Assessment of modeling and simulation in secondary computing science education

Grgurina, Nataša; Barendsen, Erik; Suhre, Cor; Zwaneveld, Bert; Veen, Klaas van

doi:10.1145/3265757.3265764

Cited by 7 publications

(9 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, looking towards school training, these skills improve attitudes such as: confidence in dealing with complexity, persistence in working with difficult problems, tolerance for ambiguity, the ability to deal with open-ended problems, and the ability to work with others to achieve a common goal and communicate it. Which is complemented by Grgurina (2021) who describes computational thinking in terms of its main concepts, such as: data collection, data analysis, data representation, problem decomposition, abstraction, algorithms and procedures, automation, modeling, and simulation Belmar (2022).…”

Section: Computer Programming and Computational Thinkingmentioning

confidence: 99%

Expert Validation of a Python Test, Reliability, Difficulty and Discrimination Indices

Garrido

2023

JED

View full text Add to dashboard Cite

In recent years, different countries have implemented the teaching of computer programming from the first grade in schools, with the aim of incorporating computational thinking as a new way of thinking that is a necessary skill for scientific and technological development, a fundamental axis for development in the 21st century. Recent reviews of the state of the art have shown that this task is only being carried out by countries that have given space to technology and science, incorporating it at the elementary school level. However, developing countries do not yet consider the significance of the issue, so to date they have not taken the necessary steps in this direction. In addition, learning computer programming is fundamental for countries to join technological development, so that they can be creators of technology and not just users of it. The problem is that there is no didactic development for the teaching of programming, nor validated evaluation instruments to quantify the learning of computer programming. The objective of this research is to validate a programming test that evaluates technical skills in the Python programming language. The instrument proposed to be validated is a 90-item test, which after validation by experts was reduced to 70 items, and after the psychometric analysis that considered the calculation of reliability, difficulty and discrimination indexes, resulted in the proposed 45-item instrument, as a standard instrument for the evaluation of learning in the Python programming language. It should be noted that the validation methodology was carried out using the classical theory of psychometric analysis.

show abstract

Section: Computer Programming and Computational Thinkingmentioning

confidence: 99%

Expert Validation of a Python Test, Reliability, Difficulty and Discrimination Indices

Garrido

2023

JED

View full text Add to dashboard Cite

show abstract

“…Louca et al (2011) constructed a framework to analyze and evaluate subject related concepts and CT aspects of computational models of physics phenomena constructed by students. Grgurina et al (2018) provide a generic practical assignment and accompanying rubrics based on SOLO taxonomy (Biggs and Tang, 2011) to assess the development and use of agent-based models of phenomena from various disciplines within secondary education CS course. Finally, So (2018) reports assessing the projects made by primary teachers where they use microcontrollers for STEM experience; however no details of the assessment are provided.…”

Section: Assessment Strategiesmentioning

confidence: 99%

“…In one case, an observation of a group discussion was used to assess the gains in learning of subject matter (Leonard et al, 2015). To assess the gains in learning of subject matter, rubrics were used five times (Cakir and Guven, 2019;Chang, 2019;Grgurina et al, 2018;Hutchins et al, 2018;Terwilliger et al, 2019); and an automated CT assessment tool once to assess not only the gains in learning of subject matter, but program comprehension and CT skills as well (Q. Burke, 2012).…”

Section: Assessment Strategiesmentioning

confidence: 99%

“…studies examine both the gains in learning of subject matter skills and the learning of CT skills or program comprehension (Basu et al, 2015;Cateté et al, 2018;Chang, 2019;Grgurina et al, 2018;Harrison et al, 2018;Hickmott et al, 2018;Israel and Lash, 2019;Jaipal-Jamani and Angeli, 2017;Jenkins, 2015;Kafai et al, 2014;Ke, 2014;Klopfer et al, 2009;Lai and Lai, 2012;Leonard et al, 2015;Litts et al, 2017;Louca et al, 2011;Merkouris and Chorianopoulos, 2018;Miller, 2019;Moreno-León and Robles, 2016;Musaeus and Musaeus, 2019;Nesiba et al, 2015;Nugent et al, 2019;Pardo, 2018;Peel and Friedrichsen, 2018;Rich et al, 2019;Rodríguez-Martínez et al, 2020;Swanson et al, 2018;Terwilliger et al, 2019;Towhidnejad et al, 2014;Urban, 2015;Valentine, 2018;Wagh et al, 2017;Weitze, 2017;Xiang and Passmore, 2015;Yadav et al, 2018;Yu and Guo, 2018).…”

