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Assessment Research and Tools: when, why, how?. Diane Ebert-May Department of Plant Biology Michigan State University [email protected] http://first2.org. The trouble with our times is that the future is not what it used to be. -Paul Valery, The Art of Poetry.

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assessment research and tools when why how
Assessment Research and Tools:when, why, how?

Diane Ebert-May

Department of Plant Biology

Michigan State University

[email protected]

http://first2.org

slide2
The trouble with our times is that the future is not what it used to be.

-Paul Valery, The Art of Poetry

q1 what is scientific teaching
Active

is participation to learn (accomplish goals)

Assessment

is evidence

Diversity

is science for all...

Q1. What is scientific teaching?
q2 what is assessment1
Q2. What is assessment?
  • Data collection with the purpose of answering questions about…
  • students’ understanding
  • students’ attitudes
  • students’ skills
  • instructional design and implementation
  • curricular reform (at multiple grainsizes)
slide6
Q3. Why do assessment?

Improve student learning and development.

Provide students and faculty

substantive feedback about student understanding.

Challenge to use disciplinary research strategies to assess learning.

slide7
So what are the issues?

Claim: Faculty need to change their teaching.

Why: Data indicate students are not learning science/math.

Therefore: ... if faculty implement effective scientific teaching,

...data will show learning gains by all.

Faculty change?

true or false
True or False?

Assessing student learning in science parallels what scientists/do as researchers.

parallel ask questions
Description:

-What is happening?

Cause:

-Does ‘x’ (teaching strategy) affect ‘y’ (understanding)?

Process or mechanism:

-Why or how does ‘x’ cause ‘y’?

Parallel: ask questions
parallel collect data
We collect data to find out what our students know.

Data helps us understand student thinking about concepts and content.

We use data to guide decisions about course/curriculum/innovative instruction

Parallel: collect data
parallel analyze data
Parallel: analyze data
  • Quantitative data - statistical analysis
  • Qualitative data
    • break into manageable units and define coding categories
    • search for patterns, quantify
    • interpret and synthesize
  • Valid and repeatable measures
guidelines for thinking about research
What did students learn? (assessment data)

Why did students respond a particular way? (research) Significant question?

What are the working hypotheses? Relevant theory..

What has already been done? Literature says...

How and why select methods? Direct investigation...

How to analyze and interpret data?

What do the results mean? Coherent reasoning...

Are findings replicable and generalizable? Critique by peers...

Guidelines for thinking about research...
slide20
System

Model

ird team at msu
IRD Team at MSU

Janet Batzli - Plant Biology [U of Wisconsin]

Doug Luckie - Physiology

Scott Harrison - Microbiology (grad student)

Tammy Long - Plant Biology

Deb Linton - Plant Biology (postdoc)

Rett Weber - Plant Biology

Heejun Lim - Chemistry Education

Duncan Sibley - Geology

Rob Pennock - Philosophy

Charles Ofria - Engineering

Rich Lenski - Microbiolgy

*National Science Foundation

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