scholarly journals Factors Influencing Students’ Decision to Drop Out of Online Courses in Brazil

2020 ◽  
Vol 9 (5) ◽  
pp. 284
Author(s):  
Isabella Moreira Pereira de Vasconcellos ◽  
Diogo Tavares Robaina ◽  
Carole Bonanni
Keyword(s):  
Online Courses ◽  
Dropout Rate ◽  
Drop Out ◽  
Course Content ◽  
Student Dropout ◽  
Educational Professionals ◽  
System Use ◽  
Relevant Variables ◽  
E Learning

In recent years, e-learning has been the fastest growing educational form in students' numbers, and this industry's market revenue (Lee, Choi, &Kim, 2013). Despite this growth, concern about the significantly higher student dropout rate of students in online courses as compared with conventional learning environments has increased. Brazil has also registered a significant increase in the number of students interested in this type of education, but the dropout rate is a considerable concern to institutions. This study’s objective was to identify the relevant variables behind online students’ dropout decision in Brazil. After a literature review that determined the ten most recurrent and relevant variables, we heard professional e-learning experts. They indicated, from their standpoint, what the most pertinent variables influencing dropout would be. Based on this, we conducted a quantitative survey with e-learning students, considering the factors indicated in the literature on this subject and educational professionals’ indications. This study's contribution was to verify that the quality support is extraordinarily relevant and has a high correlation with students' perception of Usefulness, the quality of Course Content, and ease of System Use.

scholarly journals Machine Learning Classification Algorithms for Systematic Analysis to Understand Learners Drop out of MOOCs courses

2021 ◽  
Author(s):  
Seema Rawat ◽  
Deepak Kumar ◽  
Chhaya Khattri ◽  
Praveen Kumar
Keyword(s):  
Online Courses ◽  
Research Work ◽  
Dropout Rate ◽  
Drop Out ◽  
Classification Algorithms ◽  
Systematic Analysis ◽  
Significant Percentage ◽  
Student Dropout ◽  
Machine Learning Classification ◽  
Open Courses

Abstract The increasing popularity of massively online open courses (MOOCs) has been attracting a lot of learners. Despite the popularity, it has been observed that there is a significant percentage of learners who discontinue courses and drop out of the platform. This is a problem that most of the MOOC courses face. The dropout probability of any student depends on his/her interaction with the platform, and the features of the course in which the student has enrolled. The research work is intended to study and analyze the dropout behavior of the students in online learning with identification of the reasons and to understand their impact. The current research accounts for the activity log of learners of 13 different online courses offered by Harvard and MIT during 2012 to 2013. The work examines the attributes which affects the student dropout rate. The research can be useful in improving the existing features of the MOOC courses and content to ensure persistence turnout of their learners.

scholarly journals The Investigation of Student Dropout Prediction Model in Thai Higher Education Using Educational Data Mining: A Case Study of Faculty of Science, Prince of Songkla Uni-versity

2019 ◽  
Vol 27 (1) ◽  
pp. 356-367 ◽  
Author(s):  
Jarutas Pattanaphanchai ◽  
Koranat Leelertpanyakul ◽  
Napa Theppalak
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Prediction Model ◽  
Retention Rate ◽  
Dropout Rate ◽  
Drop Out ◽  
Data Mining Technique ◽  
Student Dropout ◽  
The University

The student’s retention rate is one of the challenging issues that representing the quality of the university. A high dropout rate of students affects not only the reputation of the university but also the students’ career in the future. Therefore, there is a need of student dropout analysis in order to improve the academic plan and management to reduce students drop out from the university as well as to  enhance the quality of the higher education system. Data mining technique provides powerful methods for analysis and the prediction the dropout. This paper proposes a model for predicting students’ dropout using the dataset from the representative of the largest public university in the Southen part of Thailand. In this study, data from Faculty of Science, Prince of Songkla University was collected from academic year of 2013 to 2017. The experiment result shows that JRip rule induction is the best technique to generate a prediction model receiving the highest accuracy value of 77.30%. The results highlight the potential prediction model that can be used to detect the early state of dropping out of the student which the university can provide supporting program to improve the student retention rate

scholarly journals Technical Quality of a Mobile SPOC

2018 ◽  
Vol 12 (5) ◽  
pp. 140
Author(s):  
Naima Belarbi ◽  
Abdelwahed Namir ◽  
Nadia Chafiq ◽  
Mohammed Talbi
Keyword(s):  
Learning Environments ◽  
Online Courses ◽  
Mobile App ◽  
Technical Quality ◽  
Quality Factors ◽  
Computer Based Learning ◽  
E Learning ◽  
Computer Based ◽  
Windows Phone

