Integration of Electrical Conductivity Concept in Measurement and Instrumentation Learning: Case Study on the Use of Soil Fertility Monitoring Tool for Electrical Engineering Students

Rahmaniar Rahmaniar; Najla Lubis; Agus Junaidi; Ihsan Fahreza

doi:10.35445/alishlah.v16i4.6218

Integration of Electrical Conductivity Concept in Measurement and Instrumentation Learning: Case Study on the Use of Soil Fertility Monitoring Tool for Electrical Engineering Students

Rahmaniar Rahmaniar, Najla Lubis, Agus Junaidi, Ihsan Fahreza

Abstract

This research examines the integration of the concept of electrical conductivity in the Measurement and Instrumentation course through a soil fertility monitoring tool. Using a mixed methods approach, the study involved 30 third-year Electrical Engineering students who engaged with a specially designed soil monitoring system as a learning medium and practicum tool. Quantitative analysis showed a significant increase in student understanding, with the mean test score increasing from 42.3 (SD = 8.7) in the pre-test to 78.5 (SD = 9.5) in the post-test (p < 0.001, η² = 0.61). Practical skills assessment showed that 83% of students achieved proficiency in tool calibration, while 90% successfully integrated electrical conductivity data with other parameters. Qualitative findings showed an increase in student engagement, with 92% reporting an increase in learning motivation. This implementation resulted in three main outcomes: improved theoretical understanding through hands-on experience, improved technical measurement skills, and the development of an interdisciplinary perspective linking engineering to agricultural applications. The findings suggest that integrating real-world applications into engineering education can significantly improve technical competence and practical skills, providing a model for curriculum development in other engineering disciplines. This study offers valuable insights for engineering educators who want to bridge the gap between theoretical knowledge and practical applications in measurement and instrumentation courses.

Keywords

Electrical Conductivity, Soil Fertility Monitoring, Measurement and Instrumentation, Case-based Learning, Electrical Engineering

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References

Al Ehaideb, A., Alfadley, A., Alghilan, M. A., Farook, F. F., Alhazmi, N., Castaneda, E., … Alkadi, L. (2024). The Development and Utilization of an Electronic Assessment Software in Dental Education: A Comprehensive Evaluation. Saudi Dental Journal, 36(7), 1043–1050. https://doi.org/10.1016/j.sdentj.2024.05.010

Al Mamun, M. R., Ashik-E-Rabbani, M., Haque, M. M., & Upoma, S. M. (2024). IoT-based real-time biofloc monitoring and controlling system. Smart Agricultural Technology, 9(October), 100598. https://doi.org/10.1016/j.atech.2024.100598

Beagon, U., Kövesi, K., Tabas, B., Nørgaard, B., Lehtinen, R., Bowe, B., … Spliid, C. M. (2023). Preparing engineering students for the challenges of the SDGs: what competences are required? European Journal of Engineering Education, 48(1), 1–23. https://doi.org/10.1080/03043797.2022.2033955

Burde, J. P., Weatherby, T. S., & Kronenberger, A. (2021). An analogical simulation for teaching electric circuits: A rationale for use in lower secondary school. Physics Education, 56(5). https://doi.org/10.1088/1361-6552/ac03fe

Cottafava, D., Ascione, G. S., Corazza, L., & Dhir, A. (2022). Sustainable development goals research in higher education institutions: An interdisciplinarity assessment through an entropy-based indicator. Journal of Business Research, 151(July), 138–155. https://doi.org/10.1016/j.jbusres.2022.06.050

Debnath, D., Baidya, B., & Ghosh, S. (2023). Performance analysis of Arduino processor based hybrid power generation system (HPGS). Materials Today: Proceedings, 72, 3023–3028. https://doi.org/10.1016/j.matpr.2022.08.375

Deepti Ameta, Saurabh Tiwari, P. S. (2020). A Preliminary Study on Case-Based Learning Teaching Pedagogy: Scope in SE Education. International Symposium on Electronic Commerce. https://doi.org/DOI:10.1145/3385032.3385045

Dhanaraju, M., Chenniappan, P., Ramalingam, K., Pazhanivelan, S., & Kaliaperumal, R. (2022). Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture. Agriculture (Switzerland), 12(10), 1–26. https://doi.org/10.3390/agriculture12101745

