Exploring ChatGPT’s Role in Solving Physics Problems and Enhancing Education

The article deals with the process of formation of information and mathematical competence of the higher education of engineering specialties in the general course of physics with the use of the Simscape visual programming package. During the study, the theoretical analysis of scientific literature was carried out; systematized and generalized scientific data for the study of the essence and structure of information and mathematical competence of higher education applicants; the experience of using innovative forms of study in the general course of physics is studied; the proposed approach to the educational process was introduced; Summarized and systematized results. The methodological basis of the study is to examine the process of formation of information and mathematical competences when studying physics using the MatLab software package. The solution of the problem in the study of oscillatory processes in the mechanical system with bound oscillations of two material points with MatLab Simscape is given. The components of the process of formation of information and mathematical competence of future engineers in the general course of physics are highlighted: motivational, creative, operational and technological, activity. These components include an increase in motivation, awakening of interest in the study of physics, mathematics, informatics and desires to self-realize; ability to show creativity, readiness for critical thinking and creative comprehension of the use of knowledge, skills and skills; ability to solve physics problems, selection of techniques and methods for solving non-standard tasks, the use of mathematical and information tools when solving problems, knowledge of integration links of objects of general realization; solving professional tasks with the use of mathematical and information technologies. The main tasks during the study of the general course of physics in the process of formation of information and mathematical competence are: a positive attitude to the study of physical and mathematical disciplines and the use of knowledge gained in future professional activities; ability to correctly choose information and mathematical tools for solving applied tasks; ability in accordance with the set task to interpret and use the information received; ability to organize self-education and self-development. It has been established that this approach allows you to form an information and mathematical competence of higher education for higher education in the general course of physics, promotes the development of critical and algorithmic thinking and creative comprehension of the use of higher education acquisitions, skills and skills in the process of solving physical problems.

Based on empirical findings and theoretical considerations related to the field of expertise research, the importance of “types” and “qualities” of knowledge in relation to problem solving in physics was investigated. The students (N =138) in this study had a level of competence that corresponds to an intensive beginner college course in physics. It was found that conceptual declarative knowledge and problem scheme knowledge are excellent predictors of problem solving performance. However, a detailed analysis shows that the first knowledge type is more typical for low achievers (novices) in physics problem solving whereas the second type is predominately used by high achievers (experts). Regarding types and qualities of knowledge and their relations to problem solving, the results of a multidimensional scaling analysis (MDS) indicate that two dimensions of knowledge can be distinguished. On the extreme limits of the first dimension, which could be named “problem solving relevance vs. structure of discipline”, are the types of knowledge and the qualities of knowledge, respectively. The second dimension of knowledge could be named “single knowledge elements vs. organised knowledge units”. There are types of knowledge as well as qualities of knowledge distributed along this dimension. Consequences of these results for improving physics education are discussed.

Scientific creativity is involved in Physics problem solving and it is an important element in problem solving. Mastery of scientific creatisvity is essential because it enable higher order thinking and improve problem solving. However, not many studies are found on scientific creativity in Physics problem solving. Hence, this research was conducted to study the relationship between scientific creativity and Physics problem solving. The research involved 30 Form 4 students from Bandar Tenggara, Johor to answer several open-ended questions. The instrument used to measure scientific creativity is the Scientific Structure Creativity Model (SSCM) while the Minnesota Assessment of Problem Solving (MAPS) is used to measure Physics problem solving. The results show that the students have a moderate level of scientific creativity and low level of Physics problem solving ability. There exists a positive and significant correlation between scientific creativity and Physics problem solving with the value of r = 0.371 and p = 0.044. As a conclusion, the positive and significant relationship between scientific creativity and Physics problem solving should be seen by educators as the reason to give emphasis in strengthening creativity among the students as a means to improve the students’ ability to solve Physics problems.

This study investigates the transformative potential of Generative Artificial Intelligence (AI) in enhancing learning experiences and addressing the issue of low attention span among college students. The research focuses on four key components of AI-generated pedagogy: reducing learning challenges, enhancing learning experiences, ethical considerations, and balancing benefits and risks. Utilizing a quantitative survey-based approach, data were collected from 57 college students in the Kottayam District. The findings reveal that students perceive Generative AI as having a positive impact on learning experiences, effectively reducing learning challenges, enhancing the effectiveness of pedagogical strategies, addressing ethical concerns, and balancing benefits and risks. Notably, a strong positive correlation exists between these Generative AI-generated pedagogical components and low attention span, suggesting that Generative AI can effectively address this issue among college students and open new avenues for leveraging technology to improve educational outcomes. The present research contributes to the broader discourse on the effectiveness of technology-enhanced learning, particularly in improving engagement and attention span in educational settings. It underscores the need for further exploration of the role of Generative AI in education, emphasizing the balance of technological, ethical, and pedagogical considerations to maximize AI's benefits in learning environments.

