Massive Open Online Courses Platform Research Articles

The community of inquiry as a tool for measuring student engagement in blended massive open online courses (MOOCs): a case study of university students in a developing country

While massive open online courses (MOOCs) promise to democratise access to education, the literature reveals a nuanced understanding of engagement in these settings, especially in resource-constrained environments. Blended MOOCs combine MOOCs and physical classroom settings of contents and instructions. This study extends this discourse by focusing on blended MOOCs, which remain under-explored in the context of developing countries. The blended MOOC at the University of Cape Coast (UCC), Ghana, deals with third-party MOOCs as open educational resources (OERs) integrated with campus-based courses. UCC students have been using such blended MOOCs since 2016, when all level 100 students were mandated to enrol in a course entitled Information Technology Skills (ITS101). ITS101 is aligned to courses in a MOOC platform called Alison as an OER. Students' engagement is key to their continued use and satisfaction with online learning, such as MOOCs. However, among all the e-learning modes, students' engagement is the lowest in MOOCs, leading to high dropout rates. Blended MOOCs are one of the techniques recommended to reverse the undesirability of MOOCs, including engagement. However, few studies have been conducted on students' engagement in blended MOOCs, especially among university students in sub-Saharan Africa using MOOCs as OERs. Thus, this paper aims to measure student engagement in blended MOOCs using the revised Community of Inquiry for university students in a developing country. The rationale is to determine whether factors affect engagement positively or negatively. A two-stage cluster sampling technique was used to determine the participants for this study. A list of blended MOOC classes offered at UCC was obtained from the staff's mailing list. In the first stage, academic levels (100, 200, 300 and 800) were randomly selected from the strata using a lottery sampling technique. In the second stage, another simple random selection of blended MOOC courses or classes was used within each selected academic level. All students in the selected classes were then included in the study. Partial Least Squares Structural Equation Modelling was used to validate the model on the predictive relationships existing among the four presences (cognitive, learning, social and teaching) and engagement. Results from the structural model analysis proved a statistically significant predictive relationship among the constructs within the model. Learning presence had the most significant effect on student engagement. Thus, it should be included as one of the presences in the community of inquiry.

How to animate learners to persist in learning massive open online courses? An application of the stimulus-organism-response paradigm

Purpose The purpose of this study is to propose a research model based on the stimulus–organism–response (S–O–R) model to examine whether network externality, personalization and sociability as environmental feature antecedents to learners’ learning engagement (LE) can influence their learning persistence (LP) in massive open online courses (MOOCs). Design/methodology/approach Sample data for this study were collected from learners who had experience in taking MOOCs provided by the MOOC platform launched by a well-known university in Taiwan, and 371 usable questionnaires were analyzed using structural equation modeling in this study. Findings This study proved that learners’ perceived network externality, personalization and sociability in MOOCs positively affected their cognitive LE, psychological LE and social LE elicited by MOOCs, which jointly led to their LP in MOOCs. The results support all proposed hypotheses, and the research model accounts for 76.2% of the variance in learners’ LP in MOOCs. Originality/value This study uses the S–O–R model as a theoretical base to construct learners’ LP in MOOCs as a series of the inner process, which is affected by network externality, personalization and sociability. It is worth noting that three psychological constructs including cognitive LE, psychological LE and social LE are used to represent learners’ organismic states of MOOCs usage. To date, hedonic/utilitarian concepts are more often adopted as organisms in previous studies using the S–O–R model, and psychological constructs have received lesser attention. Hence, this study’ contribution on the application of capturing psychological constructs for completely expounding three types of environmental features as antecedents to learners’ LP in MOOCs is well documented.

