Massive Open Online Courses Platform Research Articles

Examination of relationships among technology acceptance, student engagement, and perceived learning on tourism-related MOOCs

ABSTRACT This study investigates the relationship between student technology acceptance, student engagement, and perceived learning on tourism-related massive open online courses (MOOCs). The respondents include 389 Indian university students who took tourism-related MOOCs. The results of PLS-SEM indicate the weak relationship between MOOC technology acceptance and the perceived learning of students. Technology acceptance has an impact on student engagement, but the impact is not highly significant. The findings show the strong relationship between student engagement and perceived learning in the context of tourism-MOOCs. The study suggested several ways to ensure student engagement and perceived learning to develop a positive attitude toward MOOC platforms and institutions that deliver MOOCs. Several implications are drawn from the findings, and future research directions are suggested.

“Be a better version of you!”: A corpus-driven critical discourse analysis of MOOC platforms' marketing communication

This study examines the representation, reconstruction, and promotion of the 'ideal subject' of the job market in the promotional materials of the online/life-long learning platforms known as Massive Open Online Courses (MOOCs). We take a corpus-driven critical discourse analysis to investigate the marketing language in the subscription e-mails and websites of six English-medium MOOC platforms. The analysis shows that the platforms use an array of promotional persuasion strategies, including advice-giving, autonomization and responsibilization of individuals and reinforce a self-betterment discourse to create marketable employees. Through the use of a distinct blend of higher education, marketing, and self-help discourses, the skills-oriented language explicitly references job insecurity and urges the individual to (re)build oneself tirelessly to remain demandable/marketable, neglecting an intellectual advancement angle. This ideology legitimizes the neoliberal demands for the enterprising-self and employability and feeds into one's fear of failure, ranking individuals in the society based on a value-adding/detracting practice.

Stereotyping the Chinese in Arab Nations:Effects of media use, perceived realism, and perceived Chinese-Arab relations

ABSTRACT Stereotypes have become an acknowledged barrier to effective social communication, cross-cultural engagement and to globalization. This study examines the stereotypes of the Chinese held by people in four Arab nations (Saudi Arabia, Egypt, Morocco and Yemen) within the context of China’s Belt and Road Initiative. It applies a three-pronged theoretical framework (social cognitive, cultivation, and media realism) to investigate the effects and the influence of Arab media use, of perceived realism and of perceived Chinese-Arab relations on the formation of Chinese stereotypes in those Arab nations. The study analyzes responses from 405 participants selected randomly from ‘Rwaq’ – the Arab World Massive Open Online Courses Platform. Results indicate, among other things, that Arab people have positive perceptions of Chinese-Arab relations and that most Arabs have positive stereotypes of the Chinese. Arab people’s use of Arab media and their perceived realism of media content are positively correlated with stereotypes projected in those media. In conclusion, we argue that media organizations, particularly those in Arab counties, should develop a path toward mitigating the effects of harmful or negative media-generated stereotypes.

Mobile app analytics for assessing MOOC platforms: a study of online learners’ sentiments

PurposeIn distance education, massive open online courses (MOOCs) are recent innovative approaches, which strive to provide learning content to all the participants without any demographic-related barriers. The purpose of this research is to analyse and evaluate the popularity of two MOOCs platforms, Study Webs of Active-Learning for Young Aspiring Minds (SWAYAM) and Coursera, on an android app by pursuing the paradigm of mobile analytics and deep learning.Design/methodology/approachThe analytics are performed by using the Web application AppFollow (AppFollow.io), which captured, monitored, measured and analysed the app review results.FindingsThe findings of the paper suggest MOOCs designers should consider sentiment analysis to check and evaluate users’ ratings and feedback to improvise its design.Research limitations/implicationsThe findings of the study will provide the necessary inputs and feedbacks to the app developers to understand the users’ problems, users’ perspectives and users’ requirement in a scientific and precise way. By removing the bugs and incorporating the value-added features, the MOOC platforms will become more productive and beneficial in terms of knowledge sharing, skill development and capacity building.Originality/valueThis research is novel in getting an insight into the behaviour of users towards usage of SWAYAM and Coursera apps on Android platforms through the AppFollow software and provides valuable results for the app developers in monitoring the feedbacks to enhancing the features for the satisfaction of users. The present analysis is expected to provide a benchmark for future research in this domain.

Widening Access to Applied Machine Learning with TinyML

Broadening access to both computational and educational resources is critical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost and globally accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethical considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facilitate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project competition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies.

