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.

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 MOOC Dropout Prediction Using a Hybrid Algorithm Based on Decision Tree and Extreme Learning Machine

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Jing Chen ◽  
Jun Feng ◽  
Xia Sun ◽  
Nannan Wu ◽  
Zhengzheng Yang ◽  
...  
Keyword(s):  
Decision Tree ◽  
Extreme Learning Machine ◽  
Hybrid Algorithm ◽  
Online Courses ◽  
Dropout Rate ◽  
Massive Open Online Courses ◽  
Wide Range ◽  
Good Classification ◽  
High Dropout Rate ◽  
Learning Machine

Massive Open Online Courses (MOOCs) have boomed in recent years because learners can arrange learning at their own pace. High dropout rate is a universal but unsolved problem in MOOCs. Dropout prediction has received much attention recently. A previous study reported the problem of learning behavior discrepancy leading to a wide range of fluctuation of prediction results. Besides, previous methods require iterative training which is time intensive. To address these problems, we propose DT-ELM, a novel hybrid algorithm combining decision tree and extreme learning machine (ELM), which requires no iterative training. The decision tree selects features with good classification ability. Further, it determines enhanced weights of the selected features to strengthen their classification ability. To achieve accurate prediction results, we optimize ELM structure by mapping the decision tree to ELM based on the entropy theory. Experimental results on the benchmark KDD 2015 dataset demonstrate the effectiveness of DT-ELM, which is 12.78%, 22.19%, and 6.87% higher than baseline algorithms in terms of accuracy, AUC, and F1-score, respectively.

scholarly journals Factors Affecting Students' Dropout Rate During COVID-19

2021 ◽  
Author(s):  
Anita Shuja ◽  
Prof. Dr. Akhtar Ali ◽  
Sana Shuja Ahmad Khan ◽  
Shafiqa Bilal Burki ◽  
Shaham Bilal Buki
Keyword(s):  
Third World ◽  
Dropout Rate ◽  
Drop Out ◽  
Public And Private ◽  
Factors Affecting ◽  
Financial Conditions ◽  
Teachers And Students ◽  
Problematic Issue ◽  
Major Factors ◽  
The Masses

<p>Education has always been considered as the linchpin for a country’s economic and social development. The dropout rate in schools especially in third-world countries has always been a problematic issue and the situation has further been worsened by the COVID-19 pandemic. This study primarily aims at studying the factors affecting the school dropout rate during pandemic. Lockdown is the first step that any country starts to adopt for the safety of its general public. This severely affects the masses' financial conditions, especially for the parents of students at risk, as the dropout rate increases with financial pressures. The slogan “stay home stay safe” has further aggravated the fear of the parents to send their children out and attend schools. The data for the study was collected from twenty public and private schools of two divisions, including seven districts of the province of Punjab, Pakistan, using interviews of policymakers, parents of dropouts, teachers, and students. The study is corollary to several issues already highlighted in various other articles to transpire the details of drop-out rates in developing countries in general and Pakistan in particular. The study revealed financial conditions, lockdown effects, mode of learning, government policies, fear of death, the psyche of the parents, socio-cultural effects, the role of teachers and administrators, most affected level, contributory factors were amongst the major factors. Finally, the study will analyze the effects of dropout and will help suggest measures to control the dropout rate in Pakistan in particular and third world countries in general.</p>

scholarly journals A Grounded Theory Exploration of Language Massive Open Online Courses (LMOOCs): Understanding Students’ Viewpoints

2021 ◽  
Vol 13 (5) ◽  
pp. 2577
Author(s):  
Robert Li-Wei Hsu
Keyword(s):  
Grounded Theory ◽  
Theoretical Model ◽  
Online Courses ◽  
Internet Access ◽  
Dropout Rate ◽  
Massive Open Online Courses ◽  
English For Specific Purposes ◽  
Massive Open Online ◽  
Innovation In Education ◽  
High Dropout Rate

Massive open online courses (MOOCs) have been called the biggest innovation in education in 200 years for their unique attribute of being open and free to any individual with Internet access; however, their high dropout rate has led many people to be concerned or dubious about their effectiveness and applicability. The applicability of MOOCs in English for specific purposes (ESP) courses (in this case, hospitality English) needs more empirical evidence; the present study intends to help fill this gap and extend our current understanding of this issue. This study followed a grounded theory methodology to develop a theoretical model based on a constant dialogue between collected data and the literature. The results suggest that most participants had positive perceptions of language MOOCs (LMOOCs) in general, but some doubted their applicability. Most participants said they would continue to use LMOOCs for learning, depending on the attributes of specific courses. Based on the extracted data, a conceptual model for the applicability of LMOOCs is proposed.