Facilitating social learning through learning design

In the field of technology-enhanced learning design, the rise of Web 2.0 has been the filip needed to accelerate the emergence of socially-connected global learners. These highly social learners now use the web to engage with knowledge and skill development and as such, online education has moved irrevocably beyond simply the finding and sharing of information among learners. The higher education sector is responding to this learning landscape. This is particularly relevant to the massive open online courses (MOOCs) environment where there is the potential for thousands of participants to learn through multiple open source tools with minimum intervention from educators. This social learning perspective can present a challenge for some educators. In this paper we present a work-in-progress collaborative project designed to respond to the professional development of teaching academics newly engaged in designing and teaching from a social learning perspective. We, in a central learning and teaching unit, designed and developed a professional development (PD) course that sought to build the capacity of academics going through this change: they were about to teach on a MOOC platform. Our purpose was to model the social learning framework as a method of capacity-building, but we also aimed to distil authentic social learning for the academics themselves. This resulted in creating a community of practice among educators. Further research is required to measure the impact of this capacity-building course in order to further enhance the learning experiences of academics preparing to teach on a MOOC platform.

MOOC for Pagelaran Pottery Village: Boosting Income through Design Educational Tourism

The implementation of this activity aims to design and build educative tourism infrastructure using the Massive Open Online Course (MOOC) as a means to increase the income potential of Pagelaran Village. Pagelaran Village is famous for its pottery crafts, which are rich in cultural values. However, the potential for tourism and related revenues has yet to be fully realized. Therefore, employing MOOC as a tool to provide an online learning experience for a package of pottery production training activities is expected to attract tourists and boost the income of the local community. The methods used in this implementation include field surveys, literature studies, and analysis of tourist needs and preferences. The approach adopted is the asset-based community development (ABCD) approach, which consists of five stages of activity including: 1) Discovery, 2) Dream, 3) Design, 4) Define, and 5) Destiny. The infrastructure built includes the development of the MOOC platform, offering learning materials related to history, manufacturing techniques, and the art of pottery. Additionally, practical learning facilities are also provided for tourists desiring to undertake direct courses without constraints by space and time. During initial testing, this MOOC garnered favorable responses from travelers who participated the course. The results of the implementation of this activity indicate that the use of MOOC as an educative tourism infrastructure can increase potential income in Pagelaran Village. Keywords: MOOC, income, pagelaran pottery, educational, infrastructure

Research on Joint Recommendation Algorithm for Knowledge Concepts and Learning Partners Based on Improved Multi-Gate Mixture-of-Experts

The rise of Massive Open Online Courses (MOOCs) has increased the large audience for higher education. Different learners face different learning difficulties in the process of online learning. In order to ensure the quality of teaching, online learning resource recommendation services should be more personalised and have more choices. In this paper, we propose a joint recommendation algorithm for knowledge concepts and learning partners based on improved MMoE (Multi-gate Mixture-of-Experts). Firstly, the heterogeneous information network (HIN) is constructed based on the MOOC platform and appropriate meta-paths are selected in order to extract the human–computer interaction information and student–student interaction information generated during the learners’ online learning processes more completely. Secondly, the temporal behavioural characteristics of students are obtained based on their learning paths as well as their knowledge of conceptual characteristics, and LSTM (Long Short-Term Memory) is used to mine students’ current learning interests. Finally, the gating network in MMoE is changed into an attention mechanism network, and for different tasks, multiple attention mechanism networks are used to fuse the learner’s human–computer interaction information, student–student interaction information, and interest characteristics to generate learner representations that are more in line with the respective task and to complete the tasks of knowledge conception and learning partner recommendation. Experiments on publicly available MOOC datasets show that the method proposed in this paper provides more accurate and varied personalization services to online learners compared to the latest proposed methods.

MOOCs: Determinants of Enrolment and Completion Rate among Learners of Higher Education Institutions in India

