Analysis of Data Mining Techniques for Constructing a Predictive Model for Academic Performance

Rubiano, Sandra Milena Merchán; Garcia, Jorge Alberto Duarte

doi:10.1109/tla.2016.7555255

Cited by 33 publications

(6 citation statements)

References 4 publications

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“…Most decision tree algorithms are greedy algorithms that construct decision trees in a top-down divide and retain retrospective manner starting with a set of practice samples and their associated category labeling. 14,15 2. Information gain Amount to information required for identifying the tuples on tuples attribute M between before and after tuples segmentation.…”

Section: Overviewmentioning

confidence: 99%

Design of Early Warning System for Mental Health Problems Based on Data Mining and Database

2023

Rev Bras Med Esporte

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Introduction: Data mining technology is mainly employed in the era of big data to evaluate the acquired information. Subsequently, reasoning about the data inductively is fully automated to discover possible patterns. Objective: Recently, data mining technology in the national mental health database has deepened and can be effectively used to solve various mental health early warning problems. Methods: For example, it can be applied to mine psychological data and extract the most important features and information. Results: This paper presents the design of an early warning system for mental health problems based on data mining techniques to offer some thoughts on early warning of mental health problems, including data preparation, data mining, results in analysis, and decision tree algorithm. Conclusion: The experimental results indicate that the results of the early warning system in this paper can achieve an accuracy rate of more than 96% with a high accuracy rate. Level of evidence II; Therapeutic studies - investigating treatment outcomes.

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Section: Overviewmentioning

confidence: 99%

Design of Early Warning System for Mental Health Problems Based on Data Mining and Database

2023

Rev Bras Med Esporte

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“…In addition to academic performance prediction the student performance is analyzed through data mining algorithms but most of algorithms doesn't produce accuracy on classification [5], many other factors help understand the student's overall performance. Classification techniques are described based on the predication performance and used in educational data mining [6]. The classification process is based on an artificial neural network algorithm with poor classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

Psychotic Motivation for Improving Student Performance Based On Pattern Learner Features Using Deep Neural Classifier for Bipolar Disorder Students

Peerbasha¹,

Surputheen²

2021

cibg

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Bipolar disorder is a depressive fact that makes manic illness pressures in young ages due to the non-intensive nature of brain functions, energy levels, mood-outs, and health disorders. These abnormalities may affect student performance under the learning strategies of students. Improvement of bipolar disorder affected student performance needs more data analysis forums that lead to high dimensional nature of features. The problem is that nonrelation feature analysis depends on the nature of student fitness that creates low prediction during classifications for students' motivation. To resolve this problem, a Psychotic motivation is proposed for improving student performance based on Pattern Learner Features (PLF) using Intra Segment Recurrent Deep Neural Network (ISRDNN) for bipolar disorder students. The proposed system makes student academic data's with physical fitness data collection progressive approach to predict important features to classify the result. Bipolar Disorder Influence Rate (BDIR) is usedto spill the progressive student defectives and the learning capabilities for classification result. With Intra Segment Activation Function (ISAF), the recurrent neural network is optimized to classify the result. This classifier improved the student's academic performance based on psychological motivation recommendations. Results prove that the accuracy of the proposed system produces high results compared to the previous system.

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“…Journal of Postsecondary Student Success Accurate predictions of academic performance for individual students are essential to inform interventions. To achieve this goal, researchers have identified several important attributes for predicting student academic performance, such as aspects of a student's demographic and socioeconomic background (e.g., place of birth, disability, parent academic and job background, residing region, gender, socioeconomic index, health insurance, frequency of going out with friends, financial status; Imran et al, 2019;Jain & Solanki, 2019;Purwoningsih et al, 2019;Rubiano & Garcia, 2016;Shanmugarajeshwari & Lawrance, 2016;Tenpipat & Akkarajitsakul, 2020;Zeineddine et al, 2021), pre-enrollment (e.g., high school or level 12 performance and grades, entrance qualification, SAT scores, English and math grades, awards, school they attended; Iatrellis et al, 2021;Imran et al, 2019;Jain & Solanki, 2019;Rubiano & Garcia, 2016;Tenpipat & Akkarajitsakul, 2020;Xu et al, 2017;Zeineddine et al, 2021), enrollment (e.g., enrollment date, enrollment test marks, number of courses students previously enrolled in, type of study program, study mode; Berens et al, 2019;Kemper et al, 2020), tertiary academic (e.g., attendance, number of assessment submissions, student engagement ratio, major, time left to complete degree, course credits, semester work marks, placements, count and date of attempted exams; Berens et al, 2019;Iatrellis et al, 2021;Imran et al, 2019;Kemper et al, 2020;Xu et al, 2017;Yang & Li, 2018), and LMS-based data.…”

Section: Introductionmentioning

confidence: 99%

Predictive Identification of At-Risk Students: Using Learning Management System Data

Osborne

Lang

2023

JPSS

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This paper describes a neural network model that can be used to detect at-risk students failing a particular course using only grade book data from a learning management system. By analyzing data extracted from the learning management system at the end of week 5, the model can predict with an accuracy of 88% whether the student will pass or fail a specific course. Data from the grade books from all course shells from the Spring 2022 semester (N = 22,041 rows) were analyzed, and four factors were found to be significant predictors of student success/failure: the current course grade after the fifth week of the semester and the presence of missing grades in weeks 3, 4, and 5. Several models were investigated before concluding that a neural network model had the best overall utility for the purpose of an early alert system. By categorizing students who are predicted to fail more than one course as being generally at risk, we provide a metric for those who use early warning systems to target resources to the most at-risk students and intervene before students drop out. Seventy-four percent of the students whom our model classified as being generally at risk ended up failing at least one course.

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Analysis of Data Mining Techniques for Constructing a Predictive Model for Academic Performance

Cited by 33 publications

References 4 publications

Design of Early Warning System for Mental Health Problems Based on Data Mining and Database

Design of Early Warning System for Mental Health Problems Based on Data Mining and Database

Psychotic Motivation for Improving Student Performance Based On Pattern Learner Features Using Deep Neural Classifier for Bipolar Disorder Students

Predictive Identification of At-Risk Students: Using Learning Management System Data

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