<p class="0papertitle">Computer based Learning Environments are mainly shaped by emerging environments such as Massive Open Online Courses (MOOCs), SPOCS (Small Private Online Courses) and Mobile learning. This variety challenges the quality of the content delivered in these various environments. In Moroccan higher education, SPOCS is a trending topic widely used in its context of blended learning. The present work focuses on an SPOC delivered as a hybrid mobile app and on factors that define its technical quality. The objective is to propose a set of technical quality factors which are defined following a study of literature, focusing on frameworks, labels, practices that are used to assess the quality of e-learning environments, MOOCs, SPOCs and mobile applications. ISO standards for the quality software and the guidelines for the most dominant Mobile Operating Systems (Android/IOS/Windows phone) are also considered when defining these criteria. The proposed criteria can be twofold used: 1) to assess the technical quality of an existing mobile SPOC; 2) constitutes guidelines to increase the technical quality of a new mobile SPOC</p>

A Machine Learning Approach to Detect Student Dropout at University

2021 ◽  
Vol 10 (6) ◽  
pp. 3101-3107
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Dropout Rate ◽  
Random Forest Classifier ◽  
Drop Out ◽  
Dropout Rates ◽  
Learning Technology ◽  
Student Dropout ◽  
High Dropout Rate ◽  
Academic Plan

In universities, student dropout is a major concern that reflects the university's quality. Some characteristics cause students to drop out of university. A high dropout rate of students affects the university's reputation and the student's careers in the future. Therefore, there's a requirement for student dropout analysis to enhance academic plan and management to scale back student's drop out from the university also on enhancing the standard of the upper education system. The machine learning technique provides powerful methods for the analysis and therefore the prediction of the dropout. This study uses a dataset from a university representative to develop a model for predicting student dropout. In this work, machine- learning models were used to detect dropout rates. Machine learning is being more widely used in the field of knowledge mining diagnostics. Following an examination of certain studies, we observed that dropout detection may be done using several methods. We've even used five dropout detection models. These models are Decision tree, Naïve bayes, Random Forest Classifier, SVM and KNN. We used machine-learning technology to analyze the data, and we discovered that the Random Forest classifier is highly promising for predicting dropout rates, with a training accuracy of 94% and a testing accuracy of 86%.

Instructional Design and Quality

2013 ◽  
pp. 236-250
Author(s):  
Anna Busquets ◽  
Muriel Gómez
Keyword(s):  
Instructional Design ◽  
Teaching And Learning ◽  
East Asian ◽  
Drop Out ◽  
Instructional Design Process ◽  
Course Materials ◽  
E Learning ◽  
Two Phases ◽  
East Asian Studies

Quality in e-learning should be measured from three perspectives: technology, pedagogy, and management and administration. This paper examines the pedagogical and methodological perspective, specifically in the work developed by the professors of the course “East Asian Geography”, a compulsory course of the Programme of East Asian Studies. The authors consider that the teaching and learning methodology applied to the UOC model has reached the proper level of quality when students are satisfied, follow the courses and not drop out, and perform appropriately. In that sense, satisfaction, permanence, and academic performance are the three levels of measurement of the quality of the each course and program, as well as the UOC model in general. On the basis of the data obtained and results of the first two years 2003-2004 and 2004-2005, in terms of performance and satisfaction of the students in the course “East Asian Geography”, is considered for revision and improvement. This process has two phases. In the first one, during 2005-2008, the authors focus on the instructional design process and the conceptualization of the course plan with new activities; in the second, from 2009 to present, the authors examine the design and diversification of course materials and e-learning activities.

Comprehensive E-Learning Appraisal System

2019 ◽  
pp. 289-304
Author(s):  
Jose Luis Monroy Anton ◽  
Juan Vicente Izquierdo Soriano ◽  
Maria Isabel Asensio Martinez ◽  
Felix Buendia Garcia
Keyword(s):  
Health Professionals ◽  
Online Courses ◽  
Technological Development ◽  
Healthcare Sector ◽  
Appraisal System ◽  
Virtual Training ◽  
Face To Face ◽  
E Learning ◽  
Training Courses

The healthcare sector in the 21st century presents a big technological development. All fields of medicine are deepening their knowledge, which increases the volume of material that must be handled by professionals in each specialty. This large volume of material should be taken into account by health professionals, because it contributes to a better quality of care. The traditional way of teaching has been face-to-face classes; however, with rising technologies, virtual training via computers and virtual teachers are being implemented in some institutions. This change in the way of teaching also leads to changes in how to assess the knowledge gained through this method of learning. The aim of this chapter is to provide a small analysis of online training courses for health professionals, and deepen into an appraisal system developed to integrate different complementary variables, and how they can be implemented as a method addressed to assess online courses in a more comprehensive way.

