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Training in Experimental Design: Developing scalable and adaptive computer-based science instruction

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TED Training in Experimental Design: Developing scalable and adaptive computer-based science instruction Mari Strand Cary, David Klahr Stephanie Siler, Cressida ... – PowerPoint PPT presentation

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Title: Training in Experimental Design: Developing scalable and adaptive computer-based science instruction


1
Training in Experimental DesignDeveloping
scalable and adaptive computer-based science
instruction
TED
  • Mari Strand Cary, David Klahr
  • Stephanie Siler, Cressida Magaro, Junlei Li
  • Carnegie Mellon University University of
    Pittsburgh

2
Overview of the TED project
  • Curriculum Experimental design, evaluation, and
    interpretation
  • Age 5th-8th grade students
  • Schools 6 inner city
  • 4 low SES challenging classroom environments
  • 2 mid-high SES
  • End goal Computer-based adaptive tutor
  • 1 student 1 computer in classroom environment
  • Provides individualized, adaptive instruction
  • Supplements (does not replace!) teacher

3
What do we mean by Experimental design?
  • CVS Control of Variables Strategy
  • Simple procedure for designing unconfounded
    experiments
  • (Vary one thing at a time)
  • Conceptual basis for making valid inferences from
    data
  • (Isolating the causal path)

4
CVS and RampsTest whether the ramp surface
affects the distance that a ball travels.
Variable Ramp 1 Ramp 2 Ramp 2
Confounded Unconfounded
Surface Smooth Rough Rough
Track length Short Long Short
Height High Low High
Ball Golf Rubber Golf
5
Why do we need to teach CVS?
  • Core topic in science instruction
  • State standards
  • High stakes assessments
  • Science component of NCLB
  • Has real-world applications
  • Essential to evaluating product claims, and news
    reports
  • Students do not always learn CVS on their own
    (low SES students, in particular)

6
What do students do wrong?
  • Common errors
  • Vary everything
  • Hold target variable constant and vary other
    variables
  • Partially confounded
  • Nothing varied (identical)
  • Their justifications
  • I dont know
  • You told me to test x!
  • Describe their set-up
  • Want to see if x happens
  • Want to see if this setup is better than that
    setup

7
Why do they take these approaches?
  • By accident
  • misread question
  • working carelessly
  • Are led astray
  • by saliency of physical apparatus (e.g., ramps)
  • dont understand written representations (e.g.,
    tables)
  • On purpose
  • different goals (e.g., engineering)
  • misconception of experimental logic
  • think other variable(s) dont matter
  • Just guessing

8
Whats the best way to teach CVS?
  • As a society (educators, researchers, and
    legislators), we dont know
  • Our research team knows of one effective way

9
Our basic CVS instruction
  • Students design experiments
  • Students answer questions
  • Instructor provides explicit instruction about
    CVS
  • One domain
  • Short instructional period

10
Effective in the lab and in classrooms of high
SES and achievement levels
  • One-on-one Chen Klahr (1999) Klahr Nigam
    (2004), Strand Cary Klahr (in preparation)
  • Full class Toth, Klahr Chen (2000)
  • Physical and virtual materials Triona Klahr
    (2003)

11
Would it work for lower-achieving students in
low-SES schools?
12
Effective in low-achievement classrooms (Li,
Klahr Jabbour, 2006)
  • Raises item-scores above national norms
  • Enables students to catch up with untrained
    peers from high-SES schools
  • BUT, repeated and varied forms of instruction are
    required for generalized CVS understanding
  • Many days
  • Multiple domains

13
Thus, our starting point
  • Brief, focused CVS instruction is differentially
    efficient and effective for different student
    populations, settings, and transfer tasks.
  • We want to reach ALL students!
  • To improve our instruction for the entire student
    population, we must engage in modification
    individualization

14
A computer tutor could facilitate differentiated
instruction
  • Computer-based instruction
  • Individualized self-paced
  • Provides instruction, practice, and feedback
  • Teacher freed to provide coaching as needed

15
How are we building our tutor?
  • 4 development phases
  • Iterative design process

16
4 development phases
  • Information gathering
  • What are the novice models students hold and how
    can we address those?
  • Refining the basic instruction and going
    virtual
  • Building a computer tutor with a few paths
  • Building an adaptive computer tutor with a web
    of paths

17
  • An evolving CVS computer tutor

18
Our iterative design process
19
  • What are we learning from each version that will
    help us design the final, adaptive tutor?
  • VERSION 1 (Completed)
  • Database of student biases, misconceptions,
    errors areas of difficulty
  • Inventory of successful tutoring approaches
  • familiar domains
  • instruction in prerequisite skills
  • step-by-step approach
  • Student-friendly terminology, definitions, and
    phrasing
  • Requiring explicit articulation by student

