The Epistemological Content of the Msc 

 

Over the past decade, increasing importance and attention has been attached to the potential of new technologies of information and communication (ICT) to improve teaching and learning in schools as well as helping towards the development of new jobs enhancing social cohesion.

 

STEM is a curriculum based on the idea of educating students in four specific disciplines — science, technology, engineering and mathematics — in a trans-disciplinary and applied approach. Rather than teach the four disciplines as separate and discrete subjects, STEM integrates them into a cohesive learning paradigm based on real-world-authentic  situations integrated with cotemporary teaching and learning approaches.

STEM is based on the Computational Science approach, the Computational experiment and the computational thinking. STEM in Education attempts to combine them with contemporary learning theories, new curricula and new forms of assessment in the classroom.

 

In the Master program we combine STEM with the Inquiry Based teaching and learning approach.

 

Inquiry’ is referred to in the science education literature to designate at least three distinct but interlinked categories of activity: what scientists do (investigating scientific phenomena by using scientific methods in order to explain aspects of the physical world); how students learn (by pursuing scientific questions and engaging in scientific experiments via emulating the practices and processes that scientists use); and, a pedagogy, or teaching strategy, adopted by science teachers (designing and facilitating learning activities that allow students to observe, experiment and review what is known in light of evidence).

 

Inquiry based learning has officially been  promoted as a pedagogy for improving STEM learning in many countries (Bybee et.al,  2008) and can be defined as "the deliberate process of diagnosing problems, planning experiments, distinguishing alternatives, setting up investigations, researching conjectures, sharing information, constructing models, forming coherent arguments, collecting and analyzing data" (Bell et.al,  2004).

 

Establishing inquiry-based learning environments with the use of STEM seems to be a pervasive theme in STEM Didactics and pedagogy. Using computational-based inquiry learning environments involves high level of organisation, structuring and planning in terms of allowing personalised support and varying degrees of assistance.

 

Scholz (2010) relates trans-dsciplinarity to STEM for solving messy problems whose solution is a societal challenge. This requires opening up STEM education by going beyond science through fusing methods and concepts from different disciplines. Marshall (2014)[i] associates transdiscplinarity with art/design integration’s capacity to foster conceptual skills and metacognition for moving into the core of STEM. The challenge here is to perceive transidsciplinarity as connective, implying deeper connections and correlation with varying levels of integration of disciplinary concepts, theories, methods and findings in which STEM as a discipline remains discrete.

 

 

Scholz (2010) relates trans-dsciplinarity to STEM for solving messy problems whose solution is a societal challenge. This requires opening up STEM education by going beyond science through fusing methods and concepts from different disciplines. Marshall (2014)[i] associates transdiscplinarity with art/design integration’s capacity to foster conceptual skills and metacognition for moving into the core of STEM. The challenge here is to perceive transidsciplinarity as connective, implying deeper connections and correlation with varying levels of integration of disciplinary concepts, theories, methods and findings in which STEM as a discipline remains discrete.

What Is Transdisciplinary Research?

Transdisciplinary research is, essentially, team science. In a transdisciplinary research endeavor, scientists contribute their unique expertise but work entirely outside their own discipline. They strive to understand the complexities of the whole project, rather than one part of it. Transdisciplinary research allows investigators to transcend their own disciplines to inform one another`s work, capture complexity, and create new intellectual spaces.

 

What`s The Difference?

  Transdisciplinary Research

  Multidisciplinary Research    Interdisciplinary Research 

 Collaboration in which exchanging information,altering discipline-specific approaches, sharing resources and integrating disciplines achieves a common scientific goal (Rosenfield 1992).

 Researchers from a variety of disciplines work together at some point during a project, but have separate questions, separate conclusions, and disseminate in different journals.  Researchers interact with the goal of transferring knowledge from one discipline to another. Allows researchers to inform each other's work and compare individual findings.
 
Accordingly, transdisciplinary research is defined as research efforts conducted by investigators from different disciplines working jointly to create new conceptual, theoretical, methodological, and translational innovations that integrate and move beyond discipline-specific approaches to address a common problem.
 
 
 
1. The Computational Experiment-CE
 
Computational Science can be considered as a third independent scientific methodology (the other two are the theoretical science and the experimental science), has arisen over the last twenty years and shares  characteristics with both theory and experiment, while it demands  interdisciplinary skills in Science, Mathematics, Engineering and Computer Science. According to (Landau, Páez, & Bordeianu, 2008) Computational Science focuses on the form of a problem to solve, with the components that compose the solution separated according to the scientific problem–solving paradigm: a. Problem (from science), b. Modelling (Mathematical relations between selected entities and variables), c. Simulation Method (time dependence of the state variables,  discrete, continuous or stochastic processes like e.g. Monte Carlo simulation), d. Development of the algorithm based on numerical analysis methods, e. Implementation of the algorithm (using Java, Mathematica,  Fortran etc) and f. Assessment and Visualization through exploration of the results and comparison with real data in authentic phenomena. In this framework, being able to transform a theory into an algorithm, requires significant theoretical insight, concrete Physical and Mathematical understanding as well as a mastery of the art of programming.
 
