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									CP AND ICT




      Science Education and Psychological
   Structures during the Research/Modelling -
       based teaching of Energy Levels of
                   Hydrogen

                  Sarantos Psycharis
                      ASPETE
                  www.spsycharis.gr
                spsycharis@gmail.com
CP AND ICT


             Abstract



    This study examines the effect of a computational experiment for
    prospective primary school teachers using a computer simulation
    environment experiment for the domain of “Energy Levels of
    Hydrogen”.

    The aim of the article is to investigate the impact of the computational
    experiment to:
    a) the modelling construction/modelling indicators
    b) the shift to approach to learning
    c) the shift to psychological structures and correlation of these with

    a,b.
CP AND ICT

                   1. INTRODUCTION
                   The field of Science Education



         The field of science education research is concerned with
         the development of high-level skills like concept formation,
            modelling, problem solving, meta-cognitive skills and
                            scientific procedures.

             Behavioural skills and low-level cognitive skills (e.g. the
              ability to learn and repeat definitions and laws, apply
                     formulae are accorded a lower priority.
CP AND ICT

                1. The field of Science Education



        Science education is thus changing to give more attention
         to those higher cognitive skills which cannot be acquired
        by methods based principally on learning by repetition and a
                       transmission model of teaching.
        Research in science education requires an understanding
          not only of scientific knowledge, but also of the nature of
                           science and its methods.
        Research emphasizes the central role of models in putting
           together scientific theory and modelling in conducting
                     various forms of scientific inquiry
CP AND ICT           1. The field of Science Education

                            Definition of Inquiry
                  Inquiry can be defined as "the intentional process
                   of diagnosing problems, critiquing experiments,
                 distinguishing alternatives, planning investigations,
                                searching for information,
                                   constructing models,
             forming coherent arguments" …..(Linn, Davis, & Bell, 2004).
                                     Forms of Inquiry
                    1. inquiry as a description of methods and processes that
                                      scientists use;
                   2. inquiry as a set of cognitive abilities that students might
                                         develop;
                                           and
                     3. inquiry as a collection of teaching strategies that can
                  facilitate learning about scientific inquiry and developing the
                                         abilities of inquiry
CP AND ICT          1. The field of Science Education



                            During Inquiry:
             Learner engages in scientifically oriented questions.
             Learner gives priority to evidence in responding to
               questions.
             Learner formulates explanations from evidence.
             Learner connects explanations to scientific
               knowledge.
             Learner communicates and justifies explanations.
CP AND ICT
             1. The field of Science Education
                          Teaching science by inquiry



           The publication of the "Science Education Now: A
          renewed Pedagogy for the Future of Europe" report
         (Rocard, 2007) once again brought science as inquiry to
                      the top of educational goals.



         Inquiry based learning has been officially promoted as a
            pedagogy for improving science learning in many
                   countries (e.g., Bybee et al., 2008).
CP AND ICT
              1. The field of Science Education
                                 Teaching science by inquiry

         Inquiry learning has been classified as learning science as inquiry and
                               by inquiry (Tamir, 1985).

         Learning science as inquiry includes learning about the way in which
         the scientific endeavour progresses, and analyzing the inquiry process
         performed by others, sometimes using historical perspectives (Bybee,
                                  2000.; Schwab, 1962).

        Learning by inquiry, or learning “the abilities necessary to do scientific
            inquiry” (Bybee, 2000), involves the learner in raising research
          questions, generating a hypothesis, designing experiments to verify
             them, constructing and analyzing evidence-based arguments,
          recognizing alternative explanations, and communicating scientific
                               arguments (Tamir, 1985).

              Teaching science by inquiry, requires imparting not only scientific
             information but also the abilities to do inquiry and, more deeply, an
                      understanding of what scientific inquiry is about.
CP AND ICT
             1. The field of Science Education
                                          Main questions to be answered


                                          Main Questions
                                                                 What are the conceptions
              What approaches                                    commonly held by science
                                           How are students
              to teaching and                                    teachers about science and
                                           to be motivated?
              learning science are                               how it should be taught?
              to be preferred?


                                              How are teachers
              What is the nature of the       to adopt the
              professional knowledge          innovations that    What are the specific
              that contributes to the         are suggested to    contributions that ICT
              development of science          them?               can make to science
              teaching skills?                                    teaching and learning?
CP AND ICT
             2. Modelling in Science/Engineering
             Education
             2.1 Modelling Theory in Science/Science Education



            Modelling theory is originally a theory of science, a
         theory about scientific theory and practice that emerged
                       in the philosophy of science.

