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Following trends in entertainment streaming services, online educational platforms are increasingly offering users flexible "on-demand" content options. It is important to understand how the timing of content release affects learning behaviors and firm revenue drivers. The current research studies over 67,000 users taking a marketing course before versus after a natural experiment in which the platform switched the course from a scheduled weekly-release format to an on-demand format with all content immediately available. The switch to on-demand positively impacted short-term firm revenue by increasing the number and proportion of certificate-paying users, suggesting that on-demand content can attract a broader set of consumers who value flexibility. On the downside, the switch resulted in users exhibiting lower lecture completion rates and quiz performance and taking fewer additional business courses on the platform, representing a long-term cost. The results were robust to propensity score matching and stratification. The analyses also revealed that on-demand content enabled learning patterns that deviated from a standard evenly paced schedule, including "strategic" binge learning and stretching out engagement past the recommended course period. Thus, while on-demand formats can boost revenues by bringing in more paying users, managers must consider new strategies for maintaining performance and engagement levels within these environments. [ABSTRACT FROM AUTHOR]

The document "Index" from the journal "International Perspectives on Education & Society" provides an extensive list of topics related to higher education in Southeast Asia, including issues such as academic freedom, accreditation, and governance. It covers a wide range of subjects, from the history of higher education in various countries to the impact of digital transformation on education. The document offers insights into the challenges and advancements in higher education in the region, making it a valuable resource for researchers interested in this field. [Extracted from the article]

This study examines students' engagement in online synchronous collaborative learning (OSCL) sessions in a massive open online course (MOOC). The study focuses on the role of digital technologies in facilitating students' agency in collaborative meaning-making activities. The primary data sources for the study were video recordings of OSCL sessions, which were analysed using the method of interaction analysis and interpreted through the lens of cultural-historical theory. The findings revealed four interconnected activities of collaborative meaning-making: establishing social connections and presence, reflecting on prior learning, sharing digital artefacts and reflecting on shared understanding. The progression from one activity to another unfolded as students contributed to collaborative learning activities using their own ideas and problem-solving approaches. The use of individually created digital artefacts, such as examination assignment drafts, deepened interactions and meaning-making processes, enhancing students' agency in collaborative learning. The findings of this study provide insights into pedagogical strategies that can enhance student agency in MOOC environments, highlighting the crucial role of digital artefacts in developing students' agency in collaborative learning. [ABSTRACT FROM AUTHOR]

Background: With rising awareness of the health-related impact of climate change and environmental degradation, there is clear interest by oral health professionals to deliver services in ways that minimize environmental impact. Discussion: We summarize key principles related to sustainability in dentistry. Domains for action such as disease prevention, carbon footprint reduction, and management of plastics, nitrous oxide and antibiotics are discussed. Resources are identified to support teams to implement sustainable practices including the FDI World Dental Federation's Interactive Toolkit and Massive Online Open Course. By understanding key concepts of sustainability, recognizing domain areas for action, and referencing available resources, dental teams will be empowered to move further toward sustainable practice. [ABSTRACT FROM AUTHOR]

This study investigates whether Japanese university students' motivation to communicate in English was enhanced through the application of content and language integrated learning (CLIL) practices. Fifty‐three non–English majors took a CLIL English class that utilized massive open online course (MOOC) content for a semester. They gave presentations on the MOOC of their choice after watching lectures for a certain period. A questionnaire on second‐language (L2) willingness to communicate (WTC) and international posture was administered three times throughout the semester. Analysis of variance showed significant effects on the factor having things to communicate, whereas L2 WTC and other elements of international posture did not change significantly. A qualitative analysis of students' comments on classes indicated that allowing students to learn topics of interest to them and giving presentations boosted having things to communicate, intercultural approach‐avoidance tendency, and L2 WTC. Based on these results, the author discusses the importance of students' autonomy in learning topics and the necessity of longitudinal research and increasing opportunities for students to speak English in useful and authentic ways. [ABSTRACT FROM AUTHOR]

Online education has gained significant popularity during the COVID-19 pandemic. The availability of massive open online courses has further strengthened the online learning environment. One of the considerable challenges in online learning environments is to measure the learner’s engagement to meet the educational objectives. This paper addresses the challenge and proposes a convolutional neural network-based architecture that extracts discriminative features from the face (Affective) and the upper body (Behavioral) of the learner and classifies the engagement. The performance of the proposed architecture is evaluated on the publicly available Dataset of Affective States In E-Environments (DAiSEE) and the learning-centered affective state dataset curated from open-source datasets. The experimental results demonstrate that the proposed methodology with affective and behavioral features improves the engagement measurement results. The proposed method outperforms the state-of-the-art in terms of Unweighted Average Recall (UAR), Unweighted Average Precision (UAP), and Unweighted Average F1 (UAF1) with 79.14%, 49.62%, and 55.29% values, respectively. It is threefold more computationally efficient than the previous state-of-the-art method. The proposed method also improves the accuracy of the curated dataset by 2.07% of the prior method. [ABSTRACT FROM AUTHOR]

High dropout rates and low pass rates are prevalent problems encountered by online learning platforms, which greatly hinder the development of online education. Drawing upon the theory of attention allocation, this study aimed to investigate the factors influencing the effectiveness of Massive Open Online Courses (MOOCs), as well as the potential moderating effects. To address the limitation of using course completion rates as an overall measurement, this study endeavours to measure MOOCs learning effectiveness by examining dropout rates and pass rates as separate outcome indicators. We use secondary data analysis to investigate our research questions. Specifically, we collect 8602 courses from a Chinese MOOC platform (Zhihuishu) using data‐crawling techniques, and employ regression analyses to examine our research hypotheses. The findings indicate that course quality, content richness and interactivity significantly influence course dropout rates and pass rates. Besides, content richness moderates the relationship between course quality and learning effectiveness. Furthermore, frequent online interaction is associated with lower pass rates in high‐quality courses, but the moderating effect of online interaction on dropout rates is insignificant. This study contributes to the extant literature by examining course‐level factors that affect learning effectiveness. It also offers new theoretical insights and provides valuable suggestions for the design of MOOCs. Practitioner notes What is already known about this topic Attention is a scarce resource, and the concentration level greatly influences one's academic performance. Excessive online interaction may cause distractions and hinder learning outcomes. Information overload can result in anxiety and negative emotions, thereby reducing one's willingness to continue learning. In addition, information overload may reduce perceived usefulness and negatively affect academic performance. High dropout rates and low pass rates are common problems encountered by MOOCs today, hindering the development of online education. What this paper adds This study investigates the effect of course quality, content richness and interactivity on course dropout rates and pass rates. Content richness moderates the relationship between course quality and course dropout rates / pass rates. Courses with more content have higher dropout rates and lower pass rates, particularly for high‐quality courses. Online interaction moderates the relationship between course quality and the pass rates. More frequent online interaction is associated with lower pass rates for high‐quality courses. Implications for practice and/or policy The improvement of course quality can contribute to lower dropout rates and higher pass rates, thereby fostering the healthy growth of online education. When evaluating high‐quality courses, not only the depth of course content but also the integration of resources should be considered. High‐quality online courses should carefully regulate the number of videos and manage online learning resources. In addition, it is recommended that course designers consolidate essential video resources and ensure the effective and appropriate structure of course content. High‐quality online courses should restrict the frequency of online interaction. Additionally, these courses should strategically help learners allocate their attention, with a particular emphasis on prioritizing the course video content. What is already known about this topic Attention is a scarce resource, and the concentration level greatly influences one's academic performance. Excessive online interaction may cause distractions and hinder learning outcomes. Information overload can result in anxiety and negative emotions, thereby reducing one's willingness to continue learning. In addition, information overload may reduce perceived usefulness and negatively affect academic performance. High dropout rates and low pass rates are common problems encountered by MOOCs today, hindering the development of online education. What this paper adds This study investigates the effect of course quality, content richness and interactivity on course dropout rates and pass rates. Content richness moderates the relationship between course quality and course dropout rates / pass rates. Courses with more content have higher dropout rates and lower pass rates, particularly for high‐quality courses. Online interaction moderates the relationship between course quality and the pass rates. More frequent online interaction is associated with lower pass rates for high‐quality courses. Implications for practice and/or policy The improvement of course quality can contribute to lower dropout rates and higher pass rates, thereby fostering the healthy growth of online education. When evaluating high‐quality courses, not only the depth of course content but also the integration of resources should be considered. High‐quality online courses should carefully regulate the number of videos and manage online learning resources. In addition, it is recommended that course designers consolidate essential video resources and ensure the effective and appropriate structure of course content. High‐quality online courses should restrict the frequency of online interaction. Additionally, these courses should strategically help learners allocate their attention, with a particular emphasis on prioritizing the course video content. [ABSTRACT FROM AUTHOR]

