Massive Open Online Courses Dropout Research Articles

The Impact of Motivation on MOOC Retention Rates: A Systematic Review

This systematic review investigates the effectiveness of motivational strategies on learner engagement and retention rates in Massive Open Online Courses (MOOCs). Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we analyzed 140 studies published between 2014 and 2023 from key academic databases. The objective was to identify and evaluate motivational strategies that significantly reduce MOOC dropout rates. Our findings reveal that personalized learning, interactive content, and peer collaboration are strongly correlated with increased learner engagement and persistence. These strategies align well with learners' intrinsic goals, enhancing their educational experience and adherence to courses. The review also identifies gaps, such as the need for longitudinal studies and culturally tailored motivational strategies, offering a refined agenda for future research in MOOC education. This study contributes to the field by systematically synthesizing existing research, providing new insights into effective educational strategies, and highlighting areas for improvement in MOOC design and implementation. Doi: 10.28991/ESJ-2024-SIED1-08 Full Text: PDF

Predicting Dropout in Programming MOOCs through Demographic Insights

Massive Open Online Courses (MOOCs) have gained widespread popularity for their potential to offer education to an unlimited global audience. However, they also face a critical challenge in the form of high dropout rates. This paper addresses the need to identify students at risk of dropping out early in MOOCs, enabling course organizers to provide targeted support or adapt the course content to meet students’ expectations. In this context, zero-time dropout predictors, which utilize demographic data before the course commences, hold significant potential. Despite a lack of consensus in the existing literature regarding the efficacy of demographic data in dropout prediction, this study delves into this issue to contribute new insights to the ongoing discourse. Through an extensive review of prior research and a detailed analysis of data acquired from two programming MOOCs, we aim to shed light on the relationship between students’ demographic characteristics and their likelihood of early dropout from MOOCs, using logistic regression. This research extends the current understanding of the impact of demographic features on student retention. The results indicate that age, education level, student status, nationality, and disability can be used as predictors of dropout rate, though not in every course. The findings presented here are expected to affect the development of more effective strategies for reducing MOOC dropout rates, ultimately enhancing the educational experience for online learners.

Towards an Adaptive Learning Model using Optimal Learning Paths to Prevent MOOC Dropout

Currently, massive open online courses (MOOCs) are experiencing major developments and arebecoming increasingly popular in distance learning programs. The goal is to break down inequalitiesand disseminate knowledge to everyone by creating a space for exchange and interaction.Despite the improvements to this educational model, MOOCs still have low retention rates, whichcan be attributed to a variety of factors, including learners’ heterogeneity. The paper aims toaddress the issue of low retention rates in MOOCs by introducing an innovative prediction modelthat provides the best (optimal) learning path for at-risk learners. For this purpose, learners at riskof dropping out are identified, and their courses are adapted to meet their needs and skills. A casestudy is presented to validate the effectiveness of our approach using classification algorithms forprediction and the ant colony optimization (ACO) algorithm to optimize learners’ paths.

Quantification and prediction of engagement: Applied to personalized course recommendation to reduce dropout in MOOCs

MOOCs (Massive Open Online Courses) offer tens of thousands of courses and attract hundreds of millions of online learners. After years of development, these platforms have accumulated a large number of learning action data. These data imply learners’ engagement with the enrolled courses, reflecting whether learners are willing to spend time on the courses. Meanwhile, the MOOC dropout rate remains chronically high. This paper experimentally finds that using the relationship between engagement and dropout rate can help MOOC platforms develop effective methods to reduce the dropout rate. Firstly, this paper proposes a new Quantified Engagement method named QE by using learning action data and learning duration to quantify learners’ engagement in enrolled courses. Next, an Engagement Neural Network prediction model named ENN is proposed to predict learners’ engagement in unenrolled courses. Then, applying QE, the predicted engagement by ENN, and the aforementioned relationship to personalized course recommendations to learners, ensuring that the recommended courses are likely to be completed by learners as much as possible, thus effectively reducing the dropout rate. Finally, the proposed method is evaluated on two large real-world datasets in XuetangX and KDDCUP. The RMSE and MAE of ENN are 0.1066 and 0.0727 on XuetangX and 0.062411 and 0.039621 on KDDCUP, respectively. Dropout rates were reduced by 46.99% and 10.34%, respectively, when 5% of the courses were recommended. These results demonstrate that the quantification method of engagement is valid, applying predicted engagement to personalized course recommendations and reducing dropout rates is available.