Aims: The advancement and integration of technology in education made a dramatic change in the landscape of the teaching-learning process. Massive Open Online Courses (MOOCs) as one of the emerging trends in education removed the barriers of time, space, and pace throughout the world. Diverse lifelong learners irrespective of social, cultural, economic, age and gender identity are participating in the melting pot of MOOC. This study aimed to explore the present landscape of the participation and completion rate in MOOCs by learners from formal educational institutions in the eastern part of India specifically in West Bengal. The research also aimed to identify the underlying causes of the present landscape.
 Place and Duration of Study: The study was conducted among the learners who enrolled in different programs in different Higher Education Institutions (HEIs) during the end of 2023.
 Methodology: A self-administered questionnaire in Google Forms was used to collect data from the learners. A total 298 samples have been collected. Collected data have been analyzed by descriptive statistics, independent samples ‘t’ test, ANOVA using SPSS and MS Excel.
 Results: The study depicted poor enrolment as well as completion rates in MOOCs among the learners. Lack of awareness and huge pressure on the present curriculum were the main factors behind non-enrolment. SWAYAM was the most popular MOOC platform among the students of West Bengal. Attaining knowledge, enhancing skills, and earning certificates were the main intentions of the students who enrolled in MOOCs. Lack of time, affordability, in-person proctored examination, non-cooperation of facilitators, length of courses, and uninteresting discussion forum were the major constraints to completing the MOOCs for the enrolled learners.
 Conclusion: Organizing awareness-building programs, institutional policy framing, and incorporating a credit transfer system into enrolled courses may be emphasized as immediate initiatives for re-thinking the present opportunities and challenges in higher education.

Improving undergraduate education of occupational health and occupational medicine appling massive open online courses & problem-based learning

BackgroundThe learning of Occupational Health and Occupational Medicine in undergraduate college students in China has been hindered by various factors. This study aimed to explore the effects of the application of Massive Open Online Courses (MOOC) & Problem-based learning (PBL) in the learning of Occupational Health and Occupational Medicine in undergraduate college students in China.MethodsStudents enrolled in 2017 and 2018 were categorized in the control group and study group, and received PBL learning and MOOC + PBL learning, respectively. The effects of learning were assessed by scores of final exam, satisfaction degree of students, and feedbacks.ResultsThe mean score of the final exam was not significantly different between the two groups. However, the further comparison by levels of scores showed that the percentages of good and excellent were both significantly higher in the study group than control group. The overall satisfaction degree was significantly higher in the study group than control group. In addition, the scores of the 3 dimensions of satisfaction degree, i.e. learning preparation, learning process, and learning effect, were all significantly higher in the study group than control group. The feedbacks of the students showed that they thought MOOC + PBL learning could better improve the learning efficacy, despite a substantial proportion of students reported that MOOC + PBL learning more time-consuming.ConclusionsThe findings showed that the combination of MOOC and PBL in the learning of Occupational Health and Occupational Medicine is an effective method capable of improving the learning efficacy in college students of Prophylactic Medicine. Further efforts are needed to optimize the MOOC platform to provide a friendlier interface.

ConceptThread: Visualizing Threaded Concepts in MOOC Videos.

Massive Open Online Courses (MOOCs) platforms are becoming increasingly popular in recent years. Online learners need to watch the whole course video on MOOC platforms to learn the underlying new knowledge, which is often tedious and time-consuming due to the lack of a quick overview of the covered knowledge and their structures. In this paper, we propose ConceptThread, a visual analytics approach to effectively show the concepts and the relations among them to facilitate effective online learning. Specifically, given that the majority of MOOC videos contain slides, we first leverage video processing and speech analysis techniques, including shot recognition, speech recognition and topic modeling, to extract core knowledge concepts and construct the hierarchical and temporal relations among them. Then, by using a metaphor of thread, we present a novel visualization to intuitively display the concepts based on video sequential flow, and enable learners to perform interactive visual exploration of concepts. We conducted a quantitative study, two case studies, and a user study to extensively evaluate ConceptThread. The results demonstrate the effectiveness and usability of ConceptThread in providing online learners with a quick understanding of the knowledge content of MOOC videos.

