Prediction of Dropout Students in Massive Open Online Courses Using Ensemble Learning: A Pilot Study in Post-COVID Academic Session

High dropout rate is a critical problem in MOOCs. The prime objective of this study is to identify possible dropout students at the early stage of the course and reducing the number of dropouts providing proper feedback to address the relevant factor. A prediction model based on stacking ensemble machine learning is proposed to identify whether a learner is at risk of dropping a course. The proposed stacked ensemble model outperformed with an accuracy of 93.4% compared to other popular machine learning classifiers. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Accelerating digital health literacy for the treatment of growth disorders: The impact of a massive open online course.

Background: Growth hormone deficiency (GHD) is a rare disorder characterized by inadequate secretion of growth hormone (GH) from the anterior pituitary gland. One of the challenges in optimizing GH therapy is improving adherence. Using digital interventions may overcome barriers to optimum treatment delivery. Massive open online courses (MOOCs), first introduced in 2008, are courses made available over the internet without charge to a large number of people. Here, we describe a MOOC aiming to improve digital health literacy among healthcare professionals managing patients with GHD. Based on pre- and post-course assessments, we evaluate the improvement in participants' knowledge upon completion of the MOOC. Methods: The MOOC entitled 'Telemedicine: Tools to Support Growth Disorders in a Post-COVID Era' was launched in 2021. It was designed to cover 4 weeks of online learning with an expected commitment of 2 h per week, and with two courses running per year. Learners' knowledge was assessed using pre- and post-course surveys via the FutureLearn platform. Results: Out of 219 learners enrolled in the MOOC, 31 completed both the pre- and post-course assessments. Of the evaluated learners, 74% showed improved scores in the post-course assessment, resulting in a mean score increase of 21.3%. No learner achieved 100% in the pre-course assessment, compared with 12 learners (40%) who achieved 100% in the post-course assessment. The highest score increase comparing the pre- and the post-course assessments was 40%, observed in 16% of learners. There was a statistically significant improvement in post-course assessment scores from 58.1 ± 18.9% to 72.6 ± 22.4% reflecting an improvement of 14.5% (p < 0.0005) compared to the pre-course assessment. Conclusion: This "first-of-its-kind" MOOC can improve digital health literacy in the management of growth disorders. This is a crucial step toward improving the digital capability and confidence of healthcare providers and users, and to prepare them for the technological innovations in the field of growth disorders and growth hormone therapy, with the aim of improving patient care and experience. MOOCs provide an innovative, scalable and ubiquitous solution to train large numbers of healthcare professionals in limited resource settings.

The irreplaceable role of medical massive open online courses in China during the COVID-19 pandemic.

BACKGROUND: Massive open online courses (MOOCs) have become innovative open-learning approach in medical education. This study aimed to evaluate the dynamic changes in the construction and application of medical MOOCs before and after the coronavirus disease 2019 (COVID-19) pandemic in China. METHODS: The dynamic changes of usages about medical MOOCs before and after 2020 were mainly searched on the Smart Education of China Higher Education platform, and the detailed learning profiles and outcome indicators were further analyzed using 40 national first-class medical MOOCs from 'zhihuishu' platform. RESULTS: A total of 2,405 medical MOOCs were exported from the Smart Education of China Higher Education platform, of which 1,313 (54.6%) were launched since 2020. The total and average numbers of participants of 141 national first-class medical MOOCs peaked during the initial spread of COVID-19 in 2020. We further analyzed the dynamic usage of MOOCs from 2018 to 2022 based on 40 national first-class medical MOOCs launched on the 'Zhihuishu' platform. The findings revealed that the number of registered learners (3,240 versus 2,654), questions and answers (27,005 versus 5,116) and students taking the final examination (2,782 versus 1,995) per semester were significantly higher since 2020 compared to these before 2020. Especially, the number of registered learners, registered schools, questions and answers, and students participating in online discussion, taking the unit quiz, taking final examinations and passing final examinations all peaked in the 2020 spring-summer semester. Pearson's correlation analysis found that the number of questions and answers and the number of learners who participated in online discussion were both positively correlated with the number of students who passed the final examination, and the correlation was especially strong since 2020. Moreover, the number of publications on medical MOOC research has soared since 2020 and has maintained a continuous upward trend. CONCLUSIONS: High-quality medical MOOCs have been launched rapidly since the COVID-19 pandemic in China. The number of participants and online interactions of medical MOOCs peaked during the initial spread of COVID-19 in 2020. MOOCs are reliable and valid digital sources that facilitate medical higher education and play irreplaceable roles in emergency management.