Promoting Digital Skills for Austrian Employees through a MOOC: Results and Lessons Learned from Design and Implementation

Digital skills are now essential, not only in information and communications technology (ICT) jobs, but for employees across all sectors. The aim of this article is to detail how employees’ digital skills can be fostered through a Massive Open Online Course (MOOC), how such an offer is used and what the effects of such a measure are. Using an approach oriented at action research and design-based research activities, the authors describe the basics of their finding on existing European competence frameworks for digital skills and European projects that used MOOCs, the development and design of the MOOC, the evaluation on the basis of learning analytics insights and a questionnaire, as well as a reflection. The MOOC was offered as Open Educational Resources (OER) on the Austrian MOOC platform iMOOX.at from March to April 2021, with 2083 participants, of whom 381 fully completed the course (at end of June 2021) and 489 filled out the final questionnaire.

ONCO: An Ontology Model for MOOC Platforms

In the process of searching for a particular course on e-learning platforms, it is required to browse through different platforms, and it becomes a time-consuming process. To resolve the issue, an ontology has been developed that can provide single-point access to all the e-learning platforms. The modelled ONline Course Ontology (ONCO) is based on YAMO, METHONTOLOGY and IDEF5 and built on the Protégé ontology editing tool. ONCO is integrated with sample data and later evaluated using pre-defined competency questions. Complex SPARQL queries are executed to identify the effectiveness of the constructed ontology. The modelled ontology is able to retrieve all the sampled queries. The ONCO has been developed for the efficient retrieval of similar courses from massive open online course (MOOC) platforms.

TECHNOLOGY ADOPTION AND CONTINUANCE OF MOOCS: A SYSTEMATIC LITERATURE REVIEW, SYNTHESIS AND FUTURE RESEARCH AGENDA

Massive Open Online Courses (MOOCs) have garnered a lot of attention and interest from academia and industry. The proliferation of MOOC platforms has opened up immense opportunities for fulfilling learner needs and improving the education landscape. It is important to understand the drivers of MOOC adoption to ensure its complete utilization. Research on MOOC adoption, acceptance, usage, and continuance has increased over a period of time. With this regard, the purpose of this study was to review and synthesize the research on MOOC adoption using a structured and domain-based approach in order to understand its state-of-the-art literature. Seventy empirical articles indexed in Scopus or Web of Science were identified and reviewed systematically using the Theories, Context, Characteristics, and Methods (TCCM) framework. Systematic mapping of studies was taken place to determine the year-wise publications, journal quality, citations, widely used theories, the context in terms of MOOC platforms and country of research, characteristics in terms of widely used constructs, and hypothesized relationships. The systematic literature review highlights Technology Acceptance Model is the dominant model applied in the MOOC adoption research. Further research on MOOC adoption is seen majorly in Asian economies with a narrow focus on developed economies. Although several review studies have taken place on MOOC, their focus has been on the quality, pedagogical or user perspective. This study is the first effort in synthesizing the state-of-the-art literature on MOOC adoption. Future avenues of research are suggested in terms of Theories, Context, Characteristics, and Methods to further the theoretical and practical knowledge on MOOC adoption.

Challenges and Recommendations Facing Faculty and Institutions in Delivering Massive Open Online Course (MOOC)

MOOC applications, one of the applications adopted and popular in the age, are not well known in our country. Nevertheless, there is a new educational approach in the world and it is very important to determine the difficulties in MOOC applications with various advantages in terms of eliminating these difficulties. This study examines the difficulties and recommendations faced by faculty and staff teaching at the AtademiX platform in the course of presenting MOOC. In this study, it was aimed to determine the difficulties and suggestions faced by the faculty members and the institution during the MOOC submission process. It was used to describe the case study from qualitative research methods to achieve this goal. The study was carried out in two separate groups. Groups were determined by sampling method. The first working group of the research constitutes 6 faculty members from the administrators and employees who are in charge of the administration and execution of the first MOOC platform of Turkey, AtademiX, and the second group 9 faculty members who teach through the AtademiX platform. Semi-structured interview form was prepared for evaluating the outputs of MOOC applications in terms of faculty members and institutions and data were collected. The collected data were analyzed by content analysis method. Institutions and faculty members have encountered various difficulties while presenting MOOC practice and have made various recommendations for future applications. Challenges faced by the institution can be summarized as difficulties they face due to in-house and faculty members. Difficulties faced by faculty members in the process; Individual difficulties, difficulties arising from participants, and difficulties resulting from interaction.