Development of MOOCs in Civil Engineering Education through Gamification Implementation

Background: Massive Open Online Courses (MOOCs) represent an accessible online learning platform open to everyone without specific conditions. The prominence of MOOCs has surged, particularly following the Covid-19 pandemic. Despite this global trend, the development of MOOCs in Indonesia faces challenges such as community unpreparedness and inadequate infrastructure. Traditional education perceptions persist, with many Indonesians believing in the necessity of formal education. Gamification, involving the incorporation of enjoyable and engaging game elements into non-game activities, presents an innovative approach. Given the relatively low demand for MOOCs in Indonesia, gamification is employed to enhance user motivation and retention, with the expectation of increasing student interest in utilizing MOOCs.Methods: The research methodology employed is Research and Development (RD), utilizing the Four D-Models model and gamification analysis through the Octa lysis Gamification Framework. This framework serves as the foundation for the selection of gamification elements.Results: Media validation results demonstrate an 86% feasibility rating for gamification-based MOOC products in Engineering Mechanics 1 courses, categorizing it as "Very Decent." Overall, the MOOC platform achieves a 92% feasibility rating, also falling into the "Very Decent" category.Conclusion: In conclusion, the research affirms that the development of gamification-based MOOCs in Engineering Mechanics 1 courses is viable for integration into the learning process. This is supported by the positive results from media validation and trials, both categorized as "Very Decent."

ConceptGCN: Knowledge concept recommendation in MOOCs based on knowledge graph convolutional networks and SBERT

Massive Open Online Courses (MOOCs) have gained popularity in the technology-enhanced learning (TEL) domain. To enhance the learning experience in MOOCs, educational recommender systems (ERSs) can play a crucial role by suggesting courses or learning materials that align with students' knowledge states. Thereby, understanding a student's learning needs and predicting knowledge concepts that the student might be interested in are important to provide effective recommendations. Inspired by the superior ability of knowledge graphs (KGs) in modeling the heterogeneous data in MOOCs and Graph Neural Networks (GNNs) in learning on graph-structured data, few works focusing on GNN-based recommendation of knowledge concepts in MOOCs have emerged recently. However, existing approaches in this domain have limitations mainly related to complexity, semantics, and transparency. To address these limitations, in this paper we propose ConceptGCN, an end-to-end framework that combines KGs, Graph Convolutional Networks (GCNs), and pre-trained transformer language model encoders (SBERT) to provide personalized and transparent recommendations of knowledge concepts in the MOOC platform CourseMapper. We conducted extensive offline experiments and an online user study (N=31), demonstrating the benefits of the ConceptGCN-based recommendation approach, in terms of several important user-centric aspects including accuracy, novelty, diversity, usefulness, overall satisfaction, use intentions, and reading intention. In particular, our results indicate that, if SBERT is used for the initial embeddings of items in the KG, a self-connection operation and a semantic similarity-based score function in the aggregation operation of GCN are not necessarily needed.

On the Ethical Issues Posed by the Exploitation of Users’ Data in MOOC Platforms: Capturing Learners’ Perspectives

Due notably to the emergence of massive open online courses (MOOCs), stakeholders in online education have amassed extensive databases on learners throughout the past decade. Administrators of online course platforms, for instance, possess a broad spectrum of information about their users. This information spans from users’ areas of interest to their learning habits, all of which is deduced from diverse analytics. Such circumstances have sparked intense discussions over the ethical implications and potential risks that databases present. In this article, we delve into an analysis of a survey distributed across three MOOCs with the intention to gain a deeper understanding of learners’ viewpoints on the use of their data. We first explore the perception of features and mechanisms of recommendation systems. Subsequently, we examine the issue of data transmission to third parties, particularly potential recruiters interested in applicants’ performance records on course platforms. Our findings reveal that younger generations demonstrate less resistance towards the exploitation of their data.

DEVELOPMENT OF CONCEPTUAL MODEL- EFFECTIVE TEACHING QUALITY OF ACADEMICIANS TOWARDS MOOCS IN HIGHER EDUCATION IN PAKISTAN

