ABSTRACT
MOOCs have gained a lot of popularity for past few years. Especially after the outbreak of Coronavirus, everyone is trying to gain some knowledge and skill while being at the comfort of home and making themselves safe. Due to sudden increase in the number of participants on MOOCs there is a need for an automated system to be able to assess the reviews and feedbacks given by the learners and find the sentiments behind their statements. This analysis will help trainers identify their shortcoming and make their courses even better. For sentiments analysis, several approaches may be used. This research aims to provide a system which will perform sentiments analysis on the novel dataset and show the comparison of lexicon-based vs transformer-based sentiment analysis models. For lexicon based, VADER was chosen and for transformer-based, state-of-The-Art BERT was chosen. BERT was found to be exceptionally good with an accuracy of 84% and F1-score of 0.64. © 2022 IEEE.
ABSTRACT
The COVID-19 epidemic has had a huge impact on all education systems throughout the world. The lockdown policy caused higher education to find alternative teaching solutions to serve students' needs. Consequently, MOOCs have become a fascinating solution for several universities. However, the evidence from existing research still needs to be better understood by significant factors to support learners during COVID-19. This study examined the factors influencing students' adoption intention of MOOCs in developing countries during the coronavirus outbreak. This research reports the online survey of 1,384 university students enrolled in Thai MOOC as the primary part of the curriculum. The extended UTAUT2 was proposed and analyzed using a structural equation model to improve the comprehension of students' adoption intention on Thai MOOC. The result found Performance Expectancy, Hedonistic Motivation, Habit, and Local Language Support significantly influence MOOCs adoption intentions. While Habit was found to be the most significant to students' adoption intention, only gender had a moderating effect on the relationship between Habit and Adoption intention. The overall proposed model explained 84% of the variance in MOOC adoption intention of university students in Thailand during the COVID-19 pandemic. © 2023 IEEE.
ABSTRACT
The COVID-19 pandemic has promoted the development of online teaching in various educational institutions. Different online teaching practice has shown advantages and potential problems. The combination of online and offline teaching (mixed teaching) is a new teaching practice that can exert its advantages simultaneously, and has been wildly used during the COVID-19 pandemic, even being extended to the post-pandemic era. Medical parasitology is a foundation course for medicine and a bridging course towards clinical medicine and preventive medicine. The traditional teaching of medical parasitology has presented many limitations, including outdated teaching concepts and practices, and the disconnection between theory teaching and practice teaching. In response to these difficulties, many innovative ideas and measures have been taken o reform the teaching practice of the foundation medical courses, including updating teaching program, adopting innovative teaching practice (such as blended teaching), and promoting the teaching evaluation method. In this paper, we concluded the blended teaching tools, platforms, manners, effects and evaluation methods in medical parasitology in China during the COVID-19 pandemic to provide information for the teaching reform in the post-pandemic era.Copyright © 2022, National Institute of Parasitic Diseases. All rights reserved.
ABSTRACT
In the wake of the current COVID-19 pandemic, most face-to-face activities have been restricted including the scientific program such as capacity building. This, in turn, requires rapid redesign of capacity building implementation during the pandemic period. Hence, this study aims at organizing a virtual capacity building for early-career scientists as an alternative to traditional setting which requires face-to-face interaction. This virtual capacity building focuses on the use of remote sensing for effective monitoring of ocean climate resilience. This program was developed using Massive Open Online Course (MOOC) under UTM Open Learning platform which is known as MOOC on Ocean Remote Sensing towards Climate Resilience. A total of 26 international participants successfully participated in this MOOC UTM-PORSEC. This virtual program contains 14 modules that were conducted by experienced instructors in the field of ocean remote sensing. The program evaluation has shown that the overall Programme Learning Outcomes have scored above 70%. Furthermore, 88% of the MOOC-PORSEC participants have completed all the course content, assignments and quizzes. The developed virtual capacity building has provided a new digital learning experience as well as it saves the cost of travelling and time at the expense of physical interaction.
