MOOC Platform Research Articles

Research on the development of an effective mechanism of using public online education resource platform: TOE model combined with FS-QCA

ABSTRACT The paper describes the research of causal relationships between the factors of technological, organizational, environmental, and personal contexts and their influence on the development of learning intentions in potential students. Its purpose was to develop a mechanism for designing a public online educational resource platform based on the combined TOE/fsQCA model taking into account the synergy of factors influencing student behavior at all stages of the educational process from entering the course to its completion. The research identified 4 and 3 configurations with high to medium and low intention to start learning on the MOOC platform, respectively. Since focusing on consumer demand allows for designing a high-quality MOOC platform, it is advisable to use the combined TOE/fsQCA model both at the stage of constructing the platform structure and at the learning stage to identify optimal learning trajectories taking into account the personal needs of students. Prospects for further research lie in the refinement of conventions, in-depth analysis, and a more detailed study of causal relationships that influence the behavioral motives of MOOC platform students.

The construction of a localized teaching model of the Orff music teaching method based on big data analysis

Abstract In the era of the big information wave and rapid development of economic globalization, the traditional music teaching mode in China has failed to completely develop the times. Here focuses on constructing a localized teaching mode of the Orff music teaching method in the case of cloud information analysis, firstly, understanding the definition of cloud information, using the MOOC platform design and offline traditional Orff music teaching method to build the localized teaching mode. The system is designed with SSH structure as the framework and BP-related calculation methods, with basic theory as the key point adopted by related calculation method, implementation process adopted for break-in effect inspiring rules for evaluating the actual use, analyzing the accuracy and stability of the three algorithms through experiments of three cases, and the final from experimental analysis accuracy of BP neural network algorithm is between 77% and 86%, which is higher than the accuracy and stability of the other two algorithms. Furthermore, stability is high, thus building a set of localized and innovative teaching models suitable for Chinese music education, which is conducive to improving students’ aesthetic and artistic level and bringing Chinese music to the world stage.

Revolutionizing Online Learning: The Potential of ChatGPT in Massive Open Online Courses

This paper explores the potential of ChatGPT, a language model based on the GPT-3.5 architecture, in revolutionizing the online learning experience. The paper highlights the benefits and challenges associated with MOOCs, such as their lack of personalized support and feedback, and introduces ChatGPT as a solution to some of these challenges. The paper discusses the capabilities of ChatGPT, such as language understanding, response generation, personalization, and continuous learning. A series of experiments are conducted to measure the accuracy and effectiveness of ChatGPT in answering questions from various subject areas and difficulty levels in two popular MOOCs platforms, edX and Coursera. The potential impact of ChatGPT on students' learning skills is also discussed. The findings of this paper provide valuable insights for educators, policymakers, and students who are interested in exploring the role of technology in education and the impact it may have on the future of learning.

An open-sharing platform for physical education course resources based on the MOOC network

Abstract How to optimize course learning resources and learning methods to promote higher-order learning is an important research topic in current teaching reform. Based on the background of the big data era, this paper uses the MOOC network to build an open-sharing platform for physical education teaching course resources. Firstly, the MOOC network classroom teaching model is summarized through the analysis paradigm of the MOOC platform analyzing indicators to deal with teaching big data. The third-order nonlinear model is used in the teaching model to dynamically express the physical education teaching resource-sharing design system and establish a physical education simulation model. In the simulation model, the search optimal solution is improved for resource platform search, and global search is performed when the inertia factor is large and local search is performed when it is small. The univariate validation test for group S students revealed that the mean value of the performance of the answers selected by group S students before the test compared to after the test changed from negative to positive, from -0.46 to 0.16, which was a significant improvement. It can be seen that the teaching model based on the MOOC web-supported open-sharing platform of physical education course resources has positively contributed to students’ learning performance, learning interest, and independent learning ability.