scholarly journals EKSTERNALISASI REMAJA PUTUS SEKOLAH REMAJA PUTUS SEKOLAH

2020 ◽  
Vol 1 (2) ◽  
pp. 206-220
Author(s):  
Muhammad Arwan Rosyadi ◽  
Syarifuddin Syarifuddin ◽  
Anisa Puspa Rani ◽  
Taufiq Ramdani
Keyword(s):  
Phenomenological Approach ◽  
Dropout Rate ◽  
Drop Out ◽  
Dropping Out ◽  
Subjective Knowledge ◽  
Personal Initiative ◽  
Family Economy ◽  
High Dropout Rate ◽  
Subjective Meanings ◽  
Out Of School

The high dropout rate in West Nusa Tenggara is a worrying fact behind the incessanteducation programs such as Law No. 20 of 2013 which requires 20 percent of the statebudget for education. In 2017, as many as 80 school-aged children in Guntur Macanvillage, Gunung Sari sub-district, West Lombok Regency were not in school. Besides theexternal factors (family economy) which are considered as the dominant factors causingdropout students, there is a personal initiative factor that encourages adolescents to takeaction to drop out of school. This research aims to understand: (1) the internal motives ofindividuals who encourage teenagers to drop out of school, (2) subjective knowledge aboutdropouts in teenagers dropping out of school, and (3) the form of externalizing the meaningin daily life - specifically in education and economics. This study used a qualitative researchmethod with a phenomenological approach. Then, the subjects of the study are teenagerswho dropped out of school in Guntur Macan Village. The focus and unit of analysis in thisstudy are the motives, subjective meanings, and externalization of individual actors(informants). This study finds out various motives and subjective meanings of studentdropout school. After dropping out of school, externalization in the field of education, themajority took the form of "other externalization", and the minority attended courses at theVocational Training Center. While in the economic field, the majority of teenagers droppingout of school are construction workers (peladen), and the minority are mechanics. Based onthe identification of motives, subjective meaning, and externalization of teenagers whodropped out of school in Guntur Macan Village, three categories of dropping out of schoolactions were obtained; conventional, conditional, and constructional.

scholarly journals Engagement and desertion in MOOCs: Systematic review

Comunicar ◽  
2022 ◽  
Vol 30 (70) ◽  
Author(s):  
Odiel Estrada-Molina ◽  
Dieter-Reynaldo Fuentes-Cancell
Keyword(s):  
Systematic Review ◽  
Extrinsic Motivation ◽  
Online Courses ◽  
Dropout Rate ◽  
Attrition Rate ◽  
Learning Needs ◽  
Revisión Sistemática ◽  
High Dropout Rate ◽  
Para Determinar ◽  
Future Work

Massive and open online courses (MOOCs) satisfy learning needs from the particularities of their typologies (xMOOC, tMOOC, cMOOC, iMOOC, among others) even though their high dropout rate is still latent. Recent studies reaffirm engagement as an alternative to reduce dropout rates. The literature analyzed has not yet been able to systematize responses as to how to guarantee engagement in MOOCs and thus reduce their attrition rate. And, consistent with that question, are there still challenges for teachers in this area of educational technology? These answers motivated us to carry out this systematic review to determine how engagement has been studied to help reduce the attrition rate in MOOCs. Articles from journals indexed in Scopus or WoS were reviewed applying the PRISMA protocol. At the end of the protocol, it was defined to analyze 40 studies. The results reflect that the main variables are: the design of e-activities, intrinsic and extrinsic motivation, and communication between students. This paper confirms that the main challenges to guarantee engagement in MOOCs are individualized tutoring, interactivity, and feedback. Due to the scarcity of studies that analyze the variables in an integrated way, it is proposed as future work to determine what relationships exist between these variables that interfere with engagement and dropout in MOOCs. Los cursos en línea masivos y abiertos (MOOCs) permiten satisfacer necesidades de aprendizaje desde las particularidades de sus tipologías (xMOOC, tMOOC, cMOOC, iMOOC, entre otras), sin embargo, es aún latente su alta tasa de deserción. Estudios recientes reafirman el engagement como una alternativa para disminuir los índices de deserción. La literatura analizada aún no logra sistematizar respuestas a ¿cómo garantizar el engagement en los MOOCs y disminuir así su tasa de deserción? Y, en coherencia con esa pregunta, ¿existen aún retos del profesorado en este ámbito de la tecnología educativa? Ello motivó a realizar esta revisión sistemática para determinar cómo se ha trabajado el engagement para contribuir a disminuir la tasa de deserción en los MOOCs. Se revisaron artículos de revistas indexadas en Scopus o en WoS aplicando el protocolo PRISMA. Al finalizar el protocolo se definió analizar 40 estudios. Los resultados reflejan que las principales variables son: el diseño e-actividades; la motivación intrínseca y extrínseca y; la comunicación entre los estudiantes. Se ratifica que los principales retos para garantizar el engagement en los MOOCs son: la tutoría individualizada; la interactividad; y la retroalimentación. Debido a la escasez de estudios que analicen de forma integrada las variables antes mencionadas, se propone como trabajo futuro, determinar qué relaciones existen entre estas variables que intervienen en el engagement y la deserción en los MOOCs.

