Science Education by 5PNNCbZv

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									When even parody is misinformed, how then are we to recognize ...truth?
         Alternative Conceptions in Science
          and Science Teaching Efficacy:
       Remediating Pre-service Teachers’ Alternative Conceptions
 and their effect on Science Teaching Efficacy and Reflective Judgment.


The purpose of this study was to assess the effect of
a Science Methods course experience on pre-service
teachers’ personal science teaching efficacy.


Additionally, attitudes toward science and constructivist learning
environments were measured, and possible correlation of these
attributes to the Reflective Judgment model of King and
Kitchener (1990) were investigated.


                     ...
                                                                                                                      quantitative
    Statement of Hypotheses                                      <findings>                                           analysis



  Alternative Conceptions in Science:
  There are significant differences in the number of alternative science conceptions held between the
two primary cohort groups, both at the beginning of the study and at the end (Moscow class and Coeur
d’Alene class). <yes>
  There is a significant difference in the number of alternative conceptions in science among age-
ranked participants within and across the two cohort groups. <yes>

  Alternative Conceptions and Science Teaching Efficacy:
 There are significant differences among participants holding few (0 – 6) alternative science
conceptions and their cohorts with higher numbers of alternative conceptions ( >7) with respect to
personal science teaching efficacy. <yes>
  Remediating misconceptions in a constructivist, collaborative setting focused on National Science
Education Standards contributes to changing Science Teaching Efficacy. <yes>

  Alternative Conceptions, Science Teaching Efficacy and Reflective Judgment:
  There is significant correlation among measures of science “misconceptions”, teaching efficacy
attitudes, openness to constructivist learning environments and reflective judgment.<not entirely!>
  Those students scoring highest in Reflective Judgment are also those identified as most efficacious in
personal science teaching, having remedied a larger number of their own alternative conceptions in
science, and being more open to constructivist learning environments. <not entirely!>




It was hypothesized that a heavy emphasis on standards, constructivist approaches to teaching and learning, and the
process of dispelling common misconceptions would promote participants’ efficacy in science and science teaching as
they learned to de-construct currently held beliefs and reformulate more correct personal understandings.
Classic naive theories lead to “misconceptions”
  What causes the seasons?

  What causes the “phases” of the moon?

  What are “basic states” of matter?

  Can you see, eventually, in a totally dark room?


   “...what does a cloud weigh?”


 Research shows that learner alternative conceptions
 must be addressed and fully explored in order to set the
 stage for development of successful (defensible) interpretations.
 Piaget, 1926; Piaget, 1952; Piaget, 1954; Piaget, 1970; Dewey, 1933; Dewey, 1938, Rokeach, 1960, 1968;
 Abelson, 1979; Mead, 1982; Nisbett & Ross, 1980; Posner, Strike, Hewson, & Gertzog, 1982; Pajares,
 1992; Wandersee, Mintzes, and Novak,1994; Schoon and Boone, 1998.
                                                                               quantitative
  Instruments used in this study                                               analysis




Alternative Conceptions in Science survey (ACS),
         Schoon and Boone’s (1998).

Science Teaching Efficacy Belief Instrument (STEBI),
         Enochs and Riggs (1990).

Constructivist Learning Environments Survey (CLES),
         Taylor and Fraser’s (1998).

Reasoning about Current Issues Test (RCIT),
         King and Kitchener’s (1994).



Additional information: student demographic surveys (science background,
favorite subjects, and comfort levels in teaching various topics, as well as
gender, age and recent science experiences).
                                                                               quantitative
Self-Efficacy                                                                  analysis

The construct of self-efficacy was introduced by Bandura in 1977 in his

publication "Self-Efficacy: Toward a Unifying Theory of Behavioral Change."

Self-efficacy is a measure of beliefs that are judgements about how well one
can organize and execute courses of action required to deal with prospective

situations (Bandura, 1981).



                                   Self-efficacy in science and
                                   science teaching can be measured
                                   reliably by Enochs and Riggs’
                                   (1990) Science Teaching Efficacy
                                   Belief Instrument (STEBI).
                                   The STEBI is a Likert-Scale
                                   instrument used in the pre- and
                                   post-testing of preservice teachers’
                                   science and science teaching self-
                                   efficacy.
Constructivist Learning Environments

       Constructivism:
A theory about the nature of reality and
how people understand the world around them.

