A Review of Intelligent Environmental Monitoring Systems Leveraging IoT and Machine Learning in the Context of Smart Education

Authors

  • Dr. Djuwari Universitas Nadlatul Ulama Surabaya,,Indonesia Author
  • Dr. S. R. Rajkumar Head & Librarian,Rev.Fr.E.J.Thomas .SJ.Library & Information Centre,St.Joseph’s Col-lege,Pilathara,Kannur University, Kerala, India Author
  • Rev.Fr.Rajan Fausto Manager,St.Joseph’s College,Pilathara,Kannur University ,Kerala ,India Author

DOI:

https://doi.org/10.59436/ijpsr.v1i1.2.3139-342X

Keywords:

Intelligent Environmental Monitoring,Smart Education,Internet of Things (IoT), Machine Learning (ML), Real-Time Data Analytics, Smart Classrooms, Adaptive Learning Environments, Educational Technology, Environmental Sensors

Abstract

help improve the learning experience and create healthier, more sustainable places for students. This review looks at the newest ideas, trends, and developments in envi-ronmental monitoring technologies that use the Internet of Things (IoT) and Machine Learn-ing (ML) in smart education environments. These systems collect and analyze real-time data on factors like temperature, humidity, air quality, and noise levels. This helps create learning spaces that can change and adapt, use energy more efficiently, and support student health and well-being.The review brings together recent studies on different parts of these systems, including how they are designed, the types of sensors used, how data is shared and processed, and the smart systems that make decisions.It also explains how Machine Learning helps pre-dict what might happen and detect problems early, which allows schools to manage their en-vironment more effectively. The review also discusses some main challenges, such as pro-tecting student data, making sure the systems can grow and work with existing school setups, and the need for common standards and rules.Overall, this review shows how smart environ-mental monitoring can make a big difference in creating smarter, more responsive, and inclu-sive learning environments.It also suggests where future research could go to encourage more innovation and help these technologies be used in many different types of schools and learn-ing environments.

References

Afzal A, Gul F, Muzzamil I (2025) Rethinking smart learning environments: addressing equi-ty, engagement, and future challenges. Contemp J Soc Sci Rev 3(1):1418–1432

Akour M, Alsghaier H, Aldiabat S (2020) Game-based learning approach to improve self-learning motivated students. Int J Technol Enhanc 12(2):146–160

Abdel-Basset M, Manogaran G, Mohamed M, Rushdy E (2019) Internet of things in smart education environment: Supportive framework in the decision‐making process. Concurr Comp 31(10):e4515

Alahi MEE, Sukkuea A, Tina FW, Nag A, Kurdthongmee W, Suwannarat K, Mukhopadhyay SC (2023) Integration of IoT- enabled technologies and artificial intelligence (AI) for Smart City scenario: recent advancements and future trends. Sensors 23(11):5206

Bano M, Zowghi D, Kearney M, Schuck S, Aubusson P (2018) Mobile learning for science and mathematics school education: a Systematic review of empirical evidence. Comput Educ 121:30–58

Barakina EY, Popova AV, Gorokhova SS, Voskovskaya AS (2021) Digital technologies and artificial intelligence technologies in education. Eur J Contemp Educ 10(2):285–296

Barfi KA, Bervell B, Arkorful V (2021) Integration of social media for smart pedagogy: ini-tial perceptions of senior high school students in Ghana. Educ Inf Technol 26(3):3033–3055

Chatterjee S, Majumdar D, Misra S, Damaševičius R (2020) Adoption of mobile applications for teaching-learning process in rural girls’ schools in India: an empirical study. Educ Inf Technol 25:4057–4076

Chehri A, Popova TN, Vinogradova NV, Burenina VI (2021) Use of innovation and emerg-ing technologies to address Covid-19- like pandemics challenges in education systems. Smart Edu E- Learn 2021:441–450

Chen S, Wang J (2023) Virtual reality human–computer interactive english education experi-ence system based on mobile terminal. Int J Hum Comput Int 2023:1–10

Demirbilek M (2010) Investigating attitudes of adult educators towards educational mobile media and games in eight European countries. J Inf Technol Educ Res 9(1):235–247

Dimililer K (2018) Use of Intelligent Student Mood Classification System (ISMCS) to achieve high quality in education. Qual Quant 52(1):651–662

El Janati S, Maach A, El Ghanami D (2018) SMART education framework for adaptation content presentation. Proc Comput Sci 127:436–443

Elsakova R, Kuzmina N, Kochkina D (2019) Smart technology integration in the system of bachelors’ language training. Int J Emerg Technol 14(15):25

Fu C, Jiang H, Chen X (2021) Big data intelligence for smart educational management sys-tems. J Intell Fuzzy Syst 40(2):2881–2890

Fussy, A.; Papenbrock, J. An overview of soil and soilless cultivation techniques—Chances, challenges and the neglected question of sustainability. Plants 2022, 11, 1153.