Conducting Experiments With Real-Time Cloud Computing Models: A Virtual Laboratory for Massive Open Online Courses on Computer Control Systems [Focus on Education

Massive open online courses (MOOCs) have become a recent hotspot in educational research because of their large-scale accessibility, openness, flexibility, and convenience (see “Summary”). They are an effective way to meet the growing demand for education. MOOC platforms have been created around the world, such as Coursera, founded by Stanford University; edX, cofounded by the Massachusetts Institute of Technology and Harvard University; and XUETANG, developed by Tsinghua University <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> – <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> . MOOC platforms provide students with more systematic learning opportunities than traditional teaching and possess obvious advantages in cost-effectiveness and time flexibility <xref ref-type="bibr" rid="ref4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</xref> . The Computer Control System (CCS) course at Northeastern University (NEU) in China is a MOOC launched on the iCourse website <xref ref-type="bibr" rid="ref5" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[5]</xref> , and it is offered twice a year, with nearly 7000 participants in each session. The class format currently includes videos, online discussions, homework, and examinations; experiments matching the theory lectures are absent. This is a common problem in MOOCs: experimentation is neglected because of the lack of effective methods or tools using this new platform. The drawbacks of offline experiments, such as the lack of interaction among teacher and students and insufficient supervision <xref ref-type="bibr" rid="ref6" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[6]</xref> , <xref ref-type="bibr" rid="ref7" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[7]</xref> , also prevent their wide employment in MOOCs. As a result, the lack of experimental teaching in MOOCs causes great trouble to both teachers and students, weakens the effectiveness of courses, and prohibits teaching some experimental courses via MOOCs. Fortunately, with the rapid development of science and technology, the rise of cloud technology provides a new choice for laboratory construction. Virtualization technology based on cloud computing can radically solve existing problems, such as an excessive number of students or lack of experimental resources. In addition, the large-scale data-processing ability in the cloud computing environment also makes it possible for teachers and students to interact on the MOOC platform.

Predicting Student Dropout in Self-Paced MOOC Course Using Random Forest Model

A significant problem in Massive Open Online Courses (MOOCs) is the high rate of student dropout in these courses. An effective student dropout prediction model of MOOC courses can identify the factors responsible and provide insight on how to initiate interventions to increase student success in a MOOC. Different features and various approaches are available for the prediction of student dropout in MOOC courses. In this paper, the data derived from a self-paced math course, College Algebra and Problem Solving, offered on the MOOC platform Open edX partnering with Arizona State University (ASU) from 2016 to 2020 is considered. This paper presents a model to predict the dropout of students from a MOOC course given a set of features engineered from student daily learning progress. The Random Forest Model technique in Machine Learning (ML) is used in the prediction and is evaluated using validation metrics including accuracy, precision, recall, F1-score, Area Under the Curve (AUC), and Receiver Operating Characteristic (ROC) curve. The model developed can predict the dropout or continuation of students on any given day in the MOOC course with an accuracy of 87.5%, AUC of 94.5%, precision of 88%, recall of 87.5%, and F1-score of 87.5%, respectively. The contributing features and interactions were explained using Shapely values for the prediction of the model.

Does MOOC Quality Affect Users’ Continuance Intention? Based on an Integrated Model

Massive open online course (MOOC) is an innovative educational model that has attracted widespread attention in recent years. Despite a growing number of registered users, many have given up continuously using MOOC platforms after the first-time user experience; thus, a high dropout rate has severely hindered the sustainable development of MOOC platforms. To address the problem, this study started with the quality factors of MOOC platforms and the confirmation of user expectations by integrating the D&amp;M ISS model and the expectation confirmation model into one, with the goal of identifying the factors that affect users’ continuance intention to use MOOC platforms. In this study, online questionnaires were distributed to Chinese users with experience in using MOOC platforms, and a total of 550 valid samples were recovered. In addition, the theoretical model was tested using structural equation modeling (SEM). The research results showed that there are three critical antecedents affecting the confirmation of user expectations for a MOOC platform, including information quality, system quality, and service quality, of which service quality has the greatest impact on users’ expectation confirmation. If user expectations for an MOOC platform are positively confirmed, the perceived usefulness of the platform as well as the satisfaction with it will effectively be improved. Moreover, perceived usefulness has been proven to be a critical factor affecting users’ continuance intention to use MOOC platforms, which is followed by user satisfaction. Compared to the original ECM, the integrated research model has delivered significantly improved explanatory power for users’ continuance intention. Hence, this study makes up for the insufficiency of ECM in explaining the factors affecting users’ expectation confirmation and provides theoretical support for MOOC platform developers.

