scholarly journals Special Session: Next Generation Problem Solving: Results To Date Models And Modeling Using Meas

2020 ◽  
Author(s):  
Larry Shuman ◽  
Mary Besterfield-Sacre ◽  
Brian Self ◽  
Ronald Miller ◽  
Tamara Moore ◽  
...  
10.28945/4327 ◽  
2019 ◽  

Aim/Purpose: Science is becoming a computational endeavor therefore Computational Thinking (CT) is gradually being accepted as a required skill for the 21st century science student. Students deserve relevant conceptual learning accessible through practical, constructionist approaches in cross-curricular applications therefore it is required for educators to define, practice and assess practical ways of introducing CT to science education starting from elementary school. Background: Computational Thinking is a set of problem-solving skills evolving from the computer science field. This work-in-progress research assesses the CT skills, along with science concepts, of students participating in a science program in school. The program pertains learning science by modeling and simulating real world phenomenon using an agent-based modeling practice. Methodology: This is an intervention research of a science program. It takes place as part of structured learning activities of 4th and 5th grade classes which are teacher-guided and are conducted in school. Both qualitative and quantitative evaluations are parts of the mixed methods research methodology using a variety of evaluation technique, including pretests and posttests, surveys, artifact-based interviews, in class observations and project evaluations. Contribution: CT is an emerging skill in learning science. It is requiring school systems to give increased attention for promoting students with the opportunity to engage in CT activities alongside with ways to promote a deeper understanding of science. Currently there is a lack of practical ways to do so and lack of methods to assess the results therefore it is an educational challenge. This paper presents a response to this challenge by proposing a practical program for school science courses and an assessment method. Findings: This is a research in progress which finding are based on a pilot study. The researches believe that findings may indicate improved degree of students' science understanding and problem-solving skills. Recommendations for Practitioners: Formulating computer simulations by students can have great potential on learning science with embedded CT skills. This approach could enable learners to see and interact with visualized representations of natural phenomena they create. Although most teachers do not learn about CT in their initial education, it is of paramount importance that such programs, as the one described in this research, will assist teachers with the opportunity to introduce CT into science studies. Recommendation for Researchers: Scientific simulation design in primary school is at its dawn. Future research investment and investigation should focus on assessment of aspects of the full Computational Thinking for Science taxonomy. In addition, to help teachers assess CT skills, new tools and criteria are required. Impact on Society: STEM related professions are lacking the man power required therefore the full potential of the economy of developed countries is not fulfilled. Having students acquire computational thinking skills through formal education may prepare the next generation of world class scientists and attract larger populations to these fields. Future Research: The inclusion of computational thinking as a core scientific practice in the Next Generation Science Standards is an important milestone, but there is still much work to do toward addressing the challenge of CT-Science education to grow a generation of technologically and scientifically savvy individuals. New comprehensive approaches are needed to cope with the complexity of cognitive processes related to CT.


Author(s):  
Lukas Baumanns ◽  
Benjamin Rott

AbstractThe aim of this study is to develop a descriptive phase model for problem-posing activities based on structured situations. For this purpose, 36 task-based interviews with pre-service primary and secondary mathematics teachers working in pairs who were given two structured problem-posing situations were conducted. Through an inductive-deductive category development, five types of activities (situation analysis, variation, generation, problem-solving, evaluation) were identified. These activities were coded in so-called episodes, allowing time-covering analyses of the observed processes. Recurring transitions between these episodes were observed, through which a descriptive phase model was derived. In addition, coding of the developed episode types was validated for its interrater agreement.


2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Rahmita Rahmita ◽  
Dadan Rosana

This study aims at revealing the effectiveness of the next generation science standards based 5E learning model by utilizing the local potential of environmental education centre Puntondo to enhance students’ data literacy and problem-solving abilities. This research can be categorized as an experimental study. The subjects of this study were the seventh grade students of State Junior High School 2 Takalar. Two classes were involved, totaling 64 students. The data collection technique was carried out by using the test instrument. The data analysis technique used the gain score test and t-test analysis. The results showed that the gain score for the two classes for both abilities was in the medium category, with a value indicating that the gain from the experimental class was greater than the control class. Meanwhile, the t-test value is Sig. (2.tailed) data literacy of 0.003 and problem solving of 0.008. The acquisition of the value of the two abilities shows that the Sig. (2.tailed) is less than 0.05. Based on the research results, it can be concluded that the NGSS-based 5E learning model by utilizing the local potential of EEC Puntondo is considered effective to enhance the data literacy and problem-solving abilities.


