scholarly journals Early Detection of At-Risk Undergraduate Students through Academic Performance Predictors

2017 ◽  
Vol 7 (3) ◽  
pp. 42
Author(s):  
Vikash Rowtho
Keyword(s):  
At Risk ◽  
Academic Performance ◽  
Early Detection ◽  
Undergraduate Students ◽  
At Risk Students ◽  
Regression Analyses ◽  
Linear Discriminant ◽  
Student Dropout ◽  
Performance Predictors ◽  
A New Technique

Undergraduate student dropout is gradually becoming a global problem and the 39 Small Islands Developing States (SIDS) are no exception to this trend. The purpose of this research was to develop a method that can be used for early detection of students who are at-risk of performing poorly in their undergraduate studies. A sample of 279 students participated in the study conducted in a Mauritian private tertiary academic institution. Results of regression analyses identified the variables having a significant influence on academic performance. These variables were used in a linear discriminant analysis where 74 percent of the students could be correctly classified into three categories: at-risk, pass or fail. In conclusion, this study has proposed a new technique that can be used by institutions to determine significant academic performance predictors and then identify at-risk students upon whom interventions can be implemented prior to exams to address the problem of dropouts.

scholarly journals Monitoring Students at the University: Design and Application of a Moodle Plugin

2020 ◽  
Vol 10 (10) ◽  
pp. 3469 ◽  
Author(s):  
María Consuelo Sáiz-Manzanares ◽  
Raúl Marticorena-Sánchez ◽  
César Ignacio García-Osorio
Keyword(s):  
At Risk ◽  
Early Detection ◽  
At Risk Students ◽  
Machine Learning Techniques ◽  
Dropout Rates ◽  
Dynamic Learning ◽  
Academic Risk ◽  
University Environment ◽  
Student Dropout ◽  
The University

Early detection of at-risk students is essential, especially in the university environment. Moreover, personalized learning has been shown to increase motivation and lower student dropout rates. At present, the average dropout rates among students following courses leading to the award of Spanish university degrees are around 18% and 42.8% for presential teaching and online courses, respectively. The objectives of this study are: (1) to design and to implement a Modular Object-Oriented Dynamic Learning Environment (Moodle) plugin, “eOrientation”, for the early detection of at-risk students; (2) to test the effectiveness of the “eOrientation” plugin on university students. We worked with 279 third-year students following health sciences degrees. A process for extracting information records was also implemented. In addition, a learning analytics module was developed, through which both supervised and unsupervised Machine Learning techniques can be applied. All these measures facilitated the personalized monitoring of the students and the easier detection of students at academic risk. The use of this tool could be of great importance to teachers and university governing teams, as it can assist the early detection of students at academic risk. Future studies will be aimed at testing the plugin using the Moodle environment on degree courses at other universities.

scholarly journals Academic performance of secondary education students in socio-familial risk contexts

2021 ◽  
Vol 28 (2) ◽  
Author(s):  
Daniel Rodríguez-Rodríguez ◽  
◽  
Remedios Guzmán ◽  
Keyword(s):  
At Risk ◽  
Academic Performance ◽  
Secondary Education ◽  
Self Efficacy ◽  
At Risk Students ◽  
Familial Risk ◽  
Course Evaluation ◽  
Self Concept ◽  
Cognitive Variables ◽  
Not At Risk

Introduction: The relationship that socio-familial and non-cognitive variables have on students in regards to their academic performance is a very important element for success in Secondary Education. In this study the influence of non-cognitive variables (academic self-concept, self-efficacy and perceived family affective support) and socio-familial variables (educational level and expectations of each parent) on the academic performance of secondary school students were analysed. Method: Students were grouped according to their accumulated socio-familial risk index (at-risk students, n = 305; not-at-risk students, n = 991). To measure the variables, the scales What do you think of yourself, General Self-Efficacy and Perceived Family Support were used. Socio-family variables were measured with an ad hoc questionnaire, and academic performance with the end-of-course evaluation scores. Results: The receiver operating characteristic curve showed a decrease in students’ academic performance from three or more accumulated risks. Structural Equation Modelling (SEM) was performed for each group. The results showed that for at-risk students, academic performance was mainly determined by two variables: academic self-concept and self-concept; in contrast to the not-at-risk students in which self-efficacy was the one that had the greatest effect on performance. In both groups, the parents’ expectations were the family variable with the highest incidence being performance, although, for the at-risk group, the effect was greater. Conclusions: The relevance of the identification of non-cognitive and socio-familial variables on the academic performance of at-risk students in regards to secondary education due to socio-familial factors is discussed.