20
  • What are we learning from each version that will
    help us design the final, adaptive tutor?
  • VERSION 2 (Ongoing)
  • Information regarding
  • classwide implementation of successful tutoring
    approaches
  • feasibility of multiple domains
  • effect of emphasizing domain-generality
  • interface usability
  • worksheet usability

21
  • What are we learning from each version that will
    help us design the final, adaptive tutor?
  • VERSION 3 (being developed)
  • Information regarding
  • individual tutor usability and pitfalls
  • comparative efficacy of set learning paths
  • efficacy of immediate computer feedback

22
The adaptive tutor will include
  • Pre-testing and ongoing monitoring of student
    knowledge
  • Self-paced instruction
  • Diverse topics matching students interests
  • An interactive and engaging interface
  • Teacher-controlled and/or computer-controlled
    levels of difficulty
  • Level of scaffolding, feedback, and help aligned
    with students needs
  • Computerized assessments
  • Logging capability

23
Beyond our classroom instruction
  • Where on the contextual / abstract continuum
    should this type of instruction be focused? When?
  • Single vs. multiple domains?
  • Static pictures vs. simulations vs. tabular
    representations
  • Best mix of explicit instruction, exploration,
    help, feedback, etc.

24
Questions? Comments?
Many thanks to the Institute of Education
Sciences for supporting our work
  • MariStrandCary_at_cmu.edu
  • Klahr_at_cmu.edu

25
(No Transcript)
26
  • V1 learning examples
  • VERSION 1
  • Database of student biases, misconceptions
    areas of difficulty
  • Inventory of successful tutoring approaches
  • familiar domains
  • instruction in prerequisite skills
  • step-by-step approach
  • Student-friendly terminology, definitions, and
    phrasing
  • Requiring explicit articulation of understanding
    and reasoning

Create best outcome or Most dramatic difference
Ignore the data or Biased by expectations
Pets, Sports drinks, Cars, Study habits, Running
races
Learn about all variables at once
Variable vs. Value Experiment Result vs.
Conclusion
Table format Remembering the target
variable Drawing conclusions based on the
experiment
Read carefully, Identify question, Identify
variables
Good vs. Fair vs. Informative vs. True Variable
something that can change
27
What IS an intelligent tutor?
  • Computer-based instructional system
  • Contains an artificial intelligence component
  • Encodes cognitive objectives of the instruction
  • Tracks students state of knowledge
  • Compares student performance to expert
    performance
  • Tailors multiple features of instruction to the
    student (Anderson, Boyle, Corbett, Lewis, 1990
    Anderson, Conrad, Corbett, 1989 Corbett
    Anderson, 1995 Greeno, 1976 Klahr Carver,
    1988).

28
Ramp apparatus
29
CVS and RampsA completely confounded test for
determining the effect of ramp surface on the
distance that a ball travels.
Variable Ramp 1 Ramp 2
Surface Smooth Rough
Track length Short Long
Height High Low
Ball Golf Rubber
30
Classroom CVS with urban 5th 6th graders
(Klahr, Li Jabbour, 2006)
31
Low training vs. high comparison group
Training group (5/6th grade, low achieving school)
Comparison group (5-8th grade, high achieving
school)
32
Every version
Stand-alone, detailed lesson plan with visual aids
Feedback
Assessments (formative and summative)
Asks students to explain, justify, and infer
Examples of exp. designs (good and bad)
Students designing experiments
33
Increasing complexity and adaptiveness
  • Physical apparatus ? Virtual simulations
  • Full class ? Full class individual computer
    use
  • Inflexible ? Individually-adaptive self-paced
  • One domain ? Multiple domains

34
Why SES differences?
  • Found them in our previous studies
  • Classroom environment
  • Reading comprehension
  • Experience with this type of thinking
    (expectations, appropriate challenge and/or
    scaffolding, amount of practice)

35
What if later versions are less effective than
earlier versions?
  • Stop the presses!
  • Look for obvious reasons
  • Examine lesson components individually
  • Consider what is missing

36
Prerequisites
  • Science mindset
  • Problem decomposition
  • Vocabulary!
  • Identify and understand question
  • Identify key variables
  • Notice and complete component steps
  • Analogical reasoning
  • Reading listening carefully

37
Procedures
  • Test one variable at a time
  • Make the values for the variable youre testing
    be DIFFERENT across groups.
  • Make the values for the variables youre not
    testing be the SAME across groups.

38
Concepts
  • You need to use different values for the variable
    youre testing in order to know what effect those
    different values have.
  • You need to use the same value for all the other
    variables (hold all the other variables constant
    control the other variables) so that they cant
    cause difference in the outcome.
  • If you use CVS, you can know that only the
    variable youre testing is causing the
    outcome/result/effect.
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