With the advancements in technology and the growing demand for online education, Virtual Learning Environments (VLEs) have experienced rapid development in recent years. This demand was especially evident during the COVID-19 pandemic. The incorporation of new technologies in VLEs provides new opportunities to better understand the behaviors of learners. Identifying the learning styles (LSs) of learners can greatly impact the learning process and enhance the effectiveness and satisfaction of both learners and teachers in Massive Online Open Courses (MOOCs). In this study, an approach to automatically recognize the LSs of learners based on the amount of data interactions generated in MOOC is presented. The Felder Silverman Learning Style Model (FSLSM) is used as the basis for prediction, as it is one of the most widely used models in VLEs. The data collected from the learning activities in the XuetanX platform from 08–2016 to 08–2017 was prepared using the Get and Structure Data (GSD) algorithm and then clustered using the K-means algorithm based on the Felder & Silverman Model. The learner's degree performance in each learning style was analyzed using the confusion matrix, learning curves, and performance metrics (accuracy, precision, recall, and macro/micro-averaged precision) for the neural network (NN), decision tree (DT), Random Forests (RF), Naive Bayes (NB), and K-nearest neighbors (KNN) algorithms. The evaluation results showed that the DT achieved a high accuracy rate of over 99% in predicting learners' learning styles. [ABSTRACT FROM AUTHOR]

Massive Open Online Courses (MOOCs) offer highly specialized online courses and have attracted nearly 10 million learners worldwide to participate in various educational programs. These platforms provide discussion forums that allow learners to engage with both their peers and instructors, facilitating idea exchange and seeking assistance, respectively. However, due to the substantial participant-to-instructor ratio, certain posts may go unanswered. Addressing learners' confusion is crucial. This emotional state, often experienced during the learning journey, necessitates prompt support to prevent potential dropouts. This paper proposes the application of a deep transfer learning method to automate the classification of online discussion posts based on indicators of confusion utilizing the Stanford MOOCPost dataset. The approach involves creating an explainable and adaptable deep learning model through network-based transfer learning across multiple educational domains. This model outperforms baseline methods, achieving an average accuracy of 91%. Additionally, employing data augmentation techniques enhances the model's generalizability, resulting in an 11% improvement in the F1 score. To mitigate the inherent opacity of the implemented models, Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation techniques are integrated. These explanations assess the reliability of features and provide supplementary insights into the confusion detection. By pinpointing confused posts, this work assists instructors in delivering timely responses, resolving learner confusion, providing accurate visualization of key contributing words, and reducing the dropout rate. This proactive approach ensures a smoother continuation of the learning process, consequently enhancing learner satisfaction with the educational experience. [ABSTRACT FROM AUTHOR]

Purpose: During the initial phase of the pandemic, healthcare professionals faced difficulties due to the limited availability of comprehensive learning resources on managing patients affected with coronavirus disease 2019 (COVID-19). The COVID-19 Skills Preparation Course (C19_SPACE) was tailored to meet the overwhelming demand for specialized training. The primary objective of this study was to assess the efficacy and impact of this program on enhancing clinical knowledge and to identify factors affecting this improvement. Methods: As part of the project, data were collected prospectively to measure the baseline knowledge. After the descriptive statistics, multiple and multivariate logistic regression models were executed to identify the factors associated with knowledge increase. Results: The final sample included 3140 medical doctors (MDs) and 3090 nurses (RNs). For the primary analysis, the mean value of the baseline knowledge test score of MDs was 62.41 (standard deviation, SD = 13.48), and it significantly (p < 0.001) increased to 84.65 (SD = 11.95). Factors influencing overall knowledge scores were female sex (AOR = 1.34 [1.04–1.73]), being a specialist qualified for intensive care medicine (adjusted odds ratio, AOR = 0.56, [0.33–0.96]), and performance on the pre-test (AOR = 0.91, [0.90–0.92]). As for the RNs, the mean value of the total knowledge score was 63.25 (SD = 13.53), which significantly (p < 0.001) increased to 81.51 (SD = 14.21). Factor associated with knowledge was performance on the pre-test (AOR = 0.92 [0.92–0.93]). Conclusions: C19_SPACE effectively increased the clinical knowledge of doctors and nurses. The effect was more pronounced in the program's target group of healthcare workers with less experience in the intensive care unit (ICU). Other factors associated with knowledge enhancement were sex and being a specialist in intensive care. [ABSTRACT FROM AUTHOR]

Self-directed learning (SDL) is necessary for successful learning in MOOCs. Motivation is one of the critical elements of SDL. This mixed-method study examined the design and delivery of MOOCs to motivate learners for SDL. The data collection methods included semi-structured interviews with 22 MOOC instructors, a document review of 22 MOOCs, and an online survey with 198 participants. The researchers found that MOOC instructors valued motivating learners for SDL. The strategies to motivate learners included entering motivations (i.e. decisions to participate in a course) and task motivations (i.e. learners' persistence in learning activities). For entering motivation, MOOC instructors used strategies, such as identifying the value and needs of learning, increasing self-efficacy, and using incentives. For task motivation, MOOC instructors adopted engaging instructional strategies, well-designed learning materials, immediate and constructive feedback, and social interaction. Besides, technology played an essential role in motivating learners. Implications for instructors and instructional designers were discussed at the end of the paper. [ABSTRACT FROM AUTHOR]