In partnership: A just-in-time MOOC to support continuing professional development during a pandemic

Introduction Just-in-time education has been described as rapid dissemination of accurate, up-to-date information accessible during or soon after a crisis and is recognised as an effective strategy to assist during crisis management. Background The COVID4NURSES program was developed and delivered ‘just-in-time' as a Massive Open Online Course (MOOC), to upskill nurses in Victoria, Australia to support delivery of safe patient care (Wong et al., 2022). Academics collaborated with the state department of health and practicing nurses to develop the program, which enabled the spread of workload and best use of expertise during the pandemic. Method The MOOC was delivered to over 2000 nurses from all levels and specialties and across a large geographically disparate region. A mixed methods study was undertaken following program delivery to measure knowledge improvement and to explore the confidence of nurses working in a COVID-19 environment. Data were collected through a pre-test post-test Multiple Choice Quiz, and qualitative data were collected from the asynchronous online discussion forums from 801 participants who consented to the study. Findings Findings indicated improvements in nurses' knowledge and confidence after undertaking the program. Conclusion Implementing a just-in-time MOOC during a crisis is a valuable initiative for rapid dissemination of scientific evidence to support nursing practice and positive patient outcomes.

Student performance prediction in e-learning system and evaluating effectiveness of online courses

In recent years, the number of online courses in India has skyrocketed especially due to the Covid pandemic. The most significant increments have happened in degree colleges, where 85% concur that internet based courses are important for their drawn-out procedure when contrasted with 60% in 2015. The distribution of online courses has evolved dramatically as technology has advanced. Web-based platform provides new challenges for both teachers and students. Teachers should be clear about the effectiveness of online learning in teaching students. For that, the possibilities of online learning should be compared with traditional learning. Students are evaluated based on their focus on online learning. This study aims to determine the efficacy of online courses by predicting student performance in an e-learning system. These research findings evaluate modern learning methods, highlight students' potential and help teachers understand how to assess and lead students on online platforms. © 2023 IEEE.

Designing Self-Managed E-Classroom

Distance education and e-learning have been the subject of extensive research in recent years, particularly after the advent of the COVID-19 pandemic, which drove educational institutions to move from traditional face-to-face classrooms to online learning;consequently, courses have evolved. Massive Open Online Courses, or MOOCs, are online courses that are available to anybody who wishes to join. Many MOOC educational platforms, such as Coursera, Edx, and Udacity, provide students with recorded video lectures, online readings, examinations, and student-to-student and teacher-to-teacher interactions. However, these systems have several disadvantages, including costly prices, a lack of Arabic language support, and closed-source software. This paper focuses on the design and development of a massive open online course (MOOC) platform with self-managed learning (SML);so that, it can be used as a tool to improve education in the digital era. It should also be open-source and support the Arabic language. It is specifically designed for the University of Mosul to establish courses such as competency courses. The proposed system differs from existing LMSs in the sense that the order of activities is dictated by student actions and behaviors and is not the same for all students. SML guides student activities and behavior. © 2022 IEEE.

Supporting career skills development: Exploring stakeholders' perceptions of college students' participation in an enterprise internship massive open online course (MOOC)

The Coronavirus (COVID-19) outbreak in March 2020 resulted in hundreds of companies canceling internships for college students who had accepted offers for the summer. While colleges and companies were determining how to move forward, a team of recruiters and learning professionals at a global telecommunications company engaged thousands of students in a massive open online course (MOOC) that may have fulfilled internships for some students. This novel approach to learning provided students with access to an employer-developed curriculum utilizing an enterprise internship MOOC model. This study centered on understanding career services professionals' perspectives of an enterprise internship MOOC for supporting college students' career skills development. Employing constructivist grounded theory (CGT) and the product market fit pyramid, this qualitative research explored the enterprise internship MOOC's viability. Data collection and analysis consisted of semi-structured interviews with 11 career services professionals from higher education institutions (HEIs) in the United States. Five out of 11 participants had experience with the enterprise internship MOOC while the remaining participants learned about it during the study. The artifacts collected from the web included the enterprise internship MOOC's curricula, students' comments on LinkedIn about their experiences in the program, skills information from the digital badge, and the company's marketing materials for the program. To better understand career services professionals' experiences with MOOCs, participants answered questions through the lens of a conceptual framework that deconstructed the MOOC's dimensions, aligning academic MOOCs and the enterprise internship MOOC. Data analysis utilizing the CGT methodology yielded a grounded theory called the triad of stakeholder involvement. This grounded theory supports a focused approach to understand the HEI's career emphasis, employer skills needs, and student destination for career skills development leveraging an enterprise internship MOOC. Data analysis also led to the conclusion that the enterprise internship MOOC is a viable model, given the appropriate pedagogical and technological structure. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