        The theory basically asserts that models are at the core of
         any scientific theory and that model construction and
              deployment are fundamental, if not the most
              fundamental, processes in scientific inquiry
CP AND ICT
               2. Modelling in Science/Engineering
               Education
               2.1 Modelling Theory in Science/Science Education

         According to Piet Lijnse(Models of / for Teaching Modeling-
                                    Girep 2006),
           the present focus on modelling is due to three main reasons.
         The first is the recent constructivist attention to conceptions that
         students bring to the classroom (experience the world in terms of
                        their mental models and modelling).

           A second is the present emphasis on the role of philosophy in
        science education, which has resulted in stressing the importance of
         attention for the nature of scientific knowledge and of scientific
                               models in particular.

         And thirdly, the present availability of computers that has greatly
           enhanced the possibilities for creating and testing numerical
                models, both in science and in science education.
CP AND ICT
               2. Modelling in Science/Engineering
               Education
               2.1 Modelling Theory in Science/Science Education

         According to Piet Lijnse(Models of / for Teaching Modeling-
                                    Girep 2006),
          the present focus on modelling is due to three main reasons.
         The first is the recent constructivist attention to conceptions that
         students bring to the classroom (experience the world in terms of
                        their mental models and modelling).

           A second is the present emphasis on the role of philosophy in
        science education, which has resulted in stressing the importance of
         attention for the nature of scientific knowledge and of scientific
                               models in particular.

         And thirdly, the present availability of computers that has greatly
           enhanced the possibilities for creating and testing numerical
                models, both in science and in science education.
CP AND ICT     2. Modelling in Science/Engineering
               Education
               2.2 Main approaches for Modelling in Science/Engineering

               Education

         There are two main approaches to Modeling Theory in Science
                                    Education.
                The first one is due to Schwarz and White (2005).
                             Schwarz and White (2005) :
         “There is evidence indicating that students may not understand the
          nature of models or the process of modelling even when they are
                   engaged in creating and revising models” and
                developed a teaching approach that should enable
           “students to create (computer) models that express their own
        theories, evaluate their models using criteria such as accuracy, and
         engage in discussions about models and the process of modelling”
            while they seem to focus on modelling as a means for the
                          learning of theory and models.
CP AND ICT     2. Modelling in Science/Engineering
               Education
               2.2 Main approaches for Modelling in Science/Engineering

               Education
                         Questions and to be answered:
      1. Can the teaching of models and about modelling be functionally
      integrated ?
      2. What can teaching for ‘learning to model’ if such a thing exists
      at all mean in practice? Is there something like a general
      ‘modelling competence’ and can this then be taught in a more
      explicit way than by just letting students go through some
      modelling experiences?
      3. In learning by expressive modelling students have to invent
      their own models. That is, they are in the first place supposed to
      express and test their own ideas about the world, but then the
      problem becomes how to shape those ideas into the concepts to be
      taught.
      4. In learning by explorative modelling students are in the first
      place discovering, exploring and testing a given model, but then
      the question is how to connect this properly to students’ ideas
      about the world.
      Or, in other words, do we lead students into the model, or the
      model into the students.
CP AND ICT    2. Modelling in Science/Engineering
              Education
              2.2 Main approaches for Modelling in Science/Engineering

              Education
         The second approach is due to Hestenes (1987, 2006-Girep
                              Conference).

           Hestenes (1987) : “The cognitive process of applying the
          design principles of a theory to produce a model of some
           physical object or process is called model development or
                                simply modeling” .
          Modelling takes place when applying an already known
        scientific theory (a system of design principles for modelling
                      real objects) to solve new problems.
         As a consequence, he also formulates a modelling strategy,
        as a specific problem solving strategy that, should be taught
         explicitly to students. Or, in other words, he focuses on the
                        theory applying role of modelling.
CP AND ICT   2. Modelling in Science/Engineering
             Education
             2.2 Main approaches for Modelling in Science/Engineering

             Education Hestenes’s approach
CP AND ICT
                3. ICT in Science Education.
                 ICT in Education




         The design of science curricula-in relation to ICT- should
          rely on central concepts of the discipline rather than on
                    technical short-term developments.
          Several authors have proposed lists of basic concepts or
         fundamental ideas of computer science to be included in
                             science education.