In this paper, the authors describe an investigation of Kennesaw State University's inaugural MOOC, a professional development opportunity designed to enhance educator skills in designing blended and online learning environments for their K-12 learners. Additionally, the authors discuss design aspects of the MOOC in regard to learning impact and first steps taken to evaluate the learners' perceptions of the instructional strategies used during the MOOC. The methods included an analysis of quantitative survey data, as well as a qualitative analysis of both open-ended survey questions and discussion forum responses. The findings from the investigation are presented in terms of design factors contributing to the students' learning, as well as the strategies perceived as barriers to their learning. Results of the study indicate a successful MOOC design from the learners' perspective. [ABSTRACT FROM AUTHOR]

Various studies have demonstrated how the discrimination and violence experienced by LGBT+ children and young people affect their mental health and quality of life. Training for professionals working with children and young people is a key means of helping them provide the necessary support to LGBT+ children and youth people, for handling any violence that may occur in their immediate environment, as well as for empowering them. This article discusses changes in the attitudes, beliefs and practices of professionals working with children and young people following their participation in a Massive Open Online Course (MOOC) delivered through the online platform of the University of Girona. Two surveys were carried out with the participants – the first at the beginning of the course, and the second at the end – in order to evaluate possible changes. Participants' attitudes, beliefs and practices regarding sexuality and gender diversity in childhood improved, even when taking into account the fact that many began from an initial position of support. The use of MOOC training has value, given the need to engage with sexuality and gender diversity as part of formal training for a variety of professionals working with children and young people. [ABSTRACT FROM AUTHOR]

Data infrastructures exist in a variety of formats. This article draws on the insights of senior personnel involved in developing a new data dashboard in one state jurisdiction in Australia. While literature on dashboards often focuses on the teachers and learners influenced by them, there is less attention to those involved in their development and the politics that attend their work. While the dashboard initiative holds out the hope of enabling more educationally oriented benefits, such as assisting with cross-school moderation of students' learning in various 'like-schools' in a local region/state, there was also recognition by some involved in the development process that much of the discourse around the dashboard revolved around broader systemic concerns about who should have access to which kinds of data, and that such data were 're-presentations' of actual instances of student learning which limited understanding of what could actually be done to enhance students' learning. Processes of monitoring and temporal anomalies were key to sometimes problematic governance processes associated with developing the dashboard, eliciting various affective responses amongst different actors involved in its development. [ABSTRACT FROM AUTHOR]

The objective of our study was to understand learners' experiences in an LMOOC (Language Massive Open Online Course) and to highlight those elements that make an LMOOC successful. Our research focused on three questions: how do learners perceive their overall experience in the course, which specific LMOOC components learners appreciate, and to what degree the course helped learners achieve their personal learning goals. For our study we collected quantitative and qualitative responses from 226 learners, and we created a unique four-dimensional analysis framework to code each unit of meaning in order to answer our research questions in a systematic way. Learners' overall experience was rated highly, and the majority of them think the course helped them achieve their learning objectives. Consuming content was the most referred and appreciated learning activity, with video interviews being the most frequently mentioned LMOOC component as well as perceived as a high contributor to both language and cultural knowledge acquisition. Variety was another highly appreciated aspect. In general, receptive activities were the most frequently mentioned and appreciated, perhaps reflecting their preponderance in the course over productive activities. We conclude with some recommendations for future directions in LMOOC development, particularly in the communicative realm. [ABSTRACT FROM AUTHOR]

In the context of 21st-century educational transformation, Massive Open Online Courses have emerged as a global and flexible learning opportunity. This study explores the impact of MOOC implemented at Instituto Super Técnico on student engagement and attendance, focusing on their effectiveness in higher education. A descriptive approach was applied, selecting three MOOC as the empirical field, combining quantitative data from participation logs and qualitative feedback from student surveys. The key characteristics, the challenges, and the effectiveness of educational resources were evaluated, particularly concerning student interaction and perceptions of course quality. The pedagogical analysis highlighted both successful strategies and areas for improvement. The findings offer valuable insights into the influence of MOOC on students and the institution, contributing to the broader understanding of Portugal's educational landscape. This study also proposes a framework for enhancing MOOC strategies in higher education globally, considering the potential of technological innovations in the 21st century. [ABSTRACT FROM AUTHOR]

Online courses have become increasingly popular in education and professional development because of their user-friendliness, accessibility, and ability to provide specialized training beyond formal education for workforce preparation. We demonstrate the efficacy of using a Massive Open Online Course (MOOC) to teach specialized engineering concepts through the case study of a multi-body gravitational dynamics course; this topic is not typically taught in undergraduate courses but essential for specialized roles in aerospace engineering. Our use of MOOCs to bridge the knowledge gap between engineering education and practice represents a critical advance in tools for workforce preparation. Our MOOC, Designing the Moonshot, was released to the public in June 2021 and again in June 2022 as part of an international virtual event entitled OrbitCamp (originally called AstroCamp) where we collected data from a sample of 840 participants to study learning gains through pre-post testing and retention. Participant drop-off rates were steep (66% of participants did not complete the first module) but lower among men and those with higher degrees. Statistical analysis demonstrated that participants gained significant knowledge on the 12 (of 14 total) learning outcomes for which sample sizes were sufficient. Some of this knowledge was retained three months after OrbitCamp. These results support the use of MOOCs as a tool for furthering the expertise of the engineering workforce in highly specific domains. [ABSTRACT FROM AUTHOR]

Pelatihan dasar calon pegawai negeri sipil (Latsar CPNS) is basic training for newcomer civil servants that must be followed during the pre-service period. The coronavirus disease 2019 (COVID-19) outbreak has caused face-to-face learning in the classroom to be canceled, so the learning process in the training organization must be replaced with a learning process using massive open online course (MOOC) and learning management system (LMS) with a distance learning system. This study used a modified form of the unified theory of acceptance and use of technology (UTAUT) model framework. The core factors in the UTAUT framework called facilitating conditions, will be divided into two factors. The two factors are the availability of infrastructure and devices, and internet capability (IC). The respondents of this study are newly recruited civil servants at the Ministry of Transportation of the Republic of Indonesia with 400 respondents used in the analysis process. We found that performance expectation (PE), effort expectation (EE), social influence (SI), and self-efficacy (SE) affect student behavior (SB). In addition, SI and IC affect SE. Meanwhile, the relationship between infrastructure and device availability (IDA) with SE has an insignificant result. In improving Latsar CPNS services, training organizations should pay attention to several factors that can influence SB. [ABSTRACT FROM AUTHOR]