A Review on the Learner's Performance Prediction Techniques in MOOC Courses through Data Mining

Nowadays, Massive Open Online Courses are in demand owing to their informative value, easy access and low costs. The Covid-19 pandemic era saw a lot of teaching and learning through the online resources. One of the developing fields is Educational Data Mining in which the data derived from the educational environments is collected in databases, which is further analyzed to extract some interesting patterns of information. The findings can aid in supporting the educational staff in designing a cohort that may produce better results in terms of increasing the learner's performance, identifying at-risk students, placement prediction and dropout prediction, whatever the current motive may be. In this paper, we emphasize on the techniques focusing on the performance prediction that have been applied during the years 2012 to 2022 and the attributes affecting the performance have been determined. © 2022 IEEE.

Non-Sequential Indexing of Videos using Linguistic Computation

Since the outbreak of Corona Virus Disease(COVID-19), the education sector has seen a drift from traditional in-person teaching methods to virtually-assisted learning. This new trend has paved its path for students to easily gain access to a variety of educational instructors across the globe. But online education comes with its own potential and challenges. Factors like high availability, flexibility, and affordability of the online learning platforms add to the effective deliverance of the content in this progressive present-day online learning. Some key disadvantages are lack of powerful conveyance of content to listeners and sequential navigation of videos. Linearly searching for specific topics through long videos is a common problem that students face, while learning from the internet. This research study proposes a novel approach to promote the application of non-sequential navigation of videos by identifying key-topics and automatically generating timestamps. The model utilizes Natural Language Processing (NLP) and Optical Character Recognition (OCR) techniques for determining the key topics from the video. Timestamps are identified for the keywords before they are uttered, using a novel algorithm for audio indexing. Finally, timestamps are successfully generated for every keyword. Through this study, the objective of non-sequential navigation of videos using a new audio-indexing algorithm is achieved. © 2022 IEEE

Monitoring Programming Styles in Massive Open Online Courses Using Source Embedding

In recent years there has been an exponential growth of distance learning, provided by both public and private institutions. As a matter of fact, the number of students enrolled in courses delivered through the Network, has dramatically grown, also due to the COVID-19 pandemic, which has forced millions of people not to move. Consequently, more and more courses delivered in a remote modality have been attended by a huge number of people, producing an increasing number of Massive Open Online Courses (MOOC)s. These kind of courses are imposing new challenges for teachers, especially for monitoring and assessing the community learning processes. On the one hand, the learning assessment cannot be carried out based solely on closed-ended tests, while, on the other hand, teachers cannot evaluate thousands of open-Answer assignments: They should have at their disposition a set of tools helping them monitor the community learning progress. This paper investigates the possibility of using some of the Source Code Embedding techniques, to give teachers useful information about their learners' programming styles in Massive Open Online Courses. We propose a method to visualize each student's program, included the teacher's one, as a point in a 2-D space, using the doc2vec embeddings technique. Thanks to this representation, teachers can identify in the 2-D space groups of students having similar programming styles and reason on them to start a suitable didactic feedback. Moreover, teachers can reason on the relationship between each point compared to their own point as well, considered as the truth programming style. A first experimentation using Python as the programming language is performed with encouraging results. © 2022 IEEE.

