Mining for Marks: A Comparison of Classification Algorithms when Predicting Academic Performance to Identify “Students at Risk”

2013 ◽  
pp. 541-552 ◽  
Author(s):  
Lebogang Mashiloane ◽  
Mike Mchunu
Keyword(s):  
At Risk ◽  
Academic Performance ◽  
Classification Algorithms ◽  
Students At Risk

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

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 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 Learning Factor Models of Students at Risk of Failing in the Early Stage of Tertiary Education

2016 ◽  
Vol 3 (2) ◽  
pp. 330-372 ◽  
Author(s):  
Geraldine Gray ◽  
Colm McGuinness ◽  
Philip Owende ◽  
Markus Hofmann
Keyword(s):  
At Risk ◽  
Academic Performance ◽  
Tertiary Education ◽  
Early Stage ◽  
Self Regulation ◽  
Support Vector ◽  
First Year ◽  
Learning Approaches ◽  
Students At Risk ◽  
Learning Factor

This paper reports on a study to predict students at risk of failing based on data available prior to commencement of first year of study. The study was conducted over three years, 2010 to 2012, on a student population from a range of academic disciplines, n=1,207. Data was gathered from both student enrolment data maintained by college administration, and an online, self-reporting, learner profiling tool administered during first-year student induction. Factors considered included prior academic performance, personality, motivation, self-regulation, learning approaches, age and gender.  Models were trained on data from the 2010 and 2011 student cohort, and tested on data from the 2012 student cohort. A comparison of eight classification algorithms found k-NN achieved best model accuracy (72%), but results from other models were similar, including ensembles (71%), support vector machine (70%) and a decision tree (70%). Models of subgroups by age and discipline achieved higher accuracies, but were affected by sample size; n<900 underrepresented patterns in the dataset. Results showed that factors most predictive of academic performance in first year of study at tertiary education included age, prior academic performance and self-efficacy. This study indicated that early modelling of first year students yielded informative, generalisable models that identified students at risk of failing.

scholarly journals A Method to Predict At-risk Students in Introductory Computing Courses Based on Motivation

2020 ◽  
Author(s):  
Pablo Schoeffel ◽  
Vinicius Faria Culmant Ramos ◽  
Raul Sidnei Wazlawick
Keyword(s):  
At Risk ◽  
At Risk Students ◽  
Strong Relationship ◽  
High Rate ◽  
Support Vector ◽  
Classification Algorithms ◽  
Students At Risk ◽  
Long Time ◽  
Main Components ◽  
Introductory Computing

Despite being a problem reported in a long time, the high rate of dropout and failure in computing courses remains a problem. Although there is a strong relationship between the motivation and the students outcome, few works use the motivation as a factor to identify students at risk. This work presents and evaluates a method to identify features that allow predicting at-risk students in introductory computing courses, based on four main components: pre-university factors, initial motivation, motivation through the course, and professor perception. The method created, named EMMECS, was applied with 245 students from different programs in four different universities in southern Brazil. We carried out several simulations of prediction, using ten different classification algorithms and different datasets. As a result, using support vector machine and AdaBoostM1 algorithms, we identified on average more than 80% of students that would fail, since the first week of the study. The results show that the proposed method is effective compared with related works and it has as advantages its independence of programmatic content, specific assessments, grades, and interaction with learning systems. Furthermore, the method allows the weekly prediction, with good results since the first few weeks.

Discriminant Validation of the Test of Reading Performance (TORP) for Identifying Children at Risk of Reading Difficulties

2000 ◽  
Vol 16 (2) ◽  
pp. 139-146 ◽  
Author(s):  
Padeliadu Susana ◽  
Georgios D. Sideridis
Keyword(s):  
At Risk ◽  
Elementary School Students ◽  
Reading Difficulties ◽  
Reading Performance ◽  
Low Achievers ◽  
Children At Risk ◽  
School Students ◽  
Students At Risk ◽  
Group A ◽  
The Mean

Abstract This study investigated the discriminant validation of the Test of Reading Performance (TORP), a new scale designed to evaluate the reading performance of elementary-school students. The sample consisted of 181 elementary-school students drawn from public elementary schools in northern Greece using stratified random procedures. The TORP was hypothesized to measure six constructs, namely: “letter knowledge,” “phoneme blending,” “word identification,” “syntax,” “morphology,” and “passage comprehension.” Using standard deviations (SD) from the mean, three groups of students were formed as follows: A group of low achievers in reading (N = 9) including students who scored between -1 and -1.5 SD from the mean of the group. A group of students at risk of reading difficulties (N = 6) including students who scored between -1.5 and -2 SDs below the mean of the group. A group of students at risk of serious reading difficulties (N = 6) including students who scored -2 or more SDs below the mean of the group. The rest of the students (no risk, N = 122) comprised the fourth group. Using discriminant analyses it was evaluated how well the linear combination of the 15 variables that comprised the TORP could discriminate students of different reading ability. Results indicated that correct classification rates for low achievers, those at risk for reading problems, those at risk of serious reading problems, and the no-risk group were 89%, 100%, 83%, and 97%, respectively. Evidence for partial validation of the TORP was provided through the use of confirmatory factor analysis and indices of sensitivity and specificity. It is concluded that the TORP can be ut ilized for the identification of children at risk for low achievement in reading. Analysis of the misclassified cases indicated that increased variability might have been responsible for the existing misclassification. More research is needed to determine the discriminant validation of TORP with samples of children with specific reading disabilities.

Sign in / Sign up

Export Citation Format

Share Document