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Predicting the Risk of Ultimate Low Ratings in Online Courses Using Machine Learning: Analyzing Engagement and Complexity for AI-Driven Early Instructional Communication Interventions
(2026-01-01)
Kraishan, Obada
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Jitkajornwanich, Kulsawasd
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Vijaranakul, Nattadet
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Kee, Kerk
Online asynchronous courses have become a common way of learning, especially after the COVID-19 epidemic. Despite their convenience and flexibility, these courses often suffer from low engagement and/or poor course quality, ultimately reflected by a low rating. A low rating is often noted at the end of a course, by when it is often too late to intervene or improve the course. The goal of this study is twofold. First, we aim to identify key factors influencing the ultimate ratings for online asynchronous courses. Second, we then build an AI model to predict the risk of ultimate low ratings before a course is fully completed. Inspired by Moore’s model of interaction, which includes two main factors: learner-content and learner-instructor interactions, we derived two features from the dataset: course complexity and course engagement. We used a dataset of 191,849 Udemy courses collected from May 2024 to August 2024. To predict the ultimate low ratings in advance so mid-course interventions are possible, we applied machine learning models, including Logistic Regression, Random Forest, and XGBoost. In our experiments, the XGBoost model achieved the highest performance with 71% accuracy, with course complexity and the number of reviews left by learners being the two most influential factors in predicting a low course rating. Constantly monitoring course complexity and the number of course reviews can help with early detection. The findings can provide valuable, actionable insights and guidance for online education platforms, especially for instructors to adjust their courses before the courses are complete.
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Enhancing risk communication and environmental crisis management through satellite imagery and AI for air quality index estimation
(2024-06-01)
Jitkajornwanich, Kulsawasd
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Vijaranakul, Nattadet
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Jaiyen, Saichon
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Srestasathiern, Panu
;
Lawawirojwong, Siam
Due to climate change, the air pollution problem has become more and more prominent [23]. Air pollution has impacts on people globally, and is considered one of the leading risk factors for premature death worldwide; it was ranked as number 4 according to the website [24]. A study, ‘The Global Burden of Disease,’ reported 4,506,193 deaths were caused by outdoor air pollution in 2019 [22,25]. The air pollution problem is become even more apparent when it comes to developing countries [22], including Thailand, which is considered one of the developing countries [26]. In this research, we focus and analyze the air pollution in Thailand, which has the annual average PM2.5 (particulate matter 2.5) concentration falls in between 15 and 25, classified as the interim target 2 by 2021′s WHO AQG (World Health Organization's Air Quality Guidelines) [27]. (The interim targets refer to areas where the air pollutants concentration is high, with 1 being the highest concentration and decreasing down to 4 [27,28]). However, the methodology proposed here can also be adopted in other areas as well. During the winter in Thailand, Bangkok and its surrounding metroplex have been facing the issue of air pollution (e.g., PM2.5) every year. Currently, air quality measurement is done by simply implementing physical air quality measurement devices at designated—but limited number of locations. In this work, we propose a method that allows us to estimate the Air Quality Index (AQI) on a larger scale by utilizing Landsat 8 images with machine learning techniques. We propose and compare hybrid models with pure regression models to enhance AQI prediction based on satellite images. Our hybrid model consists of two parts as follows: • The classification part and the estimation part, whereas the pure regressor model consists of only one part, which is a pure regression model for AQI estimation. • The two parts of the hybrid model work hand in hand such that the classification part classifies data points into each class of air quality standard, which is then passed to the estimation part to estimate the final AQI. From our experiments, after considering all factors and comparing their performances, we conclude that the hybrid model has a slightly better performance than the pure regressor model, although both models can achieve a generally minimum R<sup>2</sup> (R<sup>2</sup> > 0.7). We also introduced and tested an additional factor, DOY (day of year), and incorporated it into our model. Additional experiments with similar approaches are also performed and compared. And, the results also show that our hybrid model outperform them. Keywords: climate change, air pollution, air quality assessment, air quality index, AQI, machine learning, AI, Landsat 8, satellite imagery analysis, environmental data analysis, natural disaster monitoring and management, crisis and disaster management and communication.
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Air Quality Assessment Based on Landsat 8 Images Using Supervised Machine Learning Techniques
(2020-06-01)
Vijaranakul, Nattadet
;
Jaiyen, Saichon
;
Srestasathiern, Panu
;
Lawawirojwong, Siam
;
Jitkajornwanich, Kulsawasd
Since 2018 during the winter of every year (December - January), Thailand has been suffering from air pollution problems known as PM 2.5 toxic dust, affecting people's daily lives especially in Bangkok and its metroplex. To cope with this problem, one of the traditional methods used is to implement physical air quality measurement devices at specific locations. Currently there are 21 stations across Bangkok and surrounding areas. Each station can assess air quality at the station point along with the given radius, meaning that areas far away from the station will not be assessed properly. In this paper, we propose a methodology that incorporates satellite images for air quality assessment with supervised machine learning techniques. Several classification models tested in this paper are Decision Tree, Naïve Bayes, k-Nearest Neighbors (kNN), Random Forest, and Gradient Boosting. From our experiments, the highest performance model is Random Forest that has averaged accuracy of 0.914, averaged precision of 0.89, averaged recall of 0.814 and averaged F-1 score of 0.84825.
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