Identifying middle school students’ challenges in computational thinking-based science learning

Computational thinking has become a crucial skill for students because it gives them the tools to solve complicated issues using organized methods. Implementing Problem-based learning and Design-based learning in science learning has significantly enhanced students' skills. This research aims to ascertain which of the two models is better for facilitating computational thinking skills among middle school students in science learning. The study used a sample of 69 middle school students selected through random sampling. This research employs a quasi-experimental, multi-group, post-test-only design. The instrument utilized in this research is a computational thinking skills test comprising five questions. The data were analyzed using one-way ANOVA to test the hypotheses. Tukey test results indicate that the mean difference score between the PBL and DBL groups is 1.2609, with a significance value greater than 0.05. The study confirms that Problem-based learning is more effective than Design-based learning in facilitating students' computational thinking skills. However, the difference between the two is not particularly noteworthy. PBL and DBL represent viable pedagogical approaches that can enhance middle school students' computational thinking skills in science learning. Keywords: problem-based learning, design-based learning, computational thinking skill. DOI: http://dx.doi.org/10.23960/jpmipa/v25i3.pp1214-1223

This publication provides an editorial introduction to a special issue of Computer Applications in Engineering Education journal entitled Computational Thinking for STEM and Engineering Education. It provides introductory remarks to the issue and a commentary on the published material, as well as presents the views of the special issue editors in the field of science, technology, engineering and mathematics (STEM) education and research. This special issue is structured as follows. It contains articles related to the topics of computational thinking (CT), STEM, and engineering education. It starts with three overview articles based on systematic reviews, giving a structured overview of CT concepts in terms of practical educational approaches in introductory science, technology, engineering, arts and mathematics (STEAM) education. Subsequent articles present practical approaches to CT and STEAM education in such educational areas as general engineering, programming and software engineering, physical computing, robotics, electronics, mechanical, and power engineering.

<span lang="EN-US">Based on the study, computational thinking skills are influenced by science, technology, engineering and mathematics (STEM) skills, and there is a relationship between computational thinking skills and 21st-century skills. However, studies related to STEM attitudes, computational thinking and their impact on 21st-century skills are still very few and limited. The purpose of our study was to examine the impact of STEM attitudes and computational thinking on 21st-century. This research uses a quantitative approach. The participants of this study were students of a vocational school in Bantul Regency, Yogyakarta, Indonesia (N=290). Research data in STEM attitude, computational thinking, and 21st-century skills using a questionnaire. The data were analyzed using structural equation modeling techniques using the Smart PLS application. The results of the study obtained several findings, including: the model proposed in this study was valid; STEM attitude has a positive and significant effect on computational thinking; and computational thinking has a positive and significant effect on 21-st century skills. It can be argued that when STEM attitudes and computational thinking are more positive, 21-st century skills will improve. These findings have implications that curriculum development and STEM learning practices have to develop students’ computational thinking skills and 21st-century skills, especially in vocational schools.</span>

Whilst Science, Technology, Engineering and Mathematics (STEM) interdisciplinary teaching and learning in the USA K-12 education still needs greater promotion, middle school students demonstrated that they can, using low-cost, single board computers that promote the teaching of computer science (in this case Raspberry Pis), successfully engage with computer programming of digital images and videos. The context for these students’ engagement was the Advancing Out-of-School Learning in Mathematics and Engineering (AOLME) Project. This chapter describes how the processes of design, model, and implement, supported 40 Latinx middle school students’ development of computational thinking in an out-of-school setting, and how these processes promoted the genuine integrated practice and learning of technology, engineering, and mathematical concepts.

This special issue of the Canadian Journal of Science, Mathematics and Technology Education invokes questions intended to further the discourse of citizenship in science and mathematics education, such as, How do we define citizen and democracy? Is our call for student action hypocritical? Does positioning school science through the work of Ranciere present a “straw man” argument for change? To what extent does the ghost of John Dewey animate and inform a “wild pedagogy” in science education? Challenging the view of the science and mathematics curriculum as a barrier to overcome, this article argues that possibilities for developing citizenship and critical thinking can be found and developed in existing curriculum formations and practices of school science and mathematics education.

