Massive Open Online Courses Platform Research Articles

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.

Agent-Supported Peer Collaboration in MOOCs.

While massive open online courses (MOOCs) can be effective in scaling education, orchestrating collaborative learning activities for large audiences remains a non-trivial task that introduces a series of practical challenges, such as the lack of adequate human support. Even when collaboration takes place, there is uncertainty whether meaningful interactions will occur among learners. This work presents the architecture of a prototype system called PeerTalk. The system was created to enable instructors to easily incorporate real-time collaborative learning activities into their online courses. Furthermore, PeerTalk employs a conversational agent service that aims to scaffold students’ online collaboration and provide valuable guidance, which can be configured by the course instructor. In order to investigate the user-acceptance of the system, two evaluation studies took place. The first one involved a group of experts, i.e., MOOC instructors who are expected to use such a system in their course, whereas the second study featured 44 postgraduate students. The study findings were encouraging in terms of the system efficiency and usability levels, laying the foundation for a conversational agent service, which can effectively scale the support of the teaching staff and be easily integrated in MOOC platforms, creating further opportunities for valuable social interaction among learners.

The social presence of online education: how MOOC platforms in China cope with collective trauma during COVID-19

ABSTRACT In the face of the COVID-19 pandemic, offline programmes of universities in China had to be rescheduled, classes were moved online based on the government’s requirements. The paper investigated how an online learning environment – especially massive open online course (MOOC) platforms – introduced a sense of social presence in students’ lives to help them better deal with trauma and changed circumstances. To this end, the author conducted a questionnaire-based survey of 5,000 students and 620 teachers at Communication University of China from May 2020 to June 2020 and analysed the respondents’ attitudes towards online community learning during the outbreak and the associated trauma period. The survey reveals that the mimicked social networking environment ensures that students perceive social presence and makes the trauma more bearable. By applying social presence theory to the ongoing crisis and conducting a case study of online education adopted by a Chinese university, this research deepens the understanding of technical reality and social networking environment creation described by the theory. Meanwhile, by analysing co-presence, immediacy, and intimacy in community learning, this study paints a more nuanced picture of the mental state of traumatised groups in a virtual environment.

Personalized Online Learning

The success of MOOC (massive open online courses) is rapidly increasing. Most educational institutions are highly interested in these online platforms, which embrace intellectual and educational objectives and provide various opportunities for lifelong learning. However, many limitations, such as learners' diversity, lack of motivation, affected learners' outcomes, which unfortunately raised the dropout rate. Thus, multiple solutions were afforded on MOOC platforms to tackle these common problems. This paper suggests a model outline of a customizable system Context-Driven Massive Open Online Courses that could be implemented in any learning environment, and that goes hand in hand with learners' context to boost their motivation towards learning, and to help identify their learning needs. The paper introduces CD-MOOC following a learner-based approach by employing two types of users' data; long-term and short-term data assembled form learners' online traces when interacting on the platform. The data help users design their own learning path based on their context and preferences.

Massive open online courses as a resource of mixed learning (on the example of humanities)

The dominant direction in the development of online education, actively discussed by the pedagogical community, is training using massive open online courses (MOOCs). The key factor in the effectiveness of the implementation of MOOCs in the practice of educational organizations is primarily the availability of elite education. In modern conditions, which determine the priority of lifelong education (lifelong learning), it is particularly relevant to form schoolchildrens readiness for self-education, which can be facilitated by training on MOOC platforms. The broad discussion of MOOC implementation in educational practice is more concerned with university education, additional education and the system of advanced training. The use of MOOCs in school education in the publications of teachers-researchers is not considered fully enough and is mostly related to natural sciences. The paper analyzes the MOOC on the example of humanities and the features of the implementation of mixed learning models (inverted learning, autonomous group, individual trajectory) based on the use of online courses. This analysis suggests that there should be a need for more in-depth research in this area and the need to introduce elements of learning into school practice through massive open online courses. This will resolve the contradiction between the need for differentiation and individualization of training and the potential of existing unused massive open online courses.

Business Models Canvas of MOOCs: an Investigation of Sustainable Practices for MOOC Universities

Massive Open Online Course (MOOC) is defined as an online open-access course available to an unlimited number of students from any location. These online lectures provide convenience and timeliness for students, enabling them to study from anywhere and anytime. With the demand for online learning, universities require a business design model for the MOOC application that will be used as online learning with the hope that it can be carried out continuously and in the long term to support the existence of online learning. The online learning process in Indonesian education in the era of the Industrial Revolution 4.0 and the implementation of the Merdeka Belajar Kampus Merdeka (MBKM). This research aims to create a business model for the implementation of the MOOC application as an open online lecture. The stages of this research method include a literature review study related to business models by the world's leading MOOC platforms, including Coursera, EdX, Udacity, and Udemy. Then analyzed and made a business model using the nine-block canvas method and implemented the MOOC application in the context of the MBKM Curriculum.

Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations.

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. In this paper, we seek to improve Learner Profiling (LP), i.e. estimating the demographic characteristics of learners in MOOC platforms. We have focused on examining models which show promise elsewhere, but were never examined in the LP area (deep learning models) based on effective textual representations. As LP characteristics, we predict here the employment status of learners. We compare sequential and parallel ensemble deep learning architectures based on Convolutional Neural Networks and Recurrent Neural Networks, obtaining an average high accuracy of 96.3% for our best method. Next, we predict the gender of learners based on syntactic knowledge from the text. We compare different tree-structured Long-Short-Term Memory models (as state-of-the-art candidates) and provide our novel version of a Bi-directional composition function for existing architectures. In addition, we evaluate 18 different combinations of word-level encoding and sentence-level encoding functions. Based on these results, we show that our Bi-directional model outperforms all other models and the highest accuracy result among our models is the one based on the combination of FeedForward Neural Network and the Stack-augmented Parser-Interpreter Neural Network (82.60% prediction accuracy). We argue that our prediction models recommended for both demographics characteristics examined in this study can achieve high accuracy. This is additionally also the first time a sound methodological approach toward improving accuracy for learner demographics classification on MOOCs was proposed.

Research on the mechanism of knowledge diffusion in the MOOC learning forum using ERGMs

Research on the mechanism of knowledge diffusion in the MOOC learning forum using ERGMs

The digital transformation of higher education – “uni for nothin’, MOOCs for free”?

ABSTRACT In 2017, the newly-elected, Labor-led government of New Zealand boldly declared access to higher-education to be a universal right and committed to a year’s “fees-free” studentship, with the promise of eventually extending it to an entire first-degree programme. Against such a backdrop, this article will examine the role of Massive Open Online Courses (MOOCs) as surrogates for “fees-free” higher education and whether the design of such a Higher Education 4.0 platform is even a credible proposition. More specifically, the research question addressed is: can higher education be made universal in terms of access and costs through the intermediation of MOOCs? The case attempts to provide a socio-technical view of such a “value proposition” and concludes that the charter of higher education extends beyond the distribution of knowledge and skills that may perhaps be better delivered with blended learning models than MOOC platforms. A university is more than a certification of core-competencies in that it also brings about socialization and participation. With the undercurrent of design ideals such as “tech for good”, the academic community must examine whether MOOCs are credible substitutes or at-best, complementary platforms. In this era of Industry 4.0, higher education should not be about the creative destruction of what we value in universities, but their digital transformation. Regretfully, the onset of the Covid-19 pandemic has revealed gaping holes in the sectors’ readiness for online learning. The article concludes with an agenda for large Randomized Controlled Trials (RCTs) driven by Action Design Research that could fulfil the aspirations of the key stakeholder groups – students, faculty and regulators. It is intended that the case will inform policy makers on the implementation of a Blended Learning platform which draws from the relative strengths of traditional and online delivery.

An Intelligent Adaptive cMOOC “IACM” for Improving Learner’s Engagement

Despite the massive number of enrollments in MOOC (Massive Open Online Course) platforms, dropout rates are very high. This problem can be due to several factors: Social, pedagogical, prior knowledge as well as a demotivation. To deal with this type of problems, we have designed an adaptive cMOOC (Connectivist MOOC) platform for each registered learner’s profile.
 From the first human-machine interaction, the process adapts the learner's need according to a pre-established model. It is based on the processing of statistical data collected by correspondence analysis and regression algorithms. Each generated learner’s profile will provide an adaptive navigation and pedagogical activities. The intelligent system presented in this work will be able to classify learners according to their preferences and learning styles.

Assessment and Recognition of MOOCs: The State of the Art

Verification of learner identity is increasingly important for online education in examination and proctoring, and the Covid-19 pandemic has encouraged many institutions to move to online examinations at scale. This paper reviews state of the art approaches to ID verification, recognition and assessment in Massive Open Online Courses (MOOC). Desktop research included research publications, grey literature and MOOC platforms to identify current practices. The main focus was on public data from course pages on MOOC platforms, particularly courses that had been grouped to offer a single academic program, Four approaches are identified: basic ID checks, checks made by the university, proctoring and various types of interview. Our review demonstrates the absence of any universal approach. However, the emergent picture shows increasing co-ordination across relevant stakeholders (including higher education institutions, employment services and the private sector). Several more coordinated strategies (such as Europe’s Common Microcredential Framework) are highlighted. There remain significant challenges for online proctoring, including overcoming learner preferences and meeting the increased resourcing needed for human-led processes of identity verification. There remain significant ethical challenges regarding the use of learner data (especially biometrics). MOOC platforms would likely benefit from adopting strategies that are well-established in higher education institutions such as plagiarism checking software and pedagogies like e-portfolios.