Galindo-Dominguez H (2021) Flipped classroom in the educational system. Educ Technol Soc 24(3):44–60

Gomede E, Gaffo FH, Briganó GU, De Barros RM, Mendes LdS (2018) Application of com-putational intelligence to improve education in smart cities. Sensors 18(1):267

Hamhuis E, Glas C, Meelissen M (2020) Tablet assessment in primary education: are there performance differences between TIMSS’ paper‐and‐pencil test and tablet test among Dutch grade‐four students? Br J Educ Technol 51(6):2340–2358

Harris LR, Adie L, Wyatt-Smith C (2022) Learning progression– based assessments: a sys-tematic review of student and teacher uses. Rev Educ Res 92(6):996–1040

Iqbal HM, Parra-Saldivar R, Zavala-Yoe R, Ramirez-Mendoza RA (2020) Smart educational tools and learning management systems: supportive framework. Int J Interact Des M 14(4):1179–1193

Ingrao, C.; Strippoli, R.; Lagioia, G.; Huisingh, D. Water scarcity in agriculture: An overview of causes, impacts and approaches for reducing the risks. Heliyon 2023, 9, e18507.

Jan, N. Min-Allah, D. Düştegör Iot based smart water quality monitoring: Recent techniques, trends and challenges for do- mestic applications Water. (Basel), 13 (13) (2021), p. 1729

Jang S (2014) Study on service models of digital textbooks in cloud computing environment for SMART education. Int J u - e- Serv Sci Tech 7(1):73–82

Jo J, Park K, Lee D, Lim H (2014) An integrated teaching and learning assistance system meeting requirements for smart education. Wirel Pers Commun 79(4):2453–2467

Kausar S, Huahu X, Ullah A, Wenhao Z, Shabir MY (2020) Fog- assisted secure data ex-change for examination and testing in E-learning system. Mob Netw Appl 2020:1–17

Kong F, Li J, Wang Y (2020) Human-computer interactive teaching model based on fuzzy set and BP neural network. J Intell Fuzzy Syst 37(1):103–113

Lee HS, Lee J (2021) Applying artificial intelligence in physical education and future per-spectives. Sustainability 13(1):351

Lee S, Lee K (2023) Smart teachers in smart schools in a smart city: teachers as adaptive agents of educational technology reforms. Learn Media Technol 2023:1–22

Li KC, Wong BTM (2022) Research landscape of smart education: a bibliometric analysis. Interact Technol Smart Educ 19(1):3– 19

Matthew UO, Kazaure JS, Okafor NU (2021) Contemporary development in E-Learning ed-ucation, cloud computing technology & internet of things. EAI Endorsed Trans Cloud Syst 7(20):e3–e3

Meng Q, Jia J, Zhang Z (2020) A framework of smart pedagogy based on the facilitating of high order thinking skills. Interact Technol Smart Educ 17(3):251–266

Mohammed CM, Zebaree SR (2021) Sufficient comparison among cloud computing services: IaaS, PaaS, and SaaS: a review. Int J Sci Bus 5(2):17–30

Nguyen A, Ngo HN, Hong Y, Dang B, Nguyen BPT (2023) Ethical principles for artificial intelligence in education. Educ Inf Technol 28(4):4221–4241

Nikolopoulou K (2020) Secondary education teachers’ perceptions of mobile phone and tab-let use in classrooms: benefits, constraints and concerns. J Comput Educ 7(2):257–275

Omonayajo B, Al-Turjman F, Cavus N (2022) Interactive and innovative technologies for smart education. Comput Sci Inf Syst 19(3):1549–1564

Ouyang F, Xu W, Cukurova M (2023) An artificial intelligence- driven learning analytics method to examine the collaborative problem-solving process from the complex adaptive sys-tems perspective. Int J Comp Support Collab Learn 18(1):39–66

Park W, Kwon H (2023) Implementing artificial intelligence education for middle school technology education in Republic of Korea. Int J Technol Des Educ 34(1):109–135

Perwej Y, Omer MK, Sheta OE, Harb HAM, Adrees MS (2019) The future of Internet of Things (IoT) and its empowering technology. Int J Eng Sci 2019:2

Peng H, Ma S, Spector JM (2019) Personalized adaptive learning: an emerging pedagogical approach enabled by a smart learning environment. Smart Learn Environ 6(1):1–14

Qasem YA, Abdullah R, Jusoh YY, Atan R, Asadi S (2021) Analyzing continuance of cloud computing in higher education institutions: should we stay, or should we go? Sustainability 13(9):4664