Introduction - IT experts and academicians adopted e-learning to expedite quality education. quality education has a necessity; therefore, higher education is getting a good job to partner with Massive Open Online Courses (MOOCs). MOOC is the virtual learning platform to reform education by adopting information and communication technology infrastructure that aimed to increase the opportunities for quality education. MOOCs can be used by skilled and professional academicians who deliver their knowledge and expertise for acquiring learners’ ability to read, write, technical skills, and critical thinking through digital transformation. Academicians who are teaching in MOOCs should have good skills in digital literacy, online pedagogy, and willingness to accept MOOCs initiatives in HEIs in Pakistan. Purpose/Problem - This research aims to examine the impact of the teaching quality of academicians towards the teaching quality design of MOOCs in higher education institutions, and also determine their behavioral intention towards the acceptance of MOOCs initiatives. This research study is focused on MOOCs platform usage and acceptance in the province of Sindh, Pakistan. Methodology – Unified Theory of Acceptance and Use of Technology (UTAUT) model will be utilized to examine the hypothesis that influences academician intention towards accepting MOOCs. This study will be used a mixed-method approach to gather information. The Quantitative approach will be used to obtain the data collection for identifying the effects of independent and moderating variables on the dependent variable. SPSS and PLS-SEM 3 statistical tools will be used for data interpretation and analysis. The qualitative semi-structured approach will be used to gather more information about the area of the research survey by conducting interviews with MOOC administrators/developers, explanatory-sequencing approach should be used for more confirmation to validate and reliable the survey data collected. NVivo 12 will be used to analyze the unstructured text, audio, and video data; for interviews, focus groups, and journal articles. Outcome – In this research, MOOC platform has been intended to study and identify the effect of teaching quality of academicians towards MOOCs and their acceptance. The application of HEIs and MOOCs will prove to be the most promising integration for quality education in the year to come under one umbrella. Therefore, a unified MOOC can be recommended in the reflection of research investigations for successfully implementing MOOCs in Pakistan's higher education institutions.

Learning Outcome of Course Plantform, Teacher Ability and Students Satisfaction in Utilizing Massive Open Online Course (MOOCs)

The study assessed the effectiveness of course platforms, teachers’ ability, and students’ satisfaction in utilizing Massive Open Online Courses (MOOC) for learning, and the research locale is in Hunan Province of China where learners and teachers from over 40 colleges take courses using MOOC learning platform. The study was conducted in the 2023 academic year. The research design is the quantitative approach utilizing the descriptive comparative-correlational research design. The sample size was 350 students who participate in MOOC courses in Hunan Province. And for the interview questions-10 teachers participated in the interview. SPSS22.0 version was used to process and analyze the results of the questionnaire survey. Results showed that there were more male student-respondents who participated in this study. Majority of the respondents are below 20 years old, and most of them have used the platform for at least 3 years. The highest ranked dimension in effectiveness is the effectiveness of the course designs which suggests that the course designs in the MOOC platforms are extremely effective. Meanwhile, the least of the dimensions is the course materials which also suggests that the course materials are extremely effective. Students who are 20 years and below have significantly higher assessment scores for course materials, structure and presentation, and overall effectiveness of course platforms compared to those who are 22 years old. Furthermore, there are no significant differences in the assessment of the students on course design among levels of age and years of use. However, the assessment of female students on course design is significantly higher compared to male students. On the other hand, in teachers’ ability, the highest ranked dimension is the teachers’ ability in interactive learning which suggests that the teachers are extremely competent. The least of the dimensions is the teachers’ ability in their teaching styles which also suggests that the teaching styles of the teachers proved that they are extremely competent. There are no significant differences in the assessment of the students about their teacher’s ability in MOOCs and in terms of interactive learning and knowledge of course content among levels of sex, ge, and years of use. As for students’ satisfaction, he highest ranked dimension is the students’ satisfaction with the learning outcomes which suggests that the students are extremely satisfied. The least of the dimensions is the students’ satisfaction with the attitude towards the use of online courses which suggests that the students are extremely satisfied. There are no significant differences in the level of satisfaction of the students in utilizing MOOCs based on learning outcomes, course engagement, and attitude toward the use of online courses among levels of sex, age, and years of use. Moreover, there are no significant relationships between the effectiveness of MOOC course platforms and the students’ assessment on their teacher’s ability and satisfaction. However, there are significant positive relationships between the student’s assessment of their teachers’ teaching styles and teaching ability and their attitude towards the use of online courses. Some of the challenges experienced by teachers while teaching in MOOC platforms include heavy workload, substantial time commitment, insufficient support and lack of compensation, difficulty in providing individualized feedback, and high rate of course dropouts among others.