             The following central concepts are proposed as central
             concepts to be included in science education: problem,
             algorithm, process, communication, software, program,
                       computation, structure, and model.
CP AND ICT
                 3. ICT in Science Education.
                   Computational Science Education


                 Computer modelling is tied to computer simulation.

              A computer simulation is an implementation of a model that
             allows us to test the model under different conditions with the
                  objective of learning about the model's behaviour.

                             Computer modelling requires
                   (1) a description and an analysis of the problem,
              (2) the identification of the variables and the algorithms,
        (3) the implementation on a specific hardware-software platform,
         (4) the execution of the implementation and analysis of the results
                                           and
                           (5) refinement and generalization
CP AND ICT



             3. ICT in Science Education.
             Computational Science Education


                    Programming in Physics


    Redish and Wilson (1993) proposed an approach in physics teaching
    where students should be engaged in problem solving using
    programming languages. They considered that creation of
    programming could lead to more advanced code and thus to self-
    explained programming code, as well as to the algorithmic approach.
    Directly connected to that approach, is that proposed by Sloot (1994)
    as Computational Physics (CP).
CP AND ICT     3. ICT in Science Education.
                  Computational Science Education




      One of the crucial components of (CP) is the
    abstraction of a physical phenomenon to a
    conceptual model and its translation into a
    computational model that can be validated.
    This leads us to the notion of a computational
    experiment where the model and the computer take
    the place of the 'classical' experimental set-up, and
    where simulation replaces the experiment as such.
CP AND ICT
             3. ICT in Science Education.
               Computational Science Education
                       The Stages
CP AND ICT   3. ICT in Science Education.
              Computational Science Education
                 The Methodology of CP
                   (Landau et.al,2008)
CP AND ICT    4. ICT in Science Education.
                 Computational Science Education
               Computational Physics



    Tobochnik and Gould (2008) argue CP should be
    incorporated into the curriculum because it can
    elucidate the physics.
    Computation is both a language and a tool and in
    analogy to models expressed in mathematical
    statements, in CP models are expressed as
    algorithms, which in many cases are explicit
    implementations of mathematics.
                           4.
CP AND ICT        Modelling Indicators



      According to Hestenes (1999) model specification is
      composed by a model which describes 4 types of structure,
      each with internal and external components:
      1. systemic structure (specifies composition
      ,environment ,connections)
      2. geometric structure (specifies
      position ,configuration )
      3. temporal structure (specifies change in state
      variables , change by explicit functions of time
      interaction laws )
      4. interaction structure (interaction laws expressing
      interactions among causal links)
CP AND ICT   5. Psychological Structures and Science
             Education


        A science of teaching presupposes a
        science of thinking and the science of
        thinking is generally known as cognitive
        psychology (Hestenes, 1979).

        Cognitive psychology has much to offer to
        Science Education and Science education
        research is needed to link psychology with
        the sciences.
CP AND ICT     5. Psychological Structures and Science
               Education


           Two significant psychological structures related to science
              education are the self esteem and the locus of control.
        Rotter's (1966) locus of control formulation classified generalized
        beliefs concerning who or what influences things along a bipolar
                    dimension from internal to external control.

        A person is seen as having an external locus of control orientation
        when she/he perceives some line of causality between his/her own
              actions and eventual outcomes but not a controlling one.
            In other words, the outcome is made contingent upon some
        external agency, or is seen as under the control of powerful others,
           or as unpredictable due to the complexity of the surrounding
                                       events.
         On the other hand, an individual with an internal locus of control
        orientation believes that an action's outcome is directly contingent
                          upon his or her own behaviours.
CP AND ICT       5. Psychological Structures and Science
                 Education

             To test this theory, Levenson (1973) developed the multidimensional
                      Internal, Powerful Others, and Chance scale (IPC).


             The most broad and established definition of self esteem, within
          psychology, is Rosenberg’s (1965), who described it as a favourable or
                             unfavourable attitude toward the self.
        Implicit to that is that self-esteem is defined as general self-acceptance, self
                                 -regard, or valuing of oneself .

