Schrödinger: Journal of Physics Education
Schrödinger: Journal of Physics Education

Advancing Physics and Physics Education Through Research and Innovation

Scopus

54

Citation

Dimension

413

Citation

Schrödinger: Journal of Physics Education

Advancing Physics and Physics Education Through Research and Innovation

Development and Evaluation of an Arduino-Based Automatic Timer for Uniformly Accelerated Linear Motion in Physics Learning

Lutfiana Maula
Universitas Islam Negeri Walisongo
Indonesia
lutfianamaulaa@gmail.com
Sam Kaheru
University of Venda
South Africa
Share

  • Purpose of the study: This study aims to develop an Arduino Uno- and infrared sensor-based automatic timer for uniformly accelerated linear motion (UALM), evaluate its feasibility, assess student responses, and examine changes in conceptual understanding.

    Methodology: This study employed a research and development (R&D) approach adapted from Sugiyono’s model. The apparatus integrated an Arduino Uno R3, four infrared sensors, an LCD, and an adjustable inclined track, programmed using Arduino IDE. Data were collected from 12 tenth-grade students through interviews, questionnaires, technical tests, and pretest–posttest assessments. Analyses included Aiken’s V, descriptive statistics, R², paired-samples t-tests using SPSS 22, and normalized gain (N-gain).

    Main Findings: The apparatus achieved an overall Aiken’s V coefficient of 0.77, indicating validity according to the study’s criteria. Student responses reached 91.8%, categorized as very good. Differences between measured and theoretically calculated travel times ranged from 0.03 to 0.10 seconds, while comparisons between sensor-based and stopwatch measurements yielded an R² of 0.9967. Pretest–posttest results showed a statistically significant difference (p < 0.001), with an N-gain of 0.47, indicating moderate improvement in conceptual understanding.

    Novelty/Originality of this study: This study presents an integrated, classroom-oriented UALM teaching aid combining an adjustable inclined track, four infrared sensors, an Arduino Uno R3, and an LCD for automatic measurement and display of two time intervals. Its originality lies in the apparatus configuration, practical design modifications, and combined evaluation of expert validity, technical performance, student responses, and preliminary learning outcomes in secondary-school physics education.

  • How to cite

    [1]
    L. Maula and S. Kaheru, “Development and Evaluation of an Arduino-Based Automatic Timer for Uniformly Accelerated Linear Motion in Physics Learning”, Sch. Jo. Phs. Ed, vol. 7, no. 5, pp. 278–288, Oct. 2026, doi: 10.37251/sjpe.v7i5.3912.