Evaluating an Online Programming Instructional Process Organized Through Elaboration Theory

2018 ◽  
Vol 13 (4) ◽  
pp. 1-16
Author(s):  
Ünal Çakıroğlu ◽  
Mücahit Öztürk
Keyword(s):  
Online Courses ◽  
Evaluation Criteria ◽  
Instructional Technologies ◽  
Web Conferencing ◽  
Elaboration Theory ◽  
Course Content ◽  
Instructional Process ◽  
E Learning ◽  
Delivery Platform ◽  
Quality Learning

This article draws on a semester design study to evaluate the quality of an online from the point of e-learning. Adobe Connect web conferencing system was used as a delivery platform in an Introductory Programming course. The course content was specifically sequenced and elaborated in terms of elaboration theory (ET). Thirty pre-service computer teachers enrolled in instructional technologies department online program were participated to the study. The evaluation criteria included dimensions of e-learning in which both qualitative and quantitative data was used. The results indicated that the online course almost met the seven dimensions of e-learning in order to provide high quality learning outcomes. Elaborating the content provided positive contributions to the dimensions of content, interaction, learning and support. Along with the results, some implications were provided for elaborating and evaluating the content for online courses.

Association between clinically depressed subgroups, type of treatment and patient retention in the LIDO study

2003 ◽  
Vol 33 (6) ◽  
pp. 1051-1059 ◽  
Author(s):  
P. BECH ◽  
R. LUCAS ◽  
M. AMIR ◽  
D. BUSHNELL ◽  
M. MARTIN ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Statistical Significance ◽  
Dropout Rate ◽  
Drop Out ◽  
Medical Conditions ◽  
Mild Depression ◽  
Depressed Patients ◽  
Moderate Depression ◽  
Patient Retention

Background. Few data are available with which to evaluate the association between depressed subgroups, type of treatment and patient retention during episodes of major depression.Method. This observational study followed 1117 depressed patients over a 12-month period in the primary care setting of six different international sites. The patients were divided into three severity-linked subgroups: moderate to severe depression; moderate depression co-morbid with serious medical conditions; and mild depression.Results. In general, a low dropout rate was found, with significant differences in the rates across the six sites. However, while there was no statistical significance in the association between the three subgroups of depression and overall dropout rates, we did find that older patients were less likely to drop out, more depressed patients were more likely to drop out, and if patients were on antidepressants they were less likely to drop out. Among the three subgroups of depression, patients with moderate depression co-morbid with serious medical conditions received the lowest amount of antidepressants and had the lowest quality of life.Conclusion. Although the overall dropout rate in this study was found very low and did differ between the six sites, an association between the use of antidepressants and patient retention was seen. The group of patients with serious co-morbid medical conditions received fewer antidepressants even when the level of their depressive states was taken into consideration. This group was the least satisfied with treatment and had the lowest self-reported quality of life.

scholarly journals An exploration into students' perceptions regarding dropout within the chiropractic programme at a University of Technology

2018 ◽  
Author(s):  
◽  
Nqubeko Lizwilenkosi Buthelezi
Keyword(s):  
South Africa ◽  
Data Analysis ◽  
Student Teaching ◽  
Policy Development ◽  
Dropout Rate ◽  
The Body ◽  
Drop Out ◽  
Student Dropout ◽  
Contextual Design ◽  
University Of Technology