Previous research has established a close link between students' conceptions of learning and approaches to learning. Until recently, only a few quantitative studies have investigated the relationship between high school students' conceptions of learning science and the approaches they adopt to learning science. This study sought to address this gap in the literature by assessing these possible relationships empirically through the development of two questionnaires: The Conceptions of Learning Science (COLS) questionnaire and the Approaches to Learning Science (ALS) questionnaire. Four hundred and seventy‐four Taiwanese high school students were administered the COLS questionnaire and the ALS questionnaire. Results were entered into a structural equation model to elicit structural relations between students' conceptions of and their approaches to learning science. Overall, findings revealed that students holding constructivist conceptions of learning science tended to employ deep approaches to learning science. Conceptions of learning science such as “testing” and “calculate and practice” were also found to have effects on the surface approaches to learning science; the conceptions of learning science as “applying” and “understanding and seeing in a new way” had noticeable effects on deep approaches to learning science. This study employed quantitative methods to confirm further the structural relations existing between conceptions of learning science and the motives and strategies employed in learning science. Implications for implementing the study's findings into the context of the real‐world classroom are discussed. © 2007 Wiley Periodicals, Inc. Sci Ed, 92:191–220, 2008.

This study aims to examine the effects of a Novel Engineering (NE)-based AI education program on elementary school students’ computational thinking and learning engagement. A 12-session instructional program was developed and implemented in a fourth-grade classroom at an elementary school. A mixed-methods research design was employed, incorporating both quantitative and qualitative data. Pre- and post-tests were administered to assess changes in students' computational thinking and learning engagement. In addition, qualitative data was collected through instructional materials and in-depth interviews. Interview participants were selected based on high, medium, and low levels of performance in computational thinking and learning engagement. The interviews focused on how students demonstrated competencies in analysis/design and implementation/inference throughout the learning process. The results indicated that the NE-based AI education program had a statistically significant positive impact on both computational thinking and learning engagement with the students. The students actively engaged in problem-solving activities and found the tasks stimulating, which contributed to the enhancement of their computational thinking skills. Furthermore, the appropriate level of task difficulty, opportunities for autonomous participation, and active communication were identified as factors that fostered greater learning engagement. This study demonstrates the potential for developing and applying effective AI education programs to enhance elementary students’ computational thinking and learning engagement, offering valuable implications for future educational practices.

<span>This study investigates the necessity of computational thinking (CT) skills integration among science, technology, engineering, and mathematics (STEM) secondary school teachers, focusing on the development of the </span><em><span>McodE</span></em><span> pedagogical module for the topic of heat in solar still projects. Utilizing a design and development research (DDR) approach, the research aimed to design and develop a CT module tailored to enhance teachers’ pedagogical strategies in solar still project. The case study was employed with 32 STEM secondary school teachers, through purposive sampling participated in the study based on their experience in integrating technology into pedagogy. A pre-test and post-test design was employed during a two-day workshop to assess the participants’ CT skills before and after the intervention. An additional open-ended interview was conducted among three teachers to support the quantitative data. The results revealed significant improvements in various aspects of CT application. The findings demonstrated substantial increases in teachers’ abilities to integrate CT into their teaching practices. The mean for pre-test score was 43.25, which significantly increased to 67.37 in the post-test, indicating a marked enhancement in CT skills. The t-test statistics for the pre-test (t=31.04, df=31, p<.001) and post-test (t=49.94, df=31, p<.001) confirmed a highly significant differences between the pre-test and post-test scores. The <br /> open-ended interview also showed the strong interest and necessity of the CT module among the respondents. The study concludes that there is a critical need for CT module among STEM teachers to better prepare students for the demands of modern STEM education.</span>

In recent years, educators have faced challenges in designing learning that can improve students' computational thinking and creativity, especially in science and mathematics learning. This systematic review examines didactic research published between 2010 and 2023, following PRISMA guidelines. Relevant studies were identified through database searches in Scopus, Web of Science, and ERIC, using keywords related to computational thinking, creativity, science and math education. Twenty-five studies were selected for in-depth analysis through a structured screening process. These studies focus on strategies, methodologies, and assessment techniques that promote computational thinking (CT) and creativity in science and math classrooms. Particular emphasis is placed on project-based learning (PjBL) as a pedagogical framework for integrating CT and creativity into the learning process. The findings highlight how PjBL not only supports students in developing logical and systematic problem-solving skills but also fosters creative expression through open-ended, collaborative tasks. The review further explores tools and resources that enhance CT instruction and discusses how digital technologies can support creativity in science learning. The evidence highlights the effectiveness of integrating CT and creativity through PjBL within the context of science and math education.