ABSTRACT
COVID-19 pandemic was a global crisis in one hand but at the same time it positioned distance learning in the forefront of the education worldwide. Digital educational and MOOC methodologies were suddenly utilised in wide spectrum - but with mixed results. In our paper we show that MOOC type of education can be an operational model for mass personalisation in higher education. Building on Technology Acceptance Models (TAM), we demonstrate the results of an empirical exploration research proving that such an autonomous - kind of 'campusless' - digital educational solution can be acceptable to higher education students. We conclude that MOOC education has proved to be viable/relevant based on the experience of our analysed population, but only if it efficiently supports learning and is suitable to promote autonomous learning. It also means that a simple solution does not necessarily result in a positive attitude among students.
ABSTRACT
E-Learning and Massive Open Online Courses are old techniques, but since the Coronavirus, they have become more popular again. Students already suffer from a lack of concentration and motivation in traditional courses;thus, this lack affects online courses. Furthermore, another important Online Learning systems problem is the difference between learners in terms of Learning Styles, abilities, social characteristics as well as preferences, background, and other psychological and mental features. Generally, these features are not taken into account by scientists. Therefore, Deep Learning techniques and Datasets have been used to improve E-Learning systems and MOOCs in several aspects such as: predicting dropout, Learning Styles and performance of online learners, and even their attention after taking an online course. In this work, we have studied and analyzed many recent works in the area of using Deep Learning techniques to improve Online Learning systems and MOOCs. This analysis shows what researchers rely on to improve E-Learning and MOOCs and demonstrates that research does not use the definition of the appropriate Learning Style frequently. However, the most used ones are dropout and performance of learners. In another hand, learners' attention is still gap. © 2022 IEEE.
ABSTRACT
Teaching cybersecurity to children is not a trivial task due to the complexity of explaining online-threat-related topics such as sexting or cyberbullying. Moreover, most of the courses designed to raise awareness in these controversial subjects has been mainly produced in English. However, during the Covid-19 pandemic, getting access to Spanish-based training for protecting children online became a priority for parents in countries such as Ecuador, which was not prepared to have children studying at home whilst being exposed to such threats. In this paper, we show that it is feasible to design a virtual course (MOOC) to teach cybersecurity to Spanish-speaking children with acceptable usability levels and minor usability issues. Our proposed methodology combines ADDIE, DPIPE and UDL for developing the course, and uses both SUS and Nielsen as methods to assess the MOOC from an usability point of view. For enhancing user engagement, our course uses novel 3D anthropomorphic characters and storyboards to aid the learning process. Regarding usability results, our proposed MOOC has very high and acceptable scores, with around 86/100 using the SUS scale, and less than 3 in the Nielsen score. © 2022 IEEE.
ABSTRACT
With centuries-old lecture-based teaching methods, ingrained institutional biases, and obsolete classrooms, the slow pace of change in global education and academic institutions is lamentable. COVID-19, on the other hand, has acted as a catalyst for educational institutions worldwide to seek out novel solutions within a short or long timeframe. It is critical for countries to address the situation in such a way that the crisis fosters innovation and inclusion rather than exacerbating learning disparities. Schools are utilising distance learning programmes, educational apps, and platforms such as radio and the internet to reach students who live in remote areas. However, closing the so-called 'digital divide' - the divide between those with access to computers and the internet and those with limited or no access - is a difficult task. We propose in this paper the Interactive Accessible Virtual Education Network - Grand Educational Repository (i-AVEN$\vert$ GER), the world's first comprehensive futuristic hi-tech, comprehensive, and inclusive online structured learning platform with all types of interactivity, namely, educational app and portal. We've discussed the architecture and all of the associated functionalities thus far with recommended implementation. This proposed app and portal will be enhanced to ensure inclusivity for all people without regard for prejudice, as well as the incorporation of cutting-edge technology such as augmented reality, virtual reality, and artificial intelligence. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic has been a catalyst for mandatory use of technology to deliver classroom learning to students who have been forced to learn from home (LFH). Learn from home requires technology-enhanced learning (TEL) method if students are to be satisfied with online delivery of lessons. Technology-enhanced learning is used to describe the integration of technology to teaching and learning;the demand for technology-enhanced learning has been increased as a result of the lockdown. The study explores various online teaching platforms, techniques, online examination, and technology-enhanced learning methods needed to be used by university instructors to ensure that educating the students goes on unhindered. The identified variables were empirically tested with the aid of a structured questionnaire. The study concludes that there is a positive relationship between satisfaction with the delivery of online study and the perceived effectiveness of TEL among higher education students. © 2022 IGI Global. All rights reserved.