Predictive Modeling of Student Dropout in MOOCs and Self-Regulated Learning

The primary objective of this study is to examine the factors that contribute to the early prediction of Massive Open Online Courses (MOOCs) dropouts in order to identify and support at-risk students. We utilize MOOC data of specific duration, with a guided study pace. The dataset exhibits class imbalance, and we apply oversampling techniques to ensure data balancing and unbiased prediction. We examine the predictive performance of five classic classification machine learning (ML) algorithms under four different oversampling techniques and various evaluation metrics. Additionally, we explore the influence of self-reported self-regulated learning (SRL) data provided by students and various other prominent features of MOOCs as potential indicators of early stage dropout prediction. The research questions focus on (1) the performance of the classic classification ML models using various evaluation metrics before and after different methods of oversampling, (2) which self-reported data may constitute crucial predictors for dropout propensity, and (3) the effect of the SRL factor on the dropout prediction performance. The main conclusions are: (1) prominent predictors, including employment status, frequency of chat tool usage, prior subject-related experiences, gender, education, and willingness to participate, exhibit remarkable efficacy in achieving high to excellent recall performance, particularly when specific combinations of algorithms and oversampling methods are applied, (2) self-reported SRL factor, combined with easily provided/self-reported features, performed well as a predictor in terms of recall when LR and SVM algorithms were employed, (3) it is crucial to test diverse machine learning algorithms and oversampling methods in predictive modeling.

Scrutinizing student dropout issues in MOOCs using an intuitionistic fuzzy decision support system

Massive open online courses (MOOCs) are a recent e-learning programme that has received widespread acceptance among several colleges. Student dropout from MOOCs is a big worry in higher education and policy-making circles, as it occurs frequently in colleges that offer these types of courses. The majority of student dropouts are caused by causes beyond the institution’s control. Using an IF-DEMATEL (Intuitive Fuzzy Decision-making Trial and Evaluation Laboratory) approach, the primary factors and potential causal relationships for the high dropout rate were identified. The most effective aspects of massive open online courses (MOOCs) are identified using IF-DEMATEL and CIFCS. Moreover, it explains the interconnectedness of the various MOOC components. As an added measure, a number of DEMATEL techniques are used to conduct a side-by-side comparison of the results. Decisions made by the educational organisation could benefit from the findings. According to the research, there are a total of twelve indicators across four dimensions that are related to online course withdrawal amongst students. Then, experienced MOOC instructors from various higher education institutions were invited to assess the level of influence of these characteristics on each other. Academic skills and talents, prior experience, course design, feedback, social presence, and social support were identified as six primary characteristics that directly influenced student dropout in MOOCs. Interaction, course difficulty and length, dedication, motivation, and family/work circumstances have all been found to play a secondary part in student dropout in massive open online courses (MOOCs). The causal connections between the major and secondary factors were traced and discussed. The results of this study can help college professors and administrators come up with and implement effective ways to reduce the high number of students who drop out of massive open online courses (MOOCs).

Analysis and Prediction of MOOC Learners’ Dropout Behavior

With the wide spread of massive open online courses ( MOOC ), millions of people have enrolled in many courses, but the dropout rate of most courses is more than 90%. Accurately predicting the dropout rate of MOOC is of great significance to prevent learners’ dropout behavior and reduce the dropout rate of students. Using the PH278x curriculum data on the Harvard X platform in spring 2013, and based on the statistical analysis of the factors that may affect learners’ final completion of the curriculum from two aspects: learners’ own characteristics and learners’ learning behavior, we established the MOOC dropout rate prediction models based on logical regression, K nearest neighbor and random forest, respectively. Experiments with five evaluation metrics (accuracy, precision, recall, F1 and AUC) show that the prediction model based on random forest has the highest accuracy, precision, F1 and AUC, which are 91.726%, 93.0923%, 95.4145%, 0.925341, respectively, its performance is better than that of the prediction model based on logical regression and that of the model based on K-nearest neighbor, whose values of these metrics are 91.395%, 92.8674%, 95.2337%, 0.912316 and 91.726%, 93.0923%, 95.4145% and 0.925341, respectively. As for recall metrics, the value of random forest is higher than that of KNN, but slightly lower than that of logistic regression, which are 0.992476, 0.977239 and 0.978555, respectively. Then, we conclude that random forests perform best in predicting the dropout rate of MOOC learners. This study can help education staff to know the trend of learners’ dropout behavior in advance, so as to put some measures to reduce the dropout rate before it occurs, thus improving the completion rate of the curriculum.