While vocabulary learning strategies (VLSs) have primarily been taught in physical classroom settings, the potential for applying strategy training in an online environment remains under-explored. With the rising acceptance of remote education, there is a need to investigate the impacts of applying strategy training in online contexts. Hence, this study explores the effects of online deliveries on VLSs and vocabulary usage in writing among adult English as second language (ESL) learners via massive open online course (MOOC) and video conferencing platforms. A total of 50 pre-intermediate ESL learners completed a 5-week VLSs course on a MOOC while 44 students learnt from the instructor synchronously using Microsoft Teams. The data comprised questionnaires and learners' compositions, which were collected before and after treatment. The data analysis procedures involved Lextutor's version of lexical frequency profiling (LFP), descriptive and inferential statistics. The results revealed that there was a significant increase of usage in all categories of the VLSs after the course for both groups. Additionally, the effects of VLSs instruction on vocabulary usage could be reflected in both groups' writing though the choice of words might have been task-influenced. The findings from this study could yield useful insights on online vocabulary learning for ESL instructors and learners. [ABSTRACT FROM AUTHOR]

This study works on the early identification of serious but struggling students in Massive Open Online Courses (MOOCs), which facilitates a transition to a hybrid learning format. Firstly, we exclusively utilize basic behavior log features that are universally available across all MOOC platforms. Secondly, we encode each learning activity with a distinct identifier, which helps us utilize the current advances in NLP to address our problem. Thirdly, we seamlessly integrate our LSTM model with a Logistic Regression model, configured as a single dense layer with a sigmoid activation function, within the broader framework of deep learning. [ABSTRACT FROM AUTHOR]

The full implementation of MOOCs in online education offers new opportunities for integrating multidisciplinary and comprehensive STEM education. It facilitates the alignment between online learning content and learning behaviors. However, it also presents new challenges, such as a high rate of STEM dropouts. Many learners struggle to establish effective learning behaviors and fail to complete the entire learning process. This study focuses on analyzing a vast amount of MOOCs learning behavior instances and proposes a knowledge graph-based analysis and prediction model that is centered around associated STEM concepts. The objective is to provide key insights for fostering sustainable and efficient learning behaviors. By constructing a heterogeneous information network, analyzing and learning from key concepts and their relationships by the improved graph convolution neural networks, and employing adaptive recommendations based on the course-concept matrix, it is possible to generate knowledge graphs from extensive learning behavior instances, both within single course and across multiple courses. This approach accurately captures learning behavior patterns and provides guidance for effective learning paths. The data analysis process and predictive results demonstrate that the knowledge graph can effectively guide learning behaviors and enhance learning outcomes. This research offers technical support and a decision-making foundation for optimizing interdisciplinary learning process within MOOCs for STEM education. It showcases strong innovation and practicality. [ABSTRACT FROM AUTHOR]

Background: To assist in the global training of rehabilitation professionals to address the unique needs of forcibly displaced persons, a free massive open online course (MOOC) was developed. This study aimed to explore self-perceived knowledge following completion of courses within the MOOC, ability to implement changes in practice, and participant engagement with the MOOC content. Methods: The Kirkpatrick Model was used to assess the effectiveness of the course with a focus on reaction, learning, and behavior. Participant interaction with the course content was explored using thematic analysis of the MOOC's discussion forum content. Participants in the MOOC were asked to complete pre and post-surveys of self-perceived knowledge regarding the rehabilitation of displaced persons. Mean within-subject pre and post-MOOC data were compared using the Mann U Whitney test. Knowledge implementation after six months was assessed with a follow-up survey. Results: Self-reported knowledge following completion of the MOOC improved significantly on individual survey questions (p≤.004) as well as composite score (p <.001). Six months following completion of the course, participants noted the ability to implement multiple strategies into practice. Three themes emerged from the discussion forum postings: (1) Understanding the persons lived experience to provide effective, holistic, patient-centered care, (2) Communication to develop a sense of connection and facilitate the provision of quality care, and (3) The importance of culturally responsive practice. Conclusion: The MOOC effectively delivered the fundamental knowledge to address the complex needs of displaced individuals among a diverse group of practicing rehabilitation professionals and can play a role in preparing the rehabilitation workforce for increased population displacement, alongside specialist programs in related themes. [ABSTRACT FROM AUTHOR]

AbstractThis systematic literature review examines the barriers, strategies, and accessibility issues affecting the engagement and retention of learners with disabilities in Massive Open Online Courses (MOOCs). Employing the PRISMA procedures, this study systematically identifies and analyzes relevant literature from five prestigious databases: (a) Scopus, (b) ScienceDirect, (c) Emerald, (d) SpringerLink, and (e) EBSCOhost in order to identify (1) the common barriers experienced by learners with disabilities in MOOCs, (2) the strategies that can be employed to improve the engagement and participation of special needs students in MOOCs and (3) influence of accessibility on engagement and retention rates of learners with disabilities in MOOCs. After conducting the screening and eligibility analysis according to the pre-defined criteria, 19 studies of out 624 sources were finally selected between 2014–2024. The review underscores the necessity for comprehensive policy frameworks and institutional commitment to inclusivity. It emphasizes the adoption of universal design principles are essential in bridging the accessibility gap. These measures are pivotal in fostering an inclusive learning environment in MOOCs, significantly enhancing engagement and retention rates among learners with disabilities. The insights from this review provide a solid foundation for educators, policymakers, and platform developers to create more inclusive and supportive online learning experiences for students with disabilities. [ABSTRACT FROM AUTHOR]

Massive Open Online Courses (MOOCs) are increasingly popular for teachers' professional development (PD). Understanding why teachers take MOOCs and how this relates to course completion could help identify underserved needs in teachers' professional learning. In the current study, we explored this question, as well as potential gaps between intention to complete the course and actual completion. Using a sample of 3,212 participants in four PD MOOCs, we applied topic modeling to open-ended and Likert-style data to identify teachers' motivations. The results show that most participants had intrinsic or professional motivations, but a subgroup of participants had prosocial motivations, namely, they wanted to support their students. In a set of logistic regression predicting course completion, we found that participants with intrinsic motivations were less likely to complete a course and participants with prosocial motivations were more likely to do so even after controlling for their initial intention. Our study contributes to the field by, first, identifying an underexplored group of learners, the prosocial learners. More research is needed to better understand this group. We also found that among teachers taking MOOCs, intrinsic motivations were associated with lower levels of engagement, contrary to findings in other populations, making a contribution to motivation theory as well as online learning practice. We concluded that the motivation-engagement relationship is more complex than previously thought, and recommend researchers continue examining this association to understand this discrepancy. Finally, we suggest practitioners take learners' a-priori motivations into account when designing MOOCs, as these could be important for course engagement. [ABSTRACT FROM AUTHOR]

