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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