The Effects of Basic Psychological Needs, Task–Technology Fit, and Student Engagement on MOOC Learners’ Continuance Intention to Use

Massive open online courses (MOOCs) continue to remain in the spotlight as a promising future education environment. However, more than 80% of learners stop learning before attending one-third of the course. Despite a continuous spread of MOOC and high dropout rate, little has examined the antecedent factors that influence student engagement in technology enhanced MOOC learning environment from the Job Demand-Resources (JD-R) model. The purpose of this study was to empirically identify the effects of individuals’ basic psychological needs and the task–technology fit on MOOC learners’ continuance intention to use, as well as the mediating effect of student engagement in MOOCs. Based on survey data from 201 Korean-MOOC learners, structural equation modeling was employed to assess the model. The findings are as follows: The basic psychological needs in MOOCs did not directly affect continuance intention to use, but did affect student engagement; the task–technology fit of MOOCs directly affected continuance intention to use and student engagement; and student engagement in MOOCs mediated between the basic psychological needs and task–technology fit, and continuance intention to use. It directly affected continuance intention to use. Implications were suggested for designing courses in MOOCs to increase student engagement for continuance intention to use.

Dropout Rate Prediction of Massive Open Online Courses Based on Convolutional Neural Networks and Long Short-Term Memory Network

Massive open online courses (MOOC) is characterized by large scale, openness, autonomy, and personalization, attracting increasingly students to participate in learning and gaining recognition from more and more people. This paper proposes a network model based on convolutional neural networks and long short-term memory network (CNN-LSTM) for MOOC dropout prediction task. The model selects 43-dimensional behavioral features as input from students’ learning activity logs and adopts the CNN model to automatically extract continuous features over a period of time from students’ learning activity logs. At the same time, considering the time sequence of students’ learning behavior characteristics, a MOOC dropout prediction model was established by using long short-term memory network to obtain students’ learning status at different time steps. The algorithm proposed in this chapter was trained and evaluated on the public dataset provided by the KDD Cup 2015 competition. Compared with the dropout prediction methods based on LSTM and CNN-RNN, the model improved the AUC by 2.7% and 1.4%, respectively. The result in this paper is a good predictor of dropout rates and is expected to provide teaching aid to teachers.

EXPLORING THE IMPACT OF LEARNERS’ DEMOGRAPHIC CHARACTERISTICS ON COURSE COMPLETION AND DROPOUT IN MASSIVE OPEN ONLINE COURSES

MOOCs (Massive Open Online Courses) have the potential to change education by offering high-quality online courses in a variety of disciplines. However, just a few studies have investigated the impact of MOOC students' demographics on their completion rates. In this research, we investigate the impact of demographic features of learners in the completion and dropping rate of MOOCs. The data from a survey is used in this study to determine which learner demographic features may have an impact on MOOC completion and dropout. The data was analyzed using Chi-square test.The findings demonstrate that four factors, including gender, marital status, age, and educational level, have no impact on degree of MOOC completion. It was also discovered that marital status, age, and educational level, have no effect on MOOC dropout. However, we discovered a statistically significant link between gender and MOOC dropout (χ2 = 6.347, df = 1, p = 0.012). These results can be considered in future instructional initiatives.

MOOC Dropout Prediction using FIAR-ANN Model based on Learner Behavioral Features

Massive Open Online Courses (MOOCs) are a transformative technology in digital learning that incorporates new techniques through video sessions, exams, activities, and conversations. Everyone leads a successful life in their professional and personal skills learning courses during COVID-19. The research concentrated on employing video interaction analysis to characterize crucial MOOC tasks, including predicting dropouts and student achievement. Our work consists of merely generating and picking the best characteristics based on the learner behavior for evaluating the dropout measure. To locate the frequent objects for feature creation, an association rule-FP growth approach is applied. The neural network is implemented using frequent itemset-3, which is used for feature selection. The evaluation metrics are calculated by using the Multilayer Perceptron (MLP) method. The metric values were then compared to the proposed model and some base supervised machine learning models namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bayes (NB). The FIAR (Feature Importance Association Rule)-ANN(Artificial Neural Network) dropout prediction model was tested on the KDD Cup 2015 dataset and it had a high accuracy of over 92.42, which is approximately 18% better than the MLP-NN model. With the optimized parameters, we are solely focused on lowering dropout rates and increasing learner retention.