Background: Forums in massive open online courses (MOOCs) enable written exchanges on course content; hence, they can potentially facilitate learners' cognitive engagement. Given the myriad of MOOC forum messages, this engagement is commonly analysed automatically through the linguistic features of the messages. Assessing linguistic features of learners' forum messages involves consideration of the learning tasks. MOOC forum discussion tasks, however, have not been previously considered. Objective and Method: This study explores the effects of MOOC forum discussion tasks on learners' cognitive engagement. Based on the structure of observed learning outcomes (SOLO) taxonomy, we manually annotate distinct levels of cognitive engagement encouraged in forum discussion tasks and displayed by learners in messages starting discussions (i.e., thread starters). We study the linguistic features of thread starters in relation to the pedagogical design of the discussion tasks. Additionally, we use random‐forest modelling to identify the linguistic and task‐related features that help to categorise learners' cognitive engagement according to SOLO levels. Results: Manual analysis showed that learners' thread starters mainly reflect surface SOLO levels and include few academic words and cohesive language. Random‐forest modelling showed that these linguistic features, together with the SOLO levels encouraged in the discussion tasks, played an important role in identification of learners' cognitive engagement. Major Takeaways: Our results highlight the importance of the pedagogical design of MOOC forum tasks in helping learners engage cognitively. Our study also contributes to the empirical evidence that learners' linguistic choices can afford insights into the quality of their cognitive engagement. Lay Description: What is currently known about this topic: Learners' cognitive engagement in massive open online courses (MOOCs) discussion forums mostly reflects surface‐level understanding.The distinctive linguistic features of MOOC forum messages can, to some extent, reflect the quality of learners' cognitive engagement. What this paper adds: We analysed the linguistic features of MOOC forum messages that start a discussion (i.e., thread starters) and studied these features in relation to the pedagogical design of the forum discussion tasks.We found that learners strove to mobilise the cognitive processes expected in the forum discussion tasks. However, in some forums, learners' engagement might not have been aligned with the intended learning outcomes of the task. Implications for practitioners: The learning design used in MOOC discussion forums needs to inform learners explicitly about their expected cognitive contributions.To promote higher levels of cognitive engagement, MOOC instructors need to design forum discussion tasks that help learners structure their knowledge and make explicit connections between ideas. [ABSTRACT FROM AUTHOR]

This article describes an innovative Future Work 4S project and provides comprehensive feedback on a free Massive Open Online Course (MOOC) focused on environmental skills. Feedback was collected from two main target groups: educators (including university professors, teachers, and trainers) and student adult learners from Bulgaria, Greece, the Czech Republic, and Italy. The purpose of this survey is to provide an in-depth analysis of the feedback received on the Green Skills course and to highlight the main strengths identified by both educators and students. The findings reveal a high appreciation for the course's interactive and experiential learning approach by both the students and the educators. Specifically, 93% of participants agreed that the course was interesting, engaging, relevant, accessible, and developmental. The course's interactive exercises and gamification elements were particularly praised, enhancing learning and motivation. However, suggestions were made for including more practical examples and improving the uniformity of the text. [ABSTRACT FROM AUTHOR]

To date, student issues with Massive Open Online Courses (MOOCs) have only been explored in context‐specific environments. Mainstream problems such as declining student motivation during a course, massive student dropout rates, accountability, user experience, etc., persist due to the permutations and combinations of these issues. Literature is replete with a deep understanding of such problems, but the causal relationships among these issues are less focused upon. We delve into these problems by studying the interrelations among student issues that cause such problems. Garnering insights from students (N = 149) and using Total Interpretive Structural Modelling with Polarity (TISM‐P), the study has established direct and transitive relations among nine detrimental MOOC‐related student issues. The results of the study depict clear positive, negative and transitive relationships between the student issues. Matrice d'Impacts croises‐multipication applique' an classment (MICMAC) analysis was also used to assess the driving and dependence power of all issues that further allowed the model to trace out negative and positive pathways of influence. The model constructed in the study will provide a platform for future research to test these interconnections as independent factors affecting problems such as dropout rates, motivation, etc. Therefore, the TISM‐P model could further be explored to understand the behaviour of such issues, which might have far‐reaching consequences on major existing problems with MOOCs. [ABSTRACT FROM AUTHOR]

Background: Massive open online courses (MOOCs) have gained popularity as a form of distance education, highlighting the need for additional research. Various studies have systematically examined scholarly research on MOOCs. However, the reviewed academic publications on the use of MOOCs in engineering education are limited. Purpose/Hypothesis(es): This study aims to examine the implementation of MOOCs in engineering education using systematic review and bibliometric analysis methods. Design/Method: This study examines 68 studies from the Web of Science database that fall within the scope of the systematic review and 257 studies for bibliometric analysis. Results: Since 2018, there has been a growing number of studies exploring the uses of MOOCs in engineering education. The quantitative research method is the most preferred, while the mixed method is the least preferred. The study found that using MOOCs in engineering education resulted in positive student perceptions and satisfaction. However, the lack of interactivity between learners and instructors or among learners was the most frequently reported problem. Moreover, the bibliometric analysis indicates that reviewed studies extensively explore learners' engagement, interactions, and feedback processes. Recently, academic research focused on sentiment analysis, deep learning, educational technology, machine learning, and text mining. Conclusions: There is a need for further investigation into utilizing MOOCs in engineering education. This study's evaluation of MOOCs' pros and cons yields essential insights for optimizing their use in engineering education. By leveraging the positive aspects outlined in the study, educators can enhance engineering students' satisfaction and participation in MOOCs. These findings offer a valuable resource for researchers exploring MOOC‐related topics in engineering education. [ABSTRACT FROM AUTHOR]

Objective: Family physicians (FPs) worldwide have rallied to minimize the indirect effects of the disease. FPs are always on the frontline, "first in- last out", and took the lead in fighting the virus-caused disease in the acute phase of the coronavirus disease-2019 (COVID-19) epidemic. They played a vital role in examining, informing, and monitoring patients in primary care health centers, as well as in centers set up specifically for COVID-19. However, due to poor knowledge of COVID-19, which changes rapidly, FPs had to update their practical and theoretical knowledge about this novel coronavirus on a daily. Massive open online courses (MOOCs) are courses that use an online application and can reach the entire world. In this study, we aimed to determine the opinions and suggestions of FPs in meeting their educational needs related to COVID-19 through MOOCs and to create solutions. Methods: The study was planned to be held with FPs who completed MOOCs training and completed the questionnaire. Due to international participation, the survey was conducted in English. Ethical approval was obtained from the Ethics Committee of İzmir University of Economics. Participation was entirely voluntary. Results: Our study revealed that as the age of FPs increases, their confidence in their ability to treat patients also increases. FPs showed a positive attitude toward MOOCs as sources of continuous medical education and group activity (p<0.005). For the qualitative part of the study, three themes were significant: "i) opinions about concerns about changes of primary care, ii) views and attitudes about the information need and access to information, iii) attitudes and beliefs about MOOCs". Conclusion: By completion of this course, FPs broadened their knowledge about COVID-19 management. This gave them capacity to save and improve the lives of countless patients infected by the novel coronavirus worldwide. Sharing their experiences with COVID-19 could be a way to cope with stress. [ABSTRACT FROM AUTHOR]

