Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

Abidi, Syed Muhammad Raza; Hussain, Mushtaq; Xu, Yonglin; Zhang, Wu

doi:10.20944/preprints201811.0460.v1

Cited by 13 publications

(6 citation statements)

References 27 publications

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“…Here, we can see that all independent variables are significant predictors based on their P-values [62], as shown in Table 4. For statistical analysis, we used the statistical programming language R [63] and used RStudio [64] to perform basic descriptive and regression analysis.…”

Section: ) Statistical Analysismentioning

confidence: 72%

Spatiotemporal Patterns of Visitors in Urban Green Parks by Mining Social Media Big Data Based Upon WHO Reports

et al. 2020

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Green parks in urban areas are believed to enhance the well-being of residents. The importance of green spaces to support health and fitness in urban areas has recently regained interest. Reports released in 2010-2016 by the World Health Organization (WHO) on urban planning, environment, and health stated that green spaces can have a positive impact on physical activity, social and mental well-being, enhance air quality and decrease noise exposure. We analyzed the number of check-ins in various parks of Shanghai by utilizing geotagged social media network check-in data. This article presents a descriptive study using social media data by obtaining the three-year comparison of spatial and temporal patterns of park visits to raise public awareness that green parks provide a healthy environment that can be beneficial for the wellbeing of urban citizens. We investigated the visitor spatiotemporal behavior in more than 115 green parks in 10 districts of Shanghai with approximately 250,000 check-ins. We examined 3 years of geotagged data and our main findings are: (i) the spatial and temporal variations of users in urban green parks (ii) the gender differences in space and time with relation to urban green parks. The main objective of this article is to present evident data for policymakers on the advantages of providing green spaces access to urban citizens and to facilitate cities with systematic approaches to provide green space access to improve the health of urban citizens. INDEX TERMS Urban green parks, big data, social networks, spatiotemporal, KDE, data mining.

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Section: ) Statistical Analysismentioning

confidence: 72%

Spatiotemporal Patterns of Visitors in Urban Green Parks by Mining Social Media Big Data Based Upon WHO Reports

et al. 2020

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“…Data mining techniques can analyze more valuable information hidden in a large number of datasets [9]. Educational data mining is to know and analyze the situation of students from educational data through learning analysis tools [10]. Therefore, we investigated the application of various data mining techniques and machine learning models in the field of education.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Costa et al used educational data mining (EDM) techniques to predict student failure rates in introductory programming courses at an early enough stage [12]. Abidi et al identified in advance the student group who would be confused when doing algebra homework by seven machine learning methods such as random forest (RF) and gradient boosting tree (XGBoost) to help them learn knowledge and develop talent [10]. Jokhan et al developed an early warning system (EWS) to predict student performance in an IT course at a university in the South Pacific area through the correlation between online behavior and grades, with a prediction accuracy of 60.8% [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Students learning performance prediction based on feature extraction algorithm and attention-based bidirectional gated recurrent unit network

Yin,

Tang,

Zhang

et al. 2023

PLoS ONE

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With the development of information technology construction in schools, predicting student grades has become a hot area of application in current educational research. Using data mining to analyze the influencing factors of students’ performance and predict their grades can help students identify their shortcomings, optimize teachers’ teaching methods and enable parents to guide their children’s progress. However, there are no models that can achieve satisfactory predictions for education-related public datasets, and most of these weakly correlated factors in the datasets can still adversely affect the predictive effect of the model. To solve this issue and provide effective policy recommendations for the modernization of education, this paper seeks to find the best grade prediction model based on data mining. Firstly, the study uses the Factor Analyze (FA) model to extract features from the original data and achieve dimension reduction. Then, the Bidirectional Gate Recurrent Unit (BiGRU) model and attention mechanism are utilized to predict grades. Lastly, Comparing the prediction results of ablation experiments and other single models, such as linear regression (LR), back propagation neural network (BP), random forest (RF), and Gate Recurrent Unit (GRU), the FA-BiGRU-attention model achieves the best prediction effect and performs equally well in different multi-step predictions. Previously, problems with students’ grades were only detected when they had already appeared. However, the methods presented in this paper enable the prediction of students’ learning in advance and the identification of factors affecting their grades. Therefore, this study has great potential to provide data support for the improvement of educational programs, transform the traditional education industry, and ensure the sustainable development of national talents.

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“…Uskov et al (2019) examined the machine learning predictions of student academic performance in STEM (Science, Technology, Engineering, and Mathematics) education. Abidi et al (2019) investigated models for predicting confused students who try to do homework using ITS (Intelligent tutoring systems). In their studies, they used naïve Bayes (NB), generalized linear model (GLM), logistic regression (LR), deep learning (DL), decision tree (DT), random forest (RF), and gradient boosted trees (XGBoost) machine learning models.…”

Section: Introductionmentioning

confidence: 99%

Estimation of High School Entrance Examination Success Rates Using Machine Learning and Beta Regression Models

Koç¹,

Akin²

2022

Journal of Intelligent Systems: Theory and Applications

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Education is the foundation of economic, social, and cultural development for every individual and society as a whole. Students are accepted to secondary education institutions with the high school entrance examination made by the Ministry of National Education in Turkey. In this study, the success rates of the students who took the high school entrance examination in Turkey's 81 provinces in 2019 were handled with the machine learning regression and beta regression model. The present paper aimed to model, predict, and explain students' success rates using variables such as divorce rate, gross domestic product, illiteracy, and higher education populations. Support vector regression, random forest, decision tree, and beta regression model were applied to estimate success rates. Two models with the highest R 2 value were found to be beta regression and random forest models. When the prediction errors of beta regression and random forest model were examined, it seemed to be that the random forest model is relatively superior to the beta regression model in predicting the success rates. While the beta regression model was the best predictor of the success rates of Çanakkale province, the random forest model predicted the success rates of Ankara well. Also, it was seen that the variables found to be significant in the beta regression model for success rates were also crucial in the random forest model. It is recommended to use both the beta and random forest models to estimate the students' success rates.

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Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

Cited by 13 publications

References 27 publications

Spatiotemporal Patterns of Visitors in Urban Green Parks by Mining Social Media Big Data Based Upon WHO Reports

Spatiotemporal Patterns of Visitors in Urban Green Parks by Mining Social Media Big Data Based Upon WHO Reports

Students learning performance prediction based on feature extraction algorithm and attention-based bidirectional gated recurrent unit network

Estimation of High School Entrance Examination Success Rates Using Machine Learning and Beta Regression Models

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