Taking action to reduce dropout in MOOCs: Tested interventions

Massive Open Online Courses (MOOCs) have democratized access to higher education, but low entry and exit barriers make dropout rates in these courses much higher than in traditional in-person courses. Previous research has explored the main factors driving student dropout and proposed predictive models to identify students at risk. However, it is still unclear what type of interventions should be implemented to effectively reduce the dropout rate in MOOCs. We use findings from the literature to propose a theoretical framework to guide the design of interventions to reduce dropout in MOOCs. We design four interventions and implement them using A/B testing and natural experiment approaches in courses of the MITx MicroMasters® Program in Supply Chain Management (a MOOC-based program). Our findings reveal that ad hoc interventions communicated via email are not effective in reducing dropout, but interventions that modify course contents to make them more approachable for students show a positive impact on dropout reduction. By proposing a theoretical framework and uncovering the design features that can help to create effective interventions for dropout reduction, we provide a foundation for further research in the area of dropout interventions. This paper offers guidance to MOOC designers and instructors on how to improve student engagement and increase completion rates.

Factors Associated with Student Enrollment, Completion, and Dropout of Massive Open Online Courses in the Sultanate of Oman

This research aims to investigate the elements that influence learners' enrollment in, completion of and dropout from Massive Open Online Courses (MOOCs). MOOCs attract a vast number of participants, with enrollments growing at an unprecedented rate; however, only a small number of students who enroll, complete their study. In this research, data were collected from 79 participants using an online survey. Professional growth, flexible time, free of charge, and the opportunity to obtain certification were recognized as the four main factors that directly influenced student enrollment in MOOCs. The motivational variables revealed in this study that led to the effective completion of MOOCs included good-quality course videos, straightforward and clear tutor instructions, good course delivery, and useful instructor feedback, among others. The most prevalent reasons for dropout included failure to fulfil the deadline, the instructor's vocabulary, challenging assignments, and difficulty in grasping the course content. The findings of this study can provide educators and decision-makers with the information they need to better understand the factors that influence MOOC student enrollment, completion, and dropout rates, as well as relevant actions to improve the success rates of MOOC.

MOOC Dropout Prediction Based on Multi-view Learning

Massive Open Online Courses (MOOCs) provides a promising way to support education for all. Nonetheless, one central challenge is the remarkably high dropout rate, with completion rates for MOOC recently reported to be below 5%, and high dropout rates limiting their effectiveness. Building on the analysis of dropout as closely related to user and course interaction patterns, this paper presents an end-to-end multi-view interactive framework (EMIF) to predict user dropout in MOOC. We extract learning activity information, user enrolment information, and course knowledge information through a set of lD-CNNs, obtain mutual information of these and fuse the above information-view loss through a self-weighing method. Experimental evaluation of our solution on the KDDCUP 2015 and MOOCCube datasets showed better results (AUC & F1 scores). We have a better performance compared to several existing methods, without using different architectures for different datasets.

The learning behaviours of dropouts in MOOCs: A collective attention network perspective

Understanding the dropout phenomenon has advanced from viewing it as a sign of deficient quality to viewing it as an explicit sign of individual choice, which highlights the importance of investigating how dropouts learn in massive open online courses (MOOCs). Nevertheless, the short, limited and heterogeneous behaviours of individual dropouts create challenges for understanding how dropouts learn over time. Taking a systematic network perspective, this study used clickstream data to build a flow network model of collective attention to investigate how dropouts learn in XuetangX's Introduction to Psychology (2018 autumn). The results showed that the quantification of behavioural data presented a stereotypical image of dropouts, but the network analytics presented a rather different picture of how dropouts learn. Recognising the distinct roles of introductory learning resources could prevent dropping out and improve the accuracy of prediction models. Interestingly, the assessments embedded in the MOOCs performed a scaffolding role in guiding dropouts to learn. Thus, redesigning quizzes or examinations in open and flexible learning environments to construct a minimum cost network of collective attention is vital to making this online space cost effective for learners at risk.

The MOOC dropout phenomenon and retention strategies

ABSTRACT The high dropout rate from Massive Open Online Courses (MOOCs) has been a major concern of researchers and educators over the years. Although academic papers on MOOCs have mushroomed over the past ten years, few studies have focused on MOOC dropout and retention. In particular, research on hospitality and tourism MOOCs has remained nascent despite the field’s significant contribution to international business and global employment. Because of the lack of relevant literature on hospitality and tourism MOOCs, this study conducts a systematic review of the MOOC literature on the broader education field, examining the MOOC dropout phenomenon and retention strategies. The results of a content analysis based on journal articles’ main research topic show four clusters: prediction, continuance intention, motivation, and attrition. Thematic analysis is used to categorize the dropout factors into seven major themes: learning experience, interactivity, course design, technology, language, time, and situation. This paper concludes with a summary of the results, recommendations, practical implications, limitations, and directions for future research.