This research is focused on exploring the importance of STEM (Science, Technology, Engineering, and Mathematics) education in the development of critical competencies among secondary school students in the 21st century. This was aimed to assess the impact of STEM-based activities on students' creative and computational thinking skills. A quasi-experimental design that included 77 secondary school students from public and private schools in Bandar Lampung, Indonesia, who participated in STEM interventions for over 5 weeks, was adopted. Data were collected through creative thinking tests and questionnaires on CT and STEM attitudes. The results showed that students who participated in STEM activities exhibited significantly higher creative thinking scores compared to peers taught with the traditional curriculum. Specifically, the experimental group showed a progressive increase in weekly test scores, suggesting that STEM methods improved students' performance over time. Structural equation modeling (SEM) disclosed strong positive associations between attitudes towards STEM, CT, and creativity. The implications of these results outlined the need to integrate STEM education into curricula to foster essential skills for future challenges. This research contributes to the understanding of effective educational strategies and also advocates for a shift towards more interactive and integrative methods in secondary education to meet the demands of the contemporary workforce.

The Elementary School Journal Volume 84, Number 4 S1984 by The University of Chicago. All rights reserved. 001 3-5984/84/8404-0004$01.00 Assumptions, even assumptions that seem logical and reasonable, can make it difficult or impossible for people to understand scientific concepts. Everyone makes assumptions about the way the world works, assumptions like "When the sky is cloudy and dark, it will probably rain" or "Bits of wood float in water." People often use those assumptions to explain how things work: "I can see myself in a mirror because light bounces off me to the mirror and off the mirror to my eyes." Such explanations or conceptions are often based on experience and common sense; however, experience and common sense can sometimes

“Good luck on your first day as an assistant professor, Dr. Tanner! Have a great class!” On the wall above my desk, these words scream out from an otherwise encouraging note that is adorned with many exclamation points. This note has hung on my wall since my very first day as an Assistant Professor of Biology. As I was charging off to teach my first class, a senior faculty member who had been on my hiring committee slipped this note under my office door. In moments of pause years later, I still stare up at that note and breathe a sigh of relief that I had much more than luck to guide me on my first day as a college-level teacher. Although I continue to have much to learn—as all of us do no matter the number of years of teaching experience—I did arrive at the university with both formal and informal training in science education. I had had plenty of exposure to innovative pedagogical approaches, questioning strategies, and techniques for engaging diverse audiences in learning science. As a scientist educator, I had had the privilege of many years of collaboration with outstanding K–12 educators as well as a postdoctoral fellowship in science education. However, my training has been, to say the least, unconventional compared with that of my fellow junior faculty and unique in its preparation in regard to the teaching and learning of my discipline. It will not be news to anyone reading this article that university and college teaching is to a large extent a profession with no formal training. It’s startling but true that the majority of faculty members—and lecturers who often teach large numbers of students—have no formal training in the teaching and learning of their discipline. In fact, the hiring process in university science departments is structured primarily to evaluate a faculty candidate’s ability to be a productive researcher, with success measured in number of publications and magnitude of grant funds raised. Depending on the type of institution, for example, research university, state-level university, or liberal arts college, there may be a component of the faculty interview process that probes a candidate’s teaching ability, for example, requesting a statement of teaching philosophy and requiring the candidate to teach a sample lecture class. However, this sample lecture often screens for gross inadequacies, rather than looking for stellar innovations or pedagogical skills. This lack of formal, accredited training for university and college instructors stands in stark contrast to the requirements for a high school teacher who is charged with the education of students only a year junior to college freshmen. High school teachers in the United States must be credentialed as a secondary science teacher, demonstrate subject matter competency in every subject that they will be teaching, and must continually engage in professional development in the teaching and learning of their discipline throughout their career as a science teacher. With the 2002 federal No Child Left Behind legislation, the onus is upon each precollege science teacher to become “highly qualified” in terms of formal university-level training in science education. However, no such required professional training or measurable standards for teaching are required in institutions of higher education. Many policy documents have suggested standards of teaching practice in postsecondary science education (National Research Council, 1996, 1997; Siebert and McIntosh, 2001), but the extent of implementation of these ideals is unclear and has gone relatively unstudied, although national and regional accreditation boards do look at outcomes, asking colleges and universities to assess what their students have gained from four years of study at their institutions. Nonetheless, there is a striking reversal of accountability that happens when one crosses the precollege teaching to college-level teaching boundary (Table 1). During the K–12 school years, society expects K–12 teachers to be responsible for student learning. Salaries of teachers in many states are tied to student test scores, and poor student performance can potentially invoke penalties. At a college or university, several variables in the educational universe shift. Students are the ones responsible for learning. The evaluation and compensation of college-level teachers is not DOI: 10.1187/cbe.05–12–0132 Address correspondence to: Kimberly Tanner (kdtanner@sfsu.edu). CBE—Life Sciences Education Vol. 5, 1–6, Spring 2006