Acting in secret: Interaction, knowledge construction and sequential discussion patterns of partial role-assignment in a MOOC

Forum discussions have been utilised widely as a means of facilitating learning interaction and social-knowledge construction in online learning. Much research has been conducted on the instructional interventions that benefit asynchronous discussions. Role-playing, or assigning roles to discussants, has been proven effective in promoting interactivity and knowledge construction in the context of both face-to-face and online learning. However, assigning and rotating roles to thousands of learners in massive open online courses (MOOCs) and preparing them to act properly in their roles sounds impractical to MOOC instructors due to work overload. The present study provided three types of role assignment in a MOOC during various course offerings: fall offerings with no role-assignment, spring offerings with partial role-assignment and summer offerings with full role-assignment. Through the examination of the discussion patterns and role-assignment differences among 4,239 students and 5,439 posts in 56 forums, we suggest that partial role-assignment is as effective as full-role assignment. By assigning as few as 10 students with rotating roles, MOOC instructors can leverage this effective strategy while minimising their effort in preparing the discussants and moderating the discussions. These students act behind the scenes and improve the behavioural patterns of asynchronous discussions.
 Implications for practice or policy:
 
 MOOC instructors and teaching assistants can leverage a partial role-assignment strategy to improve asynchronous discussion quality with manageable effort.
 MOOC platform leaders and instructional designers may explore work-smart teaching strategies that are viable in practice without overburdening instructors.

Exploring the reliability and its influencing factors of peer assessment in massive open online courses

AbstractAs an innovative evaluation tool, peer assessment is essential in Massive Open Online Courses (MOOCs). In both formative and summative peer assessments in MOOCs, providing reliable feedback is crucial in enhancing learning outcomes. Peer assessment has been highlighted as a reliable tool in both traditional classrooms and small‐scale online learning contexts by a wealth of studies. Factors impacting the result of peer assessment have also been identified. However, such exploration in MOOCs is very limited. Through an examination of 5,722 assignment submissions and 56,794 review scores collected from 18 peer assessments in three courses on Chinese University MOOC platform, this study investigated the inter‐reliability of peer assessment in MOOCs utilizing Krippendorff's alpha and ICC[1]. It also revealed factors affecting peer assessment reliability in MOOCs. The results of this study show that students tended to mark extreme scores, and peer assessment in MOOCs seemed unreliable. In addition, the reliability of the peer assessment in these MOOCs was inversely correlated to the number of reviewers per assignment and reviews completed per reviewer. The assignment type also matters; peer assessment using e‐portfolios format was found more reliable than those using papers and proposals. Suggestions on how to improve the reliability of peer assessments in MOOCs are also provided. Practitioner notesWhat is already known about this topic Both the formative and summative peer assessment mode, providing reliable feedback is needed, which is of great value for improving the quality of online learning. The reliability and influence factors of peer assessment has been highlighted by a wealth of studies in traditional classrooms and small‐scale online learning contexts. There are still limited explorations in MOOCs. What the paper adds The students tended to give extreme scores in peer assessment in MOOC, and the peer assessment in MOOC may was not particularly reliable. The Assignment type being rated affected the reliability, and peer assessment using e‐portfolios format was found more reliable than those using papers and proposals. The reliability of peer assessment in MOOC was negative related to the number of reviewers per assignment and the reviews completed per reviewer. Implications for practice and/or policy Avoid using peer assessment as a summative assessment method, and should be assigned relatively low weights to final grades. Using papers and proposals as learning assignments can appropriately reduce the proportion of peer assessment in the final grade. Training on how to identify medium quality work should be provided. Instructional strategies should be designed to encourage learners to actively participate in peer assessments and thoroughly assess.

Interpretable MOOC recommendation: a multi-attention network for personalized learning behavior analysis

PurposeCourse recommendations are important for improving learner satisfaction and reducing dropout rates on massive open online course (MOOC) platforms. This study aims to propose an interpretable method of analyzing students' learning behaviors and recommending MOOCs by integrating multiple data sources.Design/methodology/approachThe study proposes a deep learning method of recommending MOOCs to students based on a multi-attention mechanism comprising learning records attention, word-level review attention, sentence-level review attention and course description attention. The proposed model is validated using real-world data consisting of the learning records of 6,628 students for 1,789 courses and 65,155 reviews.FindingsThe main contribution of this study is its exploration of multiple unstructured information using the proposed multi-attention network model. It provides an interpretable strategy for analyzing students' learning behaviors and conducting personalized MOOC recommendations.Practical implicationsThe findings suggest that MOOC platforms must fully utilize the information implied in course reviews to extract personalized learning preferences.Originality/valueThis study is the first attempt to recommend MOOCs by exploring students' preferences in course reviews. The proposed multi-attention mechanism improves the interpretability of MOOC recommendations.

