Enhancing the capabilities of Student Result Prediction System

Chaudhury, Pamela; Mishra, Sushruta; Tripathy, Hrudaya Kumar; Kishore, Brojo

doi:10.1145/2905055.2905150

Cited by 33 publications

(7 citation statements)

References 14 publications

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“…Nevertheless, the accuracy of the prediction systems can be improved through careful study and implementing different algorithmic features. Thus, preprocessing techniques have been applied together with classification algorithms (SVM, DT and NB) to improve prediction results [19].…”

Section: Supervised Learningmentioning

confidence: 99%

“…Accuracy is important since it can be very useful in planning educational interventions aimed at improving the results of the teaching-learning process, saving government resources and educators' time and effort [51]. Moreover, the additional use of pre-processing techniques along with classification algorithms has improved performance prediction accuracy [19].…”

Section: Student Performancementioning

confidence: 99%

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Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

2020

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Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things. These are benefited by the automation of many processes involved in usual students’ activities which handle massive volumes of data collected from software tools for technology-enhanced learning. Thus, analyzing and processing these data carefully can give us useful information about the students’ knowledge and the relationship between them and the academic tasks. This information is the source that feeds promising algorithms and methods able to predict students’ performance. In this study, almost 70 papers were analyzed to show different modern techniques widely applied for predicting students’ performance, together with the objectives they must reach in this field. These techniques and methods, which pertain to the area of Artificial Intelligence, are mainly Machine Learning, Collaborative Filtering, Recommender Systems, and Artificial Neural Networks, among others.

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Section: Supervised Learningmentioning

confidence: 99%

Section: Student Performancementioning

confidence: 99%

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

2020

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“…Different types of EDM (Educational Data Mining) have been proposed in a bibliographic study with the objective of higher accuracy via optimal feature selection and deep learning (Hussain et al, 2019). Chaudhury et al (2016) claimed that EDM is defined as the area of research that focuses on the development of techniques to explore sets of data collected in educational settings. According to the authors, the nature of these data is different from that observed in the data traditionally used in mining tasks, demanding adaptations and new approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

Student performance prediction with BPSO feature selection and CNN classifier

Begum¹,

Padmanna²

2022

Int. j. adv. appl. sci.

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Educational Data Mining (EDM) is gaining great importance as a new interdisciplinary research field related to some other areas. It is directly related to data mining (DM), the latter being a fundamental part of knowledge discovery in databases (KDD). This data is growing more and more and contains hidden knowledge that could be very useful for users (both teachers and students). It is convenient to identify such knowledge in the form of models, patterns, or any other representation scheme that allows better exploitation of the system. Data mining is revealed as the tool to achieve such discovery, giving rise to EDM. In this complex context, different techniques and learning algorithms are usually used to obtain the best results. Recently educational systems are adopting artificial intelligent systems, especially in the educational context, specific areas for extracting relevant information, such as EDM, which integrates numerous techniques that support the capture, processing, and analysis of these sets of records. The main technique associated with EDM is Machine Learning, which has been used for decades in data processing in different contexts, but with the advent of Big Data, there was an intensification in the application of this technique to extract relevant information from a huge amount of data. This paper proposes the student performance prediction using CNN (Convolution Neural Network) and BPSO (Binary Particle Swarm Optimization) based feature selection method. In this study, classifiers are made for 2-class and 5-class predictions. The proposed system claims an outperforming accuracy of 96.6% with various previous research works as well as found that the majority of attributes related to school activities as compared to data on demographic and socioeconomic characteristics.

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“…Family background. The family background includes attributes such as parents' education [53,63,64] and occupation [63,65], family size [38], parents influence, living area and other related attributes.…”

Section: Academic Non-reactivementioning

confidence: 99%

A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models

Khan

Ahmad

Jabeur

et al. 2021

Int. J. Interact. Mob. Technol.

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One of the important key applications of learning analytics is offering an opportunity to the institutions to track the student’s academic activities and provide them with real-time adaptive consultations if the student academic performance diverts towards the inadequate outcome. Still, numerous barriers exist while developing and implementing such kind of learning analytics applications. Machine learning algorithms emerge as useful tools to endorse learning analytics by building models capable of forecasting the final outcome of students based on their available attributes. The machine learning algorithm’s performance demotes with using the entire attributes and thus a vigilant selection of predicting attributes boosts the performance of the produced model. Though, several constructive techniques facilitate to identify the subset of productive attributes, however, the challenging task is to evaluate if the prediction attributes are meaningful, explicit, and controllable by the students. This paper reviews the existing literature to come up with the student’s attributes used in developing prediction models. We propose a conceptual framework which demonstrates the classification of attributes as either latent or dynamic. The latent attributes may appear significant but the student is not able to control these attribute, on the other hand, the student has command to restrain the dynamic attributes. Each of the major class is further categorized to present an opportunity to the researchers to pick constructive attributes for model development.

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Enhancing the capabilities of Student Result Prediction System

Cited by 33 publications

References 14 publications

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

Student performance prediction with BPSO feature selection and CNN classifier

A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models

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