         Self-esteem refers to a person’s sense of his or her value or the extent to
             which a person values, approves of, prizes or likes him or herself.
        The self-esteem of an individual is a subjective quantity and therefore self-
          esteem is measured by self-report using questionnaires or interviews .
        The most widely used self-esteem scale is the Rosenberg Self-Esteem Scale
          first used by Rosenberg in 1965 to study the self-esteem of high school
                                students in New York State.
CP AND ICT
              6. Approach to Learning

     Literature suggests that approaches to learning
     might be a useful way of conceptualising different
     ways in which students experienced a learning
     context.
     In the literature three approaches to learning are
     identified:
     a conceptual approach, in which the intention is to
     understand concepts;
     a “mathematical” approach, in which the focus is on
     calculation methods; and
     an information-based approach, in which the
     intention is to gather and remember information.
CP AND ICT


                    7.METHODOLOGY OF RESEARCH



      Materials and Methodology

      Forty –eight prospective primary school teachers from the University of
      Aegean participated in the research as they studied the course
      « Didactics of Natural Sciences» (Academic Year 2008-2009) using an
      interactive computer simulation created from the PhET project at the
      University of Colorado(
      http://phet.colorado.edu/en/simulation/hydrogen-atom
      ).
      Students attended the Course « Didactics of Natural Sciences» and they
      had integrated a course in General Physics during the first year of their
      studies.
CP AND ICT


             7.METHODOLOGY OF RESEARCH
CP AND ICT
                   7.METHODOLOGY OF RESEARCH




     Research Questions
     The experiment was conducted to address the following questions:
     (1) Is Computational experiment related to approach to learning and the
     recognition/use of the Modelling Indicators?
     (2) Does the approach to learning change over the duration of the
     process due to the computational experiment?
     (3) Is Computational experiment related to psychological factors like
     locus of control and self-esteem?
CP AND ICT
                    7. METHODOLOGY OF RESEARCH


     Tools of Methodology
     (1). Approach to Learning questionnaire: approach to learning
     was measured with a questionnaire provided to all students
     before and after the experiment.
     The approach to learning was divided
     into conceptual approach-Grade 3
     mathematical approach-Grade 2
     and information based approach-Grade 1
      (2). Levensen and Rosenberg questionnaire to measure locus of
     control and self-esteem
     (3). Measurement of Modelling Indicators: for the Modelling
     indicators (systemic structure, geometric structure, temporal structure
     and interaction structure), 3 stands for the fully favourable part, 2 for the
     partially favourable part and 1 for the unfavourable part.
CP AND ICT          7.METHODOLOGY OF RESEARCH-
                       MODELLING INDICATORS


     “Modeling ability” ability was assessed in this study using 10 items each
     for 10 points. Each item consists of modelling descriptions (such as
     “what is the system and what are the internal and external parts?”,
     “what are the links between the various concepts/quantities?”, “is the
     measurement of the quantity independent of the reference system?”,
     etc.).
     The fully favourable part corresponds to recognition by the student of all
     the sub components of the Modelling Indicators (systemic structure,
     geometric structure, temporal structure and interaction structure.)
     Grade 3
     The partially favourable part corresponds to recognition of some of the
     indicators Grade 2 and the
     unfavourable part to failure to recognize even one of the Indicators
     Grade 1 .
      During the experiment two experienced teachers marked the use of

     modelling indicators and reported the results to the research team.
CP AND ICT              7.METHODOLOGY OF RESEARCH-
                       QUESTIONNAIRE FOR SELF -ESTEEM
                                ROSENBERG




     Self Esteem
     Questionnaire created by Rosenberg to measure the initial value of self-esteem
     Some questions in the questionnaire involved the following issues:
     increased self-confidence, feeling stronger, feeling empowered, liking
     themselves, trusting themselves, valuing themselves.
     Change on Individual Items of the self esteem (N=48).
     Items are scored 4, 3, 2 or 1, where a score of 4 represents the highest
     self-esteem and 1 represents the lowest self-esteem. For example for the
     item «I am a person of worth, at least on an equal basis with others» , 4
     stands for the answer Strongly Agree , 3 for Agree, 2 for Disagree and 1

     for Strongly Disagree
CP AND ICT             7.METHODOLOGY OF RESEARCH-
                      QUESTIONNAIRE FOR SELF -ESTEEM
                               ROSENBERG




     To fix the scale we divided the difference (40-10) over 4(result=7.5), and
     we started from 10 up to 40.
     We classified students in 4 categories. Category 1 for students with
     scores in the range(10,18), Category 2 students with scores in the range
     (19,26), Category 3 students with scores in the range (27,34) and
     Category 4 students with scores in the range (35,40).
CP AND ICT             7.METHODOLOGY OF RESEARCH-
                   QUESTIONNAIRE FOR LOCUS OF CONTROL