Eitzel, M. V., Mhike Hove, E., Solera, J., Madzoro, S., Changarara, A., Ndlovu, D., … Ndlovu, M. (2018). Sustainable development as successful technology transfer: Empowerment through teaching, learning, and using digital participatory mapping techniques in Mazvihwa, Zimbabwe. Development Engineering, 3(October 2017), 196–208. https://doi.org/10.1016/j.deveng.2018.07.001

Fuertes-Camacho, M. T., Graell-Martín, M., Fuentes-Loss, M., & Balaguer-Fàbregas, M. C. (2019). Integrating sustainability into higher education curricula through the project method, a global learning strategy. Sustainability (Switzerland), 11(3). https://doi.org/10.3390/su11030767

Hidayati, F. H., & Evy Wisudariani, E. W. (2023). Pengaruh Pembelajaran Berbasis Kasus (Case Based Learning) dalam Meningkatkan Kreativitas dan Kemampuan Berpikir Mahasiswa. Biodik, 9(2), 180–190. https://doi.org/10.22437/biodik.v9i2.20821

Idrus, H. H., Mustamin, M., & Zulfahmidah. (2023). Evaluation of a Multidisciplinary Extracurricular Event Using Kolb’s Experiential Learning Theory: A Qualitative Study. Journal of Multidisciplinary Healthcare, 16(December), 39–40. https://doi.org/10.2147/JMDH.S402844

Iyer, R., & Mihalas, S. (2017). Cortical circuits implement optimal context integration. BioRxiv, 158360. https://doi.org/https://doi.org/10.1101/158360 ;

Junaidi, A., Afandi, M., Pangaribuan, W., & K, A. H. (2024). Enhancing Electrical Power Engineering Education in North Sumatra : Evaluating the Effectiveness and Practicality of Consortium Collaborations Between Academia and Industry. Al-Ishlah: Jurnal Pendidikan, 16, 2953–2966. https://doi.org/10.35445/alishlah.v16i3.5446

Kaur, R. (2014). Case based learning as an innovative teaching tool. International Journal of Basic & Clinical Pharmacology, 3(2), 1. https://doi.org/10.5455/2319-2003.ijbcp20140428

Kumar, A., Alghamdi, S. A., Mehbodniya, A., anul haq, M., Webber, J. L., & Navruzbek Shavkatovich, S. (2022). Smart power consumption management and alert system using IoT on big data. Sustainable Energy Technologies and Assessments, 53(October). https://doi.org/10.1016/j.seta.2022.102555

Lazanas, A. C., & Prodromidis, M. I. (2023). Electrochemical Impedance Spectroscopy─A Tutorial. ACS Measurement Science Au, 3(3), 162–193. https://doi.org/10.1021/acsmeasuresciau.2c00070

Lenoir, Y. (2022). Interdisciplinarity in basic education: interpretations, relevance and principles. International Encyclopedia of Education: Fourth Edition, 267–280. https://doi.org/10.1016/B978-0-12-818630-5.03050-5

Lim, J., & Kang, Y. (2024). Comparing the impact of PASS-BAR handoff education for new nurses between simulation-based and case-based approaches: A quasi-experimental design. Nurse Education in Practice, 79(August). https://doi.org/10.1016/j.nepr.2024.104036

Lubis, N., Wasito, M., Damayanti, R., & Hayati, D. (2024a). Analisis Kadar Unsur Hara Dari Pupuk Organik Cair Bioenzim Berbahan Dasar Limbah Pertanian Analysis of Nutrient Levels from Bioenzyme Liquid Organic Fertilizer Made from Agricultural Waste. Peningkatan Ketahanan Pangan Melalui Adaptasi Perubahan Iklim Untuk Pertanian Berkelanjutan, 661–667. Jember: Agropross, National Conference Proceedings of Agriculture. https://doi.org/2964-0172

Lubis, N., Wasito, M., Damayanti, R., & Hayati, D. (2024b). Pupuk Organik Cair Berbahan Dasar Ekoenzim dari Hasil dan Limbah Pertanian sebagai Nutrisi pada Sistem Hidroponik. BIOFARM Jurnal Ilmiah Pertanian, 20(1), 123–129.