This paper describes our equity-driven development of a chatbot curriculum for a journalism professional development center’s massive open online courses (MOOCs). A curriculum refers to entire student experiences that occur during the whole learning process in this context. We used a sequential and iterative development process with four phases to research and develop the chatbot curriculum: experimentation, evaluation, research and development reiteration, and implementation and evaluation in an ongoing course. In Phase 1, we tested whether a chatbot interface designed to answer frequently asked questions (FAQs) could provide more equitable student support than traditional FAQ pages, considering the widespread student frustration about FAQ pages. A total of 120 diverse students participated in this phase. In Phase 2, we conducted a case study to contrast the experiences of 27 non-native English users who used the FAQ chatbot, identifying what distinguished their experiences from those of 15 native English user counterparts. Based on this phase’s findings, the chatbot was further trained using an enhanced training set to better support non-native English users. In Phase 3, using a topic analysis result of 3,645 forum discussion posts from the center’s previous 21 courses, we ensured the chatbot’s expertise to cover nine additional topic themes. Phase 4 is where we examine students’ experiences with the chatbot’s latest version as a built-in course tool in MOOCs. This study suggests that systematic research and development of a chatbot curriculum is indispensable to fostering equitable learning environments in MOOCs. [ABSTRACT FROM AUTHOR]

Massive Open Online Courses (MOOCs) provide borderless opportunities to engage with content and ideas, with prospective participants from around the globe being able to easily register. The course featured in this study focused on the social and emotional well-being of adolescents, selected because of a recognized need for professional learning related to this topic. It was purposely designed for delivery as a MOOC and was designed as a 6h program around four topics to be completed over two weeks in asynchronous mode. It was delivered seven times from 2019 to 2023, with 32,969 individuals enrolled across these deliveries. The effectiveness of engaging in the course for professional learning purposes is of particular interest to this study. To that end, a convergent mixed methods study was conducted. First, quantitative and qualitative survey data collected at various course stages were examined to reveal the demographic characteristics of participants and their experiences in the course using data from surveys and comments about their experiences. The findings revealed, among other factors, that 65% were female, with just under half (47%) aged 45 years or less, nearly half (44%) held a bachelor's degree as their highest level of qualification, and 48% were employed within the teaching and education sector. The most active learners were from Europe (48%) and Asia (27%), with active learners from a total of 178 countries. The course has a high course retention index, with 51% of learners completing 51% of the course and 8383 learners completing 90% or more of the course. The qualitative findings reveal the strongly positive experiences reported by the active participants. Secondly, we examined the effectiveness of the MOOC for participants' professional learning needs by assessing the course using a framework with ten domains related to its core design features, modified for use by the course designers as a self-reflective tool. We found that the domains that scored the lowest were collaboration, interactivity, and, to a lesser extent, pedagogy. The study's limitations include the incomplete data provided as part of the MOOC protocols, and the use of a self-reflection tool, which may inadvertently incorporate bias. This study points to these gaps in the data, including the need to access longitudinal data that go beyond a focus on the design of courses to extend to the impact and outcomes of the experience. [ABSTRACT FROM AUTHOR]

The text discusses the importance of decolonising educational technology to address colonial biases and power imbalances in modern educational practices. Authors from diverse perspectives explore how technology can perpetuate biases and privilege certain knowledge systems. The Special Issue aims to uncover biases, promote inclusivity, and empower marginalized communities in educational technology design and implementation. The articles in the issue highlight the need for diverse cultural perspectives, social justice, and ethical considerations in educational technology development. The text calls for collaboration, cultural sensitivity, and dismantling of existing structures to create a more equitable and inclusive educational landscape. [Extracted from the article]

In online education settings, effective student-teacher interaction can be challenging, often leaving student feedback on forums overlooked. This paper highlights the significance of student feedback and investigates the experiences shared on Massive Open Online Courses (MOOCs) platforms through forum interactions. We combined three distinct datasets from MOOCs forums into a unified corpus suitable for training Large Language Models (LLMs) for diverse classification tasks. Our study uses Bidirectional Encoder Representations from Transformers (BERT), a Robustly Optimized BERT Pretraining Approach (RoBERTa), Generative Pre-trained Transformer 3.5 (GPT -3.5), Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA), and eXtreme Multi-head Attention Network (XLNet) to classify feedback based on urgency levels, assess topic similarity, and analyze sentiment to gauge overall classroom sentiment. The resulting multi-task learning framework addresses the classification of questions, urgency levels, and sentiment analysis concurrently, enhancing the management of student inquiries and satisfaction in MOOC environments. This research contributes methodologically by demonstrating the efficacy of LLMs in handling multifaceted feedback analysis tasks, thereby enriching the understanding of student engagement and satisfaction in online courses. [ABSTRACT FROM AUTHOR]

Conventional approaches fall short of managing the current scale of data, underscoring the significance of big data analytics in the field of data science. The study highlights the importance of big data analytics in data science, focusing on sentiment analysis and developing a machine learning framework for identifying sentiments in product reviews and assessing Massive Open Online Courses (MOOCs). Online education proved its value to the academic community during the COVID-19 pandemic, when much of the world was at a standstill. Since the coronavirus outbreak, Massive Open Online Courses (MOOCs) have taken over many public schools and undergraduate degree programs as the new norm. Nowadays, online courses are being incorporated into traditional educational programs. Therefore, it has become crucial to develop a framework that both government and nongovernment organizations could use to assess public opinion before implementing online education programs. This article offers a methodology for utilizing deep learning and natural language processing techniques to examine public sentiments toward online education and courses such as MOOCs. Public reviews from Coursera and Udemy are utilized to assess the quality of online courses. Using sentiment analysis, the proposed methodology employs the Bidirectional Encoder Representations from Transformers (BERT) model to classify public reviews into positive and negative categories. Positive and negative reviews are clustered using the k-means algorithm to evaluate the quality of MOOCs. The study's findings indicate that MOOCs with qualified professors are beneficial for learning basic concepts. But they may lack advanced knowledge, real-world experience, and enough examples. These courses are lengthy, and students request more questions after lessons to comprehend the concept. The proposed framework assists in assessing public opinion before implementing online education and may provide valuable insight to the developers of the courses. [ABSTRACT FROM AUTHOR]

This paper provides a thorough exploration of the key elements influencing the quality of Massive Open Online Courses (MOOCs), examining the quality parameters at the various stages of MOOC development and the evolving landscape of quality assessment. Many MOOCs offered by prestigious universities and business schools cover among other things, business strategies, management techniques and organizational development. The paper delves into existing literature to establish a foundation for understanding MOOC quality parameters and MOOC development stages. The background section elucidates the complexities of MOOCs and highlights the need for a nuanced evaluation of quality in these educational platforms. The paper critically examines MOOC evaluation, focusing on two key aspects: the stages of MOOCs and quality dimensions. It discusses best practices, models for high-quality MOOC development, and strategies for achieving quality objectives. Exploring existing standards, frameworks, and guidelines, it offers insights into quality measures and introduces new evaluation developments. The paper concludes with discussions on the current state of MOOC quality, proposing future research avenues. [ABSTRACT FROM AUTHOR]