MOOCs Platform: A Review and Sources

Recent years have seen a significant and eye-opening transformation in the Indian educational sector. Although "Massive Open Online Course" (MOOC) platforms have been available to students for learning purposes since the beginning, its potential has only lately been recognised. The purpose of this essay is to examine and contrast the major MOOC platforms utilised to provide courses in India, as well as the numerous difficulties they encounter. To accomplish these goals, a detailed examination of the four most established and popular MOOC platforms is conducted. NPTEL (2003), mooKIT (2014), IITBombayX (2017), and SWAYAM (2017) are among them. The lack of digital infrastructure, the necessity for investment, and learners' capacity to adapt to MOOCs are a few of the researchers' top concerns with relation to how well MOOCs perform in India. It was also realised that there may be a possible compromise in the calibre of the research work done, the requirement to raise the calibre of information produced, and the necessity to address the variety of demands of Indian students

Effect of online English learners’ perceived teacher support on self-regulation mediated by their self-efficacy

This study explored the relationships among online English learners’ perceived teacher support, self-efficacy, and self-regulation in online learning based on social cognitive theory. Structural equation modeling (SEM) with bootstrapping estimation was conducted using data from 220 online English learners engaged in blended learning on the Chinese University MOOC platform. The results showed that online English learners’ perceived teacher support positively influenced their self-efficacy and self-regulation. Moreover, self-efficacy was found to mediate the relationship between their perceived teacher support and self-regulation. On the whole, the findings detailed the effect of English learners’ perceived teacher support on their self-efficacy and self-regulation, as well as empirically identifying the mediation effect of self-efficacy in the relationship between perceived teacher support and self-regulation in an online learning environment. Related pedagogical implications for teacher online teaching, students online learning, and the Chinese University MOOC platform, and limitations were discussed.

Multi-View MOOC Quality Evaluation via Information-Aware Graph Representation Learning

In this paper, we study the problem of MOOC quality evaluation that is essential for improving the course materials, promoting students' learning efficiency, and benefiting user services. While achieving promising performances, current works still suffer from the complicated interactions and relationships of entities in MOOC platforms. To tackle the challenges, we formulate the problem as a course representation learning task based, and develop an Information-aware Graph Representation Learning(IaGRL) for multi-view MOOC quality evaluation. Specifically, We first build a MOOC Heterogeneous Network (HIN) to represent the interactions and relationships among entities in MOOC platforms. And then we decompose the MOOC HIN into multiple single-relation graphs based on meta-paths to depict multi-view semantics of courses. The course representation learning can be further converted to a multi-view graph representation task. Different from traditional graph representation learning, the learned course representations are expected to match the following three types of validity: (1) the agreement on expressiveness between the raw course portfolio and the learned course representations; (2) the consistency between the representations in each view and the unified representations; (3) the alignment between the course and MOOC platform representations. Therefore, we propose to exploit mutual information for preserving the validity of course representations. We conduct extensive experiments over real-world MOOC datasets to demonstrate the effectiveness of our proposed method.

RETRACTED ARTICLE: Simulation of English MOOC platform based on machine learning and language recognition system

RETRACTED ARTICLE: Simulation of English MOOC platform based on machine learning and language recognition system

Analysis of participation and performance of MOOC learners via latent class analysis: A retrospective study based on the data of a nursing MOOC from 2018 to 2022