Michael, B., H. (2016). Electrical Conductivity and Resistivity. https://doi.org/10.1201/B15664-29

Nair, S., Vataliya, S., Desai, D., & Lele, P. D. (2020). Designing of Digital Processing System Using ADS1115 and Arduinouno. (December).

Potortì, F., Rosa, D. La, & Palumbo, F. (2023). Enerduino-pro: Smart meter led probe using Arduino. HardwareX, 15(July 2022), e00461. https://doi.org/10.1016/j.ohx.2023.e00461

Rahmaniar. (2019). Model Flash-Nr Pada Analisis Sistem Tenaga Listrik. Universitas Negeri Padang.

Rahmaniar, R., Junaidi, A., Tarigan, A. S. P., & Butar, A. H. B. (2022). Digital Simulation of Short Circuit Current Calculation Using Graphical User Interface for Learning Media Electric Power System Analysis. International Journal Of Artificial Intelegence Research, 6(1). https://doi.org/10.29099/ijair.v6i1.360

Rico-Juan, J. R., Peña-Acuña, B., & Navarro-Martinez, O. (2024). Holistic exploration of reading comprehension skills, technology and socioeconomic factors in Spanish teenagers. Heliyon, 10(12). https://doi.org/10.1016/j.heliyon.2024.e32637

Sanchez, M. R. V., Mishima, S., Fujiwara, M., Ai, G., Jouaiti, M., Kobryn, Y., … Wagatsuma, H. (2020). Methodological design for integration of human eeg data with behavioral analyses into human-human/robot interactions in a real-world context. ICIC Express Letters, 14(7), 693–701. https://doi.org/10.24507/icicel.14.07.693

Saqr, M., López-Pernas, S., Helske, S., & Hrastinski, S. (2023). The longitudinal association between engagement and achievement varies by time, students’ profiles, and achievement state: A full program study. Computers and Education, 199(July 2022). https://doi.org/10.1016/j.compedu.2023.104787

Sun, L., & Zhou, L. (2023). Does text-based programming improve K-12 students’ CT skills? Evidence from a meta-analysis and synthesis of qualitative data in educational contexts. Thinking Skills and Creativity, 49(September). https://doi.org/10.1016/j.tsc.2023.101340

Suparya, I. K. (2024). Designing Flexibility, Accessibility, and Independent Learning through the Use of Hyperapp Blended Learning Module. Jurnal Komunikasi Pendidikan, 8(1), 1–10. https://doi.org/https://doi.org/10.32585/jurnalkomdik.v8i1.5015

Tandoc, B. T. btakahas and E. C. (2015). Media sources, credibility, and perceptions of science: Learning about how people learn about science. Sage Journals, 25(6). https://doi.org/https://doi.org/10.1177/0963662515574986

Wei, X., Liu, C., Liu, Y., Li, Y., & Zhang, K. (2023). Next location recommendation: a multi-context features integration perspective. World Wide Web, 26(4), 2051–2074. https://doi.org/10.1007/s11280-022-01126-y

Xu, G., Lin, Z., Wu, Q., Tan, J., & Chan, W. K. V. (2024). Bi-level hierarchical model with deep reinforcement learning-based extended horizon scheduling for integrated electricity-heat systems. Electric Power Systems Research, 229(April). https://doi.org/10.1016/j.epsr.2024.110195

Xu, J., Zhang, X., & Zou, L. (2022). A review: Progress and perspectives of research on the functionalities of phase change materials. Journal of Energy Storage, 54(October). https://doi.org/10.1016/j.est.2022.105341

Žalėnienė, I., & Pereira, P. (2021). Higher Education For Sustainability: A Global Perspective. Geography and Sustainability, 2(2), 99–106. https://doi.org/10.1016/j.geosus.2021.05.001

Zhang, X., Feng, G., & Sun, X. (2024). Advanced technologies of soil moisture monitoring in precision agriculture : A Review. Journal of Agriculture and Food Research, 18(May), 101473. https://doi.org/10.1016/j.jafr.2024.101473

DOI: https://doi.org/10.35445/alishlah.v16i4.6218