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 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 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 Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems

2019 ◽  
Vol 9 (24) ◽  
pp. 5523 ◽  
Author(s):  
Luiz Antonio Buschetto Macarini ◽  
Cristian Cechinel ◽  
Matheus Francisco Batista Machado ◽  
Vinicius Faria Culmant Ramos ◽  
Roberto Munoz
Keyword(s):  
At Risk ◽  
Social Presence ◽  
At Risk Students ◽  
Cognitive Presence ◽  
Teaching Presence ◽  
Machine Learning Techniques ◽  
Student Interactions ◽  
Learning Techniques ◽  
Significant Difference ◽  
Undergraduate Programs

Algorithms and programming are some of the most challenging topics faced by students during undergraduate programs. Dropout and failure rates in courses involving such topics are usually high, which has raised attention towards the development of strategies to attenuate this situation. Machine learning techniques can help in this direction by providing models able to detect at-risk students earlier. Therefore, lecturers, tutors or staff can pedagogically try to mitigate this problem. To early predict at-risk students in introductory programming courses, we present a comparative study aiming to find the best combination of datasets (set of variables) and classification algorithms. The data collected from Moodle was used to generate 13 distinct datasets based on different aspects of student interactions (cognitive presence, social presence and teaching presence) inside the virtual environment. Results show there are no statistically significant difference among models generated from the different datasets and that the counts of interactions together with derived attributes are sufficient for the task. The performances of the models varied for each semester, with the best of them able to detect students at-risk in the first week of the course with AUC ROC from 0.7 to 0.9. Moreover, the use of SMOTE to balance the datasets did not improve the performance of the models.

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.

scholarly journals A Learning Analytics Approach to Identify Students at Risk of Dropout: A Case Study with a Technical Distance Education Course

2020 ◽  
Vol 10 (11) ◽  
pp. 3998 ◽  
Author(s):  
Emanuel Marques Queiroga ◽  
João Ladislau Lopes ◽  
Kristofer Kappel ◽  
Marilton Aguiar ◽  
Ricardo Matsumura Araújo ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
At Risk ◽  
Learning Analytics ◽  
Prediction Models ◽  
Demographic Data ◽  
Characteristic Curve ◽  
At Risk Students ◽  
Virtual Learning Environment ◽  
Learning Context ◽  
Student Dropout

Contemporary education is a vast field that is concerned with the performance of education systems. In a formal e-learning context, student dropout is considered one of the main problems and has received much attention from the learning analytics research community, which has reported several approaches to the development of models for the early prediction of at-risk students. However, maximizing the results obtained by predictions is a considerable challenge. In this work, we developed a solution using only students’ interactions with the virtual learning environment and its derivative features for early predict at-risk students in a Brazilian distance technical high school course that is 103 weeks in duration. To maximize results, we developed an elitist genetic algorithm based on Darwin’s theory of natural selection for hyperparameter tuning. With the application of the proposed technique, we predicted the student at risk with an Area Under the Receiver Operating Characteristic Curve (AUROC) above 0.75 in the initial weeks of a course. The results demonstrate the viability of applying interaction count and derivative features to generate prediction models in contexts where access to demographic data is restricted. The application of a genetic algorithm to the tuning of hyperparameters classifiers can increase their performance in comparison with other techniques.

scholarly journals Don't Drop Out, Drop In: A Workshop for At-Risk Students

1999 ◽  
Vol 19 (1) ◽  
pp. 50-53 ◽  
Author(s):  
Carolyn N. Brooks-Harris ◽  
Val G. Mori ◽  
Lynne M. Higa
Keyword(s):  
At Risk ◽  
At Risk Students ◽  
Drop Out ◽  
Academic Difficulties ◽  
Intervention Method ◽  
Campus Resources ◽  
The University ◽  
Academic Decisions

This article describes a workshop that is targeted at students overcoming academic difficulties after a one-semester suspension. Participants are encouraged to use campus resources, empowered to make better personal and academic decisions, and given an opportunity to connect with other students and the university as a whole. This workshop represents an efficient intervention method that can increase retention and is easily transferable to other universities.

scholarly journals Detecting At-Risk Students With Early Interventions Using Machine Learning Techniques

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 149464-149478
Author(s):  
Raghad Al-Shabandar ◽  
Abir Jaafar Hussain ◽  
Panos Liatsis ◽  
Robert Keight
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
At Risk Students ◽  
Machine Learning Techniques ◽  
Early Interventions ◽  
Learning Techniques

Who Enrolls in College Death Education Courses? A Longitudinal Study

2009 ◽  
Vol 58 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Sarah Brabant ◽  
DeAnn Kalich
Keyword(s):  
At Risk ◽  
Empirical Evidence ◽  
At Risk Students ◽  
College Level ◽  
Death Education ◽  
Ethical Considerations ◽  
Unresolved Grief ◽  
The University ◽  
Education Courses ◽  
At Risk Student

A major concern for the pioneers in death education at the college level was the need to recognize those students who enrolled in the course in order to get help with death related issues, primarily suicidal thoughts and unresolved grief. Despite anecdotal evidence of these at-risk students, this concern has yet to be addressed adequately. This may be due in part to the paucity of empirical evidence. The authors bring over 30 combined years of experience in teaching death education at the university level. They have their own anecdotal stories. They also have empirical evidence. This article addresses the question of why students take death education courses in college by examining data collected from death education classes over a span of 20 years and 3 decades (1985–2004). The results document the magnitude and consistency of the at-risk student. The authors discuss the precautionary steps they take and call for a renewed discourse on ethical considerations in death education.

Eliminating the Achievement Gap: The 21ST Century Program

1997 ◽  
Vol 1 (6) ◽  
pp. 28-29
Author(s):  
Mary L. Hummel
Keyword(s):  
At Risk ◽  
Achievement Gap ◽  
21St Century ◽  
Support Services ◽  
At Risk Students ◽  
University Of Michigan ◽  
Minority Achievement ◽  
University Life ◽  
Minority Interests ◽  
The University

According to professor of psychology Claude Steele, practices such as support services for so-called at-risk students and the sidelining of minority interests in university life can actually undermine minority achievement. So what helps promote it? The answer for Steele and other educators at the University of Michigan is to raise expectations for all students. This is the philosophy behind the 21st Century Program.

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