Sarantos Psycharis

Bio: Sarantos Psycharis is an academic researcher from School of Pedagogical and Technological Education. The author has contributed to research in topics: Computational thinking & Science education. The author has an hindex of 12, co-authored 30 publications receiving 356 citations. Previous affiliations of Sarantos Psycharis include Liverpool Hope University.

The effects of computer programming on high school students’ reasoning skills and mathematical self-efficacy and problem solving

Abstract: In this paper we investigate whether computer programming has an impact on high school student’s reasoning skills, problem solving and self-efficacy in Mathematics. The quasi-experimental design was adopted to implement the study. The sample of the research comprised 66 high school students separated into two groups, the experimental and the control group according to their educational orientation. The research findings indicate that there is a significant difference in the reasoning skills of students that participated in the “programming course” compared to students that did not. Moreover, the self-efficacy indicator of students that participated in the experimental group showed a significant difference from students in the control group. The results however, failed to support the hypothesis that computer programming significantly enhances student’s problem solving skills.

Moodle as a Learning Environment in Promoting Conceptual Understanding for Secondary School Students

Abstract: The purpose of this study was to investigate the role of e-learning, as a pedagogical tool, for changing initial conceptions when learning about physics by using the learning management System of the Moodle platform. Our study provides an empirical exploration of the pedagogical use of Moodle Learning Management System (LMS) in order to investigate a) the change of students’ conception of fundamental issues in electricity and b) their attitudes towards the use of this LMS system. Analysis of questionnaire data shows a slight improvement in students’ performance and this difference is associated with participants’ conceptual understanding. Students had strong attitudes towards blended learning but this was not reflected upon their intention to further use the LMS, as expressed in responses to the TAM’s questionnaire.

The management of Digital Learning Objects of Natural Sciences and Digital Experiment Simulation Tools by teachers

Abstract: In the present study, we tried to find possible obstacles that Primary and Secondary education teachers face when managing Digital Learning Objects (DLOs) and/or Digital Simulation Tools (DST) in Science. One hundred seventy-six teachers from all over Greece answered the questionnaire. The results showed that the main reason for refusing to deal with DLOs and DSTs is the technological equipment. Also, the lack of adequate training level B 'results in about 25% of teachers not knowing the DSTs and 30% not knowing the DLOs. Factors such as the teaching experience, the specialty, the Pan-Hellenic examinations, the classes they teach, and the number of students they have per class negatively affect the teachers' attitude to get involved with the DLOs the DSTs. Finally, the negative attitude seems to be related to the lack of trust in the curriculum content as teachers prefer to search DLOs and DSTs on the internet connection. Further research with mixed methods of analysis would help to obtain satisfactory results.

Inquiry Based-Computational Experiment, Acquisition of Threshold Concepts and Argumentation in Science and Mathematics Education.

Abstract: Introduction Inquiry based learning and modelling in science and mathematics education 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). Significant parts of scientific research are carried out on models rather than on the real phenomena because by studying a model we can discover features of and ascertain facts about the system the model stands for. This cognitive function of models has been widely recognized in the literature, and some researchers even suggest that models give rise to a new form of reasoning, the so-called "model based reasoning" (Magnani & Nersessian, 2002) while modelling ability is also associated to model-based reasoning (Chittleborough & Treagust, 2007). It is well known that scientific theories are developed through a process of continuous elaboration and modification in which scientific models are developed and transformed to account for new phenomena that are uncovered. Similar processes are involved in students' learning of STEM concepts when they develop conceptual models (e.g., Bell et al., 2010). In a similar fashion, inquiry based learning requires from students to make successive refinements to their mental models in order to transform them to conceptual models that align to scientific theories. 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, Paez, & 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. 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: * 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. …

The Impact of a STEM Inquiry Game Learning Scenario on Computational Thinking and Computer Self-confidence

Abstract: Computational thinking is an ability which is considered to be essential for the process of problem solving in every science. The current empirical research aims to study the impact of a STEM content Inquiry based scenario using computational tools and educational games, regarding computational thinking (CT) and confidence for “computers use” of 115 students of Greek public schools of the 5th-6th grade. For the needs of this research, a didactic scenario was developed and implemented, using computational tools, such as the Arduino microcontroller, RGB Led’s while a computational model was designed and implemented. The assessment of computational thinking improvement and confidence for computers use was conducted with the use of questionnaires that were administered before and after the intervention. The findings indicate a positive influence of the intervention on the dimensions of computational thinking in the experimental group. The findings can be applied to educational settings that integrate STEM in the teaching sequence in order to enhance students’ confidence with computational experiments.

Computational Thinking 計算論的思考

Abstract: It represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use.

Science, Technology, Engineering and Mathematics (STEM)

Abstract: Research indicates that there is likely to be an increasing demand for graduates qualified in STEM within the growing sectors of the Northern Ireland economy in the coming years. In addition, an Independent Review of Economic Policy in 2009 highlighted the important role of skills in raising productivity and competitiveness, and recommended that the local education system should make preparations to meet increased demand for STEM graduates.

Enhancing the quality of argument in school science: developing teachers' practice

Abstract: This article makes a case for why teaching about ‘ideas and evidence’ requires more attention t o the teaching of argument in school science. Based on research work at Kings’ College London conducted with local teachers, it outline s various practical methods and strategies b y which this might be achieved. The difficulties and obstacles are also explored. Why argument ? Contemporary science impinges directly upon man y aspects of people’s lives. Individuals and societies have to make personal and ethical decisions about a range of s ocio-scientific issues, such as genetic engineering, reproductive technologies and food safety, based on information available through the press and other media. Often accounts of ne w developments in s cience report contested claims. Evaluating such reports is not straightforward as it requires, for instance, the ability to assess whether the evidence is valid and reliable, to distinguis h correlations from causes or hypotheses from observations (Millar and Osborne, 1998). Within the context of a s ociety where scientific issues increasingly dominate the contemporary landscape (Beck, 1992; Giddens, 1999), there is an urgent need to improve the quality of y oung people’s understanding of the nature of scientific ‘argument’. Consequently, an important task for science education is to develop children’s ability to understand and practise valid ways of arguing in a s cientific context. They need to be able to recognise not onl y the strengths but also the limitations of s uc h arguments. In our work, then, we have sought to stud y whether the quality of y oung people’s ‘argument’ about scientific issues, and their critical capabilities, can be enhanced in science lessons. For instance, ca n the abilities to r eason, use and criticise argument within a scientific context be taught? And, perhaps more importantly, can these abilities be improved? This is what we are attempting to do in our project ‘Enhancing the Quality of Argument in School Science’ (EQuASS), funded by the Economic and Social Science Research Council. First, it is important to point out that by ‘argument’ we do not mean the pejorative use of the word wit h its confrontational connotations. We mean the putting forward of r easons where claims are justified b y relating them to the dat a on which they are based. Evidence for any claim consists of at least tw o components – dat a and warrants. Warrants are essentially the means by which the data are related t o claims providing the justification for belief. Thus the claim that diversity of species is a product of random variation and selection by the environment was supported originally by Darwin’s data on the variety of finches’ beaks found in the Galapagos. The warrant was that each adaptation gave each species a competitive advantage that ensured their survival on a particular island. A s imple representation of a n argument is provided by Toulmin (1958) (Figure 1).