    More Citation Formats

  • 1
    Abstract views
    1
    Downloads

    Metrics — Badges

    1. L. González-Rodríguez and A. Plasencia-Salgueiro, “Uncertainty-Aware autonomous mobile robot navigation with deep reinforcement learning,” in Koubaa, A., Azar, A.T. (eds) Deep Learning for Unmanned Systems. Studies in Computational Intelligence, A. Koubaa and A. T. Azar, Eds., Cham: Springer International Publishing, 2021, pp. 225–257. doi: 10.1007/978-3-030-77939-9_7.
    2. Mark E Pryer, John Cronin, Jonathon Neville, and Aaron Uthoff, “Kinematics and kinetics of pick-up acceleration,” Int. J. Sports Sci. Coach., vol. 20, no. 6, pp. 2796–2804, Dec. 2025, doi: 10.1177/17479541251350474.
    3. V. Borish and H. J. Lewandowski, “Seeing quantum effects in experiments,” Phys. Rev. Phys. Educ. Res., vol. 19, no. 2, p. 20144, Oct. 2023, doi: 10.1103/PhysRevPhysEducRes.19.020144.
    4. Z. Patterson and L. Ding, “Epistemic framing analysis of secondary students during instruction on quantum physics,” Phys. Rev. Phys. Educ. Res., vol. 21, no. 1, p. 10116, Mar. 2025, doi: 10.1103/PhysRevPhysEducRes.21.010116.
    5. P.-H. Liu, “A structuralist view for interpreting the role of mathematics in physics,” ZDM – Math. Educ., vol. 54, no. 7, pp. 1657–1670, 2022, doi: 10.1007/s11858-022-01438-3.
    6. S. Puntambekar, D. Gnesdilow, C. Dornfeld Tissenbaum, N. H. Narayanan, and N. S. Rebello, “Supporting middle school students’ science talk: A comparison of physical and virtual labs,” J. Res. Sci. Teach., vol. 58, no. 3, pp. 392–419, Mar. 2021, doi: https://doi.org/10.1002/tea.21664.
    7. H. Van Le, “Silence in the classroom: Unraveling the cultural dynamics affecting participation and critical thinking in Malaysian and Vietnamese engineering education,” Cogent Educ., vol. 11, no. 1, p. 2404780, Dec. 2024, doi: 10.1080/2331186X.2024.2404780.
    8. H. J. Banda and J. Nzabahimana, “Effect of integrating physics education technology simulations on students’ conceptual understanding in physics: A review of literature,” Phys. Rev. Phys. Educ. Res., vol. 17, no. 2, p. 23108, Dec. 2021, doi: 10.1103/PhysRevPhysEducRes.17.023108.
    9. M. Hitier and A. S. González-Martín, “Derivatives and the Study of Motion at the Intersection of Calculus and Mechanics: a Praxeological Analysis of Practices at the College Level,” Int. J. Res. Undergrad. Math. Educ., vol. 8, no. 2, pp. 293–317, 2022, doi: 10.1007/s40753-022-00182-z.
    10. A. Cashman and T. O’Mahony, “Student understanding of kinematics: a qualitative assessment,” Eur. J. Eng. Educ., vol. 47, no. 6, pp. 886–909, Nov. 2022, doi: 10.1080/03043797.2022.2073200.
    11. J. Tito Cruz, V. R. Coluci, and R. Moraes, “ORUN-VR2: A VR serious game on the projectile kinematics: design, evaluation, and learning outcomes,” Virtual Real., vol. 27, no. 3, pp. 2583–2604, 2023, doi: 10.1007/s10055-023-00824-w.
    12. P. Alstein, K. Krijtenburg-Lewerissa, and W. R. van Joolingen, “Teaching and learning special relativity theory in secondary and lower undergraduate education: A literature review,” Phys. Rev. Phys. Educ. Res., vol. 17, no. 2, p. 23101, Jul. 2021, doi: 10.1103/PhysRevPhysEducRes.17.023101.
    13. F. Outay, H. A. Mengash, and M. Adnan, “Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: Recent advances and challenges,” Transp. Res. Part A Policy Pract., vol. 141, pp. 116–129, 2020, doi: 10.1016/j.tra.2020.09.018.
    14. A. Salatino, A. Prével, E. Caspar, and S. Lo Bue, “Influence of AI behavior on human moral decisions, agency, and responsibility,” Sci. Rep., vol. 15, no. 1, p. 12329, 2025, doi: 10.1038/s41598-025-95587-6.
    15. Maria Teresa Restivo, Diana Urbano, and Maria Fátima Chouzal, “Instrumentation for measurement: A case study on conceptual versus problem-solving knowledge,” Int. J. Mech. Eng. Educ., vol. 53, no. 2, pp. 411–431, Apr. 2025, doi: 10.1177/03064190241230057.
