Feature Extraction Model to Identify At -- Risk Level of Students in Academia

Singh, Mamta; Singh, Jyoti; Rawal, Arpana

doi:10.1109/icit.2014.68

Cited by 8 publications

(3 citation statements)

References 4 publications

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“…This will improve the quality of learning and accuracy of a model [50]. It is also an effective way of reducing dimensionality and increasing learning accuracy [68].…”

Section: S M Muthukrishnan Et Al J Fundam Appl Sci 2017 9(4s) 7mentioning

confidence: 99%

Systematic mapping review on student’s performance analysis using big data predictive model

Muthukrishnan

Govindasamy²,

Mustapha

2018

J. Fundam and Appl Sci.

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This paper classify the various existing predicting models that are used for monitoring and improving students' performance at schools and higher learning institutions. It analyses all the areas within the educational data mining methodology. Two databases were chosen for this study and a systematic mapping study was performed. Due to the very infant stage of this research area, only 114 articles published from 2012 till 2016 were identified. Within this, a total of 59 articles were reviewed and classified. There is an increased interest and research in the area of educational data mining, particularly in improving students' performance with various predictive and prescriptive models. Most of the models are devised for pedagogical improvements ultimately. It is a huge scarcity in producing portable predictive models that fits into any educational environment. There is more research needed in the educational big data.

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“…This will improve the quality of learning and accuracy of a model [50]. It is also an effective way of reducing dimensionality and increasing learning accuracy [68].…”

Section: S M Muthukrishnan Et Al J Fundam Appl Sci 2017 9(4s) 7mentioning

confidence: 99%

Systematic mapping review on student’s performance analysis using big data predictive model

Muthukrishnan

Govindasamy²,

Mustapha

2018

J. Fundam and Appl Sci.

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“…To improve the performance of the system, methods are explored, for example, feature extraction, features selection, etc. In the research [21,22] researchers do the extraction of features relating to student's achievements. Meanwhile, the other research applies features selection on student's psychomotor domain [23], on student's academic performance [24,25].…”

Section: Introductionmentioning

confidence: 99%

Identifying Dominant Characteristics of Students’ Cognitive Domain on Clustering-based Classification

Yamasari¹,

Nugroho²,

Yoshimoto³

et al. 2020

IJIES

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The rapid growth of information and communications technology-based educational tools generates a large volume of student data with many features (characteristics). However, the mining process in the clustering task of student data is not often done optimally, so the performance of the system decreases. To overcome this problem, we propose a discretization method on logistic regression to determine the most optimal number of clusters. Additionally, we introduce a technique that combines the features selection using a filter-and wrapper-based procedures (HFS) to identify the dominant features of the students' cognitive domains. Furthermore, we evaluate the identification result by three clustering methods, namely: K-means, EM, and Farthest first. Finally, we propose the clustering-based classification so the results can be measured by using the classification metrics. Here, we apply two evaluation techniques, namely: cross-validation and percentage split. The experimental results indicate that our approach describes predominance, in terms of classification metrics over conventional methods. Our approach is around 10,847-11,134 percent higher in terms of accuracy average than the original features on both the assessment techniques. Also, this approach significantly reduces the time taken to create a prototype between 0.0167-0.027 seconds. This gives the impact on a significant reduction in the model created to the number of unsuitable students on classes based on the cognitive domain, namely: 3-12 students.

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“…Singh, M., et al [8] proposed the model for feature extraction for identifying the student's risk level in academic activities. This research uses Naïve Bayes classifier to predict the features which are used for recognizing the performance of the second year students in their computer and application course by taking partially relevant or fully relevant features.…”

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confidence: 99%

An Efficient Clustering Based Feature Selection for Predicting Student Performance

Rani¹

2017

IJET

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Abstract− The student failure prediction at school has turn into a very complicated challenge owing to both the large number of factors which can influence the high performance of students and the balanced nature of student's databases which are maintaining by Educational Data Mining (EDM) techniques. The main goal of this study is to detect and remove the both irrelevant and redundant features that can be used to enhance the classification accuracy in predicting the student performance. This goal is achieved by establishing the novel technique for feature or attributes selection process by hybrid of Artificial fish swarm-Cuckoo Search optimization algorithm to remove the irrelevant features or obtaining relevant features. Also, Non-negative Matrix Factorization Clustering algorithm (NMFC) performs the removal of redundant feature or attributes which are presented in the relevant features. The performance of this technique is analyzed by using the student database which comprises the gathering of student's information from different colleges. For analyzing the performance of this technique, the comparative evaluation is carried out between the classifiers used in this research such as Prism and J48 without the feature selection and classifiers with our proposed technique. The experimental consequences illustrate that hybrid of artificial fish swarm-cuckoo search optimization feature selection along with NMFC approach is accomplishing high accuracy rate than other techniques. This study facilitates us to enhance the performance of the student's failure and dropout prediction. In other words, this helps to increase the accuracy of the classification result.Keywords− Educational Data Mining (EDM), Feature Selection, Non-negative Matrix Factorization Clustering (NMFC), Artificial fish swarm, Cuckoo search optimization I. INTRODUCTION Educational Data Mining (EDM) refers the research field which is concerned with the applications that are developed by means of using data mining, machine learning and statistics for generation of information from educational settings and by using these methods the student's performance in the settings where they learn the knowledge. This EDM interests on the development of advanced tools and algorithms to realize the data patterns. EDM investigates the learning theories and updates the educational practice.Educational Data Mining (EDM) is appearing as research area with computational and psychological methods to understand the students learning performance. In modern world, EDM has the ability to log finegrained data about student's computer-based learning atmosphere which increases the significance in developing techniques to analyse the high amounts of data generated in educational settings.Generally, EDM consists of four processes [1] such as pre-processing, validation, prediction and decisionmaking. Pre-processing involves classification, regression, clustering and etc. The classified features should valid to avoid overfitting. The valid features are predicted to know about the stu...

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Feature Extraction Model to Identify At -- Risk Level of Students in Academia

Cited by 8 publications

References 4 publications

Systematic mapping review on student’s performance analysis using big data predictive model

Systematic mapping review on student’s performance analysis using big data predictive model

Identifying Dominant Characteristics of Students’ Cognitive Domain on Clustering-based Classification

An Efficient Clustering Based Feature Selection for Predicting Student Performance

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