ABSTRACT
COVID-19 has affected human life since its advent. And to counteract its spread, humankind adopts social distancing, which encourages remote working for employees, and online learning for students. Many universities and schools quickly adopted e-learning solutions without much consideration of security, while it is important to consider users' privacy. Unfortunately, digital learning spaces face security vulnerabilities, risks and threats and are not spared from cyber-attacks. To ensure the security and privacy of e-learning solutions used by universities and schools, we analyzed how MOOCs and Organizations offering online courses long before COVID-19 deal with their users' privacy and personal data. In this study, we considered some popular platforms from The United States (Coursera, EdX, Udemy), Europe and the United Kingdom (FutureLearn, FUN MOOC, EduOpen), and Asia (XuetangX, SWAYAM, and K-MOOC). We discussed the personal data collected by these platforms, the purposes for which these data are collected, the different legislation for processing and storing data, and how the platforms ensure user privacy. © 2022 IEEE.
ABSTRACT
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.
ABSTRACT
New Engineering Education (NEE) has become increasingly important in higher education in China. The COVID-19 pandemic has forced higher education institutions to adopt online and hybrid modes of instruction globally, with Massive Open Online Courses (MOOCs) becoming a primary educational tool. In this paper, we explore the impacts of online/hybrid modes on NEE courses in the context of the COVID-19 pandemic. We hypothesized that the rapid transition to online/hybrid modes may have negatively affected students due to insufficient preparation by teachers. This hypothesis was tested through an investigation involving 787 students conducted in 2020 and 2021. The results indicated that, while most students considered online/hybrid modes a helpful tool for their studies, 28.69–36.74% of participants reported significant issues. Through one-to-one interviews with negative/neutral attitude students, we found that insufficient and burdensome communication/interaction with classmates and teachers was the main contributor to these issues. We conclude by proposing a conceptual path to address the issues raised in the study, as well as offering the obtained results as a critical reference for educational researchers seeking to improve the quality of online/hybrid courses and contribute to the field of education. © 2023 by the authors.
ABSTRACT
Real-time learning in health emergencies is a critical mechanism to provide frontline health workers, responders, decision-makers and the public with access to the latest knowledge to save lives, reduce disease transmission and protect the vulnerable. The World Health Organization (WHO) established the OpenWHO.org learning platform to meet this need. Courses are free, self-paced, accessible in low-bandwidth and offline formats, and available in national and local languages. Multilingual production was prioritized and expanded to meet the learning needs of the COVID-19 pandemic. Enrolment data from the introductory COVID-19 course, which has more than 1 million enrolments across 45 language versions, were examined according to language and geographical reach to assess how multilingual availability contributes to equity in learning. The analysis found that most language versions had uptake clustered in key countries where native speakers are concentrated, while use of some translations was more broadly dispersed. In nearly ¾ of the available language versions of the course, more than ⅓ of enrolments were found in the top country of use. The findings suggest that multilingual courses served as entryways for learners who may not have otherwise been able to participate, even as enrolment numbers likely underestimate the impact. A production policy that prioritizes translation of open online courses into diverse languages contributes to equity in access to public health knowledge at the global and country levels during health emergencies.
ABSTRACT
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.