Teaching problem solving is one of the most important topics of physics education while students have big troubles with physics problem solving. The aim of this research is to investigate the impact of extended problem-solving strategy instruction on the development of pre-service science teacher’s problem-solving, critical thinking, metacognitive awareness, and logical reasoning skills. Extended Problem-Solving Strategy has been developed for university physics courses by researcher. This strategy has importantance in terms of covering many previous strategies in physics education literature and including many new steps. The model of the research consisted of an experimental design with pre-test and post-test control groups. Pre-services randomly assigned to the experimental (N=30) and control groups (N=30). The results of the research indicate that the post scores of the experimental group students significantly higher than control group students after the implementations in terms of metacognitive awareness, critical thinking, problem solving and logical thinking skills.This research revealed the positive effects of the “Extended Problem-Solving Strategy” implementation in the physics course at the university level on the skills which are listed among the 21st Century skills and each of these skills affects the other skills positively

This study examined the effect of transfer of learning on undergraduate students of physics education. Because mathematical methods are valuable for presenting and solving physics problems, students of physics must learn to use their knowledge of mathematics in a physical context rather than struggling with complicated mathematical problems. This study sought to determine whether or not practice with physics problems by a physics instructor for the course could help the undergraduate students transfer their mathematics knowledge to a physics context. To do this, the experimental and control groups of physics education students were provided with an exam sheet and interviewed about difficulties after passing the course taught by either a physics or mathematics instructor. After completion of the course, the student responses to the exam and interviews were analyzed qualitatively. It was concluded that simply learning mathematics by the control group suppressed their initiative and caused them to resist completing the physics problems. The experimental group felt capable of attempting this kind of problem, but lacked additional factors that prevented them from producing complete solutions.

The majority of physics learning in high school is still considered difficult to understand and has an impact on low ability to solve the problems given. This research aims to (1) determine students' knowledge abilities in solving physics problems on thermodynamic concepts. (2) Knowing students' understanding abilities in solving physics problems on thermodynamic concepts. (3) Knowing students' application abilities in solving physics problems on thermodynamic concepts. (4) a description of students' analytical abilities in solving physics problems on thermodynamic concepts. This research method uses a quantitative descriptive research design. The subjects in this research were class XI students at SMAN 2 Binongko. The data collection technique used essay test questions which were then given to class XI students. The research results show that (1) Students' ability to solve physics questions at the knowledge level (C1) is categorized as high with a percentage of 44%. (2) Students' ability to solve physics questions at the understanding level (C2) is categorized as moderate with a percentage of 36%. (3) Students' ability to solve physics questions at the applied level (C3) is categorized as very low with a percentage of 64%. (4) Students' ability to solve physics questions at the analysis level (C4) is categorized as very low with a percentage of 75%.

This dissertation presents a series of neuroimaging investigations and achievements that strive to deepen and broaden our understanding of human problem solving and physics learning. Neuroscience conceives of dynamic relationships between behavior, experience, and brain structure and function, but how neural changes enable human learning across classroom instruction remains an open question. At the same time, physics is a challenging area of study in which introductory students regularly struggle to achieve success across university instruction. Research and initiatives in neuroeducation promise a new understanding into the interactions between biology and education, including the neural mechanisms of learning and development. These insights may be particularly useful in understanding how students learn, which is crucial for helping them succeed. Towards this end, we utilize methods in functional magnetic resonance imaging (fMRI), as informed by education theory, research, and practice, to investigate the neural mechanisms of problem solving and learning in students across semester-long University-level introductory physics learning environments. In the first study, we review and synthesize the neuroimaging problem solving literature and perform quantitative coordinate-based meta-analysis on 280 problem solving experiments to characterize the common and dissociable brain networks that underlie human problem solving across different representational contexts. Then, we describe the Understanding the Neural Mechanisms of Physics Learning project, which was designed to study functional brain changes associated with learning and problem solving in undergraduate physics students before and after a semester of introductory physics instruction. We present the development, facilitation, and data acquisition for this longitudinal data collection project. We then perform a sequence of fMRI analyses of these data and characterize the first-time observations of brain networks underlying physics problem solving in students after university physics instruction. We measure sustained and sequential brain activity and functional connectivity during physics problem solving, test brain-behavior relationships between accuracy, difficulty, strategy, and conceptualization of physics ideas, and describe differences in student physics-related brain function linked with dissociations in conceptual approach. The implications of these results to inform effective instructional practices are discussed. Then, we consider how classroom learning impacts the development of student brain function by examining changes in physics problem solving-related brain activity in students before and after they completed a semester-long Modeling Instruction physics course. Our results provide the first neurobiological evidence that physics learning environments drive the functional reorganization of large-scale brain networks in physics students. Through this collection of work, we demonstrate how neuroscience studies of learning can