scholarly journals Effectiveness of Tutoring to Improve Academic Performance in Nursing Students at the University of Seville

2017 ◽  
Vol 6 (2) ◽  
pp. 93-102 ◽  
Author(s):  
María Dolores Guerra-Martín ◽  
Marta Lima-Serrano ◽  
Joaquín Salvador Lima-Rodríguez
Keyword(s):  
At Risk ◽  
Academic Performance ◽  
Nursing Students ◽  
At Risk Students ◽  
Intervention Group ◽  
Academic Failure ◽  
Program Satisfaction ◽  
Tutoring Program ◽  
Information Comprehension ◽  
Education Support

In response to the increase of Higher Education support provided to tutoring programs, this paper presents the design, implementation and evaluation of a tutoring program to improve the academic performance of at-risk students enrolled in the last year of a nursing degree characterized by academic failure (failed courses). A controlled experimental study was carried out to evaluate a tutoring program that included a minimum of nine meetings performed by an expert professor as tutor. A questionnaire for assessing the academic needs was designed and interventions were performed when responses were: nothing, a little or something. Medium to large effects were found in the progress of failed course to passed course (p =.000, rφ = .30), improving the information about courses (p < .001, d = 2.01), the information comprehension (p < .001, d = 0.85) and the strategies to improve academic performance (p < .001, d = 1.37). The intervention group students’ response highlighted program satisfaction and effectiveness. The significance of the study lies in reinforcing the formal tutoring as a tool to improve academic performance in at-risk students.

scholarly journals Learning to Identify At-Risk Students in Distance Education Using Interaction Counts

2016 ◽  
Vol 23 (2) ◽  
pp. 124 ◽  
Author(s):  
Douglas Detoni ◽  
Cristian Cechinel ◽  
Ricardo Araujo Matsumura ◽  
Daniela Francisco Brauner
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
At Risk Students ◽  
Drop Out ◽  
Support Vector ◽  
Learning Models ◽  
Data Set ◽  
Student Dropout ◽  
Vector Machines ◽  
Machine Learning Models

Student dropout is one of the main problems faced by distance learning courses. One of the major challenges for researchers is to develop methods to predict the behavior of students so that teachers and tutors are able to identify at-risk students as early as possible and provide assistance before they drop out or fail in their courses. Machine Learning models have been used to predict or classify students in these settings. However, while these models have shown promising results in several settings, they usually attain these results using attributes that are not immediately transferable to other courses or platforms. In this paper, we provide a methodology to classify students using only interaction counts from each student. We evaluate this methodology on a data set from two majors based on the Moodle platform. We run experiments consisting of training and evaluating three machine learning models (Support Vector Machines, Naive Bayes and Adaboost decision trees) under different scenarios. We provide evidences that patterns from interaction counts can provide useful information for classifying at-risk students. This classification allows the customization of the activities presented to at-risk students (automatically or through tutors) as an attempt to avoid students drop out.

scholarly journals Using Heart Rate to Predict Students’ Academic Performance

2019 ◽  
Vol 8 (3) ◽  
pp. 5916-5920
Keyword(s):  
At Risk ◽  
Heart Rate ◽  
Academic Performance ◽  
Prediction Models ◽  
At Risk Students ◽  
Rate Data ◽  
Threshold Values ◽  
Heart Rate Data ◽  
Rate Fluctuation ◽  
Remedial Intervention

Timeliness was a missing factor in many studies on Academic Performance Prediction to identify at-risk students. This study embarked on a search to evaluate the feasibility of predicting students’ performance based on heart rate data collected during classes. This dimension of data was collected in the first four weeks after semester commencement to validate accurate prediction that will enable educationists to introduce remedial intervention to at-risk students. Another aim of this study is to determine the best threshold values for the different types of heart rate fluctuations that can be used in predicting academic achievements. The threshold values were tested further to verify whether the prediction model for individual course or combined courses was more accurate. Results revealed that heart rate data alone can achieve a maximum prediction accuracy of 88% and recall of 100%. Threshold values calculated in derived heart rate fluctuation types produces the best results. Prediction models for individual courses outperform the model using average threshold values of all courses.