BackgroundAlthough massive open online courses have been widely used in nurse education, few studies have evaluated MOOC learner behavioral characteristics. Understanding MOOC learners' participation and performance parameters is helpful for further development and administration of this educational approach. ObjectivesTo categorize nursing MOOC learners according to their different learning participation and to compare the differences in learning performance of different types of MOOC learners. DesignRetrospective. Settings and participantsParticipants evaluated in this study were learners of the Health Assessment MOOC on a Chinese MOOC platform for nine semesters from 2018 to 2022. MethodsVia latent class analysis, MOOC learners were categorized according to the number of times they participated in each topic test and the final exam. Differences in scores of each topic test and the final exam, case discussion number, and total evaluation score among different learners were compared. ResultsUsing latent class analysis, MOOC learners were categorized as committed (28.96 %), negative (16.08 %), mid-term dropout (12.78 %) and early dropout (42.18 %) learners. Committed learners performed best and no significant difference were found among other learner types on most topic tests and the final exam. Committed learners participated in case discussions most actively. According to total evaluations, committed, mid-term dropout, early dropout, and negative learners performed from best to worst. ConclusionHealth Assessment MOOC learners were categorized using five-years of data. Committed learners performed best. No significant difference in performance was found for other learners on most topic tests and the final exam. Understanding learner characteristics and educational behavior is critical for effective design and administration of future MOOC learning approaches.

Interval Grey Hesitant Fuzzy Set and Its Applications in Decision-Making

The use of interval-valued hesitant fuzzy sets (IVHFS) can aid decision-makers in evaluating a variable using multiple interval numbers, making it a valuable tool for addressing decision-making problems. However, it fails to obtain information with greyness. The grey fuzzy set (GFS) can improve this problem but studies on it have lost the advantages of IVHFS. In order to improve the accuracy of decision-making and obtain more reasonable results, it is important to enhance the description of real-life information. We combined IVHFS and GFS and defined a novel fuzzy set named interval grey hesitant fuzzy set (IGHFS), in which possible degrees of grey numbers are designed to indicate the upper and lower limits of the interval number. Meanwhile, its basic operational laws, score function, entropy method, and distance measures are proposed. And then, a multicriteria decision-making (MCDM) model IGHFS-TOPSIS is developed based on them. Finally, an example of MOOC platform selection issues for teaching courses illustrates the effectiveness and feasibility of the decision model under the IGHFS.

Empirical analysis of integrated teaching mode of international trade based on practical training data detection and neural network

With the continuous development of China's foreign trade, the foreign trade industry requires universities to train more application-oriented talents with specific theoretical basis and professional knowledge. However, on the one hand, the gap between international trade professionals within enterprises is widening; on the other hand, many international trade graduates are not engaged in the foreign trade industry because their professional skills are not recognized, resulting in a waste of teaching resources. The root of this dilemma lies in the serious disconnection between the traditional training mode of international trade professionals and the international development background of Chinese foreign trade enterprises. This paper focuses on neural network technology and then analyzes the integrated teaching mode of international trade. The integrated teaching resources studied in this paper come from various sources, mainly including traditional textbooks, courseware and other resources, as well as self-made MOOC courses and other MOOC platform course resources and other online teaching resources. In this paper, the framework of the integrated teaching model is mainly designed from three aspects: pre-analysis, curriculum design, assessment and evaluation. It can be found that the following practice on integrated teaching mode is very successful in MOOC, which improves students' self-study ability and also cultivates their ability to cooperate and communicate. This proves that the integrated teaching model of international trade in this paper is effective. Through the research and discussion of neural network technology, this paper applies it to the integrated teaching mode, which provides a more powerful technical guarantee for the training of international trade talents.

Chatbot Development using APIs and Integration into the MOOC


 In recent years, chatbot technologies have evolved into modern information and communication technology applications that perform many virtual tasks, including learning. One of the challenges in improving the chatbot is the insufficient knowledge base of chatbots, including education-oriented conversational agents, the challenges in connecting the chatbot with course content on Massive Open Online Course platforms. In this study, a chatbot was developed to answer questions using publicly available technologies, specifically Application Program Interfaces (APIs) that promise convenient user accessibility via APIs, such as the Facebook Messenger platform along with wit.ai API, Canvas MOOC API, and Wikipedia API. API technologies were used to connect the chatbot to selected course content on the MOOC platform as well as to large knowledge bases such as Wikipedia to expand the knowledge base of the Conversational Agent. The course selected for the chatbot integration was on general informatics topics. Most course participants interacted with the chatbot via the Facebook Messenger platform using their handheld devices. Thus, integrating the chatbot into a widely used platform such as Facebook Messenger is a convenient and effective way for reaching learners. The API technology enabled an efficient connection between the chatbot and third-party apps, including the Messenger app, wit.ai, Canvas MOOC, and Wikipedia. This was due to the variety, richness, manipulation capabilities, and format of data that an API can transfer. In addition, the Wikipedia API seemed to be a vast source of information for expanding the chatbot's knowledge base. Not all of the queries posed to the chatbot were part of the course content. Some participants questioned the personality of the chatbot and were curious about the persona of the conversational agent. This suggests that a chatbot that has been endowed with some personality traits is stimulating and more likely to be accepted by learners.