The impact of MOOC aggregators on the development of language education

Due to a large number of massive open online courses (MOOC), it becomes more and more difficult for the users to determine which course best suits their needs. To improve the efficiency of course selection, a place is needed where they can visually compare the offers of different MOOC providers. The article aims to identify the number and characteristics of massive open online courses for language learning (MOOLC) using MOOC aggregators and to trace their impact on the development of language education. The article presents different approaches to such concepts as MOOC, MOOLC, MOOC platform, MOOC aggregator and MOOC provider. The article determines MOOC aggregators, which would allow identifying the number and characteristics of MOOLC, and examines the possibilities of some MOOC platforms, which offer courses for English language learning, basing on the selected criteria for evaluation of the existing MOOC platforms. The author concludes that nowadays, the ideal MOOC platform, which could offer the necessary conditions for creating an ideal MOOLC, does not exist.

Reviewer Experience vs. Expertise: Which Matters More for Good Course Reviews in Online Learning?

With a surging number of online courses on MOOC (Massive Open Online Course) platforms, online learners face increasing difficulties in choosing which courses to take. Online course reviews posted by previous learners provide valuable information for prospective learners to make informed course selections. This research investigates the effects of reviewer experience and expertise on reviewer competence in contributing high-quality and helpful reviews for online courses. The empirical study of 39,114 online reviews from 3276 online courses on a leading MOOC platform in China reveals that both reviewer experience and expertise positively affect reviewer competence in contributing helpful reviews. In particular, the effect of reviewer expertise on reviewer competence in contributing helpful reviews is much more prominent than that of reviewer experience. Reviewer experience and expertise do not interact in enhancing reviewer competence. The analysis also reveals distinct groups of reviewers. Specifically, reviewers with low expertise and low experience contribute the majority of the reviews; reviewers with high expertise and high experience are rare, accounting for a small portion of the reviews; the rest of the reviews are from reviewers with high expertise, but low experience, or those with low expertise, but high experience. Our work offers a new analytical approach to online learning and online review literature by considering reviewer experience and expertise as reviewer competence dimensions. The results suggest the necessity of focusing on reviewer expertise, instead of reviewer experience, in choosing and recommending reviewers for online courses.

Examining the implementation of academic vocabulary, lexical density, and speech rate features on OpenCourseWare and MOOC lectures

ABSTRACT OpenCourseWare (OCW) and Massive Open Online Courses (MOOCs) have gained increasing popularity and importance because they greatly expand students’ access to university lectures. Although the main component of OCW and MOOC platforms is lectures, their differences may potentially affect the difficulty of academic listening comprehension. This study thus explores the extent to which the differences between OCW and MOOC lectures may affect L2 academic listening comprehension in terms of three affecting factors: academic vocabulary coverage, lexical density, and speech rate. The result shows that both OCW and MOOC lectures have a high academic vocabulary coverage, with many more higher-level academic words appearing in the current MOOC lectures. Moreover, the lexical density and speech rate of MOOC lectures are significantly higher than those of OCW lectures, suggesting that students may need to process denser information delivered at a faster speech rate when viewing MOOC lectures. It is thus concluded that comprehending MOOC lectures may demand higher-level academic listening skills and a larger academic vocabulary size. The findings can provide implications for EAP instruction in terms of using OCW and MOOC lectures as academic listening materials for students with different academic vocabulary knowledge and listening proficiency levels.

Construction of the Quality Evaluation Index System of MOOC Platforms Based on the User Perspective

Massive open online courses (MOOCs) have become a mainstream form of online learning. At present, various countries are vigorously developing MOOC platforms, which provide a helpful platform for people to acquire knowledge and skills. However, the quality of each MOOC platform is different, which is a challenge for learners seeking excellent courses. Since the evaluation of MOOC quality is a multiple criteria decision-making issue, it is important to find the major dimensions and criteria that determine the quality of platforms. This paper determines the weight of each dimension and criterion by using the best worst method (BWM). The results indicate that content accuracy has the greatest impact on MOOC quality. This paper selected five well-known domestic MOOC websites as research objects and used the VIKOR analysis method to rank the platform quality of the five chosen websites. The results show that IMOOC and Xuedong are ranked as the top two websites. This research result helps learners deepen their understanding of MOOC platforms and can serve as a reference for MOOC platforms to improve their quality. Techniques to reduce the uncertainty of expert judgments (such as rough sets, fuzzy theory, grey correlation, etc.) and models that clarify the influence relationship between criteria (DEMATEL-ANP) can be applied in future research.

MOOC Behavior Analysis and Academic Performance Prediction Based on Entropy.