Students Experiencing Homelessness

2020 ◽  
pp. 125-146
Author(s):  
Diana Bowman ◽  
Patricia A. Popp
Keyword(s):  
At Risk ◽  
Academic Performance ◽  
School Districts ◽  
At Risk Students ◽  
Poor Health ◽  
Children And Youth ◽  
Federal Legislation ◽  
School Mobility ◽  
Poor Academic Performance ◽  
Education Of Students

Children and youth who experience homelessness are among the most vulnerable and invisible of at-risk students. Poor academic performance and low graduation rates result from school mobility, unmet basic needs, poor health, and trauma. Teachers can mitigate the impacts of homelessness on students by making the most of the brief time a homeless student may be in their classroom, being an accessible and caring adult in the child’s or youth’s life, and working with the school district’s homeless liaison to connect the child or youth to supports both in the school and in the community. Teachers should be familiar with the McKinney-Vento Act, which is federal legislation that ensures that schools and school districts remove barriers to the education of students experiencing homelessness. Services may include tutoring, transportation, free meals, and counseling. Schools can be a haven for safety, normalcy, and hope for children and youth who experience homelessness.

High Versus Low Implementation of Instructional Support Teams

1999 ◽  
Vol 20 (3) ◽  
pp. 170-183 ◽  
Author(s):  
Joseph F. Kovaleski ◽  
Edward E. Gickling ◽  
Henry Morrow ◽  
Paul R. Swank
Keyword(s):  
At Risk ◽  
Academic Performance ◽  
At Risk Students ◽  
Instructional Support ◽  
Task Completion ◽  
Academic Learning ◽  
Time On Task ◽  
Support Teams ◽  
Instructional Support Team ◽  
High Degree

In 1990, the Commonwealth of Pennsylvania implemented a statewide instructional support team (IST) process to provide prereferral assessment and intervention for at-risk students in 500 school districts. The current study examined the academic performance of students affected by this process as contrasted with other at-risk students who did not have access to it. The dependent measures were academic learning time (time on task, task completion, and task comprehension). The results indicated that students supported by ISTs had greater levels of academic performance only when their schools implemented the IST process to a high degree. Low IST implementation produced no differences in academic performance in schools that had not implemented IST. The importance of implementing a promising program according to critical design features is discussed.

Meeting the At-Risk Challenge: Empowering Nursing Students Through Mentoring

2004 ◽  
Vol 1 (1) ◽  
Author(s):  
Lori L. Candela ◽  
Susan Kowalski ◽  
Dianne Cyrkiel ◽  
Deborah Warner
Keyword(s):  
At Risk ◽  
Nursing Students ◽  
Learning Strategies ◽  
Undergraduate Students ◽  
Nursing Faculty ◽  
At Risk Students ◽  
Learning Gain ◽  
Nursing Courses ◽  
The University ◽  
Faculty Mentors

Wanting to improve student retention, progression, and graduation, the nursing faculty of the University of Nevada, Las Vegas developed a program for undergraduate students. Designated faculty mentors are available for academically at-risk students, or any student wanting to improve learning skills. Through mentoring sessions, students are helped to assess their learning difficulties, develop individualized prescription plans for learning, gain support during implementation of learning strategies, and evaluate results. Implemented in 2002, the program reflects positive outcomes. Of the 29 students who were referred to the program, only 3 were unsuccessful in passing their nursing courses. Student evaluations of the program reflect the value of the mentoring experience. The program has subsequently developed in the areas of advertising, diagnostic student testing, and student access to support resources.

Sign in / Sign up

Export Citation Format

Share Document