Massive Online Research Articles

The effect of learning strategies adopted in K12 schools on student learning in massive open online courses

AbstractBackgroundGiven students' lack of self‐directed learning skills and the growing concern about implementing massive online open courses (MOOCs) in K12 education, learning strategies are needed to facilitate MOOC learning. Many studies have provided different strategies for effective learning in MOOCs. However, there is still limited research to confirm whether these strategies effectively support MOOC learning when deployed by secondary schools, which are not MOOC developers.ObjectivesThis mixed‐method study examines the provision of different learning strategies, including a learning guide, an academic mentoring programme, a training workshop, and a reimbursement scheme, and investigates the impact of these strategies on student learning in MOOCs.MethodsThe study uses two data sources: (1) surveys with 40 participants and (2) semi‐structured interviews with 12 participants. The quantitative data were analysed with descriptive statistics and Fisher's exact test, and the qualitative data were analysed with thematic analysis.Results and ConclusionThe findings indicate that MOOC learners had positive attitudes towards the overall school support, with mentoring enjoying great popularity amongst the students. In terms of the effectiveness of the school support, the mentoring programme and reimbursement scheme were more effective than the university training and learning guide. The paper discusses the implications for researchers and educators.

Self-Study of the MOOC English Pronunciation in a Global World: Metaphonetic Awareness and English Accent Variation

This paper reports on a study in which Polish first-year university students of English, self-studied the massive open and online course (henceforth MOOC) entitled “Pronunciation in a Global World” to gain some knowledge on the fundamentals of phonetics (the notion of comprehensibility, nativeness and identity; vowels, consonants and selected suprasegmentals) and English accent variation. Its two main goals are: firstly, to examine the MOOC’s impact on the participants’ understanding of basic phonetic concepts and, secondly, to obtain the users’ assessment of this MOOC’s attractiveness and usefulness.In general, the results do not give evidence for the positive influence of the MOOC course on the students’ meta-awareness of English phonetics, since there are statistically significant differences in only three of sixty-eight questions between the experimental and control group. Nevertheless, many informants regard the course as useful (72%) and attractive (49%).Although the results do not support the hypothesis of the MOOC’s beneficial role in facilitating the understanding of English phonetics our stand is that this online training could complement classroom teaching as a form of blended learning.

Identifying opportunities to innovate project management education in the digital age

This paper focuses on innovative learning methods (serious games, simulation games, virtual and augmented reality, the learning-by-doing concept, and Massive Online Open Courses) used in project management education. The use of these learning methods in project management education is crucial, as this subject needs to be practiced in real life. This paper aims to examine the current state of research in this area to determine which learning methods are most commonly used in project management education, what impact they have on the ability to learn, and whether these methods are beginning to transfer to the online environment. To this end, a systematic literature review was conducted in the Web of Science database, which resulted in 53 papers. The results show that game-based learning is the most frequently researched method (43 times). The impact of the analysed methods on the ability to learn was found to have been confirmed 23 times, especially in game-based learning research (18 times). On the other hand, the online environment is not predominant, and these methods are mostly taught on-site (28 times).

Consumer preference analysis: Diverse preference learning with online ratings

In the information age, there is a growing need to process and analyze the great number of online reviews to understand consumer preferences and product reputations. Instead of addressing all online reviews as a simple group decision-making problem in the existing research, we propose a new preference learning (PL) mechanism to extract preferences by analyzing the diversity of preferences across different time frames. First, we collect and process online ratings from e-commerce platforms. Then, we construct an online optimization model based on online mirror descent to learn priority vectors that reflect various consumer preferences. We incorporate multiple learners to capture evolving preferences. In addition, we design experiments to verify the model involving the validity, robustness, as well as suggested parameters ranges. The model helps consumers and businesses capture ongoing preferences from massive online reviews. Importantly, the PL mechanism is innovatively designed to detect different preferences and learn different types of priorities with generating ratings online. The model offers more accurate preference information and represents a broader range of consumers’ behaviors.

Applying andragogy for integrating a MOOC into a formal online learning experience in computer engineering

In this paper, we present the results of a research experience of implementing andragogy in a learning environment designed to better meet the needs of adult learners studying part-time at a distance university. The learning environment was composed of a learning experience on a formal distance university online course that has been enriched with a non-formal component based on students' participation in a Massive Online Open Course (MOOC) related to the same topic. The non-formal experience was designed to consolidate the learning of specific content that involved difficult concepts and foster collaborative skills. The university online course is in the field of computer science and human-computer interaction. The instructional design, including the course assignments, has been guided by Knowles’ principles of andragogy. Results from the data analysis of five years of academic results and student satisfaction has helped to understand the learning experience from including a MOOC in adult distance formal learning.

