scholarly journals Modeling E-Behaviour, Personality and Academic Performance with Machine Learning

2021 ◽  
Vol 11 (22) ◽  
pp. 10546
Author(s):  
Serepu Bill-William Seota ◽  
Richard Klein ◽  
Terence van Zyl
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Academic Performance ◽  
Student Performance ◽  
Correlation Coefficients ◽  
Students At Risk ◽  
Intervention Process ◽  
Population Correlation ◽  
Online Behaviour ◽  
Personality Features

The analysis of student performance involves data modelling that enables the formulation of hypotheses and insights about student behaviour and personality. We extract online behaviours as proxies to Extraversion and Conscientiousness, which have been proven to correlate with academic performance. The proxies of personalities we obtain yield significant (p<0.05) population correlation coefficients for traits against grade—0.846 for Extraversion and 0.319 for Conscientiousness. Furthermore, we demonstrate that a student’s e-behaviour and personality can be used with deep learning (LSTM) to predict and forecast whether a student is at risk of failing the year. Machine learning procedures followed in this report provide a methodology to timeously identify students who are likely to become at risk of poor academic performance. Using engineered online behaviour and personality features, we obtain a classification accuracy (κ) of students at risk of 0.51. Lastly, we show that we can design an intervention process using machine learning that supplements the existing performance analysis and intervention methods. The methodology presented in this article provides metrics that measure the factors that affect student performance and complement the existing performance evaluation and intervention systems in education.

scholarly journals A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques

2021 ◽  
Vol 11 (9) ◽  
pp. 552
Author(s):  
Balqis Albreiki ◽  
Nazar Zaki ◽  
Hany Alashwal
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
At Risk ◽  
Learning Environment ◽  
Student Performance ◽  
Critical Role ◽  
Educational Data Mining ◽  
Drop Out ◽  
Machine Learning Techniques ◽  
Students At Risk

Educational Data Mining plays a critical role in advancing the learning environment by contributing state-of-the-art methods, techniques, and applications. The recent development provides valuable tools for understanding the student learning environment by exploring and utilizing educational data using machine learning and data mining techniques. Modern academic institutions operate in a highly competitive and complex environment. Analyzing performance, providing high-quality education, strategies for evaluating the students’ performance, and future actions are among the prevailing challenges universities face. Student intervention plans must be implemented in these universities to overcome problems experienced by the students during their studies. In this systematic review, the relevant EDM literature related to identifying student dropouts and students at risk from 2009 to 2021 is reviewed. The review results indicated that various Machine Learning (ML) techniques are used to understand and overcome the underlying challenges; predicting students at risk and students drop out prediction. Moreover, most studies use two types of datasets: data from student colleges/university databases and online learning platforms. ML methods were confirmed to play essential roles in predicting students at risk and dropout rates, thus improving the students’ performance.

An Intervention Program for Advancing the Academic Performance of International Pathway “STAR”

2018 ◽  
pp. 71-95
Author(s):  
Donna Marie Velliaris
Keyword(s):  
Academic Performance ◽  
Student Performance ◽  
Study Skills ◽  
Intervention Program ◽  
Independent Study ◽  
Evidence Based ◽  
Self Directed Learning ◽  
Students At Risk ◽  
Support Strategy ◽  
Directed Learning

As part of an intervention and support strategy, this chapter discusses the evidence-based merits of a tertiary skills development (TSD) course delivered at the Eynesbury Institute of Business and Technology (EIBT) to “students at risk” (STAR). The effectiveness of the TSD course was measured via quantitative means by comparing students' academic performance before, during, and after TSD intervention. It was found that student performance analysed over three consecutive trimesters underwent a significant improvement when the support strategy was provided, followed by a small downturn in performance when the support was removed and students were again relying solely on their independent study skills and self-directed learning.

scholarly journals Identifying Students at Risk of Failing a Subject by Using Learning Analytics for Subsequent Customised Tutoring

2019 ◽  
Vol 9 (3) ◽  
pp. 448 ◽  
Author(s):  
Fredys Simanca ◽  
Rubén González Crespo ◽  
Luis Rodríguez-Baena ◽  
Daniel Burgos
Keyword(s):  
At Risk ◽  
Academic Performance ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
Virtual Classroom ◽  
Sufficient Information ◽  
Students At Risk ◽  
Theoretical Structure ◽  
Analysis Technique ◽  
Assessment Matrix