What learners hold to be “true” will be based on
what mentally works for them, what makes
sense within their conceptual framework.

 “Constructed” new knowledge builds upon prior knowledge,
 intuition, reasoned experience and collaborative idea making.

 (von Glaserfeld, 1989, and Lorsback and Tobin,1992, among others)
Models of Epistemic Development
Models of epistemological development are necessarily arbitrary and at best
provide only silhouettes of thinking and behavior. Their value lies in structuring
our understanding of cognitive development, and guiding research by simplifying the
process of identifying criteria for study and isolating aspects of interest to the
researcher.

Models are commonly structured as a series of incremental stages in which
an individual’s beliefs about the nature and justification of knowledge grow
increasingly more sophisticated.

Piaget (1920s - 1970s) Genetic Epistemology
Intellectual Development Stages:
Sensorimotor, Preoperational, Concrete Operational, Formal Operational

Cognitive Development Stages:
Maturation, Experience, Interaction, Equilibrium.

Schema: assimilation, accretion; accommodation, tuning, discrimination,
restructuring, creation.
Models of Epistemic Development continued…

Perry (1968) Intellectual & Moral Development Model
9 stage developmental scale:
Polar view: we-right v. others-wrong
Diversity of opinion, uncertainty considered with further research.
Diversity and uncertainty as legitimate but alternative viewpoints temporary.
Diversity and uncertainty as legitimate, alternative viewpoints: forcing choice of
   status quo thinking or progression to contextualized relativistic reasoning.
All knowledge and values as contextual and relativistic.
Commitment to relativism: choice made, qualified, contextualized.
(3 additional stages) Accepting implications of commitment.


Belenky, Clinchy, Goldberger, & Tarule (1960-70s) Women’s Ways of Knowing
5 Categories:
Silence
Received Knowledge
Subjective Knowledge
Procedural Knowledge
Constructed Knowledge

Marcia Baxter-Magolda (1992) Epistemological Reflection Model
4 stages:
Absolute Knowing
Transitional Knowing
Independent Knowing
Contextual Knowing
Models of Epistemic Development continued…

King & Kitchener’s Reflective Judgment Model
Beginning in the early 1970s Patricia King and Karen Kitchener sought exclusively an
epistemic (not moral) developmental characterization in cognition. Their Reflective Judgment
(RJ) model is based upon how knowers perceive and solve ill-structured problems, those
questions whose answers depend on a contextual perspective.

The RJ model stages describe a progression in sophistication of views of knowledge, origins of
knowledge, and certainty of knowledge, as well as concepts of justification. The basic
Reflective Judgment model categories are (in order of increasing sophistication) include:

Pre-reflective Thinking, Quasi-reflective Thinking, and Reflective Thinking.
                                Re flectiv e Judgment Model S ta ges


    RJM Stages              View o f Kno wledg e               Concept of Justification
                            "truth isÉ"                        "I know because, what I believ e isÉ"


    Pre-Reflec tiv e        Absolutely certain or              Authority based (known).
             (1 Ğ 3)          temporarily unc ertain.          Opinion based (unknown).

    Quasi-Reflec tiv e      Alway s unc ertain.                Contextual, observ er-
            (4 Ğ 5)                                              dependent justif ication.

    Reflec tiv e            Outc ome of proc es s,             Weight of the ev idence,
               (6 Ğ 7)       represented as the                 of ev aluation, weak nes s of
                             mos t c omplete,                   alternativ es,v alue of interpretations.
                              plausible modelbased on
                              the c urrent ev idenc e.
                                                            After Carr, 1998 (adapted from King & Kitchener, 1994)
Critical Reflective Thinking & the Reflective Judgment Model

Dewey (1938) stressed that in teaching problem solving, students “must engage in
reflective thinking to evaluate the potential solutions to problems in light of existing
information... Further, Dewey said, “uncertainty is a characteristic of the search for
knowledge”… it is a process of constructing ever more plausible solutions to ill-structured
challenges.

Bandura (1986) considered self-reflection the most uniquely human capability, for
through this form of self-referent thought people evaluate and alter their own thinking and
behavior.