Coffee Chemistry, as Microlearning Content for Public Courses at Universities’ MOOC System, A Preliminary Attempt

An attempt to have a scientific course for the public from higher education institutions was done by preparing a good set of contents for one particular topic of interest, which is also a science topic. Coffee chemistry is the topic chosen and some content was developed, including videos, audio-embedded social media, infographics and motion graphics packed with 8 topics. The choice of coffee amongst many sciences’ topic is considered strategic in bringing science to all audiences of society. Science content must explain nature as the object in wholistic concepts, starting from smaller parts of each division, including chemistry. The microlearning approach is employed in the MOOC platform from The State University of Malang, Indonesia. This also is one of the three main tasks of higher education institutions which can be delivered in the MOOC system.

Personalized Hybrid Recommendation Algorithm for MOOCs Based on Learners’ Dynamic Preferences and Multidimensional Capabilities

In the MOOCs context, learners experience information overload. Thus, it is necessary to improve personalized recommendation algorithms for learners. The current recommendation algorithm focuses mainly on the learners’ course ratings. However, the choice of courses is not only based on the learners’ interests and preferences. It is also affected by learners’ knowledge domains and learning capabilities, all of which change dynamically over time. Therefore, this study proposes a personalized hybrid recommendation algorithm combining clustering with collaborative filtering. First, data on learners’ course rating preferences, course attribute preferences, and multidimensional capabilities that match course traits are used based on multidimensional item response theory. Second, considering that learners’ preferences and multidimensional capabilities change dynamically over time, the Ebbinghaus forgetting curve is introduced by integrating memory weights to improve the accuracy and interpretation of the proposed recommendation algorithm for MOOCs. Finally, the performance of the proposed recommendation algorithm is investigated using data from Coursera, an internationally renowned MOOCs platform. The experimental results show that the proposed recommendation algorithm is superior to the baseline algorithms. Accordingly, relevant suggestions are proposed for the development of MOOCs.

Learning processes, memory development, and knowledge sharing via mobile applications using MOOCs

This paper aims to analyse the impact of MOOC-based mobile applications on the learning process, memory development, knowledge sharing, and student motivation to study. The study analyzes the existing electronic educational resources of universities. The study design includes two types of surveys (before and after MOOC-based training). The sample population included 662 students of 1–4 courses. A survey conducted before the training showed that 54% of respondents rated their technical competence level as average, 43% rated it as high, and 3% rated it as low. About 83% of respondents noted that mobile applications and online educational courses have affected their academic performance in learning, memory development, and learning ability. The post-training survey results indicated an improvement in the group performance of students who learned with mobile applications on MOOC platforms. The practical significance of the research is determined by the possibility of using the program based on MOOC mobile applications at different faculties of the university. The paper also supplements existing studies with new data on the impact of learning using MOOCs mobile applications on the educational process.

Design of Embedded Knowledge Service Interactive System in University Library Based on MOOC Data Stream Retrieval

In view of this, the idea of applying knowledge graph to MOOC platform resource retrieval is proposed, and a knowledge graph oriented to MOOC data is constructed through entity recognition, relationship analysis, course knowledge point discovery and other methods, and applied to data stream retrieval on embedded devices The system architecture; improved the storage and retrieval efficiency during clustering; designed a clustering algorithm that can capture the temporal characteristics of the skewed distribution of data in a real-time or asynchronous manner. From the fields of scientific research, teaching and social science, a more comprehensive and analyzed the knowledge transfer service from the service process. Finally, it puts forward the embedded knowledge transfer service strategy of university library for the collaborative innovation of industry, university and research.