CNN-Bi-LSTM Model for MOOC Forum Post Classification

The discussion forum of the massive open online course (MOOC) is a platform for students to communicate with teachers, teaching assistants, and platform managers. It is one of the important factors related to course quality. A reasonable classification of discussion posts in the forum will help students better communicate and solve problems, so as to improve the quality of teaching. Aiming at the classification of discussion forum posts, this paper proposes a text classification model integrating convolutional neural networks (CNN) and bidirectional long-short-term memory (Bi-LSTM). Firstly, the user types and behavior characteristics are analyzed to build the taxonomy. The taxonomy includes three categories: course related, teacher related and platform related. Then, a text classification model is constructed based on CNN and Bi-LSTM. In order to verify the effectiveness of the proposed model, it is applied to the classification of 19285 discussion posts from the MOOC platform of icourse163.org. The overall classification accuracy of the proposed model is 93.6%, which is 12%, 10%, and 8% higher than traditional machine learning methods, CNN and Bi-LSTM, respectively. The model is used for automatic text classification in MOOC discussion forum, which can provide effective help and support for learners, teachers and platform managers, and improve the automation level of MOOC platform.

Vocational College Students’ Learning Efficiency on The Use of Computer English MOOC in Hybrid Learning

With the improvement of educational technology modernization, the promoting effect of network technology on education has been widely concerned by educational circles Massive Open Online Courses (MOOCs) have emerged as a popular platform for learners to access and utilize educational resources. This paper aims to investigate the relationship between various aspects of hybrid learning in the Computer English MOOC platform, including students' learning objectives, methods, concentration, collaborative skills, teacher supervision, and learning efficiency. The research utilizes a quantitative approach to analyze data obtained from 168 vocational college students majoring in computer-related subjects. During the Covid-19 pandemic, it was not always possible for students to go to school in person, but when it came to learning Computer English, they had clear goals. Students were able to get complete and organized information through hybrid learning. And there is a strong relationship between learning cooperation and learning proficiency via hybrid learning. Learning cooperation has the most significant influence on learning proficiency. This study lies in its potential to bridge the knowledge gap regarding the specific factors that impact English language acquisition in the vocational college setting. This research seeks to provide valuable insights and recommendations to inform pedagogical practices, curriculum development, and institutional policies in higher vocational colleges in China.

The Application Research of Distributed Interface Coordination Based on Deep Reinforcement Learning in English MOOC Platform

Developing information technology and other business management firms is not the same as technological advancement. The only way we can declare that complete technical progress is in place and operating well in the nation is if the education system has been built. The educational system is set up to instruct pupils via wireless technologies and online classes. Advancement in online classes has resulted in the Massive Open Online Courses (MOOC) Platform. The advantage of the MOOC platform is that it is a platform that provides free online courses for anyone to register and continue the course at their own pace. On this MOOC platform, implementing Deep Reinforcement Learning (DRL) aids in choosing the course and learning through an intelligent mechanism. The transmission between students and teachers will significantly rise because of this teaching strategy through the Distributed Interface Coordination (DIC) mechanism where the resources are distributed in the wireless networking environment. Additionally, the registered students should possess privileges to access the resources. This combination of DIC with DRL can be termed as DIC-DRL model for the proposed system. The data was collected using a low packet rate and outdated wireless connection technologies. The suggested model has a 97.67 percent accuracy rate in comparison with the existing model's support vector machine (SVM) mechanism.