Massive open online courses (MOOC) are the largest initiative in eLearning, with the support of universities across the world. To increase course satisfaction in MOOCs, learners' must relate to the courses that best suit their needs and interests. The goal of recommendation systems is to suggest items to users based on their preferences and past behaviour. A course recommender system makes recommendations based on the similarity of courses and past interactions with the MOOC platform. With a huge volume of online courses on multiple learning platforms, it has been difficult for learners to identify the course of their interest. To address these challenges, a novel framework for hybrid MOOC course recommendations is proposed to recommend courses from multiple learning platforms. It uses web scraping techniques to collect course data from various MOOC providers, such as Coursera, Udemy, and edX platforms. With the real time dataset, a deep learning chatbot captures the personalized learning requirements of learners and recommends using a user-user collaborative approach with the valence aware dictionary and sentiment reasoner (VADER) for sentiment analysis. It enhances the accuracy of recommendations with an root-meansquare error (RMSE) value of 0.541. [ABSTRACT FROM AUTHOR]

The United Nations adopted the sustainable development goal of "quality education" as one of its objectives. In emergency teaching and learning amid the outbreak in 2019, the emphasis has been placed on providing a versatile and easily accessible lifelong learning experience to ensure high-quality education. One type of online e-learning course is the massive open online course (MOOC). It provides a free course that may be taken whenever and anywhere. However, difficulties have come up regarding student performance, course completion, and dropping out as a result of quality assurance of e-learning platforms like MOOC. In order to keep students interested in the course until the end, this study will review and recommend MOOC strategies. The strategies in enhancing for MOOC engagement reveals that they include development, collaboration between educators and students, and evaluation. Self-regulation learning is a crucial motivation in retention from dropping out of the MOOC facilitates participation through the utilization of innovative pedagogies, as well as the interaction between students and educators on both the MOOC platforms and social media platforms according to research. Course information and instructional design are also found to attract learners to complete the course. Giving prizes for completing MOOC assignments and tests is an extra choice in retaining from dropping out the course. The contribution of study is MOOC online learning engagement strategies are introduced. By developing a good course design, collaboration between students and educators and evaluation performance based can enhance students' engagement to completion of the course. [ABSTRACT FROM AUTHOR]

This paper addresses undergraduates' perception and behavioral intention (BI) toward Massive open online courses (MOOCs). It considers the moderating effect of culture to further explore the perceptions of students with different cultures to engage in MOOCs, providing MOOC suppliers with practical suggestions. 770 undergraduates in Spain and China are involved in this study. The proposed research model incorporating the technology acceptance model and theory of planned behavior is confirmed as a desirable theoretical model. Additionally, the moderating effect of culture has been confirmed between Spanish and Chinese students. The findings show Perceived usefulness and Perceived ease of use are effective in forming attitude (ATT). Besides, ATT and subjective norm, and perceived behavioral control are confirmed as vital factors impacting BI. Regarding the moderating effect of culture, significant differences have been detected to account for their behavior. [ABSTRACT FROM AUTHOR]

Background. On one hand, education is a specific form of social interaction where participants’ trust is a prerequisite rather than an outcome. On the other hand, the contemporary informational environment, in which new educational practices emerge, is replete with unverified and false information that complicates trust. This creates a paradox: online education simultaneously demands both swift trust and epistemic vigilance. Swift trust and epistemic vigilance are understood as two regimes of critical thinking, and the factors influencing the switch between these regimes are the subject of this study. Purpose. To identify the factors that trigger different regimes of critical thinking in online learning. Materials and methods. The theoretical part of the study is based on the synthesis of epistemic trust concept, theory of swift trust and social-epistemological approach to critical thinking. Based on this, the article presents a pilot survey of students of massive open online courses of Lobachevsky University online learning platform (N=83). Results. The main finding is that the key factor in switching between swift trust and epistemic vigilance is the previous negative online learning experiences. having such experience is consolidated and sharply reduces expectations from any subsequent online learning. The absence of negative experiences, on the contrary, increases swift trust and reduces epistemic vigilance. The main mechanisms of switching between regimes of critical thinking in online education are presented as epistemic delegation, appeal to statistics, logical-grammatical representation, and visual representation. Sponsorship information. The work was funded with a grant from the Russian Science Foundation (project № 24-28-00809 “Critical thinking studies: fundamental research on critical thinking as an interdisciplinary problem”)

Objective: This study aims to evaluate the impact of virtual simulation experiment teaching model and Massive Open Online Course (MOOC) teaching model on the teaching effect in debridement teaching. Methods: The study adopted a quasi-experimental design and used virtual simulation technology to construct a virtual simulation experimental teaching platform for debridement. This study was conducted at the Second Clinical College of Wuhan University. The experimental group was composed of 135 third-year clinical medicine students in the 2020 grade, who received the virtual simulation experimental teaching model; the control group was 122 third-year students in the same major in the 2019 grade, who used the MOOC teaching model. The performance of the two groups of students was evaluated through theoretical tests and animal experiment operation. In addition, the effectiveness of the experimental teaching model and student satisfaction were evaluated through questionnaire surveys. Results: The theoretical test scores and animal experiment report scores of the experimental group were significantly higher than those of the control group, and the debridement animal experiment operation time of the experimental group was shorter than that of the control group, and the difference was statistically significant (P < 0.05). The post-class questionnaire survey of the experimental group showed that most students were satisfied with the virtual simulation experimental teaching model and believed that it represented the future teaching trend. Conclusions: In the teaching of debridement, virtual simulation experiment is an effective t teaching model, which not only helps to improve student performance, but also significantly reduces skill operation time and is recognized by students. [ABSTRACT FROM AUTHOR]

Quantum computing is an exciting field with high disruptive potential, but very difficult to access. For this reason, many approaches to teaching quantum computing are being developed worldwide. This always raises questions about the didactic concept, the content actually taught, and how to measure the success of the teaching concept. In 2022 and 2023, the authors taught a total of nine two-week MOOCs (massive open online courses) with different possible learning paths on the Hasso Plattner Institute's OpenHPI platform. The purpose of the platform is to make computer science education available to everyone free of charge. The nine quantum courses form a self-contained curriculum. A total of more than 17,000 course attendances have been taken by about 7400 natural persons, and the number is still rising. This paper presents the course concept and evaluates the anonymized data on the background of the participants, their behaviour in the courses, and their learning success. This paper is the first to analyze such a large dataset of MOOC-based quantum computing education. The summarized results are a heterogeneous personal background of the participants biased towards IT professionals, a majority following the didactic recommendations, and a high success rate, which is strongly correlatated with following the didactic recommendations. The amount of data from such a large group of quantum computing learners provides many avenues for further research in the field of quantum computing education. The analyses show that the MOOCs are a low-threshold concept for getting into quantum computing. It was very well received by the participants. The concept can serve as an entry point and guide for the design of quantum computing courses. [ABSTRACT FROM AUTHOR]