Section: Subject Matter Skills In Combination With Program Comprehens...mentioning

confidence: 99%

See 1 more Smart Citation

Interdisciplinary Integration of Computational Thinking in K-12 Education: A Systematic Review

Yeni,

Grgurina,

Saeli

et al. 2023

Informatics in Education

View full text Add to dashboard Cite

There is an increasing interest in the integration of computational thinking (CT) in the K-12 curriculum. By integrating CT into other disciplines, the aim is to equip students with essential skills to navigate domain-specific challenges. This study conducts a systematic review of 108 peer-reviewed scientific papers to analyze in which K-12 subjects CT is being integrated, learning objectives, CT integration levels, instructional strategies, technologies and tools employed, assessment strategies, research designs and educational stages of participants. The findings reveal that: (a) over two-thirds of the CT integration studies predominantly focus on science and mathematics; (b) the majority of the studies implement CT at the substitution level rather than achieving a transformation impact; (c) active learning is a commonly mentioned instructional strategy, with block-based languages and physical devices being frequently utilized tools; (d) in terms of assessment, the emphasis primarily lies in evaluating attitudes towards technology or the learning context, rather than developing valid and reliable assessment instruments. These findings shed light on the current state of CT integration in K-12 education. The identified trends provide valuable insights for educators, curriculum designers, and policymakers seeking to effectively incorporate CT across various disciplines in a manner that fosters meaningful skill development with an interdisciplinary approach. By leveraging these insights, we can strive to enhance CT integration efforts, ensuring the holistic development of students' computational thinking abilities and promoting their preparedness for the increasingly interdisciplinary domains of digital world.

show abstract

“…Conversely, work by Grgurina et al focuses on assessing modeling activities in a secondary grade computing classroom [7]. The assessment uses a combination of the Revised Bloom's Taxonomy [9] and SOLO taxonomy [2] to evaluate students' written answers to a number of questions regarding their models on multiple dimensions ranging from prestructural (information makes no sense) to extended abstract (generalization and transfer) for areas of design, experimentation, and reflection on the model.…”

Section: Background and Related Workmentioning

confidence: 99%

Ceo

Lytle

Cateté

Dong

et al. 2019

Proceedings of the ACM Conference on Global Computing Education

View full text Add to dashboard Cite

With the increased demand for introducing computational thinking (CT) in K-12 classrooms, educational researchers are developing integrated lesson plans that can teach CT fundamentals in noncomputing specific classrooms. Although these lessons reach more students through the core curriculum, proper evaluation methods are needed to ensure the quality of the design and integration. As part of a research practice partnership, we work to infuse researchbacked curricula into science courses. We find a three-pronged approach of evaluation can help us make better decisions on how to improve experimental curricula for active classrooms. This CEO model uses three data sources (student code traces, exit ticket responses, and field observations) as a triangulated approach that can be used to identify programming behavior among novice developers, preferred task ordering for the assignment, and scaffolding recommendations to teachers. This approach allows us to evaluate the practical implementations of our initiative and create a focused approach for designing more effective lessons.

show abstract

Assessment of modeling and simulation in secondary computing science education

Cited by 7 publications

References 27 publications

Expert Validation of a Python Test, Reliability, Difficulty and Discrimination Indices

Expert Validation of a Python Test, Reliability, Difficulty and Discrimination Indices

Interdisciplinary Integration of Computational Thinking in K-12 Education: A Systematic Review

Ceo

Contact Info

Product

Resources

About