In recent years, massive open online courses (MOOCs) have received widespread attention owing to their flexibility and free access, which has attracted millions of online learners to participate in courses. With the wide application of MOOCs in educational institutions, a large amount of learners’ log data exist in the MOOCs platform, and this lays a solid data foundation for exploring learners’ online learning behaviors. Using data mining techniques to process these log data and then analyze the relationship between learner behavior and academic performance has become a hot topic of research. Firstly, this paper summarizes the commonly used predictive models in the relevant research fields. Based on the behavior log data of learners participating in 12 courses in MOOCs, an entropy-based indicator quantifying behavior change trends is proposed, which explores the relationships between behavior change trends and learners’ academic performance. Next, we build a set of behavioral features, which further analyze the relationships between behaviors and academic performance. The results demonstrate that entropy has a certain correlation with the corresponding behavior, which can effectively represent the change trends of behavior. Finally, to verify the effectiveness and importance of the predictive features, we choose four benchmark models to predict learners’ academic performance and compare them with the previous relevant research results. The results show that the proposed feature selection-based model can effectively identify the key features and obtain good prediction performance. Furthermore, our prediction results are better than the related studies in the performance prediction based on the same Xuetang MOOC platform, which demonstrates that the combination of the selected learner-related features (behavioral features + behavior entropy) can lead to a much better prediction performance.

Like, Comment and Share: Understanding Language Learning Experience of Gifted Students through Massive Open Online Course (MOOC) Platform

Massive Open Online Course (MOOC) gained prominence as an alternative teaching and learning platform for many educators embarking on developing university courses online. In Malaysia, the initiative at lower secondary school was quite recent and not many teachers were willing to invest the time and effort to develop the MOOC courses. This study examined the secondary school gifted students’ experience in learning a language subject using a new online course in a MOOC platform. All students from the lower secondary levels of a gifted education institution enrolled in the English subject MOOC and their engagements and experiences were recorded in their journal and classroom discussions. Their text and verbal responses were analysed and reported to better understand the various issues and challenges that they had encountered. Students accredited certain positive aspects of MOOC and they found it to be useful, flexible and engaging. More than 80 per cent relished the social meeting elements such as like, comment and share. The contents and activities helped them learn and practise the English language. The study outcomes disclosed the use of MOOC as a social learning platform that offers engaging learning. The online teaching and learning process could benefit from the social media features found in MOOC which are motivating to the gifted students. For educators, this platform offers useful alternatives and strategies to develop their teaching contents online and utilise it as an enrichment programme in the gifted and talented education institution.

Can MOOC Instructor Be Portrayed by Semantic Features? Using Discourse and Clustering Analysis to Identify Lecture-Style of Instructors in MOOCs

Nowadays, most courses in massive open online course (MOOC) platforms are xMOOCs, which are based on the traditional instruction-driven principle. Course lecture is still the key component of the course. Thus, analyzing lectures of the instructors of xMOOCs would be helpful to evaluate the course quality and provide feedback to instructors and researchers. The current study aimed to portray the lecture styles of instructors in MOOCs from the perspective of natural language processing. Specifically, 129 course transcripts were downloaded from two major MOOC platforms. Two semantic analysis tools (linguistic inquiry and word count and Coh-Metrix) were used to extract semantic features including self-reference, tone, effect, cognitive words, cohesion, complex words, and sentence length. On the basis of the comments of students, course video review, and the results of cluster analysis, we found four different lecture styles: “perfect,” “communicative,” “balanced,” and “serious.” Significant differences were found between the different lecture styles within different disciplines for notes taking, discussion posts, and overall course satisfaction. Future studies could use fine-grained log data to verify the results of our study and explore how to use the results of natural language processing to improve the lecture of instructors in both MOOCs and traditional classes.

PEMANFAATAN MASSIVE OPEN ONLINE COURSES DALAM PEMBELAJARAN AL QUR’AN DI ERA DIGITAL

This article aims to discuss the use of Massive Open Online Course (MOOC) media in learning the Qur'an in the digital era. The research method used in this study is a descriptive qualitative approach. In the current era, technological developments have brought the world of education to think about issuing learning innovations that are ready to meet the demands of the times. This includes online-based open courses or commonly referred to as Massive Open Online Courses (MOOC). The success of several MOOC platforms in providing various courses, such as English, Mathematics, Management, Physics, Graphic Design and so on is read as an opportunity also to develop online and open learning of the Qur'an. The use of MOOC media will be able to accommodate a large number of participants from various regions. This opportunity was then responded by several educational institutions to provide an online and open Qur'an learning platform, such as the Intensive Tahfidz, Griya Al Qur'an, Baitul Qur'an Daarul Qur'an, Tahsin Online Academy, and Qur'an Edu Center.