Multi-Model Stacking Ensemble Learning for Dropout Prediction in MOOCs

In recent years, with the rapid development of streaming media technology, massive open online courses (MOOC) have attracted unprecedented attention. Compared with the traditional offline teaching mode, the MOOC of online teaching has a higher degree of openness. Users can arbitrarily interrupt the study of a course according to their own interests, and have a higher dropout rate. The existing single machine learning model needs to be improved in terms of prediction accuracy. In this paper, a dropout prediction model based on multi-model stacking ensemble learning (MMSE) is proposed to further improve the accuracy of MOOC dropout prediction. The model mainly includes two parts: data preprocessing and model building. In the data preprocessing part, the student’s log records are used to design data features in weeks. In the model building part, a two-layer ensemble learning model is established. In the first layer, 5-fold cross-validation is used to train five different base classifiers. The second layer uses the XGBoost algorithm to combine the prediction results of the first layer to predict MOOC dropout. The experimental results on the KDDcup2015 real data set show that the MMSE model has achieved better results than the single model.

Power of Attention in MOOC Dropout Prediction

The dropout rate of massive open online courses (MOOC) has been significantly high, which makes its prediction an important problem. In this article, we try to transfer the knowledge gained in the field of Natural Language Processing into the field of MOOC dropout prediction, due to the high similarity between them. More specifically, we attempt to study and show the powerful use of attention and conditional random field, both of which have been very popular architectures when solving NLP problems. A novel neural network structure is designed as the combination of these techniques. Extensive experimental results demonstrate that the proposed approach is effective.

MOOC Dropout Prediction Using FWTS-CNN Model Based on Fused Feature Weighting and Time Series

High dropout rates have been a major problem affecting the development of Massive Open Online Courses (MOOCs). Student dropout prediction can help teachers identify students who are tending to fail and provide extra help in a timely manner, helping to improve the effectiveness of online learning. In recent years, the use of convolutional neural networks for dropout prediction has yielded good results. However, traditional convolutional neural networks use automatic feature extraction, which does not consider the importance of the learner’s behavior features and the effect of the time series of behavior on dropout, so it is difficult to guarantee the final prediction effect. To solve this problem, this article proposes a convolutional neural network model FWTS-CNN that integrates feature weighting and behavioral time series. It extracts continuous behavioral features from the learner’s log of learning activities, filters key features and ranks them by importance based on the decision tree, then weights the continuous behavioral features based on importance, and finally builds a convolutional neural network model based on behavioral time series and weighted features. Experiments on the KDD Cup 2015 dataset show that the FWTS-CNN dropout prediction model has a high accuracy, which can reach more than 87%, an improvement of about 2% over using the CNN algorithm alone. The FWTS-CNN model integrates the effects of behavioral features and behavior time on dropout, effectively improving the accuracy of dropout prediction.

A machine learning-based methodology to predict learners’ dropout, success or failure in MOOCs

Purpose Even if MOOCs (massive open online courses) are becoming a trend in distance learning, they suffer from a very high rate of learners’ dropout, and as a result, on average, only 10 per cent of enrolled learners manage to obtain their certificates of achievement. This paper aims to give tutors a clearer vision for an effective and personalized intervention as a solution to “retain” each type of learner at risk of dropping out. Design/methodology/approach This paper presents a methodology to provide predictions on learners’ behaviors. This work, which uses a Stanford data set, was divided into several phases, namely, a data extraction, an exploratory study and then a multivariate analysis to reduce dimensionality and to extract the most relevant features. The second step was the comparison between five machine learning algorithms. Finally, the authors used the principle of association rules to extract similarities between the behaviors of learners who dropped out from the MOOC. Findings The results of this work have given that deep learning ensures the best predictions in terms of accuracy, which is an average of 95.8 per cent, and is comparable to other measures such as precision, AUC, Recall and F1 score. Originality/value Many research studies have tried to tackle the MOOC dropout problem by proposing different dropout predictive models. In the same context, comes the present proposal with which the authors have tried to predict not only learners at a risk of dropping out of the MOOCs but also those who will succeed or fail.