     There are three separate scales use to measure one’s locus of control: Internal
     Scale,
     Powerful Others Scale, and Chance Scale.
     There are eight items on each of the three scales, which are presented to the
     subject as one unified attitude scale of 24 items.
     To score each scale add up the points of the circled answers for the items
     appropriate for that scale. (The three scales are identified by the letters “I,”
     “P,” and “C”).
     Each subject receives three scores indicative of his or her locus of control on
     the three dimensions of I, P, and C.
CP AND ICT            7.METHODOLOGY OF RESEARCH-
                  QUESTIONNAIRE FOR LOCUS OF CONTROL




     Questionnaire created by Levenson to measure the value of locus of
     control.
     Sample questions used:
     Whether or not I get to be a leader depends mostly on my ability (I).
     I feel like what happens in my life is mostly determined by powerful people
     (P)
     To a great extent my life is controlled by accidental happenings (C).

               Scoring for the I, P, and C Scales:
               4 stands for the answer Strongly Agree ,
               3 for Agree, 2 for Disagree and
               1 for Strongly Disagree
CP AND ICT           7.METHODOLOGY OF RESEARCH-
                 QUESTIONNAIRE FOR LOCUS OF CONTROL




     For example item with maximum (I) should score 32, with minimum (P)
     score 8 and with minimum C score 8.
     We consider that an item with Internal scale should have good score at I
     and bad score at P,C.
CP AND ICT            7.METHODOLOGY OF RESEARCH-
                  QUESTIONNAIRE FOR LOCUS OF CONTROL




     For example item with maximum (I) should score 32, with minimum (P)
     score 8 and with minimum C score 8. We consider that an item with Internal
     scale should have good score at I and bad score at P,C.
     To fix the range with divided the difference (32-8) by 4 and we started from 8
     up to 32.
     We classified students in 4 categories. For example for Internal Scale (I),
     Category 1 includes students with scores in the range (8, 14 ), Category 2
     students with scores in the range (15,20), Category 3 students with scores in
     the range(21,26), and Category 4 for students with scores in the range (27,32).
CP AND ICT
                 8. RESULTS -MODELLING INDICATORS




                                                                    Std. Error
                                 Mean     N        Std. Deviation     Mean

                    Modelling    1.8333       48          .75324         .10872
                    Indicators
                    before

                    Modelling    2.3333       48          .75324         .10872
                    Indicators
                    after




     Modelling Indicators before and after the computational experiment.
            (Mbefore=1,833,SD=0,753, Mafter=2,333,SD=0,753,df=47,t=-5,310
            and since the 95% confidence interval is from -0,689 to -0,310,
            the difference is statistically significant at level p<0,001).
CP AND ICT
                           8. RESULTS –APPROACH TO LEARNING




                                                                    Std. Error
                                 Mean     N        Std. Deviation     Mean

                   Approach to   1.8333       48          .78098         .11272
                   learning
                   before
                   Approach to   2.3125       48          .71923         .10381
                   learning
                   after


      Approach to learning before and after the computational experiment.
             (Mbefore=1,833,SD=0,780, Mafter=2,312,SD=0,719,df=47,t=5,51
              and since the 95% confidence interval is from -0,677 to -0,280
             the difference is statistically significant at level p<0,001).
CP AND ICT
                                 8. RESULTS –SELF-ESTEEM




                                                              Std. Error
                          Mean      N        Std. Deviation     Mean
                 Self-    21.6667       48        6.60539          .95341
                 esteem
                 before

                 Self-    29.3333       48        5.96919          .86158
                 esteem
                 after



          Self-esteem before and after the computational experiment.
       (Mbefore=21,666,SD=6,605, Mafter=29,333,SD=5,969,df=47,t=-10,535
                    and since the 95% confidence interval is
                from -9,130 to -6,202 the difference is statistically
                          significant at level p<0,001).
CP AND ICT   8. RESULTS
             Self Esteem
CP AND ICT           8. RESULTS-LOCUS OF CONTROL
                              Internal Scale


       Internal Scale before and after the computational experiment.
              (Mbefore=18,9167,SD=6,548, Mafter=24,250,SD=5,544,df=47,t=-7,534
               and since the 95% confidence interval is from -6,757 to -3,909
               the difference is statistically significant at level p<0,001).


