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 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.

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%.

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 A Review on Prediction of Academic Performance of Students at-Risk Using Data Mining Techniques

2017 ◽  
Vol 5 (1) ◽  
pp. 30-39 ◽  
Author(s):  
Preet Kamal ◽  
Sachin Ahuja
Keyword(s):  
Data Mining ◽  
Learning Process ◽  
High Probability ◽  
Research Work ◽  
Educational Data Mining ◽  
Dropout Rate ◽  
Educational Background ◽  
Drop Out ◽  
Teaching Learning ◽  
The Right

Educational data mining is the procedure of converting raw data collected from educational databases into some useful information. It can be helpful in designing and answering research questions like performance prediction of students in academics, factors that affect the students’ performance, help the teachers in understanding the problems faced by the students to understand the course content and complexity of the subject taken so that the teachers can take timely action to control the dropout rate. This also includes improving the teaching learning process so that the interventions can be taken at the right time to improve the performance of the student. This paper is the review of the research work done in the field of educational data mining for the prediction of students’ performance. The factors that influence the performance of the students i.e. the type of classrooms they attend such as traditional or on-line, socio-economic, educational background of the family, attitude toward studies and challenges faced by the students during course progress. These factors leads to the categorization of the students into three groups “Low-Risk”: who have High probability of succeeding, “Medium-Risk”: who may succeed in their examination, “High-Risk”: who have High probability of failing or drop-out. It elaborates the different ways to improve the teaching learning process by providing the students personal assistance, notes, class-assignments and special class tests. The most efficient techniques that are used in educational data mining are also reviewed such as; classification, regression, clustering and and prediction.

scholarly journals The Connection between Extracurricular, Leisure Time Activities, Religiosity and the Reasons for Drop-out

2019 ◽  
Vol 1 (1) ◽  
pp. 55-67
Author(s):  
Judit Váradi ◽  
Zsuzsanna Demeter-Karászi ◽  
Klára Kovács
Keyword(s):  
Education Research ◽  
Extracurricular Activities ◽  
Leisure Time ◽  
Tertiary Education ◽  
Dropout Rate ◽  
Further Education ◽  
Drop Out ◽  
Dropping Out ◽  
Educational Sociology ◽  
Student Dropout

The interruption of tertiary education and the reduction in the dropout rate have been a central issue in educational sociology and education research. Exploring the possible reasons for dropping out can significantly contribute to reducing the trend. Our aim is to map the links between students dropping out and individual factors. Consequently, we investigate the connection between extracurricular and leisure-time activities, health behaviour and religiosity in relation to dropout. This is explained by the fact that one of the axioms of the literature on dropout is that belonging to civil networks usually strengthens the commitment to the successful completion of studies. In our analysis, we used the database created during the research carried out in 2018 by the Center for Higher Education Research and Development (CHERD-H) in the framework of project No. 123847 of the National Research, Development and Innovation Fund of Hungary, entitled The Role of Social and Organisational Factors in Student Dropout (DEPART 2018, N=605). Our results show that the neglect of study obligations among those who are disappointed in the course and further education is closely related to the shift in value preferences and an increase in the time spent with entertainment activities and partying. It can also be stated that students take part indifferent types of extracurricular activities only to a limited extent, and the different forms of participation in activities and religiosity are not related to the causes of dropout.

scholarly journals Understanding Dropouts in MOOCs

2019 ◽  
Vol 33 ◽  
pp. 517-524 ◽  
Author(s):  
Wenzheng Feng ◽  
Jie Tang ◽  
Tracy Xiao Liu
Keyword(s):  
Online Courses ◽  
Interaction Network ◽  
Dropout Rate ◽  
Drop Out ◽  
Feature Interaction ◽  
Modeling Framework ◽  
High Dropout Rate ◽  
Feature Values ◽  
Major Factors ◽  
Method Model

Massive open online courses (MOOCs) have developed rapidly in recent years, and have attracted millions of online users. However, a central challenge is the extremely high dropout rate — recent reports show that the completion rate in MOOCs is below 5% (Onah, Sinclair, and Boyatt 2014; Kizilcec, Piech, and Schneider 2013; Seaton et al. 2014).What are the major factors that cause the users to drop out?What are the major motivations for the users to study in MOOCs? In this paper, employing a dataset from XuetangX1, one of the largest MOOCs in China, we conduct a systematical study for the dropout problem in MOOCs. We found that the users’ learning behavior can be clustered into several distinct categories. Our statistics also reveal high correlation between dropouts of different courses and strong influence between friends’ dropout behaviors. Based on the gained insights, we propose a Context-aware Feature Interaction Network (CFIN) to model and to predict users’ dropout behavior. CFIN utilizes context-smoothing technique to smooth feature values with different context, and use attention mechanism to combine user and course information into the modeling framework. Experiments on two large datasets show that the proposed method achieves better performance than several state-of-the-art methods. The proposed method model has been deployed on a real system to help improve user retention.

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 Implementation of Remote Interventions in Older Adults: Lessons Learned During COVID-19 Isolation

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 950-950
Author(s):  
Jamie Rincker ◽  
Jessica Wallis ◽  
Angela Fruik ◽  
Alyssa King ◽  
Kenlyn Young ◽  
...  
Keyword(s):  
Older Adults ◽  
Weight Loss ◽  
Nutrition Education ◽  
Underserved Populations ◽  
Smart Phone ◽  
Dropout Rate ◽  
Lessons Learned ◽  
Drop Out ◽  
Electronic Submission ◽  
Weight Loss Interventions

Abstract Recommendations for older adults to socially isolate during the COVID-19 pandemic will have lasting impacts on body weight and physical activity. Due to the pandemic, two in-person RCT weight-loss interventions in obese older adults with prediabetes, Veterans Achieving Weight Loss and Optimizing Resilience-Using Protein (VALOR-UP, n=12) and the Egg-Supplemented Pre-Diabetes Intervention Trial (EGGSPDITE, n=7), were converted to remote formats and weekly nutrition (EGGSPDITE and VALOR-UP) and exercise (VALOR-UP only) classes were delivered using synchronous videoconference technology (Webex); classes were accessed via tablet/desktop/laptop or smart phone. Steps taken to transition participants to remote formats included technology training, implementation of staff tech-support, and delivery of nutrition education, tablets, scales, and exercise bands. The time to successfully transition participants was 1 week for early adopters (n=10) and up to 4 weeks for those with significant technology barriers (n=9); their difficulties included internet access, camera and microphone access and use, and electronic submission of weight and food records. Even with these challenges, in the first 3 months of remote delivery, participant dropout rate was low (10.5%, n=2), attendance was high (87.6% nutrition class (n=19); 76.4% exercise class (VALOR-UP, n=12)), and weight loss was successful (>2.5% loss (n=13); >5% loss (n=8)), showing that lifestyle interventions can be successfully adapted for remote delivery. Remote interventions also have potential for use in non-pandemic times to reach underserved populations who often have high drop-out rates due to caretaker roles, transportation limitations, and work schedules. These barriers were significantly reduced using a virtual intervention platform.

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