work (some, at least): new kinds of science here, combination of math and biology, science becoming more and more important. This should eventually become part of the frameworks, because you'll never get there unless you have these kind of projects.

FE: Need to be much clearer about changes in science, with examples.

PH: First Frontier challenge not as useful, but still potentially interesting, because, this is aimed at need or minorities, etc. This is not the major focus of our project. Later, though, increase capacity for more students, etc. Specifically directed at deepening and expanding access to learning.

Low entry level for this: Addresses this question still

Expectations for what the teachers have covered: will come out of this with more genetics, be clear about the guidelines for this, mitosis, meiosis, basic structure of DNA, etc.  

DM: Idea of QTL analysis stayed in black box for a while, some pieces didnt' understand until I saw data; until I could see spreadsheet with 200 offspring and see data; this made sense to me then. After this, made sense to me in a way that I wasn't getting until later on. Postdoc came to her and asked her to explain to him/her.

PH: If it requires any hiccup on the part of the professional is a red flag for understanding for students. Note this.

RS: Phenotype/genotype diagram: where does observable phenomenon come from? Environment, genetics or personal history. Trying to isolate genetic component; this may vary from 0% to 100%. Some connect heavily to genetics, and those are the ones we want to look at.

PH: If need to show to expert learners more than once, it is tough. If/when we succeed, it's a big deal. 

RS: They see it, play with it for a day. Next week, give them new dataset, go off and play with it and do it. Students observing underlying a hidden thing, can use statistics to find where it is. Good at catching those concepts. Confident that we can present this in a way that this will come across. Getting base concepts in place before we throw mice at them. Finding QTL and then needing to look it up was difficult for students.

BN: Male vs. female differences with dragons or technology? PH: Don't see a systematic effect. Interesting to go and look. 

PV: Should look in field test, but probably only anecdotal evidence in pilot…

BN: Differences in learning in pilot, can show that we do/don't need to modify to see success for diff. genders.

PH: Evolution and genetics – religious or belief difficulty can be an issue.

Scaffolding inquiry techniques?

PH: Big idea for reviewer reading this in 10 min. on a plane? What should he/she remember? Big idea might be that through cyber-enabled tools, can introduce real science into the classroom. Connection between real science and real data. Around tools at some level, and how you use these tools. What do you do with the tools, given what you know. Inquiry becomes important – though we must assume genetics here. 

PV: Too much science as inquiry here not to make it explicit. Must be hit hard as a result. Also using tool as a tool of inquiry. What types of questions might ask, to develop compelling explanation based on evidence.

RS: Budget for phenotyping/genotyping, etc.

Collaboration not best place to focus resources right now: can still use this as argument in the proposal.

BN: Logging – if you can convince reviewer that there is a hint of problem solving. 

DM: Not just a directed inquiry, 

PH: Standard reviewer comment – just going to collect dribble file of every mouse click, etc. Not analyzed. Very important that we have a good example of something we did where the collected data showed that kids managed to learn.

Multiple parameter - change only one parameter at a time; much of time not useful