                                                                     Std. Error
                                 Mean      N        Std. Deviation     Mean

                      Locus      18.9167       48        6.54878          .94524
                      of
                      Control
                      before
                      -
                      Internal
                      Scale
                      Locus      24.2500       48        5.54479          .80032
                      of
                      Control
                      after -
                      Internal
                      Scale
CP AND ICT          8. RESULTS-LOCUS OF CONTROL-
                          Powerful Others Scale

   Powerful Others Scale before and after the computational experiment.
   (Mbefore=21,666SD=5,075, Mafter=14,458,SD=3,940,df=47,t=10,467
    and since the 95% confidence interval is from 5.82293 to 8.59374
   the difference is statistically significant at level p<0,001).




                                                                       Std. Error
                                   Mean      N        Std. Deviation     Mean

                        Locus Of   21.6667       48        5.07532          .73256
                        Control
                        before
                        (Powerf
                        ul
                        Others
                        Scale)
                        Locus Of   14.4583       48        3.94083          .56881
                        Control
                        after
                        (Powerf
                        ul
                        Others
                        Scale)
CP AND ICT         8. RESULTS-LOCUS OF CONTROL –
                             Chance Scale

      Powerful Others Scale before and after the computational
      experiment.
      (Mbefore=21,708 SD=5,086, Mafter=14,625,SD=3,895df=47,t=10,727
       and since the 95% confidence interval is from 5.75494 to 8.41173
      the difference is statistically significant at level p<0,001).


                                                                       Std. Error
                                   Mean      N        Std. Deviation     Mean

                        Locus Of   21.7083       48        5.08631          .73415
                        Control
                        before
                        (Chance
                        Scale)

                        Locus Of   14.6250       48        3.89558          .56228
                        Control
                        after
                        (Chance
                        Scale)
CP AND ICT
                        8. RESULTS-CORRELATIONS




     The correlation of Self -esteem (after the experiment) with the use of
     modelling indicators (after the experiment)
                                                                          Correlations

                                                                                           Modelling
                                                                                           indicators       Self-esteem
                                                                                            after the        after the
                                                                                          experiment        experiment

                                        Modelling             Pearson Correlation                       1          .382**
                                        indicators after
                                        the experiment        Sig. (2-tailed)                                       .007



                                                              N                                        48             48

                                        Self-esteem           Pearson Correlation               .382**                    1
                                        after the
                                        experiment            Sig. (2-tailed)                     .007



                                                              N                                        48             48

                                        **. Correlation is significant at the 0.01 level (2-tailed).
CP AND ICT
                       8. RESULTS-CORRELATIONS




     The correlation of approach to learning (after the experiment) with the
     use of modelling indicators (after the experiment) is
                                                                      Correlations

                                                                                      Modelling      Approach to
                                                                                      indicators    Learning after
                                                                                       after the         the
                                                                                     experiment      experiment

                                     Modelling           Pearson Correlation                    1           .628**
                                     indicators
                                     after the           Sig. (2-tailed)                                     .000
                                     experiment

                                                         N                                     48              48

                                     Approach to         Pearson Correlation               .628**                1
                                     Learning after
                                     the                 Sig. (2-tailed)                    .000
                                     experiment

                                                         N                                     48              48

                                     **. Correlation is significant at the 0.01 level (2-tailed).
CP AND ICT
                       8. RESULTS-CORRELATIONS




     The correlation of approach to learning (after the experiment) with the
     Self –esteem (after the experiment) is
                                                                      Correlations

                                                                                     Approach to
                                                                                    Learning after      Self-esteem
                                                                                         the             after the
                                                                                     experiment         experiment

                                    Approach to         Pearson Correlation                         1          .396**
                                    Learning after
                                    the                 Sig. (2-tailed)                                         .005
                                    experiment

                                                        N                                          48             48

                                    Self-esteem         Pearson Correlation                   .396**               1
                                    after the
                                    experiment          Sig. (2-tailed)                        .005



                                                        N                                          48             48

                                    **. Correlation is significant at the 0.01 level (2-tailed).
CP AND ICT
                       8. RESULTS-CORRELATIONS




     The correlation of Modelling Indicators with the Locus of Control (after
     the experiment) is
                                                                     Correlations

                                                                                      Modelling         Locus Of
                                                                                      indicators       Control after
                                                                                      after the            the
                                                                                      experiment       experiment

                                 Modelling                Pearson Correlation                     1            .357*
                                 indicators after the
                                 experiment               Sig. (2-tailed)                                       .013



                                                          N                                      48               48

                                 Locus Of Control         Pearson Correlation                  .357*               1
                                 after the
                                 experiment               Sig. (2-tailed)                      .013



                                                          N                                      48               48

                                 *. Correlation is significant at the 0.05 level (2-tailed).
CP AND ICT
             Thank you

								
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