King and Kitchener (1990) suggest that critical thinking and reflective thinking
during science inquiry hinge upon recognizing the structure of problems and potential
solutions. Well-structured problems including puzzles and mental exercises, have
complete and definite solutions, ... Ill-structured problems have low degrees of
completeness, low certainty in solutions proposed, require alternative scenario thinking,
are context-sensitive, and “best solutions” are identified through a process of evaluating
logical strengths to arguments, evidence, and application.
                                                                             quantitative
Analysis and Results                  (quantitative research results)        analysis



From an analysis of pre- and post-treatment survey
results, it was found that the two sections of the
Science Methods class differed significantly in the
degree to which students were able to remediate
common science misconceptions, and also the
extents to which their personal science teaching
efficacy changed as a result of the course experience.

The primary analytical procedures employed were
Multiple Regression and Analysis of Variance.



Caveats are mentioned throughout the descriptions of the results and
interpretations made therefrom, the most serious of which are the
limitations imposed on interpretation and generalizability of findings due
to small sample size.
                                                    quantitative
Classic naive theories & alternative conceptions:   analysis


(Schoon & Boone, 1998)
                                                                                               quantitative
Alternative Conceptions: pre- and post-treatment                                               analysis




 t-tests of independence were run on Alternative Conceptions in
 Science in order to evaluate the significance of differences in
 pre- and post-treatment scores for the entire sample.
     Test of independence for pre- and post-treatment on Alternative Conceptions in Science.
     t-tests for Paired Samples ACS

     2-tail
     Variable     Mean         SD            SE of Mean # pairs         Corr           Sig
     ACS_POS      .7297        .157          .021       58              .605          .000
     ACS_PRE      .5990        .124          .016

     Paired Differences
       Mean      SD SE of Mean t-value         df   2-tail Sig
     .1307     .128 .017       7.77            57     .000 95% CI (.097, .164)
Hypothesis 1b: There is a significant difference in the number of alternative                                         quantitative
                                                                                                                      analysis
conceptions in science among age-ranked participants within and across the two
cohort groups.


Multiple Regression of ACS with SCK, ST, CLES-rel, CLES-un, AgeRank, and Scibk college and
highschool science background as independent variables.

      From coefficients for the independent variables listed in column B in the table below. The
      estimated regression equation can be written as follows:
           Y = 0.421 +0.3100xSCK + 0.043xAgeRank + 0.006xSciBk(coll) + 0.004xSciBk(hsch) -
           0.003xCLESper/rel – 0.040xCLESunc –0.049xST

            Regression Coefficients for ACS
            Variable B     SE B 95% Confdnce Intrvl B Beta   T Sig T
            CONTENT .310015 .116917 .075179 .544850 .358258      2.652 .0107
            STUDTYPE -.049322 .069441 -.188799 .090156 -.185696    -.710 .4808
            CLSRELPR -.003936 .022634 -.049398 .041526 -.022798   -.174 .8626
            CLSUNPR -.040121 .020748 -.081795 .001553 -.253329 -1.934 .0588
            AGERANK .043661 .024562 -.005673 .092994 .485826      1.778 .0816
            COLSCIBK .006921 .011073 -.015320 .029161 .079865     .625 .5348
            HSSCIBK .004662 .006573 -.008539 .017864 .093964     .709 .4814
            (Constant .421206 .128238 .163632 .678781       3.285 .0019

      From the coefficient for SCK (Science Content Knowledge) shows the highest positive value and indicates that:
       ACS scores increase by 0.310 for every change of 1 in the value of the SCK score.

  The ANOVA indicates a significant main effect.
  Analysis of Variance for ACS.
            DF      Sum of Squares    Mean Square       F            Sig F
  Regression      7         .24800      .03543          2.82186      .0147
  Residual     50          .62774      .01255
Alternative Conceptions and Science Teaching Efficacy Hypotheses                                      continued...   quantitative
                                                                                                                     analysis



Hypothesis 2b: Remediating misconceptions in a constructivist, collaborative
setting focused on National Science Education Standards contributes to
changing Science Teaching Efficacy.

Personal Science Teaching Efficacy Analysis (PSTE: dependent variable)
Independent variables= ACS, CLES-rel, CLES-un, ATS, SCK, AgeRank, Gender, and ST.