    16. K. Achuthan, D. Raghavan, B. Shankar, S. P. Francis, and V. K. Kolil, “Impact of remote experimentation, interactivity and platform effectiveness on laboratory learning outcomes,” Int. J. Educ. Technol. High. Educ., vol. 18, no. 1, p. 38, 2021, doi: 10.1186/s41239-021-00272-z.
    17. K. SuriyaKrishnaan, G. Mahendra, D. Sankar, and K. S. Yamuna, “Wireless data transferring of soldier health monitoring and position tracking system using arduino,” in Inventive Systems and Control. Lecture Notes in Networks and Systems, V. Suma, Z. Baig, S. Kolandapalayam Shanmugam, and P. Lorenz, Eds., Singapore: Springer Nature Singapore, 2022, pp. 761–774.
    18. S. M. N. Howlader, M. M. Hossain, S. Khanom, S. Sarker, and B. Barua, “An Intelligent Car Locating System Based on Arduino for a Massive Parking Place,” in Multi-Strategy Learning Environment, V. Vimal, I. Perikos, A. Mukherjee, and V. Piuri, Eds., Singapore: Springer Nature Singapore, 2024, pp. 19–33.
    19. A. Mukhopadhyay, A. Rao, P. Joshi, and V. Harish, “Edge based blind spot avoidance and speed monitoring for emergency vehicles,” Procedia Comput. Sci., vol. 235, pp. 284–302, 2024, doi: https://doi.org/10.1016/j.procs.2024.04.030.
    20. R. Girwidz, H. Theyßen, and R. Widenhorn, “Experiments in physics teaching,” in Physics Education, H. E. Fischer and R. Girwidz, Eds., Cham: Springer International Publishing, 2021, pp. 269–296. doi: 10.1007/978-3-030-87391-2_10.
    21. S. Becker, P. Klein, A. Gößling, and J. Kuhn, “Investigating dynamic visualizations of multiple representations using mobile video analysis in physics lessons: Effects on emotion, cognitive load and conceptual understanding,” Zeitschrift für Didakt. der Naturwissenschaften, vol. 26, no. 1, pp. 123–142, 2020, doi: 10.1007/s40573-020-00116-9.
    22. G. Liu and N. Fang, “The effects of enhanced hands-on experimentation on correcting student misconceptions about work and energy in engineering mechanics,” Res. Sci. Technol. Educ., vol. 41, no. 2, pp. 462–481, 2023, doi: 10.1080/02635143.2021.1909555.
    23. S. Flegr, J. Kuhn, and K. Scheiter, “When the whole is greater than the sum of its parts: Combining real and virtual experiments in science education,” Comput. Educ., vol. 197, p. 104745, 2023, doi: 10.1016/j.compedu.2023.104745.
    24. P. Coleman and A. Hosein, “Using voluntary laboratory simulations as preparatory tasks to improve conceptual knowledge and engagement,” Eur. J. Eng. Educ., vol. 48, no. 5, pp. 899–912, Sep. 2023, doi: 10.1080/03043797.2022.2160969.
    25. X.-M. Wang, X.-H. Yu, D. Yu, G.-J. Hwang, and M. Lan, “Does combining real and virtual experiments improve learning achievement in physics? Evidence from a meta-analysis (2001–2021),” Educ. Res. Rev., vol. 46, p. 100661, 2025, doi: 10.1016/j.edurev.2024.100661.
    26. J. Kranz, A. Baur, and A. Möller, “Learners’ challenges in understanding and performing experiments: A systematic review of the literature,” Stud. Sci. Educ., vol. 59, no. 2, pp. 321–367, Jul. 2023, doi: 10.1080/03057267.2022.2138151.
    27. A. Van den Beemt, S. Groothuijsen, L. Ozkan, and W. Hendrix, “Remote labs in higher engineering education: Engaging students with active learning pedagogy,” J. Comput. High. Educ., vol. 35, no. 2, pp. 320–340, 2023, doi: 10.1007/s12528-022-09331-4.
    28. M. Tong, S. Chen, Y. Niu, and C. Xue, “Effects of speed, motion type, and stimulus size on dynamic visual search: A study of radar human–machine interface,” Displays, vol. 77, p. 102374, 2023, doi: 10.1016/j.displa.2023.102374.
    29. S. Han, “Improvement of an LCD-based calibration site for reliable focus-dependent camera calibration,” KSCE J. Civ. Eng., vol. 26, no. 3, pp. 1365–1375, 2022, doi: 10.1007/s12205-021-5464-x.