Massive Open Online Course (MOOC) for the Detection and Intervention of Suicidal Risk Patients: Evaluation and Lessons Learned in times of Covid-19

MOOCs can be used to provide specialized and continuing medical education in times of Covid-19. The procedure to evaluate the satisfaction of this MOOC aimed at primary care health professionals for the detection and management of suicidal risk had descriptive statistics, Cronbach's Alpha, and CHAID analysis (Chi-square Automatic Interaction Detector) to find the factor that most influenced the satisfaction of this course. This evaluation was complemented with thematic analysis. CHAID analysis of this MOOC course, the satisfaction of 53% Excellent was explained by the Course Content Assessment with a value of P <.001. The results of the thematic analysis were that 75% of the learning obtained corresponds to the general objective of the course. 53% of the most relevant topics of this MOOC were considered useful and of interest to their profession. Health professionals liked the final interview and the practical cases, they requested more real cases to better manage the risk of suicide. The achievement of the objective of this MOOC helps to contribute to the prevention of suicide. We can learn that this type of course is feasible at a technological level and that it requires a great commitment or interest from health professionals to carry it out satisfactorily in times of Covid-19. © 2022 IEEE.

Online Course on First Aid in Emergency Medicine on the International Coursera Platform

The pandemic of the novel coronavirus infection (COVID-19) has had an enormous impact on the education system. Higher educational institutions were forced to adapt to remote interaction with students and urgently switch to online learning. Currently, there are many educational platforms, among which one of the leading is the Californian Coursera service. In 2020, Kazan Federal University developed and published the first Coursera course on first aid — “Staying Alive! First Aid in Emergency”. The purpose of the current study was to demonstrate the course results in one calendar year. We analyzed the general indicators of Coursera — the global online learning platform. We reviewed the course indicators from December 21, 2020, to December 21, 2021. The research assesses the advantages and disadvantages of using online courses in the medical education system. It was concluded that massive open online courses effectively teach theoretical disciplines. In case of implementation of applied courses, it is necessary to search and introduce new approaches for practical skill development. © 2022 Sklifosovsky Research Institute for Emergency Medicine. All rights reserved.

Researchers' Perception of MOOCs: An Indian Case Study

Massive open online courses have gained popularity during the coronavirus induced lockdown. In India educational institutions have been working online since last fifteen months. During this time research scholar enrolled in higher educational institution have taken MOOC course to augment knowledge. This study is an attempt to understand the researchers' perception of MOOCs. It uses the qualitative approach to understand the experience of learning with MOOCs, especially during the pandemic. It outlines the ways in which researchers benefited by taking MOOCs while highlighting their grievances. This study also has a lot of implications for the researchers and facilitators of MOOCs as it points at the lacunae in the application of their products which once rectified will help them yield desired results. © 2022 IEEE.

Assessment of Modified BAT Algorithm for MOOC Learner Influence Maximization

Identification of a small group of individuals based on their maximal influence cascade is influence maximization. During the COVID-19 pandemic, discussion forums on the Massive Open Online Course (MOOC) platform have become a paramount interaction medium among learners, and the identification of influential learners evolved as a substantial research issue. In this research paper, an optimization function based on an independent cascade is established for the discussion forum influence maximization problem. A modified version of the BAT algorithm is proposed which memorizes the bad experience of the BAT. The proposed Modified algorithm helps the BAT to remember the worst location that has already been traversed for a reliable estimation in an optimized manner while exploring the best solution. Further, the performance of BAT and Modified BAT for influence maximization on the discussion forum network of a MOOC platform is evaluated which shows the excellent performance of modified BAT. Convergence graph for different populations on deviating probability depicts the effective performance of modified BAT over generic BAT algorithm. © 2022 ACM.

CSDLEEG: Identifying Confused Students Based on EEG Using Multi-View Deep Learning

Distance learning has dramatically increased in recent years because of advanced technology. In addition, numerous universities had to offer courses in online mode in 2020 and 2021 because of the COVID-19 pandemic. However, there are more challenges in distance learning than in the traditional learning method (e.g., feedback and interaction). Recently, researchers started using simple EEG headsets to identify confused students during online courses based on machine learning approaches. However, they faced unpleasant accuracy using traditional machine learning algorithms or nondeep neural networks. In this paper, we present a data-driven approach based on a multi-view deep learning technique called CSDLEEG to identify confused students. We employ the students' demographic information and EEG signals to feed our novel neural networks. The results show that our proposed approach is superior to state-of-the-art methods for 98% accuracy and 98% F1-score. © 2022 IEEE.