The estimated regression equation can be written as follows:
             Y = 0.594 + 1.190xSCK + 0.829xACS + 0.276xCLESper + 0.259xATS + 0.151xCLESun
             + 0.150xAgeRank –0.205xGender – 0.231xST

Regression Coefficients
Variable      B SE B % Confdnce Intrvl B Beta T Sig T
CLESper/rel .276626 .104768 .066087 .487164 .318621 2.640 .0111
CLESuncert .151548 .085132 -.019530 .322627 .215799 1.780 .0813
ATS        .259403 .230166 -.203134 .721940 .147090 1.127 .2652
SCK        1.190473 .519989 .145517 2.235429 .276101 2.289 .0264
Gender -.205062 .192321 -.591546 .181422 -.139668 -1.066 .2915
ST       -.231255 .288475 -.810967 .348458 -.174739 -.802 .4266
AgeRank .150034 .098721 -.048353 .348421 .335055 1.520 .1350
ACS         .829949 .458676 -.091795 1.751694 .210361 1.809 .0765
(Constant) .594539 .897827 -1.209711 2.398789        .662 .5109



PSTE scores increase by 1.190 for every change of 1 in the value of SCK.
Other coefficients show relationships that indicate far less linear relationship: ACS (coeff= 0.829), CLESper/rel
(coeff=0.276), Attitude (coeff= 0.259), CLES science uncertainty (coeff=0.151), and AgeRank (coeff=0.150). Two of
the independent variables show an inverse linear relation: coeff= -0.205 for Gender, and coeff= -0.231 for Student
Type (ST).
Reflective Judgment results...   quantitative
                                 analysis
Reflective Judgment results...                                                                  quantitative
                                                                                                analysis




 <outliers: participants in prior “constructivist” and reflective Integrated Science course.>
“Optimizing the Instructional Moment”
                                                       qualitative
                                                       analysis



 In this “methods space” the teacher - student
 relationship can be envisioned as a flexible link
 connecting expert and learner occupants that are in
 constant motion (tension) drifting around in the
 medium of inquiry.




 “Every learner in every learning environment
 interfaces with the environment at his sensory
 receptors”. (Keegan, M., 1993).
                                                         qualitative
 Qualitative Research Results                            analysis



The inverse relationship between teacher and
learner volition (willful intent) and activity through
the four methods. The trends are curved with
increasingly dramatic relative volition from stage to
stage across the methods spectrum..




After Keegan (1993)
qualitative
analysis
                                                        qualitative
Are presevice teachers ready for autonomous learning?   analysis




    [student reflection: March, 1998]

    This class is a waste of time, I haven’t been
    taught anything! I’ve had to learn everything
    myself.
    [student reflection: March 11, 1998]

    I don't rate myself very highly using this
    method.... I've had to rely more on my
    personal motivation for learning and pushing
    myself to get assignments done on my own
    time. It's been a difficult adjustment. I'm used
    to having the entire semester laid out in
    simple terms where you only have 3 or 4 tests
    and maybe a paper. This class is definently
    different!
                                                      qualitative
[student reflection: February 20, 1998]               analysis


I've never had a class like this so it's hard to
get used to. It seems like each instructor
gives a different answer to our questions. The
class is very chaotic and I get frusterated with
not knowing exactly what's going on. I really
like structer (sp)! I learn better when I feel like
I know whats going on.


I liked the deforestation-sphere activity
looking things up. I learned a lot about
deforestation and its effects on the
environment.
[student reflections....]                                                qualitative
                                                                         analysis
I think much differently about science in general than I did before
taking this course. This course has really provided me the insight
that I don't need to know everything about science to teach it
effectively. I feel that my confidence level alone is much higher
after taking this course, not to mention how I think that science is
effectively taught. For me, science was always a lecture, do some
random activity, fill out a worksheet, and then turn it in. These
types of activities always left me thinking that if I got a bad grade,
I'd obviously gotten the wrong answer, so I began to feel that I
wasn't good at science because I didn't have the right answer.


Now I think science isn't about right or wrong answers, but the
process you took to get there. It isn't about doing random
experiments. I like the idea of having students come up with their
own experiment for something. The more students are allowed to
explore their processes of finding information out for themselves,
the more independent that student will be. The teacher won't be
such a needed figure in my classroom to tell the students exactly
what they should do. The students will figure that out for
themselves, with me in the process to facilitate their learning,
should they need it.

								
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