Motivation and Factors Affecting Pre-Service English Teacher in Joining Webinar Session

In the pandemic due to COVID-19, a massive online learning process is being carried out in education. This study aims to investigate types of motivation and identify factors that influence motivation among pre-service English teachers to participate in webinars as learning material in a distance learning context. This research is descriptive research using a mixed methods research design. The sample for this research consisted of 96 teachers. The methods used to collect data were questionnaires and interviews. The data collection instrument uses a questionnaire sheet and interview guide. The data was tabulated and analyzed quantitatively using descriptive statistical analysis to categorize the data and determine the percentage of data. The research results show that the motivation of pre-service English teachers is dominated by extrinsic motivation. It has a significant impact on student motivation to take part in webinars. Several important points about respondents' motivation for webinars are useful for increasing and enhancing their motivation for participating in webinars to increase professional development as prospective English teachers.

A fog‐computing architecture for network public opinion monitoring based on intelligent semantic recognition

AbstractCurrently, the online data contains rich user emotional information and public opinion information. These data can provide massive support for network public opinion monitoring and analysis. However, there are two problems in the network public opinion analysis of the online data. On the one hand, a vast amount of online data with discursiveness and concealment are processed in the cloud platforms, which consumes a long time. On the other hand, the massive online public opinion data is disperse and hidden, resulting in the dependence on manual screening for the analysis of public opinion. Therefore, it is still an important challenge to study the efficient and low‐latency extraction of valuable information from network public opinion. In this paper, we proposed a fog computing based framework using the technologies of intelligent semantic recognition and data mining for the analysis of network public opinion. Firstly, we build a fog computing architecture to collect the text data of network public opinion. Then, an efficient network public opinion model is constructed by intelligence semantic recognition. Finally, we achieve the function of public opinion analysis and early warning. The experimental results show that the method proposed in this paper achieves better performance against some existing methods.

Inovasi Sistem Informasi STUNEDUCATE sebagai Upaya Meningkatkan Indeks Literasi Stunting Masyarakat Kabupaten Enrekang Sulawesi Selatan

This community service program aims to provide innovation to the Enrekang Regency Stunting Reduction Acceleration Team (TPPS) in conducting stunting counselling to the Enrekang Community. This service method uses STUNEDUCATE information system training with target partners, namely the Team for the Acceleration of Stunting Reduction (TPPS) of Enrekang Regency, South Sulawesi Province. The evaluation instrument of this activity is a questionnaire and analyzed descriptively. The result of this service activity is the creation of new innovations for TPPS Enrekang Regency in terms of conducting online-based stunting counselling. TPPS can provide stunting education online so that TPPS is no longer constrained by distance and time. With the massive online counselling by TPPS through the STUNEDUCATE information system, the stunting literacy index of the Enrekang Regency Community can be further increased, and the stunting rate can be achieved in accordance with Presidential Regulation Number 72 of 2021 concerning the acceleration of stunting reduction.

Cognitive control across the lifespan: Congruency effects reveal divergent developmental trajectories.

The Simon, Stroop, and Eriksen flanker tasks are commonly used to assess cognitive control across the lifespan. However, it remains unclear whether these three tasks in fact measure the same cognitive abilities and in the same proportion. We take a developmental approach to this question: if the Simon, Stroop, and flanker tasks all roughly measure the same capacity, they should show similar patterns of age-related change. We present data from two massive online cross-sectional studies: Study 1 included 9,585 native English speakers between 10 and 80 years of age who completed the Simon and Stroop tasks, and Study 2 included 13,448 English speakers between 10 and 79 years of age who completed the flanker task. Of the three tasks, only the flanker task revealed an inverted U-shaped developmental trajectory, with performance improving until approximately 23 years of age and declining starting around 40 years of age. Performance on the Simon and Stroop tasks peaked around 34 and 26 years of age, respectively, and did not decline significantly in later life, though it is possible that age-related declines would be observed with more difficult versions of the tasks. Although the Simon and Stroop tasks are commonly interpreted to target similar underlying processes, we observed near zero correlations between the congruency effects observed in each task in terms of both accuracy and response time. We discuss these results in light of recent debates regarding the suitability of these tasks for assessing developmental and individual differences in cognitive control. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Empowering Deaf Learners: The Promise of Sign Language MOOCs

