motivated and had less anxiety with enhanced critical thinking.IntroductionEducators are saddled with the responsibility of ensuring every learning objective is met whilecreating an engaging student environment [1]. Educators must ensure that every experiment isdesigned with practical applications in mind and implemented in a safe environment. This aids theinstructors in facilitating critical thinking amongst the learners, ensuring that they can proffersolutions to essential questions. These guides and resources are models that support progressivelearning and peer-to-peer collaborations. Also, they can foster an inclusive learning atmosphereand encourage continuous improvement. Laboratory sessions are an integral part of the richlearning experience
traditional daytime undergraduate curriculum. By"compressed version," we mean teaching the same program as the daytime undergraduateprogram, with the same level of demand, but in a shorter period and without makingsignificant adaptations to the reality of "working students."At least initially, there exists a perception that generally, only a minority of students enteruniversity driven by intrinsic motivation [1], and an even smaller portion envisions a careeras an academic or researcher [2]. Typically, motivations tend to be more pragmatic,especially acquiring a degree and the necessary learning to gain access to employment orundertaking ventures that allow them to make a living. Working students aim to obtain aprofessional degree that offers them
inclusion of 3D printing and advanced data analysissoftware in physics labs to enrich educational outcomes.Keywords: Physics Education Research, Educational Innovation, STEM Education, Kinematics,Experimental Physics, 3D Printing Technology, Tracker SoftwareINTRODUCTIONPhysics education research has increasingly highlighted the need for improvements in laboratoryinstruction, particularly in fostering conceptual understanding and experimental design skills [1]and [2]. Holmes and Wieman argue that traditional introductory physics labs often fail toreinforce conceptual learning effectively [3]. Additionally, model-based reasoning has beenidentified as a crucial component in experimental physics learning [4]. This study contributes tothis ongoing
skills and competencies are highly indemand, and these skills and competencies are mostly found and taught in the science educationdiscipline. And one of these disciplines is physics education, which deals with the fundamentalsof the interaction of energy and matter, as well as engineering and technology. The teaching andlearning mechanisms in physics for engineering students involve innovative approaches aimed atenhancing conceptual understanding and promoting deep learning. Research emphasizes the shiftfrom traditional teaching methods to more interactive and inquiry-based strategies to engagestudents effectively [1]. Interactive simulations play a crucial role in teaching physics, particularlyelectrostatics, as they significantly improve
. The key advantages of this approach were access to equipment,flexibility on when and how experiments are conducted, and the curiosity-driven engagementfostered. However, this approach lacks one-on-one engagement, academic dishonesty, and theuse of specialized equipment [1], [2]. It established a difference and, in some respects, increasedstudent engagement. The development of troubleshooting skills and confidence in settingexperiments are a few key observations [3]–[5].The COVID-19 pandemic, which hinders knowledge transfer to students by restrictingmobility and providing significant logistical and safety issues, has rendered this traditional modeof instruction ineffective. With little to no time to consider the effects of the transitions on
theteaching and learning of a physics course through the students' perception. The modifiedILD has the same three stages as the original ILD, with two main differences in whoperforms the experiment and when it is performed. Specifically, the three phases in themodified ILD are 1) predict, 2) experiment (by students working in groups, not theinstructor), and 3) reflect (in groups, not individually). The first phase, prediction, beginswith the analysis of a physical situation in which students have to predict the behavior ofthe situation based on the knowledge imparted in the session by the instructor. This occursat the end of the instructor's exposition. The second phase occurs in the laboratory sectionof the course and relates to students' experience
promote the following competences in students: • Develop mathematical models that represent physical phenomena using statistical, computational, and simulation tools, among others. • Verify and validate models using appropriate techniques. • Predict the results of systems through models.Based on previous academic experiences [1-6], as well as the active learning approach [7-12],and assessment methods related to competency-based assessment [13-22], the aim is todevelop physics modeling competence and soft skills among second-year students inEngineering courses. We propose that students undertake experimental projects that align withthe main course syllabus, specifically focusing on Oscillations
theeffectiveness of immersive (panoramic) videos with hotspots as pre-class materials withinthe flipped classroom approach. This paper presents the implementation of thistechnology in a classic physics experiment on oblique launches, conducted withapproximately 400 first-year engineering students at XXXXXX. These students weredivided into laboratory classes, working in teams of 3 to 4.The paper tests the hypothesis that an immersive video—explaining in detail theexperimental apparatus, the concepts involved, and the experimental procedure throughhotspots—before the class, would promote greater autonomy in modeling and executingthe experiment. The proposal aimed at analyzing: 1. The increase in student engagement with the flipped classroom methodology
stakeholders in QISE education for amore diverse QISE workforce. We suggest strategies based on the findings of this study such asintegrating QISE into existing engineering courses, investing in the development of QISE coursesand programs at non-PhD-granting institutions, and making courses with QISE content accessibleto students from a variety of majors.IntroductionIn recent years, quantum technology has emerged as a federal priority driving investment inQuantum Information Science and Engineering (QISE) research and education. The NationalQuantum Initiative (NQI) Act was one of the first pieces of legislation in the US to establish thepriority [1]. Although it emphasized primarily the need for financial investment in research, theNQI act also calls
disciplines, highlighting the foundational role of physics in shaping theseperceptions and skills [1]. Furthermore, the relationship between physics and mathematics isemphasized in educational frameworks that aim to enhance student's understanding of bothsubjects, facilitating a more cohesive learning experience [2] [3]. This interconnectedness isessential for engineering students, as they often encounter complex problems requiring a solidgrasp of physics and mathematical principles.However, several studies have pointed out that students often perceive these subjects aschallenging, affecting their motivation and performance. Research indicates that students usuallyview physics as one of the more difficult subjects within the STEM (Science, Technology
NILdesktop equipment; selection of a template; making the sample; characterization of samples byoptical microscopy and scanning electron microscopy; lab report; literature search exercise;classroom presentation. In addition, students learn about career opportunities related tonanoimprint lithography and semiconductor industry. The course activities are well aligned withthe ABET general criteria for engineering that include requirements for both basic science andbroad education components, instruction on modern equipment, and development of leadership,and written and oral communication skills.IntroductionThe CHIPS and Science Act of 2022 [1] has provided funding specific for the development andin support of domestic semiconductor and
self-efficacy and attitudes toward physics) in the developmentof spatial reasoning skills among secondary school students. The research addressed three corequestions: (1) How do educational environments, indicated by school types, influence spatialreasoning development? (2) What is the predictive power of physics performance on spatialreasoning abilities? (3) How do students’ self-efficacy and attitudes toward physics, influenced bypersonal and teacher factors, impact their spatial reasoning performance?This study employed a quantitative approach using penalized regression models (Lasso and Ridgeregression) to identify key predictors of spatial reasoning performance. The sample consisted of251 senior secondary school physics students from