Learning analytics (LA) has become a key area of study in educology, where it could assist in customising teaching and learning. Accordingly, it is precisely this data analysis technique that is used in a sensor—AnalyTIC—designed to identify students who are at risk of failing a course, and to prompt subsequent tutoring. This instrument provides the teacher and the student with the necessary information to evaluate academic performance by using a risk assessment matrix; the teacher can then customise any tutoring for a student having problems, as well as adapt the course contents. The sensor was validated in a study involving 39 students in the first term of the Environmental Engineering program at the Cooperative University of Colombia. Participants were all enrolled in an Algorithms course. Our findings led us to assert that it is vital to identify struggling students so that teachers can take corrective measures. The sensor was initially created based on the theoretical structure of the processes and/or phases of LA. A virtual classroom was built after these phases were identified, and the tool for applying the phases was then developed. After the tool was validated, it was established that students’ educational experiences are more dynamic when teachers have sufficient information for decision-making, and that tutoring and content adaptation boost the students’ academic performance.

scholarly journals Early Identification of Students at Risk for Poor Academic Performance in Clinical Clerkships

2000 ◽  
Vol 75 (Supplement) ◽  
pp. S78-S80 ◽  
Author(s):  
SCOTT A. FIELDS ◽  
CYNTHIA MORRIS ◽  
WILLIAM L. TOFFLER ◽  
EDWARD J. KEENAN
Keyword(s):  
At Risk ◽  
Academic Performance ◽  
Early Identification ◽  
Students At Risk ◽  
Poor Academic Performance ◽  
Clinical Clerkships

Machine Learning for the Identification of Students at Risk of Academic Desertion

2019 ◽  
pp. 462-473
Author(s):  
Leidy Daniela Forero Zea ◽  
Yudy Fernanda Piñeros Reina ◽  
José Ignacio Rodríguez Molano
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Students At Risk

scholarly journals Identifying Students at Risk to Academic Dropout in Higher Education

2021 ◽  
Vol 11 (8) ◽  
pp. 427
Author(s):  
María Gómez Gallego ◽  
Alfonso Palazón Perez de los Cobos ◽  
Juan Cándido Gómez Gallego
Keyword(s):  
At Risk ◽  
Academic Performance ◽  
Learning Strategies ◽  
Cognitive Functions ◽  
Academic Failure ◽  
Dropout Rate ◽  
Cognitive Capacity ◽  
Students At Risk ◽  
Learning Skills ◽  
Increased Risk

A main goal of the university institution should be to reduce the desertion of its students, in fact, the dropout rate constitutes a basic indicator in the accreditation processes of university centers. Thus, evaluating the cognitive functions and learning skills of students with an increased risk of academic failure can be useful for the adoption of strategies for preventing and reducing school dropout. In this research, cognitive functions and learning skills in 284 university students were evaluated. Academic performance predictors were identified, and conglomerates analysis was carried out to establish groups according to those variables. The stability and validity of the conglomerates were tested with discriminant analyzes and comparison tests. The variables associated significantly to academic performance were: attention, intelligence, motivation, metacognition and affective components. The conglomerate analysis suggested a three-group solution: (1) students with cognitive skills of moderate to high, but deficient learning strategies; (2) students with cognitive and learning capabilities of moderate to high; (3) students with cognitive functions low and moderate learning capacity. Students from groups 1 and 3 showed worse academic performance; 83.3% of students at risk of desertion belonged to such groups. Two groups of students have been identified with the highest risk of academic failure: those with poor cognitive capacity and those with bad learning skills.

scholarly journals A Machine Learning Approach for Predicting Student Performance

2019 ◽  
pp. 462-465
Author(s):  
C. Selvi ◽  
R. Shalini ◽  
V. Navaneethan ◽  
L. Santhiya
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Student Performance ◽  
Learning Model ◽  
Learning Approach ◽  
Economic Prosperity ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Novel Method ◽  
Predicting Student Performance

An University’s reputation and its standard are weighted by its students performance and their part in the future economic prosperity of the nation, hence a novel method of predicting the student’s upcoming academic performance is really essential to provide a pre-requisite information upon their performances. A machine learning model can be developed to predict the student’s upcoming scores or their entire performance depending upon their previous academic performances.

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