scholarly journals Investigating Student Satisfaction With Online Courses

2021 ◽  
Vol 17 (3) ◽  
pp. 12-28
Author(s):  
Jeetendra Pande ◽  
Mythili G.
Keyword(s):  
Student Satisfaction ◽  
Support Services ◽  
Online Courses ◽  
Dropout Rate ◽  
Satisfaction Survey ◽  
Statistical Measures ◽  
High Dropout Rate ◽  
The University ◽  
Students Satisfaction ◽  
Study Center

The educational system has moved towards digitization and online learning in the past two decades. The institutions are focusing on delivering online courses to facilitate the students. Uttarakhand Open University offered online courses to reach the unreached learners. High dropout rate from MOOCs is a global concern. Learners' satisfaction survey is one of the important instruments to investigate the reasons of discontinuance from an online course. Detailed analysis of learners' satisfaction survey will help the educators understand learners' expectations from the course and they can work on these factors which leads to increasing the learners' satisfaction with MOOCs and thereby address high-dropout rates from MOOCs. This paper investigates the students' satisfaction of online courses on academic counselling, assignments, and examination marking process and various support services provided by the university. A structed questionnaire of 5-point Likert scale was administrated using Google form. The data (269 valid responses) have been analysed quantitatively by implying statistical measures. The findings show that academic counselling provided by the university to students are conceptual, clear, and knowledgeable. The students are comfortable on assignment, examination, and their result declarations. Along with this the online services, different initiatives and other support services provided by university are discussed in detail and suggested for further enhancements. Furthermore, it is also concluded that the study center structure is adequate, and personnel at study center are very helpful with students.

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 Dropout Time and Learners’ Performance in Computer Programming MOOCs

2021 ◽  
Vol 11 (10) ◽  
pp. 643
Author(s):  
Marili Rõõm ◽  
Marina Lepp ◽  
Piret Luik
Keyword(s):  
Quantitative Study ◽  
Computer Programming ◽  
Online Courses ◽  
Dropout Rate ◽  
Massive Open Online Courses ◽  
Dropping Out ◽  
Massive Open Online ◽  
High Dropout Rate ◽  
Different Levels

One of the problems regarding MOOCs (Massive Open Online Courses) is the high dropout rate. Although dropout periods have been studied, there is still a lack of understanding of how dropout differs for MOOCs with different levels of difficulty. A quantitative study was conducted to determine the periods with the highest dropouts in computer programming MOOCs and the performance of the dropouts on the course before dropping out. Four occurrences of three MOOCs, with different durations, difficulty of the topic, and the degree of supportive methods, were included. The results showed that dropout was highest at the beginning of all studied courses. Learners also dropped out before the project. In the easier and shorter courses, most dropouts were successful until they quit the course. In longer and more difficult courses, learners mainly dropped out in the week they started due to experiencing problems with the course activities. It is suggested to recommend that learners take courses at a level that suits them if their current course is too easy or difficult and encourage learners to use course resources for help. It would be a good idea to provide learners with example topics to assist them in starting with a project.

scholarly journals Gamification Increases Completion Rates in Massive Open Online Courses

2022 ◽  
Vol 18 (1) ◽  
pp. 0-0
Keyword(s):  
Online Courses ◽  
Course Completion ◽  
Dropout Rate ◽  
Completion Rate ◽  
Massive Open Online Courses ◽  
Dropout Rates ◽  
Completion Rates ◽  
Massive Open Online ◽  
High Dropout Rate ◽  
Course Completion Rate

Massive Open Online Courses (MOOCs) aim at unlimited participation and open access via the web. There are concerns about the actual value of such courses. This is predominantly due to higher dropout rates. According to studies, only 7-13% go on to complete these courses. The high dropout rate in MOOCs is a challenge for education providers. This paper aims to explore reasons for high dropout rates within MOOCs and how they can be minimized. With this in mind, two research questions have been set for this study: 1) Why do MOOC participants not complete their courses? 2) How can the course completion rate be increased? Implementation of the strategies investigated in this paper can increase completion rates in MOOCs. In conclusion, after analyzing the collected data, the final results have shown that gamification increased the completion rate of MOOCs.

Sign in / Sign up

Export Citation Format

Share Document