ABSTRACT
With advancements in e-learning technology, students may power them by interacting with the eLearning environment, such that the teacher is no longer the gatekeeper of instruction. This research attempts to examine students' prediction performance based on their interaction with educational activities in MOODLE and MOOCs;this was accomplished via the use of student log files and some extra data about the specific student. In order to discover the best approach for the student's prediction, the performance prediction was explored using Decision Tree (C4.5 algorithm), Artificial Neural Network, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithm techniques. Furthermore, log file analysis shows that the rate of interaction with the e-learning context has a substantial influence on their performance, with students with the highest interaction on the MOODLE performing better than someone with low interactivity rates. According to the data, students are more interested on e-learning MOODLE than MOOCs, and as a result, they are missing out on the benefits of the available resources on MOOCs, such as viewing lecture videos and participating in quizzes, which may help them with their studies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The use of online courses is growing worldwide that has opened the door for the interested students to learn comfortably from their locations especially, during Covid-19 pandemic. However, an important aspect of traditional classroom is real-time students' feedback for content delivery and interactive sessions, which is missing in online courses. The aim of this work is to bridge this gap by providing an automatic recognition system for engagement level of the students during online courses using deep transfer learning. In this paper, a CNN based method is proposed to predict the level of engagement while watching online class sessions. The CNN based method consists of two different modalities including: (1) pre-trained network based transfer learning for feature extraction from image data, (2) support vector machine (SVM) classifier for classification. Ten different pre-trained networks are used in the proposed method. The superiority of the method is evaluated on the dataset created using images of graduate students. Of all pre-trained networks, Resnet50 and VGG16 achieved highest classification accuracy of 72.34% and 71.77% using the proposed approach respectively. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Due to the COVID-19, Chinese universities have moved their English courses online. Students took lessons through various online learning platforms, especially MOOCs. However, MOOCs' problems such as low course completion rate, poor learning engagement, and learning efficiency harm the education quality. This study explores whether Bilibili, an online entertainment platform providing English videos, has better affordances in online English learning. Through a comparative analysis of the two platforms using questionnaires and SPSS software, the results show that compared to MOOCs, respondents perceived Bilibili to have a higher level of interactivity. In addition, respondents regarded Bilibili's screen bullets to have more significant positive effects on their English learning than MOOCs' discussion forums, especially in triggering their English learning interest. Still, the difference is not prominent in learning English grammar. Based on the results, the study argues that universities should choose between MOOCs and Bilibili according to the different characteristics of English skills. © 2022 Owner/Author.
ABSTRACT
Mandarine Academy is an Ed-Tech company that specializes in innovative corporate training techniques such as personalized Massive Open Online Courses (MOOCs), web conferences, etc. With more than 550K users spread across 100 active e-learning platforms. The company creates online pedagogical content (videos, quizzes, documents, etc.) on daily basis to support the digitization of work environments and to keep up with current trends. Mandarine Academy provided us with access to Mooc.office365-training.com. A publicly available MOOC in both French and English versions to conduct research on recommender systems in online learning environments. Mandarine Academy collects user feedback using two types of ratings: Explicit (Like Button, Social share, Bookmarks), and Implicit (Watch Time, Page View). Unfortunately, explicit ratings are underutilized. Most users avoid the burden of stating their preferences explicitly. To address this, we shift our attention to implicit interactions, which generate more data that can be significant in some cases. Implicit Ratings are what constitute Mandarine Academy Recommender System (MARS) Dataset. We believe that the degree of viewing has an impact on the overall impression, for this reason, we applied changes to the implicit data and made a part of it similar to the explicit rating format found in other known datasets (e.g., Movielens). This paper presents two real-world dataset variations that consist of 89,000 explicit ratings and 276,000 implicit ratings. Data was collected starting early 2016 until late 2021. Chosen users had rated at least one item. To protect their privacy, sensitive information has been removed. To the best of our knowledge, this is the first publicly available real-world dataset of E-Learning recommendations in both French and English with mixed ratings (implicit and explicit), allowing the research community to focus on pre-and post-COVID-19 behavior in online learning.
ABSTRACT
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.
ABSTRACT
Massive Open Online Courses (MOOCs) have recently gained great attention. However, the biggest challenge to the success of MOOCs is their low completion rate. During the lockdown of the COVID-19 pandemic, MOOCs were in high demand by many higher education institutions to replace their face-to-face lessons. MOOCs have great potential to grow and reinvent the way of learning in the 21st century. This study uses the Virtual Learning Environment (VLE) effectiveness model to understand how the five key factors (learner, instructor, course, technology system, and interactivity) influence student learning satisfaction from a holistic approach and determine the best predictor of student learning satisfaction in the MOOC learning environment. A set of online data based on a 5-point Likert scale was collected from 333 undergraduate students from the top five public universities in Malaysia whose students are actively using MOOCs in their learning. The Partial Least Squares Structural Equation Modelling (PLS-SEM) technique was used to analyse the data. The empirical results revealed that all factors significantly influence student learning satisfaction positively. Learner and interactivity factors were the strongest predictors in determining student learning satisfaction in MOOCs. These findings provide an empirically justified framework for developing successful online courses such as MOOCs in higher education.