In the realm of education, deaf students face significant communication barriers that limit their integration and success within intellectual communities. Due to the use of distinct mother languages, a communication gap arises between deaf and non-deaf communities. This poses serious difficulties for deaf students, who communicate primarily in sign language and are unable to fluently read materials written in spoken language. As most didactic materials in higher education are available exclusively in spoken languages, this further limits the opportunities and chances for deaf students to succeed. While technological advancements have made it possible to create tools and services that translate between spoken and sign languages, they are often expensive and not widely available in educational settings. The proposal is a pedagogical model that seeks to overcome these obstacles and foster inclusivity by offering deaf students access to educational resources in their mother language – sign language. This is accomplished using Massive Online Open Courses (MOOCs) paired with automatic sign language translation technology. To demonstrate this model's effectiveness, a digital literacy course for schoolteachers was developed and evaluated within a group of users, including deaf students. The paper discusses the state-of-the-art of digital educational content available to the deaf, the process of translating digital learning materials into sign languages, the specifications needed to structure educational content for the deaf, and an example of the development methodology for creating multilingual learning objects for the deaf. The evaluation results indicate that MOOC is inclusive and positively impacts deaf students' learning outcomes. The proposed pedagogical model has significant implications for improving access to education and promoting inclusivity for deaf students worldwide. By utilizing technological advancements and creating inclusive digital educational materials, it is possible to bridge the communication gap between deaf and non-deaf individuals, thereby enabling deaf students to acquire, expand, and improve their knowledge in various subjects.

Strengthening Primary Health Care Through MOOCs (Massive Open Online Courses): A Cross-Sectional Study

Objective: After the increased number of COVID-19-positive patients, a plateau phase and a decrease in the numbers were expected, followed by a second phase which could lead to an increased health system workload. The importance of training well-educated and qualified healthcare professionals (HPs) has been reconsidered. The rapid spread of the epidemic revealed the limitations of traditional method education. Massive online courses (MOOCs) were offered as a solution to keep the information up-to-date and accessible. The aim of our study was to evaluate the perceptions and experiences of healthcare professionals on strengthening the health system during the COVID-19 pandemic.
 Methods: In this descriptive cross-sectional multi-centered study, an online survey was applied to HPs between February-May 2021. There were 28 participating countries. Due to international participation, the survey was conducted in English. 
 Results: There were 88 female and 87 male participants. Most of the participants were from Europe and Euroasia. Most of them reported their income as $501-$1500 US Dollars). The duration of job experience was between 5-15 years. 111 of them have completed a postdoctoral degree. 
 Conclusion: According to the survey, all the groups reported as the effect of strengthening primary care by MOOC was positive.

Building lean improvement skills at scale: an evaluation of a massive open online course in the English NHS

BackgroundInternationally, healthcare systems face challenges from population demographics and rising care costs. Systematic methods of quality improvement (QI) are considered key to delivering needed transformation and change. Large-scale training in...

Towards Understanding Metaverse Engagement via Social Patterns and Reward Mechanism: A Case Study of Nova Empire

With the constant fusion of virtual and reality, a new vision of human beings has emerged—Metaverse. At present, the development of metaverse is still in its infancy, and although the industry has put feverish investment into it, there are still many problems that need to be discussed in academia. The first thing is user engagement. Metaverse relies heavily on massive online users to realize its social value. In other words, user engagement is the foundation of the metaverse ecosystem. Therefore, our research uses <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Nova Empire</i> as a case study, aiming at understanding metaverse engagement via complexity analysis of social patterns and reward mechanisms. Specifically, the behavioral data of 46 954 players in September 2021 are used for analysis. Our observations show that social behavior is not the main factor for user engagement. Then, we perform a correlation analysis of trigger times for different gaming behaviors to verify the above observations. The results prove that the main factors affecting user engagement are game tasks and reward mechanisms in the early stage and social gaming behaviors environment with alliances in the middle and late stages. Finally, we discuss the implications of our findings for the design of future games and propose operation strategies on user engagement to improve the metaverse ecosystem and other online social space.