    30. K. Wang, “Dynamic prediction of adolescent sports injury probability based on the improved ARIMA model in the background of digital physical education teaching,” Sci. Rep., vol. 16, no. 1, p. 1903, 2025, doi: 10.1038/s41598-025-31591-0.
    31. K. Hippalgaonkar, Q. Li, X. Wang, J. W. Fisher, J. Kirkpatrick, and T. Buonassisi, “Knowledge-integrated machine learning for materials: Lessons from gameplaying and robotics,” Nat. Rev. Mater., vol. 8, no. 4, pp. 241–260, 2023, doi: 10.1038/s41578-022-00513-1.
    32. T. Pierratos, “Encounter with a rectilinear uniformly accelerated motion in a flipped classroom: Enhancing students’ data processing skills,” Phys. Educ., vol. 56, no. 5, p. 55017, 2021, doi: 10.1088/1361-6552/ac080b.
    33. R. Hartono and T. Pahlevi, “Development of assessment instruments based on hots at Surabaya vocational high school,” JISAE J. Indones. Student Assess. Eval., vol. 6, no. 2 SE-Articles, pp. 210–218, Aug. 2020, doi: 10.21009/jisae.v6i2.15853.
    34. P. C. Zulfiani and S. Sugiyono, “Developing an interactive learning multimedia basic competence for using facial care tools with technology in students of vocational education,” J. Phys. Conf. Ser., vol. 1700, no. 1, p. 12089, 2020, doi: 10.1088/1742-6596/1700/1/012089.
    35. K. Okoye and S. Hosseini, “T-test Statistics in R: Independent Samples, Paired Sample, and One Sample T-tests,” in R Programming: Statistical Data Analysis in Research, K. Okoye and S. Hosseini, Eds., Singapore: Springer Nature Singapore, 2024, pp. 159–186. doi: 10.1007/978-981-97-3385-9_8.
    36. A. Hashemifardnia, S. Shafiee, F. Rahimi Esfahani, and M. Sepehri, “Effects of flipped instruction on iranian intermediate EFL learners’ speaking complexity, accuracy, and fluency,” Cogent Educ., vol. 8, no. 1, p. 1987375, Jan. 2021, doi: 10.1080/2331186X.2021.1987375.
    37. N. Barz, M. Benick, L. Dörrenbächer-Ulrich, and F. Perels, “The effect of digital game-based learning interventions on cognitive, metacognitive, and affective-motivational learning outcomes in school: A meta-analysis,” Rev. Educ. Res., vol. 94, no. 2, pp. 193–227, Apr. 2024, doi: 10.3102/00346543231167795.
    38. B. Wu, X. Yu, and X. Gu, “Effectiveness of immersive virtual reality using head-mounted displays on learning performance: A meta-analysis,” Br. J. Educ. Technol., vol. 51, no. 6, pp. 1991–2005, Nov. 2020, doi: https://doi.org/10.1111/bjet.13023.
    39. J. Bernhard, “What matters for students’ learning in the laboratory? Do not neglect the role of experimental equipment!,” Instr. Sci., vol. 46, no. 6, pp. 819–846, 2018, doi: 10.1007/s11251-018-9469-x.
    40. J. Winkelmann, S. F. C. Wenzel, M. Ullrich, H. Horz, and R. Erb, “Three practices of experimenting in the physics classroom – impact on students’ content knowledge acquisition,” Int. J. Sci. Educ., vol. 48, no. 9, pp. 1311–1333, Jun. 2026, doi: 10.1080/09500693.2025.2453954.
    41. F. Coştu and H. Bayram, “From cookbook lab to PEODE-based lab: Redesigning lab activities to foster conceptual understanding,” Res. Sci. Technol. Educ., vol. 43, no. 3, pp. 818–839, Jul. 2025, doi: 10.1080/02635143.2024.2342884.
    42. F. E. Babalola, S. J. Fakoyede, F. B. Ojobola, M. A. Ayodeji, S. K. Ekundayo, and D. O. Alabi, “Evaluating the effectiveness of multimedia integration in enhancing physics instruction in secondary schools,” Discov. Educ., vol. 5, no. 1, p. 46, 2025, doi: 10.1007/s44217-025-00871-2.
    43. J. M. May, “Historical analysis of innovation and research in physics instructional laboratories: Recurring themes and future directions,” Phys. Rev. Phys. Educ. Res., vol. 19, no. 2, p. 20168, Dec. 2023, doi: 10.1103/PhysRevPhysEducRes.19.020168.