2011 ◽  
Vol 9 (4) ◽  
pp. 397-397
Author(s):  
J.K. Pringle ◽  
N.J. Cassidy ◽  
P. Styles ◽  
I.G. Stimpson ◽  
S.M. Toon

2010 ◽  
Vol 8 (6) ◽  
pp. 503-518 ◽  
Author(s):  
J.K. Pringle ◽  
N.J. Cassidy ◽  
P. Styles ◽  
I.G. Stimpson ◽  
S.M. Toon

Author(s):  
Arnetha F. Ball

In 1950, Erik Erickson introduced the concept of generativity in psychosocial development when referring to an individual’s desire to produce new knowledge that contributes to the guidance of the next generation. Nearly fifty years later, Epstein built on the term generativity in his research when referring to the generation of new or novel behavior in problem-solving. According to Epstein, generativity theory is a formal, predictive, empirically based theory of ongoing behavior in novel environments. Because it can be used to predict generative behavior and engineer new performances, it is also predictive of creativity and offers important contributions to the study of the transformative processes needed by teachers who desire to work effectively with students in culturally and linguistically complex classrooms. The evolution of theories of generativity can be traced from their use in studies of psychosocial development, to their use in studies of education, teacher education, and the preparation of teachers who work effectively in complex, 21st century classrooms. It should be noted that the theme that runs throughout the research literature on generativity over the last seventy years is a focus on using the term generativity theory to refer to a formal, predictive theory of creative behavior in individuals. When applied to education and the development of teachers to teach in culturally and linguistically complex classrooms, it is important to note that oftentimes teachers—many of whom have never worked with diverse student populations before—must develop the ability to translate their desire to teach into a conscious concern to serve the next generation—into a generative commitment to teach all students. They must make decisions to establish goals for generative behavior and then turn those decisions into generative actions and the creation of effective pedagogical solutions that meet the needs of their diverse students. One meaning of generative behavior is to generate things and people, to be creative, productive, and fruitful, to “give birth” to creative pedagogical problem-solving both figuratively and literally. The scholarship on generativity theory emphasizes the notion that generativity, unlike simple altruism or general prosocial behavior, involves the creation of a product or legacy. The qualities emphasized in generativity theory are the qualities needed by teachers who hope to be effective in their work with diverse populations. Generative behavior involves the conservation, restoration, preservation, cultivation, nurturance, or maintenance of that which is deemed worthy of such behavior, as in nurturing children and adapting traditions that link generations and assure continuity over time—through generative concern, action, and narration. Reflection is not enough. Rather, generative action that stems directly from teachers’ commitment, enhanced belief, and stimulated by concern, inner desire and cultural demand is needed. Generative action—which includes the behaviors of creating, maintaining, and offering to others—is the ultimate result of generativity. Narrations of generativity and the use of writing as a pedagogical tool for deep thinking are two means by which the complex relations among demand, desire, concern, belief, internalization, commitment, and action can be captured and analyzed.


2019 ◽  
Vol 23 (08) ◽  
pp. 1940004
Author(s):  
PAUL J. WOODFIELD ◽  
KENNETH HUSTED

We explore how knowledge sharing impacts innovation across generations of a family firm. We argue that each generation contributes to the knowledge pool differently, and there can be different levels of hostility towards sharing knowledge that can impact a family firm’s ability to innovate. We present two models distinguishing the source of knowledge from the receiver of knowledge for each generation. When the senior generation is the source of knowledge, business tends to be as per usual. Conversely, when the source of knowledge is the next generation, this can lead to new approaches to doing business being introduced, with potential for innovation activities and outcomes. We suggest that to minimise hoarding and rejection of knowledge, strategies need to be in place to avoid redundancy in the knowledge production and problem-solving processes.


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