Refueling Mental Health: Digital Reading Predicts Psychological Resilience for Left‐behind Children in Rural China

ABSTRACTBeyond the easy and equal access to massive online contents, will digital reading bring more social welfare values for disadvantaged groups? Take left‐behind children (LBC) in rural China as example, whose mental health issue has widely aroused public concern. Lacking of sufficient parental supervision and educational resources leads to insufficient development of psychological resilience and makes them vulnerable to mental health problems. As psychological resilience is a critical protective factor for maintaining mental health, in this paper, we wonder whether digital reading could perform as an alternative way with easy accessibility and numerous resources to supplement LBC's resilience development. We conducted a field questionnaire study on LBC (N = 217) and investigated the predictive effect of digital reading on psychological resilience. After controlling sociodemographic variables and common resilience protective factors, hierarchical regression results demonstrated that digital reading predicted an additional 4.3% of the variation in resilience above the control variables, indicating digital reading a positive promoter of LBC's psychological resilience. Moreover, by systematically exploring the fine‐grained digital reading variables, we also found intrinsic motivation to read and recreational digital reading (e.g., reading comics and communicating online) to be the two most stronger predictors of psychological resilience.

Massive Online Open Courses for Emergency Health: Analysis of Global Use, Adaptation, and Sharing of Courses on the OpenWHO Platform (Preprint)

Massive Online Open Courses for Emergency Health: Analysis of Global Use, Adaptation, and Sharing of Courses on the OpenWHO Platform (Preprint)

Nuclear decommissioning: project management and leadership

Abstract. Management and leadership for safety relate to managerial competencies necessary to develop, promote and sustain a safety culture and to set goals, lead others and manage knowledge and projects to enhance safety performance. The development of these competencies is needed to enrich and complement the predominant technical background and skills of engineers and/or managers involved in the nuclear sector and particularly in decommissioning and dismantling (D&amp;amp;D) projects. The recent recognition of the importance of managing for safety led the International Atomic Energy Agency (IAEA) to develop formal safety requirements that are now implemented by its member states. This includes the need to develop training and education for beginning- and mid-career managers with nuclear safety responsibilities and, considering the time frame of D&amp;amp;D projects, for future generations of managers. Training and education challenges are acute in all Instrument for Nuclear Safety Cooperation (INSC) and European countries, where managers need to develop knowledge and comprehensive safety-related competencies to run the D&amp;amp;D projects of nuclear facilities in a context where the generational change of managers in the nuclear field is happening fast. In 2016/2017 the IAEA and the European Commission (EC) developed a cooperative framework to jointly address a similar challenge related to operation and regulatory oversight of nuclear installations. The development of these projects was possible by funding from the European Union (EU) through its INSC instrument. The first project, led by the IAEA in 2017, was the development of a pilot school for safety leadership at the University Côte d'Azur (UCA), France. Encouraged by this success, the agency has since then developed the syllabus into a 2-week programme, still based on experiential learning, which is offered to IAEA member states who wish to organise sessions for their managers (regulatory bodies or industry). The second project, named ELSE, was operated by UCA and aimed to develop training to help managers acquire leadership for safety capabilities, which are key professional requirements in complex, high-risk and highly regulated sectors such as the nuclear sector. The originality of the ELSE project stemmed from its science-based approach, integrating the most recent findings of management and other social sciences. The dedicated ELSE training programme is composed of a massive online open course (MOOC), a 10 d of face-to-face training and an individually tutored project. Based on the success of these experiences, the EU decided to prolong these actions in the field of nuclear D&amp;amp;D, leading to the start-up, in 2023, of the Decommissioning Management and Leadership for Safety Education (DMaLSE) project. This project has also been entrusted to UCA, in partnership with SKEMA Business School, the Karlsruhe Institute of Technology (KIT) and Jacques Repussard Conseil. DMaLSE has two main objectives, namely to develop a science-based training programme for future D&amp;amp;D project managers and to extend the impact of the project through bachelor-degree-level on-site training for operators involved in D&amp;amp;D projects.