The Next Generation Science Standards call for engineering design solutions using computer technologies, but many K-12 classrooms either do not have accessible technologies or the teacher pedagogical knowledge on how to effectively implement engineering design into the curriculum. Without access to computer or mobile technologies, teachers are faced with a challenge integrating design thinking and computational thinking into inquiry-based teaching practices. Embedding design thinking and computational thinking within a socioscientific framework is a possible solution. Socioscientific issues, design thinking, and computational thinking are thoroughly researched and published constructs. There has been some research suggesting a connection between design thinking and computational thinking, however pilot data suggest a potential intersection of the three concepts which became the crux for our research. This study sought to construct a pedagogical framework amalgamating socioscienfific issues, design thinking, and computational thinking for science teacher practice in classrooms without the use of technology. Practicing science teachers enrolled in a master’s level class served as the study’s participants and they were charged with designing a mixed reality Serious Educational Game embedding socioscientific issues in a climate change context. Incorporating a case study design with triangulated data sources that were collected through reflective journals, design documents, and observations, an iterative process was used to analyze data to build trustworthiness. Results suggest initial hypotheses of the potential commonality between the three constructs were not completely accurate, however a refined model emerged. Articulation of how to integrate the new model using socioscientific issues, design thinking, and computational thinking are discussed.

The PISA data results show that computational thinking abilities are still lacking. Computational thinking ability is influenced by gender. This research aims to analyze patterns of computational thinking skills of prospective elementary school teachers based on gender at 8 universities in Indonesia. In this research, the components of computational thinking skills analyzed are abstraction, algorithmic, decomposition, and pattern recognition. This research is a mix method research with research subjects as many as 234 prospective elementary school teachers at 8 higher educational institutions. The instruments used were test and interviews. The data analysis technique used is a quantitative data analysis technique using SEM PLS and for qualitative data analysis using miles and Huberman. The research results show that computational thinking skills are still low on the decomposition and pattern recognition components. Based on the SEM PLS test results, it shows that computational thinking abilities are related to gender. In general, the computational thinking ability of female students is slightly higher in all sub-indicators than men and there are differences in the pattern of computational thinking ability between male and female elementary school teacher prospective. The ability of prospective female elementary school teachers to answer in more detail and more structured, while the answers of male prospective teachers are shorter and less comprehensive. Recommendations for developing computational thinking skills by developing problem-based learning, contextual project-based learning and STEAM based learning.

While various studies have focused on the significance of computational thinking (CT) for the future career paths of individuals in science, technology, engineering, and mathematics (STEM), few studies have focused on computational thinking for non-STEM college students. This study explores the relationship between computational thinking and learning satisfaction for non-STEM-major college students. A conceptual model is proposed to examine the structural relationships among computational thinking, self-efficacy, self-exploration, enjoyment and learning satisfaction in an AppInventor-based liberal education course. Collecting data from 190 undergraduate students from Taiwan and analyzing the data by using partial least squares (PLS) methods, the research framework confirms the six proposed hypotheses. These results show that both computational thinking and enjoyment play significant roles in both self-exploration and digital self-efficacy. Moreover, digital self-efficacy and self-exploration also have a significant positive influence on learning satisfaction. These findings have implications for influencing the learning outcomes of non-STEM-major college students, computational thinking course instructors, and computational thinking relevant policies.