Artificial Intelligence System in Aid of Pedagogical Engineering for Knowledge Assessment on MOOC Platforms: Open EdX and Moodle

The aim of this research is to provide a novel educational model with the goal of reducing the expenses associated with manual question production and meeting the demand for a continual supply of new questions on MOOC platforms such as Moodle or Open EDX. We considered integrating machine-learning methods with natural language processing in order to increase the number and validity of assessing questions. To accomplish this, we developed a system that generates multilingual questions automatically.
 Various kinds of evaluation were conducted with two factors in mind: evaluating MOOC learners' competency and the similarity of the generated questions to those created by humans. The first evaluation is based on subjective judgment by three MOOC creators, while the second is based on replies from MOOC participants on machine-generated and human-created questions. Both evaluations revealed that the machine-generated questions performed on par with the human-created questions in terms of evaluating skills and similarity. Moreover, the results demonstrate that most of the produced questions (up to 82 percent) enhance e-assessment when the new suggested technology is used.

LivHeart: A Multi Organ‐on‐Chip Platform to Study Off‐Target Cardiotoxicity of Drugs Upon Liver Metabolism

AbstractThe drug discovery and development process is still long, costly, and highly risky. The principal attrition factor is undetected toxicity, with hepatic and cardiac toxicities playing a critical role and being the main responsible of safety‐related drug withdrawals from the market. Multi Organs‐on‐Chip (MOoC) represent a disruptive solution to study drug‐related effects on several organs simultaneously and to efficiently predict drug toxicity in preclinical trials. Specifically focusing on drug safety, different technological features are applied here to develop versatile MOoC platforms encompassing two culture chambers for generating and controlling the type of communication between a metabolically competent liver model and a functional 3D heart model. The administration of the drug Terfenadine, a cardiotoxic compound liver‐metabolized into the noncardiotoxic Fexofenadine, proves that liver metabolism and a fine control over drug diffusion are fundamental to elicit a physio‐pathological cardiac response. From these results, an optimized LivHeart platform is developed to house a liver model and a cardiac construct that can be mechanically trained to achieve a beating microtissue, whose electrophysiology can be directly recorded in vitro. The platform is proved able to predict off‐target cardiotoxicity of Terfenadine after liver metabolism both in terms of cell viability and functionality.

Learning Path Planning Based on Knowledge Graph on MOOC Platform

Based on the edX platform, this paper designs the knowledge graph model by combining expert method and reference method, selects Neo4j as the knowledge storage database of knowledge graph; selects Echarts as the visualization tool of Neo4j, and finally realizes the visual search and display of knowledge points on MOOC platform, and gives learning feedback and update according to the learning situation of learners. When planning the learning path through the designed knowledge graph, Ripplenet algorithm is introduced to enrich the diversity of courses in the learning path planning between courses. Professional learning path planning is provided for learners to improve the learning efficiency of online learning.

An online teaching process monitoring method of MOOC platform based on video recognition

In order to overcome the problem of low monitoring accuracy of traditional methods, a new online teaching process monitoring method based on video recognition for MOOC platform is proposed. The weight coefficient of online teaching process monitoring information of MOOC platform is calculated, and the monitoring information is reconstructed. On this basis, video recognition method is used to mine the online teaching process monitoring information of MOOC platform. Through the construction of monitoring model, the online teaching process monitoring of MOOC platform is realised. The experimental results show that the mean square error analysis results of the monitoring method based on video recognition verify that the effect of different gender students is different. The method has high correction coefficient, high monitoring accuracy and good application effect, which can effectively improve the effect of students' online learning.