Teaching Reform of Microprocessor Course under Smart Forestry Background: A Case Study of Micro-Lesson Design for RS-485 Interface
DOI:
https://doi.org/10.63236/Keywords:
Microprocessor Course, Smart forestry, Micro-lesson design, on-site teaching, engineering educationAbstract
Microprocessor systems are fundamental to equipment intelligence; the rapid advancement of smart forestry has created new demands for reforming the Microprocessor Course in forestry universities. Traditional teaching methods often emphasized theory over practice and general applications over industry-specific characteristics, limiting students’ ability to solve real-world forestry engineering problems. This study presented a teaching reform initiative at Nanjing Forestry University, using the RS-485 interface as an example to design a scenario-based micro-lesson rooted in smart forestry. The reform integrated on-site teaching at forestry bases, instrument measurement demonstrations, and ideological and political elements, aiming to cultivate electromechanical talents with agricultural and forestry expertise. A quasi-experimental design was employed with two cohorts: the questionnaire analysis included valid responses from the control class (Grade 2023 of Intelligent Manufacturing, n=57) received the existing PPT-based instructional approach, while the experimental class (Grade 2024 of Intelligent Manufacturing, n=54) adopted the proposed micro-lesson model. Questionnaire responses indicated higher proportions of favorable ratings in the experimental cohort across knowledge comprehension, learning interest, engineering cognition, and professional identity; The findings suggest that the scenario-based micro-lesson may help connect theoretical learning with industrial practice and support students' perceived engagement and professional relevance. This reform provides a practical reference for improving microprocessor education and supporting talent development in smart forestry.
References
Arion, I. D., Morar, I. M., Truta, A. M., Chereches, I. A., Isarie, V. I., & Arion, F. H. (2025). Internet of Things (IoT)-based applications in smart forestry: A conceptual and technological analysis. Forests, 17(1), Article 44. https://doi.org/10.3390/f17010044
Behseresht, S., Love, A., Valdez Pastrana, O. A., & Park, Y. H. (2024). Enhancing fused deposition modeling precision with serial communication-driven closed-loop control and image analysis for fault diagnosis-correction. Materials, 17(7), Article 1459. https://doi.org/10.3390/ma17071459
Bibi, M., Skarmeta, A. F., & Khan, S. Z. (2026). Revolutionizing pest control: Harnessing cutting-edge technologies in controlling forest pests. Frontiers in Forests and Global Change, 9, Article 1777311. https://doi.org/10.3389/ffgc.2026.1777311
Castro, A., Martínez-Osuna, J. F., Michel, R., Escoto-Rodríguez, M., Bullock, S. H., Cueva, A., López-Reyes, E., Reimer, J., Salazar, M., Villarreal, S., & Vargas, R. (2017). A low-cost modular data-acquisition system for monitoring biometeorological variables. Computers and Electronics in Agriculture, 141, 357–371. https://doi.org/10.1016/j.compag.2017.08.010
Damaševičius, R., Mozgeris, G., Kurti, A., & Maskeliūnas, R. (2024). Digital transformation of the future of forestry: An exploration of key concepts in the principles behind Forest 4.0. Frontiers in Forests and Global Change, 7, Article 1424327. https://doi.org/10.3389/ffgc.2024.1424327
Daniela, L. (2021). Smart pedagogy as a driving wheel for technology-enhanced learning. Technology, Knowledge and Learning, 26(4), 711–718. https://doi.org/10.1007/s10758-021-09536-z
Dong, B., Zhang, M., Li, S., Xie, L., Xie, B., & Chen, L. (2025). How digital intelligence integration boosts forestry ecological productivity: Evidence from China. Forests, 16(8), Article 1343. https://doi.org/10.3390/f16081343
Ehrlich-Sommer, F., Hoenigsberger, F., Gollob, C., Nothdurft, A., Stampfer, K., & Holzinger, A. (2024). Sensors for digital transformation in smart forestry. Sensors, 24(3), Article 798. https://doi.org/10.3390/s24030798
Farrar, M. J. (2021). Arduino microprocessors as a platform for modeling framework labs. American Journal of Physics, 89(6), 596–602. https://doi.org/10.1119/10.0004856
Friedrich, G. R., & Reggiani, G. H. (2024). Data communication for low resources IoT devices: RS485 over electrical wires. IEEE Embedded Systems Letters, 16(1), 53–56. https://doi.org/10.1109/les.2023.3250162
Fu, R., Zhang, H., Wang, G., Zhu, X., Sun, H., Wang, L., Cui, Z., Zhang, J., & Yu, L. (2026). Improving the accuracy of DBH estimation in Chinese fir using multi-source data fusion and interpretable machine learning algorithms. Smart Forestry, 1(1), 1–15. https://doi.org/10.48130/smartfor-0026-0004
Gong, W., Choi, P., & Kim, D. K. (2020). Hardware implementation of lightweight block ciphers for IoT sensors. Journal of Semiconductor Technology and Science, 20(4), 381–389. https://doi.org/10.5573/jsts.2020.20.4.381