The Epistemology of the Misc 01 

In order to describe inquiry based learning as a search process, (Klahr & Dunbar, 1998) introduced two spaces, the hypothesis and the experimental spaces. (Psycharis, 2013) added one more space, the prediction space, in order to address the introduction of modelling and the comparison of data produced by the model with real data. In the “prediction space” the  Computational Science methodology is implemented through the development of models of simulations that favor the so called “Computational Thinking -(CT)”. According to (Psycharis,2013), the three spaces of the Computational Science methodology should include issues form (CT), namely: a)logically organizing and analyzing data, b) representation of data  through abstractions such as models and simulations and  c) algorithmic thinking.

In this context, the three spaces of the Computational Science methodology include: 

    1. The hypotheses space, where the students, in cooperation with the teacher, decide, clarify and state the hypotheses of the problem to be studied, as well as the variables included in the problem and the possible relations between the variables.

    2. The experimental space, which includes the model and the method of simulation for the problems under study. In this space the learners are engaged in the scientific method writing models according to the variables they included and the interaction laws that govern the phenomenon. In this space students collect the data from their model and analyze them, while they try to connect them with the theory they have been taught,

    3. The prediction space, where the results, conclusions or solutions formulated in the experimental space, are checked through the analytical (Mathematical) solution as well as with data from the real world.
 
Bell et al. (2010), identified nine main science inquiry processes supported by different computational environments that could be used in an integrated –Inquiry based  and STEM approach-, namely: orienting and asking questions; generating hypotheses; planning; investigating; analyzing and interpreting; exploring and creating models; evaluating and concluding; communicating; predicting. The nine inquiry tools of (Bell et.al, 2010) are closely related to the essential features of inquiry based learning (Asay & Orgill, 2010), namely: Question (the learner engages in scientifically oriented questions), Evidence (the learner gives priority to evidence), Analysis (the learner analyses evidence), Explain (the learner formulates explanations from evidence), Connect (the learner connects explanations to scientific knowledge and Communicate (the learner communicates and justifies explanations). Our learning and teaching approach integrates the inquiry based approach and (CE) through the relation  of the (CE) spaces, namely the hypotheses space, the experimental space and the prediction space with the essential features of inquiry, the Computational Thinking(CT) features  and the inquiry tools of  (Bell et al., 2010).
 
In Table  below, we present the interrelation between the spaces of the (CE) and the features and the tools of inquiry.
 
Table. The interrelation of the CE spaces, the inquiry features and the inquiry tools
Spaces of the (CE) (Psycharis, 2013) Essential Features of Inquiry      Inquiry tools   
Hypotheses  space Question Orienting and asking questions; generating hypotheses
Experimental space

Evidence

Analyze

Explain

Planning-Investigating

Analysis and interpretation

Modelling

Prediction Space

Connect

Communicate

Conclusion-Evaluation-Prediction
 
 
Computational experiment approach considers models as the fundamental instructional units of Inquiry Based Science and Mathematics Education (IBSE) and STEM Education ,where the model take the place of the “classical” experimental set-up and simulation replaces the experiment.
 
 
2. The computational Thinking
 

The International Society for Technology in Education (ISTE) and the Computer Science Teachers Association (CSTA) have collaborated with leaders from higher education, industry, and K–12 education to develop an operational definition of computational thinking. The operational definition provides a framework and vocabulary for computational thinking that will resonate with all K–12 educators. ISTE and CSTA gathered feedback by survey from nearly 700 computer science teachers, researchers, and practitioners who indicated overwhelming support for the operational definition.

Computational thinking (CT) is a problem-solving process that includes (but is not limited to) the following characteristics: 

• Formulating problems in a way that enables us to use a computer and other tools to help solve them.

• Logically organizing and analyzing data

• Representing data through abstractions such as models and simulations

• Automating solutions through algorithmic thinking (a series of ordered steps)

• Identifying, analyzing, and implementing possible solutions with the goal of achieving the most efficient and effective combination of steps and resources

• Generalizing and transferring this problem solving process to a wide variety of problems

These skills are supported and enhanced by a number of dispositions or attitudes that are essential dimensions of CT. These dispositions or attitudes include:

• Confidence in dealing with complexity

• Persistence in working with difficult problems

• Tolerance for ambiguity

• The ability to deal with open ended problems

• The ability to communicate and work with others to achieve a common goal or solution

 

3. Teachers Training and STEM –STEM in Schools

 

According to Rodger W. Bybee-J Sci Teacher Educ (2014) 25:211–221
……….there must be  specific educational shifts—interconnecting science and engineering practices, disciplinary core ideas, crosscutting concepts;
recognizing learning progressions; including engineering; addressing the nature of science,….
NGSS standards have three dimensions: (1) disciplinary core ideas, (2) scientific and engineering practices, and (3) crosscutting concepts.
Perhaps the most significant challenge is experiences with the integration of three dimensions as this suggests the need for investigations and simulations.