Introduction: Chiropractic is a health profession specialising in the diagnosis, treatment and prevention of disorders affecting the bones, joints, muscles and nerves in the body. It is a type of alternative or complimentary medicine concerned with the relationship between the body's structure and its functioning. The Durban University of Technology (DUT) and University of Johannesburg are the two internationally accredited academic institutions in South Africa to offer the chiropractic programme. The Chiropractic Department at the DUT is one of 13 departments within the Faculty of Health Sciences. A student who successfully completes the chiropractic-training programme becomes registered as doctor of chiropractic by the Allied Health Professions Council of South Africa under Act 63 of 1982 (as amended). However, a number of students drop out from the chiropractic programme before completion. Some of these students transfer to other programmes; others deregister and leave the university, while others are excluded because of the progression rule or because of having exceeded the maximum duration of the programme. Aim of the study: The aim of the study was to explore and describe the perceptions of the students regarding dropping out from the chiropractic programme at the DUT. The study aimed to answer three research questions, which were: 1) what are the perceptions of students regarding dropout from the chiropractic programme at the DUT? 2) what are the determinants of student dropout from the chiropractic programme at the DUT? and 3) how can the dropout rate in the chiropractic programme at the DUT be minimised? Methodology: A qualitative, explorative, descriptive and contextual design was employed. The DUT was used as a data collection site. Data was collected between May and June 2018 using one-on-one semi structured interviews with 12 former students who were previously registered for the chiropractic programme and dropped out before completion. Tesch’s eight steps of data analysis guided thematic data analysis. Findings: The students’ perceptions regarding dropout from the chiropractic programme were grouped into five major themes and several subthemes. The major themes included financial constraints, post course employment, personal, course related and socio- cultural factors. All these themes were, according to the participants, determinants of student dropout from the chiropractic programme. Recommendation from the study findings focused on how the dropout rate in the chiropractic programme could be minimised. Conclusion: The study discovered that, according to the students’ perceptions, there are several determinants of the high dropout rate from the chiropractic programme. Some of these are intrinsic chiropractic programme factors such as course structure, workload and assessment strategy. However, other determinants are outside the programme and generic to all university disciplines/programmes. Nevertheless, it is still critical that attention be given to all determining factors to facilitate retention of students into the chiropractic programme. Recommendations: The following recommendations with special reference to policy development and implementation, institutional management and practice, chiropractic education and further research, are presented. The national and institutional policies regarding application and administration of financial aid should be reviewed and guidelines for application and appeals procedures should be made known to students. Student teaching and assessment strategies should be reviewed periodically and input from students be invited. The Chiropractic Department should ensure that information about the programme and qualification is made available to the public. The chiropractic curriculum should include entrepreneurship to provide information and guidance on how to set up own private practice. The chiropractic programme should institute measures of decolonising the programme in order to address challenges of racial discrimination. A broader research study on reasons for student dropout is recommended.

scholarly journals Factors Affecting the Quality of E-Learning During the COVID-19 Pandemic from the Perspective of Higher Education Students

10.28945/4628 ◽  
2020 ◽  
Vol 19 ◽  
pp. 731-753
Author(s):  
Kesavan Vadakalu Elumalai ◽  
Jayendira P Sankar ◽  
Kalaichelvi R ◽  
Jeena Ann John ◽  
Nidhi Menon ◽  
...  
Keyword(s):  
Higher Education ◽  
Social Support ◽  
Administrative Support ◽  
Course Design ◽  
Educational Institutions ◽  
Learner Characteristics ◽  
Course Content ◽  
E Learning ◽  
Instructor Characteristics

Aim/Purpose: The objective of the research was to study the relationship of seven independent factors: administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technical support on quality of e-learning in higher education during the COVID-19 pandemic. Further, the study analyzes the moderating effect(s) of gender and level of the course on the quality of e-learning in higher education during the COVID-19 pandemic. objective of the research was to study the relationship of seven independent factors: administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technical support on quality of e-learning in higher education during COVID-19 pandemic. Background: The COVID-19 pandemic situation has impacted the entire education system, especially universities, and brought a new phase in education “e-learning.” The learning supported with electronic technology like online classes and portals to access the courses outside the classroom is known as e-learning. This study aimed to point out the variables influencing the quality of e-learning, such as administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technological support. Methodology: An inferential statistics cross-sectional study was conducted of the students of higher education institutions in India and the Kingdom of Saudi Arabia with a self-administered questionnaire to learn the students’ perception of e-learning. All levels of undergraduate and postgraduate students took part in the study with a sample size of 784. Ultimately, this study used a Structural Equation Modelling (SEM) approach to find the positive relationship between the quality of e-learning and the seven independent variables and two moderating variables in the higher education sector. Contribution: The study aims to explore the quality of e-learning in higher education from the students’ perspective. The study was analyzed based on the student’s data collected from the higher educational institutions of India and Saudi Arabia. The study will support the top management and administrators of higher educational institutions in decision making. Findings: The findings revealed that there is a positive relationship between the set of variables and the quality of e-learning in the higher education sector. Also, there is a significant difference in the perception of the students between gender, level of the course, and quality of e-learning in the higher education sector during the COVID-19 pandemic. Recommendations for Practitioners: The results of the study can help top management and administrators of higher educational institutions to improve their actions. Higher educational institutions need to concentrate on the study outcomes related to administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technological support to enhance the quality of e-learning. The study revealed that there should be a difference in the procedure of providing e-learning based on the level of the course and gender of the students. Recommendation for Researchers: The results were examined and interpreted in detail, based on the perspective of the students, and concluded with a view for future research. The study will be beneficial for academic researchers from different countries with a different set of students and framework. Impact on Society: The study revealed that the positive results of the students’ perspective on the quality of e-learning would help the policy-makers of the country in providing the learning process during the COVID-19 pandemic. Also, the result explored the importance of the quality aspects of e-learning for improvement. Future Research: There is a need for future studies to expose the quality of e-learning in higher education in the post-COVID-19 pandemic. Further researchers will bring the performance level of e-learning during the COVID-19 pandemic.

Sign in / Sign up

Export Citation Format

Share Document