INVESTIGATING INHIBITING FACTORS AFFECTING MOOCS ADOPTION AMONG TEACHERS IN HIGHER EDUCATION INSTITUTIONS OF INDIA

Massive Open Online Courses (MOOCs) has the potential to eradicate the digital divide and endorse education globally among the learners in the education industry. Through MOOCs platforms, teachers can upgrade their knowledge skills and abilities in their respective fields. The present study aims to identify the inhibiting factors underlying the adoption of MOOCs by teachers employed in higher education institutions. The research has employed an Interpretative Phenomenological Analysis of qualitative research methodology. In this research, teachers from the identified Universities of Uttarakhand, India are selected as the respondent to investigate the factors affecting the adoption of MOOCs. Findings reveal that technological barriers, financial barriers, and lack of awareness act as inhibiting factors in adopting MOOCs by teachers of HEI. This study also contributes to the literature by assessing the factors influencing teachers’ intention to adopt Massive Open Online Courses for learning. Through this paper, the three stakeholders of MOOCs, that is teachers, HEI and MOOC provider are suggested measures that will enhance the support and acceptability of the online courses.

Construction of Video Courses of Physical Education and Health Education in Colleges and Universities under the MOOC Platform

In the traditional construction process of educational video courses for physical and health education in universities, numerous problems are encountered, such as small storage capacity, significant delay error, and decreased information throughput rate. Therefore, an educational video course for physical and health education in universities based on the Massive Open Online Course (MOOC) platform is constructed. The course is built by constructing a creative environment for video courses, establishing a video course model under the MOOC platform, and the caching code design for video courses. Upon numerous modifications and test runs, with the campus network’s help, the construction of a network teaching video for physical and health theory courses is realized. Our proposed approach’s experimental result shows that the designed course’s frame rate is 10% higher than that of the traditional course designing. The node’s storage capacity is up to 130%, the delay error is significantly reduced, and the information throughput rate is not affected by the increase of network load.

Massive Open Online Course Fast Adaptable Computer Engineering Education Model

Massive Open Online Course (MOOC) is a new online education model that provides new opportunities and challenges for the development and reform of teaching in colleges and universities. This paper first builds a MOOC-based model of influencing factors of blended learning adaptability. Through investigation and research, it is found that the six influencing factors all have different ways and different degrees of influence on learning adaptability. Among them, learning motivation has a direct and significant impact on learning adaptability, and learning self-efficacy not only has a direct and significant impact on learning adaptability but also has an indirect impact on learning adaptability through learning motivation. Teacher teaching has indirect effects on learning adaptability through learning self-efficacy, learning motivation, and learning support. Learning support not only has a direct and significant impact on learning adaptability but also has an indirect impact on learning adaptability through learning motivation. MOOC platform has a direct and significant impact on learning adaptability. The quality of course content has a direct and significant impact on learning adaptability through the MOOC platform and learning motivation. The creative results of this research are mainly embodied in the combination of theory and practice, exploring and forming related results based on MOOC-based college students’ learning adaptability. According to the MOOC-based learning-adaptive intervention model, teaching practice is carried out to verify the effect of the intervention model from two aspects of student procedural performance and result performance. Finally, in view of the problems existing in traditional computer teaching in colleges and universities, combined with the design ideas and basic characteristics of MOOC, the teaching mode reform of computer courses is analyzed and discussed, and a MOOC-based computer engineering education teaching mode is proposed.

Capacity Tracing-Enhanced Course Recommendation in MOOCs

Massive open online courses (MOOCs) have been an important learning tool in education. In order to reduce the high dropout rate and improve learners' satisfactions, it is urgent for MOOCs platform to provide course recommendation and tutoring service. To achieve it, it is necessary to determine and trace learners' learning state. Cognitive diagnosis in psychometric is a good way to quantify learners' capacities, but it demands explicit learner feedback, which does not always exist in MOOCs platform, such a typical weak-interaction scenario. Therefore, in this article, multidimensional item response theory (MIRT) is exploratively integrated into recommendation models in MOOCs by introducing a time-effectiveness hypothesis to obtain the implicit response on a followed course. To dynamically update learners' capacities by considering real-time and capacity multidimensionality, MIRT is extended to a capacity tracing model. The estimation for learner capacity is treated as attributes and integrated into collaborative filtering framework in course recommendation. To the best of our knowledge, this is the first work to integrate capacity tracing into course recommendation in MOOCs. Extensive experiments are conducted on a real-world dataset, demonstrating that the capacity tracing-enhanced course recommendation has improved effectiveness and explainability in MOOCs.