The purpose of this study was to examine the effects of peer instruction (PI) on performance, gender gap, conceptual learning and problem solving in physics. The students enrolled in two sections of a physics course were selected; one section was treatment group (TG) and the other section was control group (CG). 42 students in TG were instructed with PI whereas 56 students in CG were instructed with conventional instruction. Data were collected using Force Concept Inventory, final examination problems, and students’ opinions about the instruction. The results indicated that PI had more positive effect on students’ conceptual learning and problem solving than conventional instruction. The conceptual learning with PI did not show any statistically significant difference between female and male students in TG. However, the quantitative problem solving performance after PI revealed that male students performed better than female students. The female and male students’ conceptual learning and problem solving performance did not change noticeably in CG. The students in TG also changed their perspective on solving a problem and understanding a concept. They found the method helpful to connect the quantitative solution with related concepts. Key words: conceptual learning, gender gap, peer instruction, physics education, problem solving.

Aim/Purpose: This study aims to explore factors influencing engineering students’ acceptance of the Google Classroom platform in communication skills courses to ensure more active engagement and better performance. Background: In response to the underutilization and hesitancy in adopting educational technologies, this study investigates the factors influencing engineering students’ acceptance of Google Classroom in a Middle Eastern university. Despite the potential benefits of such technologies, their integration faces challenges due to cultural factors and resistance from educators and students alike. Methodology: The study utilized a Technology Acceptance Model-based questionnaire distributed via Google Forms to 140 engineering students to analyze the acceptance of Google Classroom. Data analysis was conducted using structural equation modeling with Smart PLS, focusing on critical constructs like ease of use and perceived usefulness. Limitations due to the sample size and single-institution scope are acknowledged, which may affect the generalizability of the findings. Contribution: This study outlines practical steps for educators to enhance learning by fostering a user-friendly environment and supporting student proficiency with technology. It highlights the importance of policies encouraging educational technology adoption and urges developers to focus on user-centered features. Additionally, the study calls for collaboration among educators, policymakers, and developers to create engaging and compelling learning experiences. Findings: Findings unveil the significant impact of user satisfaction on perceived ease of use and usefulness, subsequently influencing attitudes. Furthermore, the study identifies the substantial influence of subjective norms and attitudes on intentions and the consequential impact of intentions on self-perception of academic success. Recommendations for Practitioners: The study advocates for educators to cultivate a motivating environment that fosters active engagement with the Google Classroom platform by raising students’ satisfaction and positive attitudes. Recommendation for Researchers: The study encourages further investigation into the long-term effects of technology integration on students’ academic performance and learning outcomes and exploration of additional variables or moderators that may influence technology acceptance in educational settings. Impact on Society: By understanding the factors influencing engineering students’ acceptance of Google Classroom, educators can better integrate technology into communication skills courses, potentially improving student engagement and academic performance and preparing students for success in a technologically driven society. Future Research: Longitudinal studies tracking students’ technology adoption patterns over time would also contribute to understanding the sustained impact of technology integration on educational practices and outcomes.

This study delves into the insights and experiences of teachers regarding the influence of AI-driven gamification on student motivation, engagement, and learning outcomes. Through qualitative research methods, teacher perspectives are explored to unravel the multifaceted aspects of AI-enhanced gamification within educational settings. The study identifies several significant themes, including positive perceptions of AI-driven gamification, challenges in implementation, elevated motivation and engagement, enhanced learning experiences, improved learning outcomes, and the complexities of assessing long-term effects. The findings underscore the transformative potential of AI-driven gamification, as educators highlight its ability to create democratic, effective, and transformative learning environments. Nevertheless, the study also uncovers challenges related to professional development, technical glitches, and curriculum alignment in the implementation of this innovative approach. Moreover, the study reveals a resounding consensus among teachers regarding the positive impact of AI-driven gamification on student motivation, participation, and learning experiences. Teachers emphasize how AI-driven gamification can turn ordinary learning into enjoyable and meaningful journeys, offering students autonomy and real-time feedback. Furthermore, teachers believe that AI-driven gamification contributes to improved learning outcomes, fostering a deeper understanding of subject matter, knowledge retention, and enhanced problem-solving skills. Despite these short-term gains, teachers acknowledge the need for longitudinal studies to comprehensively assess the long-term effects of AI-driven gamification on learning outcomes.