 

According to the same article …………

There is a strong need for “Include Engineering Design”
The Next Generation Science Standards (NGSS) includes both science and engineering. Science and engineering practices and crosscutting concepts are designed as an integral component of the standards.
Consistently, science teachers express a concern about their lack of understanding engineering, in particular the differences between scientific inquiry and engineering design.

 

The Epistemology of the Misc 02

Figure. The Next Generation Science Standards (NGSS)

 

 

In Figures below we present some aspect for inclusion of STEM in school education

 

 

The Epistemology of the Misc 03

Figure. STEM in School education

 

 

The Epistemology of the Misc 04

Figure. STEM in School education

 

The Epistemology of the Misc 05

Figure. STEM in School education

 

 

 

 

References

Asay, L. D., & Orgill, MK.  (2010). Analysis of essential features of inquiry found in articles published in The Science Teacher, 1998-2007. Journal of Science Teacher Education, 21, p. 57-79.

Bell, P. L., Hoadley, C., & Linn, M.C.(2004). Design-based research as educational inquiry. In M. C. Linn, E. A. Davis & P. L. Bell (Eds.), Internet environments for science education. Mahwah, NJ: Lawrence Erlbaum Associates.

Bell, T., Urhahne, D., Schanze, S., & Ploetzner, R.(2010). Collaborative inquiry learning: models, tools and challenges. International Journal of Science Education, 32(3), p. 349-377.

Bybee, RW.,Trowbridge, LW.,.& Powell, JC. (2008). Teaching Secondary School Science: Strategies for Developing Scientific Literacy. New Jersey Merrill.

Hestenes, D. (1999). The scientific method. American Journal of Physics, 67, p. 273-276.

Klahr, D., & Dunbar, K.  (1998). Dual space search during scientific reasoning. Cognitive Science, 12, p. 1-48.

Kuhn, D.(1993). Science as argument: Implications for teaching and learning scientific thinking. Science Education, 77(3), p. 319–337.

Landau, RH., Páez, J. & Bordeianu, C.(2008). A Survey of Computational Physics: Introductory Computational Science. Princeton and Oxford: Princeton University Press. 

Marshall, J.(2014):Transdisciplinarity and Art integration: Toward a new understanding of art-based learning across curriculum. Studies in Art Education. 55:2, 104-127

https://www.oecd.org/pisa/keyfindings/pisa-2012-results-overview.pdf

Psycharis, S. (2013). The effect of the computational models on learning performance, scientific reasoning, epistemic beliefs and argumentation. Computers & Education- Volume 68, October 2013, p. 253–265.

Psycharis, S.,Botsari,E.,Mantas, P.,&Loukeris, D.(2013),‘The impact of the Computational Inquiry Based Experiment on Metacognitive Experiences, Modelling Indicators and Learning Performance’. Computers & Education,CAE2501,PII:S0360-1315(13)00278-9, DOI: 10.1016/j.compedu.2013.10.001

Psycharis, S., Botsari,E.&Chatzarakis, G.(2014),‘Examining the Effects of Learning Styles, Epistemic Beliefs and the Computational Experiment Methodology on Learners’ Performance Using the Easy Java Simulator Tool in STEM Disciplines. Journal of Educational Computing Research’; 2014, Volume 51(1). http://jrnledcompresearch.com/index.php/jecr

Psycharis, S., (2015).‘Inquiry Based- Computational Experiment, Acquisition of Threshold Concepts and Argumentation in Science and Mathematics Education (Accepted for publication at Journal “Educational Technology & Society”-http://www.ifets.info/ets_journal/preprint.php).

 

Psycharis, S. (2016). ‘The Impact of Computational Experiment and Formative Assessment in Inquiry Based Teaching and Learning Approach in STEM Education ;  Journal of Science Education,25(2),316-326 and Technology (JOST) DOI 10.1007/s10956-015-9595-z 

http://icss2010.net/download/documents/24-JUNE/Sessione-5_Sustainability-science-education/5_R-W-Scholz-and-M-Stauffacher.pdf

Xie,C., Tinker, R., Tinker, B.,  Pallant, A., Damelin, D., & Berenfeld, B.( 2011). Computational Experiments for Science Education. Science, 332, p.1516-1517.