New Categorization of Practical Works Activities with Hybridization of Bloom's Taxonomy, Grimard's Pyramid, and Specific MOOC

Due to the outbreak of the coronavirus pandemic and the total confinement imposed on all countries to prevent the spread of the virus, Massive open online courses (MOOC) systems have been widely used in recent years, and have attracted more attention in educational institutions, especially. But MOOCs intended for learning practical work have not been adequately addressed. However, everyone knows that the chances of dropping out of MOOCs are very high compared to conventional offline courses. Researchers have implemented extensive and diverse methods to determine the reasons behind learner attrition or lack of interest to apply timely interventions. We decided to address the dropout problem due to the lack of motivation among learners, with special practical works MOOCs. We have hybridized two methodologies: cognitive levels of learners, namely, Bloom's taxonomy and Grimard's pyramid for motivation this hybridization allowed us to create a new categorization for practical works, and we propose a new MOOC for learning practical works activities for programming languages in computer science. The main objective of this MOOC platform is to automatically generate practical works of different levels of complexity to be solved according to the level of motivation related to the learner. It composed into three principal components: IMMS survey component, motivation component, and practical works generator component.

Completion factor in massive open online course in developing countries: A literature review in 2015-2021

Massive Open Online Course (MOOCs) has helped develop the education sector since the Covid-19 pandemic, though it causes low retention. This study used a systematic literature review method to analyze the factors affecting the retention level of MOOCs participants in developing countries and worldwide. A total of 89 publications in the Scopus journal during 2015-2021 and 26 published in developing countries were examined. The results showed that the factors affecting the retention level of MOOCs' participants include perceived ease of use, usefulness, social influence, and self-efficacy. However, motivation was an insignificant factor in developing countries despite being significant worldwide. Infrastructure was an internal factor for the retention level among participants, though this study can be further expanded using better methods. © 2022 Birlesik Dunya Yenilik Arastirma ve Yayincilik Merkezi. All rights r eserved.

Half-full or half-empty? The impact of influential events pre- and intra- COVID 19 pandemic on enrolment, progression and completion of online courses

The SARS-CoV-2 outbreak in 2019 brought an enormous uncertainty and unexpected consequences to the world and in Mexico, students, staff, and professors of higher education were no exception. The online lifestyle became vital for continuous education through various means of remote learning including alternative credentials offered via Massive open online course (MOOCS). Upon announcement of lockdown across the country, Tecnologico de Monterrey, a leading university in Mexico and Latin America, provided free access to the content of Coursera to its community to facilitate a sustained learning environment for all the members. The prolonged COVID-19 pandemic and related news have negatively immersed the community in an environment of fear and anxiety. To monitor the learning progress of the members, understand their response to unprecedented circumstances and provide the community with timely support, we have monitored the course pursuit, progression and the status of completion in Coursera registered by 36,591 users. The data provided insight to the gender of the users enrolling to 6 distinctly different categories of courses and the degree of commitment they demonstrated to the undertaken learning activities. The results showed Computer Science and Programming as one of the most requested course types, and, together with Engineering MOOCS greatly dominated by male users. A timeline of influential events that took place nationally and/or internationally over the course of the 11 months from January 2020 to February 2021 was comprised and compared to the registration, progression and completion of the courses in an attempt to understand the response of our community to the milestones of this concerning global health event. While the results highlight a general struggle to remain motivated and finalize the registered courses, the national and international positive and negative connotations created considerably different responses in participants across different time periods. The current article takes a close look into dynamic of the users of the online courses across this challenging milestone. © 2022 IEEE.

ONLINE EDUCATION OF REMOTE SENSING IN CHINA DURING THE COVID-19 PANDEMIC: A CASE OF STUDY IN JIANGSU NORMAL UNIVERSITY

Affected by the Coronavirus Disease (COVID-19) pandemic, almost all students in China have to study online at home from February to June, 2020. In this paper, we discussed the forms of online courses and took Jiangsu Normal University as an example to introduce the online courses of remote sensing in China. The results of the satisfaction survey show that more than 90% of the respondents agree with online courses and believe that online courses can at least meet basic learning needs in the age of COVID-19, and more than 60% of respondents claimed that they had met or exceeded their learning expectations. The major advantages of online course include reducing the gathering of people and thus the risk of infection. However, there are still some problems with online courses, and we hope that these problems can be solved well in the future. © 2021 IEEE