A Review Selection Method Based on Consumer Decision Phases in E-commerce

A valuable small subset strategically selected from massive online reviews is beneficial to improve consumers’ decision-making efficiency in e-commerce. Existing review selection methods primarily concentrate on the informativeness of reviews and aim to find a subset of reviews that can reflect the informational properties of the original review set. However, changes in consumers’ review diets during the two-phase decision process are not fully considered. In this study, we propose a novel review selection problem of finding a diet-matched review subset with high diversity and representativeness, which can better adapt to consumers’ review-diet conversion from attribute-oriented to experience-oriented reviews between two decision phases. A novel decision-phase-based review selection method named DPRS is further proposed, which involves two steps: review classification and review selection. In the review classification step, the probability of a review being attribute-oriented or experience-oriented is estimated by prior knowledge-aware attentive neural network. In the second step, a novel heuristic algorithm, namely, stepwise non-dominated selection with superiority strategy, is introduced to seek the solution to the review selection problem. Extensive experiments on a real-world dataset demonstrate that DPRS outperforms state-of-the-art methods in terms of both review classification and review selection.

Multiplayer Online Battle Arena (MOBA) Games: Improving Negative Atmosphere with Social Robots and AI Teammates

Electronic sports show significant user churn caused by a toxic gaming atmosphere, and current GUI-based interventions are insufficient to address the issue. Based on the theoretical framework of Perceiving and Experiencing Fictional Characters, a new hybrid interaction interface and paradigm combined with tangibles is proposed to counter negative mood. To support the frustrated users of Massive Online Battle Arena (MOBA) games, we added AI teammates for better personal performance and social robots for the disclosure of negative mood. We hypothesized that AI teammates’ invisibility and anonymity would mitigate negative emotions; an effect amplified by the presence of social robots. A comparative experiment was conducted with 111 participants. Social robots for emotion-oriented coping improved user mood but AI teammates for problem-oriented coping did so better, although their higher levels of experienced anonymity may not have been preferred. Unexpectedly, conversing with a robot after playing with an AI teammate brought the mood back to that experienced when talking to a robot alone, while increasing the distancing tendencies. With this in mind, AI and social robots can counter the negative atmosphere in MOBA games, positively contributing to game design and empathic human–computer interaction.

The engage taxonomy: SDT-based measurable engagement indicators for MOOCs and their evaluation

Massive Online Open Course (MOOC) platforms are considered a distinctive way to deliver a modern educational experience, open to a worldwide public. However, student engagement in MOOCs is a less explored area, although it is known that MOOCs suffer from one of the highest dropout rates within learning environments in general, and in e-learning in particular. A special challenge in this area is finding early, measurable indicators of engagement. This paper tackles this issue with a unique blend of data analytics and NLP and machine learning techniques together with a solid foundation in psychological theories. Importantly, we show for the first time how Self-Determination Theory (SDT) can be mapped onto concrete features extracted from tracking student behaviour on MOOCs. We map the dimensions of Autonomy, Relatedness and Competence, leading to methods to characterise engaged and disengaged MOOC student behaviours, and exploring what triggers and promotes MOOC students’ interest and engagement. The paper further contributes by building the Engage Taxonomy, the first taxonomy of MOOC engagement tracking parameters, mapped over 4 engagement theories: SDT, Drive, ET, Process of Engagement. Moreover, we define and analyse students’ engagement tracking, with a larger than usual body of content (6 MOOC courses from two different universities with 26 runs spanning between 2013 and 2018) and students (initially around 218.235). Importantly, the paper also serves as the first large-scale evaluation of the SDT theory itself, providing a blueprint for large-scale theory evaluation. It also provides for the first-time metrics for measurable engagement in MOOCs, including specific measures for Autonomy, Relatedness and Competence; it evaluates these based on existing (and expanded) measures of success in MOOCs: Completion rate, Correct Answer ratio and Reply ratio. In addition, to further illustrate the use of the proposed SDT metrics, this study is the first to use SDT constructs extracted from the first week, to predict active and non-active students in the following week.

Comparison of Multiple Machine Learning Algorithms for Music Genre Classification

With the fast advance of the Internet and the continuous improvement of computer technology, speech recognition has been applied in many fields, and speech recognition has broad prospects for development. Music and audio classification technology can add category labels to music based on music content, which is of great significance in the research and application of efficient organization, retrieval and recommendation of music resources. In order to efficiently classify audio from massive online music data and help users to obtain the most suitable music style, a deep learning classification algorithm based on convolutional neural network (CNN) is proposed. To examine its effectiveness, it is compared with traditional machine learning algorithm. First, the original music data set was preprocessed and then feature extraction was carried out to obtain music features and transform them into spectral maps. Traditional machine learning model and deep learning component model were used for simulation experiments. The testing accuracy of the deep learning model is up to 92%, verifying the model's superiority.