Quantification and prediction of engagement: Applied to personalized course recommendation to reduce dropout in MOOCs

MOOCs (Massive Open Online Courses) offer tens of thousands of courses and attract hundreds of millions of online learners. After years of development, these platforms have accumulated a large number of learning action data. These data imply learners’ engagement with the enrolled courses, reflecting whether learners are willing to spend time on the courses. Meanwhile, the MOOC dropout rate remains chronically high. This paper experimentally finds that using the relationship between engagement and dropout rate can help MOOC platforms develop effective methods to reduce the dropout rate. Firstly, this paper proposes a new Quantified Engagement method named QE by using learning action data and learning duration to quantify learners’ engagement in enrolled courses. Next, an Engagement Neural Network prediction model named ENN is proposed to predict learners’ engagement in unenrolled courses. Then, applying QE, the predicted engagement by ENN, and the aforementioned relationship to personalized course recommendations to learners, ensuring that the recommended courses are likely to be completed by learners as much as possible, thus effectively reducing the dropout rate. Finally, the proposed method is evaluated on two large real-world datasets in XuetangX and KDDCUP. The RMSE and MAE of ENN are 0.1066 and 0.0727 on XuetangX and 0.062411 and 0.039621 on KDDCUP, respectively. Dropout rates were reduced by 46.99% and 10.34%, respectively, when 5% of the courses were recommended. These results demonstrate that the quantification method of engagement is valid, applying predicted engagement to personalized course recommendations and reducing dropout rates is available.

A Conceptual Persuasive Development Framework to Change Students’ Behaviour in Massive Open Online Courses: A Review

Some experts attribute the relatively low completion rates of Massive Open Online Courses (MOOCs) partly to user dissatisfaction with the system. Live instructors are absent from MOOCs due to their delivery through virtual learning platforms. A distinctive feature distinguishing MOOCs from other e-learning systems is the significantly higher ratio between users and instructors. Consequently, the main challenges include limited interaction between students and study materials and the heightened need for instructor guidance. Consequently, enhancing the design of the existing MOOCs system is imperative to create a more engaging learning experience. Previous studies have attempted to incorporate persuasive design elements into e-learning systems. However, these studies must integrate persuasive design with motivational factors and effective learning strategies to encourage student behavior change and increase student engagement. The present study utilizes prior literature to establish a conceptual framework for persuasive e-learning development, known as PEDAL, which integrates motivational factors, learning strategies, and persuasive design principles. The initial section of the paper introduces issues related to student motivation, learning strategies, the MOOCs platform, and the potential impact of persuasive technology on enhancing the effectiveness of MOOCs. The subsequent section elucidates the methodology employed for the literature search. The third section explains the mechanisms of the PEDAL framework and discusses relevant previous literature that contributed to its development. Finally, the last section outlines the framework's limitations and potential future improvement. The paper also outlines how the proposed conceptual framework can be applied to design an effective e-learning system.

Multimodal Discourse Analysis of MOOC Videos

Massive Open Online Courses (MOOC), as a newly developed teaching mode, is just stepping into the classes of universities and the view of researchers. There are more and more professors and famous teachers in the universities beginning to make their own MOOC, which includes recording videos, designing questions and arranging discussions through MOOC platform, so as to develop rich teaching contents and vivid teaching mode, which contributes to attracting the learning interests of many learners. Especially during the outbreak of the new crown virus, online teaching has become an essential means of teaching in contemporary colleges and universities. In this thesis, the characteristics of MOOC videos and MDA method are combined, that is to say, it chooses one MOOC-related to English teaching as the research object and used MDA to study the videos in it，so as to present the distinguished characteristics of MOOC.

Learning Cataloguing and Classification: Is It on Popular Massive Open Online Courses (MOOC) Platforms?

The paper explores if cataloguing and classification (CAT/CLASS) can be learnt on popular MOOC platforms and what can be done if nonexistent. Different MOOC platforms were explored with a concentration on EdX and Coursera. Literature on MOOCs was examined, and findings were reported. It was observed that some library science courses are available on MOOC platforms. However, no CAT/CLASS courses are available on EdX or Coursera. The majority of their library science courses did not cover the technical service aspects of librarianship. As a result, recommendations were made on how library stakeholders can play proactive roles in introducing CAT/CLASS to MOOC platforms. This is one of the first papers to examine the subject of CAT/CLASS on MOOC platforms.