Hafiz, F., Emon, M. J. H., Hossain, M. A., Mukta, M. S. H., Islam, S., & Shatabda, S. (2025). Design of a microprocessors and microcontrollers laboratory course addressing complex engineering problems and activities. Computer Applications in Engineering Education, 33(2), Article e70006. https://doi.org/10.1002/cae.70006
Jacko, P., Bereš, M., Kováčová, I., Molnár, J., Vince, T., Dziak, J., Fecko, B., Gans, I., & Kováč, D. (2022). Remote IoT education laboratory for microcontrollers based on the STM32 chips. Sensors, 22(4), Article 1440. https://doi.org/10.3390/s22041440
Jeong, H., Kim, J., Kwon, J., & Park, I. C. (2026). Area-efficient word-serial architecture for Hamming encoding and decoding. Microprocessors and Microsystems, 122, Article 105274. https://doi.org/10.1016/j.micpro.2026.105274
Li, S., Zheng, N., Gao, Y., Quan, C., & Liu, W. (2021). Make experimental device to be a toy - the experimental teaching in digital logic circuit course. In 2021 IEEE Frontiers in Education Conference (FIE) (pp. 1–5). IEEE. https://doi.org/10.1109/fie49875.2021.9637282
Lou, X., Chen, S., Sun, L., Zheng, X., Huang, S., Dong, C., Wu, D., Liang, H., & Jiang, G. (2026). Development of low-power forest fire water bucket liquid level and fire situation monitoring device. Forests, 17(1), Article 126. https://doi.org/10.3390/f17010126
Maxworth, A. (2023). Designing engineering courses with embedded virtual and real experimentations. Education Sciences, 13(6), Article 610. https://doi.org/10.3390/educsci13060610
Ouederni, M. (2021). Compatibility checking for asynchronously communicating software. Science of Computer Programming, 205, Article 102569. https://doi.org/10.1016/j.scico.2020.102569
Pasricha, S. (2022). Embedded systems education: Experiences with application-driven pedagogy. IEEE Embedded Systems Letters, 14(4), 167–170. https://doi.org/10.1109/les.2022.3175686
Ren, R., Zhang, K., Qi, L., Zhao, M., Xie, W., Zhou, C., & Li, M. (2026). Monitoring carbon stock change at the individual-plant scale: A methodological review and integrative framework. Forests, 17(5), Article 563. https://doi.org/10.3390/f17050563
Ryalat, M., Franco, E., Elmoaqet, H., Almtireen, N., & Al-Refai, G. (2024). The integration of advanced mechatronic systems into Industry 4.0 for smart manufacturing. Sustainability, 16(19), Article 8504. https://doi.org/10.3390/su16198504
Shi, J., Zhang, Z., Gao, R., & Chen, Z. (2025). Cultivating talents at tertiary agricultural institutions in China for sustainable and intelligent development. Sustainability, 17(21), Article 9754. https://doi.org/10.3390/su17219754
Sládek, I., Skovajsa, M., Kuchár, P., Kafková, J., Šedivý, T., & Gašpar, G. (2025). A universal I2C-to-RS-485 module for industrial sensing. Electronics, 14(18), Article 3675. https://doi.org/10.3390/electronics14183675
Sudharsan, B., Breslin, J. G., & Ali, M. I. (2022). ML-MCU: A framework to train ML classifiers on MCU-based IoT edge devices. IEEE Internet of Things Journal, 9(16), 15007–15017. https://doi.org/10.1109/jiot.2021.3098166
Szabó, L., Jurca, N. F., Popa, D. C., & Ferenci, S. (2025). Embedding the Sustainable Development Goals in electromechanical engineering curricula: Preparing engineers for sustainable industry. In 2025 International Conference on Electromechanical and Energy Systems
(SIELMEN) (pp. 257–262). IEEE. https://doi.org/10.1109/sielmen67352.2025.11260649
Taali, Desky, A. H. A., Pratama, A. J., & Setiawan, H. (2024). Effectiveness of blended learning model in microprocessor course with Google Classroom. Jurnal Penelitian Pendidikan IPA, 10(7), 3674–3680. https://doi.org/10.29303/jppipa.v10i7.7754
Torresan, C., Benito Garzón, M., O’Grady, M., Robson, T. M., Picchi, G., Panzacchi, P., Tomelleri, E., Smith, M., Marshall, J., Wingate, L., Tognetti, R., Rustad, L. E., & Kneeshaw, D. (2021). A new generation of sensors and monitoring tools to support climate-smart forestry practices. Canadian Journal of Forest Research, 51(12), 1751–1765. https://doi.org/10.1139/cjfr-2020-0295
Tupac-Yupanqui, M., Vidal-Silva, C., Pavesi-Farriol, L., Sanchez Ortiz, A., Cardenas-Cobo, J., & Pereira, F. (2022). Exploiting Arduino features to develop programming competencies. IEEE Access, 10, 20602–20615. https://doi.org/10.1109/access.2022.3150101
Vatandaslar, C., Boston, K., Ucar, Z., Narine, L. L., Madden, M., & Akay, A. E. (2025). Precision forestry revisited. Remote Sensing, 17(20), Article 3465. https://doi.org/10.3390/rs17203465
Venanzi, R., Latterini, F., Civitarese, V., & Picchio, R. (2023). Recent applications of smart technologies for monitoring the sustainability of forest operations. Forests, 14(7), Article 1503. https://doi.org/10.3390/f14071503
Wardiyani, G. C., Karna, N., & Istikmal (2021). Designing a teaching aid for microprocessor class: Case study microprocessor interconnection with memory. In 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) (pp. 958–963). IEEE. https://doi.org/10.1109/iemtronics52119.2021.9422497
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