The integration of virtual reality (VR) into educational settings has shown significant potential in enhancing learning experiences, particularly in complex fields such as Internet of Things (IoT). This paper presents the design and development of VRIOTS, a VR-based learning platform aimed at providing students with hands-on experience in IoT projects without the need for expensive physical hardware. The platform simulates real-world IoT environments, allowing students to interact with virtual devices and systems, thereby improving their understanding, retention, and practical skills. Using an Agile development methodology and iterative user testing with 35 participants, we evaluated learning engagement through a user engagement scale and pre-post knowledge tests. The findings demonstrated significant improvements in knowledge retention, learning engagement, and task completion rate. Moreover, it was shown that VR-based learning not only enhances knowledge retention and student learning engagement but also makes IoT education more accessible and cost-effective. This research highlights the importance of VR in overcoming the limitations of traditional learning methods and preparing students for future careers in the IoT and related fields.

The notion that teachers and students can incorporate digital technology to support science investigations and enhance learning experiences has received considerable interest from researchers, practitioners, and policymakers (Loveless & Ellis, 2001; New Zealand Ministry of Education, 2007; Somekh, 2007). For instance, the World Wide Web offers easy access to multimodal and up-to-date information and opportunities to interact with people and information, compared to facilitating face-to-face meetings or using standard texts (Cowie, Moreland, Jones, & Otrel-Cass, 2008; Slotta & Linn, 2000). Careful orchestration of digital technology in classrooms has the potential to enhance understanding of science ideas, promote learners’ independence, motivation and engagement in science, and support visualizing investigations and science learning. However, this requires that teachers and students have sandpit time, which means time to practice, and reflect for, the use of digital technology (Otrel-Cass, Cowie, & Khoo, 2011). It has been argued that if teachers want their students to learn about what it means to think and work as a scientist, then they should be involved in activities that are authentic and meaningful. This means that students should get opportunities to apply their growing scientific literacy, practice decision making (Roth, van Eijck, Reis, & Hsu, 2008), and learn about social practices and discourses that contribute to the way scientists generate knowledge (Kovalainen & Kumpulainen, 2009). This demand for authenticity is challenging the traditional school environment, because activities that involve students as self-directed learners, who investigate, interpret, and assess the trustworthiness of information from a variety of sources, for the purpose of answering their own questions, are not easily achieved (Duschl, 2008; Otrel-Cass et al., 2011). Although Information and Communication Technology (ICT) has been identified to provide a suite of tools that support such endeavors, digital technology alone will not change teacher practices in science classrooms. If digital technology is to contribute to transforming science learning, those involved with shaping teacher pedagogy, including researchers and teacher educators, need to explore how teachers can use the creative, collaborative, experimental, and evaluative possibilities ICT may have to offer (Somekh, 2007). It is also not enough to assume that twenty-first century students may be digitally literate in using technology for recreational purposes, such as social networking, and to then believe that they can, or want, to automatically transfer those skills into educational settings (Kennedy, Judd, Churchward, Gray, & Krause, 2008). Such oversimplifications of digital technology use and practices in science, or other subjects, may lead to less productive teaching and learning outcomes, and alienate both teachers and students from using digital technology in class.

A research that aims to determine the profile of student’s creative who have a concrete sequential thinking styles, sequential abstract, concrete random, and abstract random in solving physics problems at the State University of Makassar. This study is a qualitative research. The sample was taken by using purposive random sampling technique. The data obtained were analyzed using Miles and Hubermann model. The results describe creativity of student thinking style in solving physics problems. The conclusion shows that the concrete sequential of thinking’s style "less creative", abstract sequential "quite creative", random concrete "less creative" and abstract random "creative" in solving physics problems at the State University of Makassar

This study aims to describe the creativity in thinking of junior high school students in solving physics problems. Creativity in thinking is important for students to make themselves understand a physics concept, such as force, work, etc. Creativity in thinking is also needed so that students can find several possible problem solving according to their potential and are not fixated on a method because basically every student has their own imagination and thought process. Twelve students in the class VIII J of SMP Negeri 1 Jombang participated in the study. Data obtained from student's thinking creativity test instrument which is strengthened by data from student and teacher interviews. Data analysis referred to three indicators of creativity used in the study: fluency, flexibility, and originality. The results showed that the students' thinking fluency with high ability and some students with medium and low abilities got high scores. The highest score of the flexibility indicator was obtained by students with low abilities. High scores for the originality indicator were also obtained by several students from the high, medium, and low ability groups. High creativity scores are not only obtained by high-ability students but also medium, and low-ability students. The creativity of students' thinking in solving physics problems is different. Students' knowledge and experience play a role in the differences in students' creativity in thinking fluently, flexible, and new. So that teachers can use learning methods that provide direct experience to students to make it easier for students to understand a concept and also improve students' mathematical operational abilities to make it easier for them to solve a problem that requires mathematical calculations.