Raes A, Depaepe F (2020) A longitudinal study to understand students’ acceptance of technological reform. When experiences exceed expectations. Educ Inf Technol 25(1):533–552

Regan PM, Jesse J (2019) Ethical challenges of edtech, big data and personalized learning: twenty-first century student sorting and tracking. Ethics Inf Technol 21:167–179

Shapsough SY, Zualkernan IA (2020) A generic IoT architecture for ubiquitous context-aware learning. IEEE Trans Learn Technol 13(3):449–464

Shadiev R, Liu T, Hwang WY (2020) Review of research on mobile-assisted language learn-ing in familiar, authentic environments. Br J Educ Technol 51(3):709–720

Shu X, Gu X (2023) An empirical study of a smart education model enabled by the edu-metaverse to enhance better learning outcomes for students. Systems 11(2):75

Siriwardhana Y, Porambage P, Liyanage M, Ylianttila M (2021) A survey on mobile aug-mented reality with 5 G mobile edge computing: architectures, applications, and technical aspects. IEEE Commun Surv Tutor 23(2):1160–1192

Tang Y, Liang J, Hare R, Wang FY (2020) A personalized learning system for parallel intel-ligent education. IEEE Trans Comput Soc Syst 7(2):352–361

Tham JC, Verhulsdonck G (2023) Smart education in smart cities: layered implications for networked and ubiquitous learning. IEEE Trans. Technol Soc 4(1):87–95

Tobar-Muñoz H, Baldiris S, Fabregat R (2017) Augmented reality game-based learning: en-riching students’ experience during reading comprehension activities. J Educ Comput Res 55(7):901–936

UNESCO IITE, BNU, ISTE (2022) Report on National Smart Education Frame-work

https://iite.unesco.org/publications/report-onnational-smart- education-framework/. Ac-cessed 2 Feb 2023

Vallejo-Correa P, Monsalve-Pulido J, Tabares-Betancur M (2021) A systematic mapping re-view of context-aware analysis and its approach to mobile learning and ubiquitous learning processes. Comput Sci Rev 39:100335

van Leeuwen A, Janssen J (2019) A systematic review of teacher guidance during collabora-tive learning in primary and secondary education. Educ Res Rev 27:71–89

Wagner M, Urhahne D (2021) Disentangling the effects of flipped classroom instruction in EFL secondary education: when is it effective and for whom? Learn Instr 75:101490

Wang C, Zhao M, Wang Q, Li M (2020) A sentinel-based peer assessment mechanism for collaborative learning. Comput Mater Con 65(3)

Wang C, Zhao M, Wang Q, Li M (2020) A sentinel-based peer assessment mechanism for collaborative learning. Comput Mater Con 65(3)

Xie H, Chu HC, Hwang GJ, Wang CC (2019) Trends and development in technology-enhanced adaptive/personalized learning: a systematic review of journal publications from 2007 to 2017. Comput Educ 140:103599

Xu D, Glick D, Rodriguez F, Cung B, Li Q, Warschauer M (2020) Does blended instruction enhance English language learning in developing countries? Evidence from Mexico. Br J Educ Technol 51(1):211–227

Yang J, Sun Y, Lin R, Zhu H (2024) Strategic framework and global trends of national smart education policies. Hum Soc Sci Commun 11(1):1–13

Yang J, Shi G, Zhuang R, Wang Y, Huang R (2022) 5 G and smart education: educational reform based on intelligent technology. Front Educ China 17(4):490–509

Yoon M, Hill J, Kim D (2021) Designing supports for promoting self-regulated learning in the flipped classroom. J Comput High Educ 33:98–418

Yu J, Denham AR, Searight E (2022) A systematic review of augmented reality game-based learning in STEM education. Educ Technol. Educ Technol Res Dev 70(4):1169–1194

Zhou Y, Huang C, Hu Q, Zhu J, Tang Y (2018) Personalized learning full-path recommenda-tion model based on LSTM neural networks. Inf Sci 444:135–152

Zeeshan K, Hämäläinen T, Neittaanmäki P (2022) Internet of Things for sustainable smart education: an overview. Sustainability-Basel 14(7):4293

Zhou B (2022) Building a smart education ecosystem from a metaverse perspective. Mob Inf Syst 2022:1–10

Downloads

Published

2025-12-25

How to Cite

Dr. Djuwari, Dr. S. R. Rajkumar, & Rev.Fr.Rajan Fausto. (2025). A Review of Intelligent Environmental Monitoring Systems Leveraging IoT and Machine Learning in the Context of Smart Education. International Journal of Primary and Secondary Research (IJPSR), 1(1), 5-16. https://doi.org/10.59436/ijpsr.v1i1.2.3139-342X