An evaluation of machine learning approaches in educational environments: a systematic literature review

Rocha, Lucio Agostinho

doi:10.1590/1983-3652.2025.54910

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Articles • Texto livre 18 • 2025 • https://doi.org/10.1590/1983-3652.2025.54910 linkcopy

An evaluation of machine learning approaches in educational environments: a systematic literature review

Uma avaliação de abordagens de aprendizado de máquina em ambientes educacionais: uma revisão sistemática da literatura

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Abstract

The goal of this article is to investigate how methodologies based on machine learning are being applied in educational environments to enhance the quality of learning in graduation courses. The method is presented through a systematic review of literature on the main methodologies of systems based on artificial intelligence applied in smart classrooms. The results indicate that in the popularization of machine learning, the widespread use of these technologies presents new challenges for educators as learning agents who investigate learning strategies alongside their learners. The learning with these recent technologies faces challenges regarding the veracity, soundness, and quality of information. In the classroom, it is important to take into account the legal aspects and ethics principles involved in these recent machine learning advances. Besides, these issues involve how popular platforms combine data from multiple sources to generate useful information with reduced human intervention. Nowadays, society lives connected to a myriad of interconnected computational devices. In this sense, the classrooms are evolving into learning spaces where students learn together in a collaborative way and shape soft-skills abilities, such as communication, relationship, and collaboration.

Keywords:
Educational research; Smart classroom; Machine learning; Learning processes; Artificial Intelligence

Items of Inclusion Criteria	Items of Exclusion Criteria
a) The studies that address the concepts of the search string: (“artificial intelligence” OR “machine learning”) AND (“educational research” AND “smart classroom”).	a) The proposals and concepts not applied in the classroom;
b) The articles written in English published in the last five years, starting from 2020 until the first half of 2024.	b) The papers without pedagogical analysis of artificial intelligence technologies in graduation courses.
c) The practical application of artificial intelligence to the improvement of learning in graduation courses.

Catalogue Sources	Step 1 Selection Phase: Inclusion criteria	Step 2 Evaluation Phase: Exclusion criteria (a)	Step 3 Synthesis Phase: Exclusion criteria (b)
IEEE	72	32	26
Elsevier	9	2	2
DOAJ	9	5	5
Google Scholar	78	42	40
Total	168	81	73

Methodology	Software Tools	Smart Classrooms
1. Massive Open Online Courses (MOOCs) and Online platforms for e-learning.	a) Google Classroom; b) Canvas; c) Blackboard; d) Moodle; e) BigBlueButton; f) chat online meeting like videoconference (For example: Zoom, WebEx, Google Meeting, Skype, Microsoft Teams); g) online quizzes; h) YouTube; i) Facebook; j) 2U; k) Loud Cloud; l) Adaptcourseware; m) Anewspring; n) customized e-learning platforms based on Fog/Cloud Computing and others.	Zhang et al. (2022), Bhatnagar; Sharma (2022), Shaik et al., 2022, Wu et al. (2021), Syzdykbayeva; Baikulova; Kerimbayeva (2021).
2. Natural Language Processing (NLP)	a) Chatbots; b) chatGPT, and others.	Shaik et al., (2022), Tang; Li (2023).
3. Virtual/Augmented Mixed Reality	a) Neural networks; b) Markov chains; c) deep learning frameworks (e.g. Caffe); d) trained based models from public datasets; e) OpenPose; f) Tensorflow, and others.	Zhang et al. (2020), Haghighi et al. (2021).
4. Gamification	a) Learning management systems (LMS); b) immersive reality; c) simulations with serious game; d) interactive media; e) HelloTalk; f) Lingualeo; g) Hello English; h) Memrise; i) Duolingo; j) browser games and others.	Dahalan; Alias; Shaharom (2023), Syzdykbayeva; Baikulova; Kerimbayeva (2021), Zhu; Guo; Yang (2023).
5. Big Data Analysis	a) Citespace; b) VOSviewer; c) Bibliometrix; d) Pytorch; e) word clouds; f) Spssau; g) Ucinet; h) Netdraw; i) datasets from questionnaires; j) public facial expression datasets; k) ML algorithms, and others.	Bhatnagar; Sharma (2023), Chai; Ye (2024), Bansal; Ahmed (2023), Madhani; Shah; Swarnalatha et al. (2022), Li; Yao (2023).
6. Performance Prediction	a) Random Forest (RF); b) K-nearest neighbors (KNN); c) artificial neural networks (ANN); d) support vector machine (SVM); e) Gradient Boosted Regression Trees (GBRT); f) Long Short-Term Memory (LSTM); and others.	He; Hu (2022), Luan; Tsai (2021), Zhao et al. (2020), Zhao et al. (2022), Wu et al. (2021).
7. Monitoring and Surveillance with Computer Vision	a) Computer vision algorithms such as YOLO, R-CNN series, etc.; b) recording softwares, and others.	Pang; Lai; Zhang et al. (2023), Song; Zhao; Xiong et al. (2022), Shanshan; Hui; Liqiao (2024).

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