Students Performance Prediction in Online Courses Using Machine Learning Algorithms

Advances in Information and Communications Technology (ICT) have increased the growth of Massive open online courses (MOOCs) applied in distance learning environments. Various tools have been utilized to deliver interactive content including pictures, figures, and videos that can motivate the learners to build new cognitive skills. High ranking universities have adopted MOOCs as an efficient dashboard platform where learners from around the world can participate in such courses. The students learning progress is evaluated by using set computer-marked assessments. In particular, the computer gives immediate feedback to the student once he or she completes the online assessmentsThe researchers claim that student success rate in an online course can be related to their performance at the previous session in addition to the level of engagement. Insufficient attention has been paid by literature to evaluate whether student performance and engagement in the prior assessments could affect student achievement in the next assessmentsIn this paper, two predictive models have been designed namely students’ assessments grades and final students’ performance. The models can be used to detect the factors that influence students’ learning achievement in MOOCs. The result shows that both models gain feasible and accurate results. The lowest RSME gain by RF acquire a value of 8.131 for students assessments grades model while GBM yields the highest accuracy in final students’ performance, an average value of 0.086 was achieved

Analyzing Learners Behavior in MOOCs: An Examination of Performance and Motivation Using a Data-Driven Approach

Massive Open Online Courses (MOOCs) have been experiencing increasing use and popularity in highly ranked universities in recent years. The opportunity of accessing high quality courseware content within such platforms, while eliminating the burden of educational, financial and geographical obstacles has led to a rapid growth in participant numbers. The increasing number and diversity of participating learners has opened up new horizons to the research community for the investigation of effective learning environments. Learning Analytics has been used to investigate the impact of engagement on student performance. However, extensive literature review indicates that there is little research on the impact of MOOCs, particularly in analyzing the link between behavioral engagement and motivation as predictors of learning outcomes. In this study, we consider a dataset, which originates from online courses provided by Harvard University and Massachusetts Institute of Technology, delivered through the edX platform [1]. Two sets of empirical experiments are conducted using both statistical and machine learning techniques. Statistical methods are used to examine the association between engagement level and performance, including the consideration of learner educational backgrounds. The results indicate a significant gap between success and failure outcome learner groups, where successful learners are found to read and watch course material to a higher degree. Machine learning algorithms are used to automatically detect learners who are lacking in motivation at an early time in the course, thus providing instructors with insight in regards to student withdrawal

Machine Learning Approaches to Predict Learning Outcomes in Massive Open Online Courses

With the rapid advancements in technology, Massive Open Online Courses (MOOCs) have become the most popular form of online educational delivery, largely due to the removal of geographical and financial barriers for participants. A large number of learners globally enrol in such courses. Despite the flexible accessibility, results indicate that the completion rate is quite low. Educational Data Mining and Learning Analytics are emerging fields of research that aim to enhance the delivery of education through the application of various statistical and machine learning approaches. An extensive literature survey indicates that no significant research is available within the area of MOOC data analysis, in particular considering the behavioural patterns of users. In this paper, therefore, two sets of features, based on learner behavioural patterns, were compared in terms of their suitability for predicting the course outcome of learners participating in MOOCs. Our Exploratory Data Analysis demonstrates that there is strong correlation between click steam actions and successful learner outcomes. Various Machine Learning algorithms have been applied to enhance the accuracy of classifier models. Simulation results from our investigation have shown that Random Forest achieved viable performance for our prediction problem, obtaining the highest performance of the models tested. Conversely, Linear Discriminant Analysis achieved the lowest relative performance, though represented only a marginal reduction in performance relative to the Random Forest

The Application of Gaussian Mixture Models for the Identification of At-Risk Learners in Massive Open Online Courses

With high learner withdrawal rates in the setting of MOOC plat-forms, the early identification of at risk student groups has be-come increasingly important. Although many prior studies con-sider the dropout issue in form of a sequence classification prob-lem, such works address only a limited set of behavioral dynamics, typically recorded as sequance of weekly interval, neglecting important contextual factors such as assignment deadlines that may be important components of student latent engagement. In this paper we therefore aim to investigate the use of Gaussian Mixture Models for the incorporation such im-portant dynamics, providing an analytical assessment of the in-fluence of latent engagement on students and their subsequent risk of leaving the course. Additionally, linear regression and , k- nearest neighbors classifiers were used to provide a performance comparison. The features used in the study were constructed from student behavioral records, capturing activity over time, which were subsequently organized into six time intervals, corre-sponding to assignment submission dates. Results obtained from the classification procedure yielded an F1-Measure of 0.835 for the Gaussian Mixture Model, indicating that such an approach holds promise for the identification of at risk students within the MOOC setting

Artificial Intelligence for Detecting Preterm Uterine Activity in Gynacology and Obstertric Care

Preterm birth brings considerable emotional and economic costs to families and society. However, despite extensive research into understanding the risk factors, the prediction of patient mechanisms and improvements to obstetrical practice, the UK National Health Service still annually spends more than £2.95 billion on this issue. Diagnosis of labour in normal pregnancies is important for minimizing unnecessary hospitalisations, interventions and expenses. Moreover, accurate identification of spontaneous preterm labour would also allow clinicians to start necessary treatments early in women with true labour and avert unnecessary treatment and hospitalisation for women who are simply having preterm contractions, but who are not in true labour. In this research, the Electrohysterography signals have been used to detect preterm births, because Electrohysterography signals provide a strong basis for objective prediction and diagnosis of preterm birth. This has been achieved using an open dataset, which contains 262 records for women who delivered at term and 38 who delivered prematurely. Three different machine learning algorithm were used to identify these records. The results illustrate that the Random Forest performed the best of sensitivity 97%, specificity of 85%, Area under the Receiver Operator curve (AUROC) of 94% and mean square error rate of 14%

