Comparative Analysis of Machine Learning Models for Students’ Performance Prediction

Ismail, Leila; Materwala, Huned; Hennebelle, Alain

doi:10.1007/978-3-030-71782-7_14

Cited by 17 publications

(5 citation statements)

References 17 publications

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“…However, we have seen less than 40% of research articles in the literature considered adopting feature selection methods/algorithms for their models. Many studies in the literature have tested their predictive models with and without using feature selection methods [86][87][88][89][90] , and their models revealed better results when applying feature selection methods. Albreiki et al's systematic literature review 15 also revealed that most of the research articles did not consider feature selection methods/algorithms for identifying features that are relevant to the prediction models.…”

Section: Discussionmentioning

confidence: 99%

Contextualizing the current state of research on the use of machine learning for student performance prediction: A systematic literature review

Alalawi

Athauda

Chiong

2023

Engineering Reports

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Today, educational institutions produce large amounts of data with the deployment of learning management systems. These large datasets provide an untapped potential to support and enhance decision‐making and operations. In recent times, machine learning (ML) has been applied to develop models utilizing this “big” data to assist in decision‐making. This study presents a systematic literature review into the application of ML to predict student performance. A total of 162 research articles from January 2010 to October 2022 were critically reviewed and analyzed by applying Kitchenham's systematic literature review approach. Our analysis categorized the literature predicting students' academic performance into two categories: (i) predicting student performance in assessments, courses or programs, and identifying students at‐risk of failing their course/program (129 studies); and (ii) predicting student dropout or retention in a course or program (33 studies). Classification is the most commonly used approach for predicting student performance (138 studies), followed by regression (25 studies) and clustering (9 studies). Supervised learning methods are used more often than semi‐supervised learning. Five most popular ML algorithms include the Decision Tree, Random Forest, Naïve Bayes, Artificial Neural Network, and Support Vector Machine. Historical records of students' grades and class performance, academic related data from learning management systems, and students' demographics are the most common features used for predicting students' performance. The most common methods used for feature selection are Information Gain‐based selection algorithms, Correlation‐based feature selection, and Gain Ratio. The general platforms/tools/libraries used in the studies include WEKA, Python, R, Rapid Miner, and MATLAB. We also investigated possible actions considered in the literature to help at‐risk students. We only found very few studies that deployed remedial actions and evaluated their impact on students' performance. In conclusion, ML has shown great potential in the prediction of student performance, but also has many areas of further research.

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Section: Discussionmentioning

confidence: 99%

Contextualizing the current state of research on the use of machine learning for student performance prediction: A systematic literature review

Alalawi

Athauda

Chiong

2023

Engineering Reports

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“…Ayya Nadar Janki Ammal college Sivakasi, Tamil Nadu from computer application department used classification techniques based on C5.0 algorithm, to help learner and teacher to improve the performance as pass or reappear [5]. Comparative analysis of different machine learning algorithm that is DT, NB, ANN, SVM and RF are done for student performance prediction, on student academic data set, Portuguese [6]. Accuracy rate and F measure of data set is measured with two forms that is with attribute selection and without attribute selection.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Student Performance Using Machine Learning Techniques: A Review

Yadav¹,

Deshmukh²

2023

Advances in Computer Science Research

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Data science and machine learning, over the years have proven very well-organized and significant in many sectors including education. Machine learning is an aspect of artificial intelligence in which a computing system can able to learn from data and make conclusions. The recent development in education sector provides assessment tools to predict the student performance by exploring education data using machine learning and data mining techniques. Student performance assessment is an important measurement metrics in education which affects the university accreditation. Student performance improvement plan must be implemented in those universities, by counselling the low performer students. It helps both students and teachers to overcome the problems experienced by the student during studies and teaching techniques of teachers. In this review paper, different student performance prediction literature related to find out low performer student. The survey results indicated that different machine learning techniques are used to overcome the problems related to predicting student at risk and assessment of student performance. Machine learning techniques plays an important role in progress and prediction of student performance, thus improving student performance prediction system.

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“…It is based on a pay-per-use model and can be provisioned with minimal management effort. With the emergence of the IoT and big data analytics applications in various domains such as healthcare [ 125 , 126 , 127 ], education [ 128 , 129 , 130 ], transportation [ 131 ], banking [ 132 , 133 ], energy utilities [ 134 , 135 ], and entertainment [ 136 , 137 ], cloud computing provides a sandbox for data processing and storage, enabling the deployment of compute-intensive smart city applications [ 138 ]. However, considering the distance between the IoT devices and the remote cloud servers, the latency requirements of time-critical applications may be violated.…”

Section: Taxonomy Of Technology-enabled Smart City Applications In 6g...mentioning

confidence: 99%

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

Ismail

Buyya

2022

Sensors

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The recent upsurge of smart cities’ applications and their building blocks in terms of the Internet of Things (IoT), Artificial Intelligence (AI), federated and distributed learning, big data analytics, blockchain, and edge-cloud computing has urged the design of the upcoming 6G network generation, due to their stringent requirements in terms of the quality of services (QoS), availability, and dependability to satisfy a Service-Level-Agreement (SLA) for the end users. Industries and academia have started to design 6G networks and propose the use of AI in its protocols and operations. Published papers on the topic discuss either the requirements of applications via a top-down approach or the network requirements in terms of agility, performance, and energy saving using a down-top perspective. In contrast, this paper adopts a holistic outlook, considering the applications, the middleware, the underlying technologies, and the 6G network systems towards an intelligent and integrated computing, communication, coordination, and decision-making ecosystem. In particular, we discuss the temporal evolution of the wireless network generations’ development to capture the applications, middleware, and technological requirements that led to the development of the network generation systems from 1G to AI-enabled 6G and its employed self-learning models. We provide a taxonomy of the technology-enabled smart city applications’ systems and present insights into those systems for the realization of a trustworthy and efficient smart city ecosystem. We propose future research directions in 6G networks for smart city applications.

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Comparative Analysis of Machine Learning Models for Students’ Performance Prediction

Cited by 17 publications

References 17 publications

Contextualizing the current state of research on the use of machine learning for student performance prediction: A systematic literature review

Contextualizing the current state of research on the use of machine learning for student performance prediction: A systematic literature review

Prediction of Student Performance Using Machine Learning Techniques: A Review

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

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