Students dropout factor prediction using EDM techniques

Anjana, Pradeep; Das, Smija; Kizhekkethottam, Jubilant J

doi:10.1109/icsns.2015.7292372

Cited by 36 publications

(16 citation statements)

References 7 publications

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“…The work in [74] utilized education data mining to analyze the factors affecting student academic performance which contributed towards the student failure and dropout. The authors showed that their techniques enabled the identification of weak students shown to have poor performance.…”

Section: ) Dropout Prediction and Academic Early Warning Systemsmentioning

confidence: 99%

Big Educational Data & Analytics: Survey, Architecture and Challenges

Ang

Ge²,

Seng

2020

IEEE Access

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The proliferation of mobile devices and the rapid development of information and communication technologies (ICT) have seen increasingly large volume and variety of data being generated at an unprecedented pace. Big data have started to demonstrate significant values in higher education. This paper gives several contributions to the state-of-the-art for Big data in higher education and learning technologies research. Currently, there is no comprehensive survey or literature review for Big educational data. Most literature reviews from a few authors have focused on one of these fields: educational mining, learning analytics with discussions on one or two aspects such as Big data technologies without educational focus, social media data in education, etc. Most of these literature reviews are short and insufficient to provide more inclusive reviews for Big educational data. In this paper, we present a comprehensive literature review of the current and emerging paradigms for Big educational data. The survey is presented in five parts: (1) The first part presents an overview and classification of Big education research to show the full landscape in this field, which also gives a concise summary of the overall scope of this paper; (2) The second part presents a discussion for the various data sources from education platforms or systems including learning management systems (LMS), massive open online courses (MOOC), learning object repository (LOR), OpenCourseWare (OCW), open educational resources (OER), social media, linked data and mobile learning contributing to Big education data; (3) The third part presents the data collection, data mining and databases in Big education data; (4) The fourth part presents the technological aspects including Big data platforms and architectures such as Hadoop, Spark, Samza and Big data tools for Big education data; and (5) The fifth part presents different approaches of data analytics for Big education data. This part provides a more inclusive discussion on data analytics which is beyond traditional forms of learning analysis in higher education. This includes predictive analytics, learning analytics including collaborative, behavior, personal learnings and assessment, followed by recommendation systems, graph analytics, visual analytics, immersive learning and analytics, etc. The final part of the paper discusses social (e.g. privacy and ethical issues) and technological challenges for Big data in education. This part also illustrates the technological challenges faced by giving an example for utilizing graph-based analytics for a cross-institution learning analytics scenario. INDEX TERMS Big data, learning technologies, educational data, learning analytics.

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Section: ) Dropout Prediction and Academic Early Warning Systemsmentioning

confidence: 99%

Big Educational Data & Analytics: Survey, Architecture and Challenges

Ang

Ge²,

Seng

2020

IEEE Access

View full text Add to dashboard Cite

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“…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]. Here, the previous studies have not yet made a combination of feature selection Table 1.…”

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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“…There has been a growing interest in using the great amount of information gathered in the educational system. Some problems arise when large amounts of data are stored in an student data base known as Educational Data Mining (EDM) [3][4][5]. EDM in many organizations has been widely used.…”

Section: Introductionmentioning

confidence: 99%

Predicting Student Academic Performance with Ensemble Classification Method on Imbalanced Educational Data

Chowdary*,

Prasanna,

Krishna T

et al. 2019

IJRTE

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Education benefits a person in sustaining his present and future by assuring the goal of life. At present universities and colleges are mainly focusing to improve the academic performance of the students. Recently, many studies have concentrated on employing several machine learning models in the field of higher education to assist both the teachers and the students to identify their problems and can take remedial measures to improve their performances. Some of the earlier studies have been discussed about class imbalance problem and achieved poor prediction outcomes due to low performance of the classifiers. In this paper, we aim to improve the classification/prediction outcomes with a rule-based ensemble model based on various sampling strategies by addressing the class imbalance problem. The dataset used for this study has been collected from Vignan’s Lara and Vignan’s Nirula institutions based on considering various factors such as Attendance Percentage, No of Backlogs, Adjustable Nature, Concentration, Result History, Discipline in class, Usage of Social Media, Degree of Intelligence, Understanding of Subjects, Event Participation, Time Management, Extra Classes, Alternative Learning Skills, Logical Thinking, Bad Habits, Parents Education, Health Condition, Planning for higher studies, Family Support, Time Management, and Aggregate. To evaluate the efficiency, we also considered and compared our original dataset with different benchmark datasets and the performance measures of the proposed method is also tested with various sampling methods based on a learning rate parameter ranging between 0.1 and 0.8. The original data set with the re-sampling method with the proposed method achieved maximum precision values at a learning rate 0.3 with an accuracy rate of 98.36%. Finally, the obtained results were also compared with several baseline classifiers like Naïve Bayes, SVM, MLP, KNN, and OneR on the collected original datasets.

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Students dropout factor prediction using EDM techniques

Cited by 36 publications

References 7 publications

Big Educational Data & Analytics: Survey, Architecture and Challenges

Big Educational Data & Analytics: Survey, Architecture and Challenges

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

Predicting Student Academic Performance with Ensemble Classification Method on Imbalanced Educational Data

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