Towards the Discrimination of Primary and Secondary Headache: An Intelligent Systems Approach

We consider the use of intelligent systems to address the long-standing medical problem of diagnostic differentiation between harmful (secondary) and benign (primary) headache conditions. In secondary headaches, the condition is caused by an underlying pathology, in contrast to primary headaches where the production of pain represents the sole constituent of the disorder. Conventional diagnostic paradigms carry an unacceptable risk of misdiagnosis, leaving patients open to potentially catastrophic consequences. Intelligent systems approaches, grounded in artificial intelligence, are adopted in this study as a potential means to unite contributions from multiple settings, including medicine, the life sciences, pervasive computation, sensor technologies, and autonomous intelligent agency, in the fight against headache uncertainty. In this paper, we therefore present the first steps in our research towards a data intensive, unified approach to headache dichotomisation. We begin by presenting a background to headache and its classification, followed by analysis of the space of confounding symptoms, in addition to the problem of primary and secondary condition discrimination. Finally, we proceed to report results of a preliminary case study, in which the epileptic seizure is considered as a manifestation of a headache generating neuropathology. It was found that our classification approach, based on supervised machine learning, represents a promising direction, with a best area under curve test outcome of 0.915. We conclude that intelligent systems, in conjunction with biosignals, could be suitable for classification of a more general set of pathologies, while facilitating the medicalisation of arbitrary settings

The Utilisiation of composite Machine Learning models for the Classification of Medical Datasets For Sickle Cell Disease

The increase growth of health information systems has provided a significant way to deliver great change in medical domains. Up to this date, the majority of medical centres and hospitals continue to use manual approaches for determining the correct medication dosage for sickle cell disease. Such methods depend completely on the experience of medical consultants to determine accurate medication dosages, which can be slow to analyse, time consuming and stressful. The aim of this paper is to provide a robust approach to various applications of machine learning in medical domain problems. The initial case study addressed in this paper considers the classification of medication dosage levels for the treatment of sickle cell disease. This study base on different architectures of machine learning in order to maximise accuracy and performance. The leading motivation for such automated dosage analysis is to enable healthcare organisations to provide accurate therapy recommendations based on previous data. The results obtained from a range of models during our experiments have shown that a composite model, comprising a Neural Network learner, trained using the Levenberg-Marquardt algorithm, combined with a Random Forest learner, produced the best results when compared to other models with an Area under the Curve of 0.995

Detecting At-Risk Students with Early Interventions Using Machine Learning Techniques

Massive Open Online Courses (MOOCs) have shown rapid development in recent years, allowing learners to access high-quality digital material. Because of facilitated learning and the flexibility of the teaching environment, the number of participants is rapidly growing. However, extensive research reports that the high attrition rate and low completion rate are major concerns. In this paper, the early identification of students who are at risk of withdrew and failure is provided. Therefore, two models are constructed namely at-risk student model and learning achievement model. The models have the potential to detect the students who are in danger of failing and withdrawal at the early stage of the online course. The result reveals that all classifiers gain good accuracy across both models, the highest performance yield by GBM with the value of 0.894, 0.952 for first, second model respectively, while RF yield the value of 0.866, in at-risk student framework achieved the lowest accuracy. The proposed frameworks can be used to assist instructors in delivering intensive intervention support to at-risk students

A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction

In this paper, a novel application of machine learning algorithms including Neural Network architecture is presented for the prediction of flood severity. Floods are considered natural disasters that cause wide scale devastation to areas affected. The phenomenon of flooding is commonly caused by runoff from rivers and precipitation, specifically during periods of extremely high rainfall. Due to the concerns surrounding global warming and extreme ecological effects, flooding is considered a serious problem that has a negative impact on infrastructure and humankind. This paper attempts to address the issue of flood mitigation through the presentation of a new flood dataset, comprising 2000 annotated flood events, where the severity of the outcome is categorised according to 3 target classes, demonstrating the respective severities of floods. The paper also presents various types of machine learning algorithms for predicting flood severity and classifying outcomes into three classes, normal, abnormal, and high-risk floods. Extensive research indicates that artificial intelligence algorithms could produce enhancement when utilised for the pre-processing of flood data. These approaches helped in acquiring better accuracy in the classification techniques. Neural network architectures generally produce good outcomes in many applications, however, our experiments results illustrated that random forest classifier yields the optimal results in comparison with the benchmarked models

Training Neural networks for Experimental models: Classifying Biomedical Datasets for Sickle Cell Disease

This paper presents the use of various type of neural network architectures for the classification of medical data. Extensive research has indicated that neural networks generate significant improvements when used for the pre-processing of medical time-series data signals and have assisted in obtaining high accuracy in the classification of medical data. Up to date, most of hospitals and healthcare sectors in the United Kingdom are using manual approach for analysing patient input for sickle cell disease, which depends on clinician’s experience that can lead to time consuming and stress to patents. The results obtained from a range of models during our experiments have shown that the proposed Back-propagation trained feed-forward neural network classifier generated significantly better outcomes over the other range of classifiers. Using the ROC curve, experiments results showed the following outcomes for our models, in order of best to worst: Back-propagation trained feed-forward neural net classifier: 0.989, Functional Link neural Network: 0.972, in comparison to the Radial basis neural Network Classifiers with areas of 0.875, and the Voted Perception classifier: 0.766. A Linear Neural Network was used as baseline classifier to illustrate the importance of the previous models, producing an area of 0.849, followed by a random guessing model with an area of 0.524