Concerns over the quality of teaching in massive open online courses devoted to language learning (LMOOCs) have prompted extensive research on learning behavior in such courses. The purpose of this study is to gain a better understanding of autonomous learning check-ins – that is, individuals sharing their own learning records and/or other information about their learning-related experience – a novel behavior that has not been studied in previous LMOOC research. Using learning analytics, we found that just 6.2% (n = 699) of a sample of 11,293 learners autonomously engaged in check-in behavior, and that the content of these learners' check-ins varied considerably according to their contexts and the language skills they were seeking to acquire. We further found (1) a positive association between check-in behavior and LMOOC completion; (2) that students who chose to check in earned relatively low grades on unit quizzes, especially in their early stage of learning, but outperformed the non-check-in group significantly in final exam scores; and (3) that those who checked in engaged with a significantly wider array of in-LMOOC learning components than those who did not, and thus accessed a wider system of language-learning experiences. Taken together, these results confirm that check-in behavior can aid the process of learning in an LMOOC and further highlight this behavior's wider potential to aid self-directed autonomous online learning. [ABSTRACT FROM AUTHOR]

Language MOOC research has experienced a notable evolution from practice to conceptuality since its emergence as a subdiscipline of computer-assisted language learning. The versatility of the MOOC format for language learning has led to experimental designs that combine linguistic acquisition with other educational activities. This has been considered to be conducive to new ways of understanding how language learning occurs in LMOOCs, although there is no solid classification of LMOOCs subtypes to date based on course design. This study aimed to contribute to the conceptualisation of the field by creating a taxonomy for existing LMOOCs. Grounded theory strategies were adopted, so evidence was systematically collected to develop conceptual categories based on a thorough analysis process of the syllabus and short description of 432 courses. As a result, six LMOOC modalities emerged from the analysis: general language learning LMOOCs, LMOOCs for academic purposes, LMOOCs for professional purposes, LMOOCs focused on a specific language skill development, cultural-oriented LMOOCs, and meta-language learning LMOOCs. This study means a significant contribution to the LMOOC research field inasmuch as it is one of the first empirical-based attempts to broaden the definition of LMOOC. [ABSTRACT FROM AUTHOR]

Online asynchronous interaction is considered a core part of online teacher training, which has an important impact on learners' learning experience and learning outcomes. How to provide immediate and effective feedback through technical support based on the learners' interactive content and enhance interactive connection has become a key issue in massive online teacher training. This study designed an automated feedback framework from feedback strategy, feedback way, and feedback type. Then, we designed an emotional-cognitive feedback approach and an emotional-cognitive-metacognitive feedback approach based on automated analysis of online asynchronous interactions. A massive online teacher training course was conducted to provide automated feedback to 1438 learners in the online asynchronous interaction. Results showed that the two approaches enabled learners to interact more positively, slow down the growth of the dropout rate, and help learners adjust their emotional state and cognitive level. Particularly, the emotional-cognitive-metacognitive feedback approach could facilitate learners' self-regulation and improve feedback quality. Through questionnaires and semi-structured interviews, we found that learners were obsessed by automated emotional-cognitive-metacognitive feedback approach and they believed that it was helpful for their learning. This study is of great significance and application value to widely carry out high-quality, massive and personalized online learning. [ABSTRACT FROM AUTHOR]

Different from the group formation approaches led by teachers, learners' generative learning objectives and the independent choice of collaborative partners are important in the Internet learning environment. This study takes the cMOOC (connectivist massive open online course) 5.0 "Internet + education: dialogue between theory and practice" as a case course. Data on learners' interactive behaviours, generative texts, and personal attributes were collected through the course platform and questionnaires. Social network analysis and natural language processing were adopted to build a variety of collaborator relationship networks, and the quadratic assignment procedure was used to reveal the impact of the social, topic and attribute relationships of learners before collaboration on the collaborative relationship. The results show that individuals with close social relations, similar topics, similar age and learning experiences before collaboration are more likely to establish collaborative relationships; among them, close social relations and close ages are the key driving factors affecting the choice of partners. In addition, topic similarity can further promote the establishment of collaborative relationships among individuals with close social relations. The findings will help community-based courses optimise partner recommendation strategies and promote collaborative problem-solving and collective knowledge creation. [ABSTRACT FROM AUTHOR]

Massive Open Online Courses (MOOCs) are platforms teachers can use to seek guidance on different strategies to update their teaching skills. This study finds the effectiveness of MOOCs in developing professional performance and skills of school teachers. It tries to show the extent to which school teachers can benefit from these courses and how they apply them in their practical teaching. A mixed-method sequential explanatory (following up the quantitative findings with qualitative ones) method by using multiple data sources, including the MOOC system's log files, final grades and the discussion forum, with additional of face to face class snapshots was used. Some of the data were analysed using statistical, correlations and clustering models, with others coded in excel to develop different themes based on reported experiences. Results indicated how TPD core features uncover school teachers' knowledge and skills level through their learning behaviour variables for the MOOC platform, which will help to transform their pedagogical and instructional approaches while shifting their classrooms into 21st Century classrooms. Generally, this study contributes fresh insights into the literature on the importance of MOOCs for teachers' professional growth while sustaining their work and connections for networked learning. Implications for future studies are also discussed. [ABSTRACT FROM AUTHOR]

The interactive mechanism between social and concept networks is a key question in connectivist learning, which explains the impact of interaction on cognitive development and knowledge generation. The successful practice of "Internet Change Education: Dialogue between Theory and Practice", the first cMOOC in China, provides data support and research context for exploring the learning rules. This study proposed an evaluation framework of individual social network status in cMOOCs and a model for concept network characteristics analysis in cMOOCs. Analysis from 10,598 interactive data points of cMOOC1.0 using complex network analysis, topic clustering algorithm and correlation analysis found that there was a correlation between a learner's individual network status and concept network characteristics, and there are two kinds of special connectivist learners in cMOOC learning: someone with higher network status but lower knowledge output, and someone with higher knowledge output but weaker network status. Furthermore, we put forward three practical suggestions: (1) facilitators should focus on guiding learners' expression and connection to important individuals; (2) according to different correlation trends, facilitators can flexibly tailor activity design strategies, such as providing additional wayfinding support for learners with low centrality; and (3) carry out adaptive learning evaluations and design diversified certificates. [ABSTRACT FROM AUTHOR]

Although digital advances have enabled hundreds of universities to offer massive open online courses (MOOCs) to millions of students worldwide, the high dropout rate on these MOOCs indicates a disjunct between the potential of the offer and the success of its uptake. Many studies point to issues relating to interpersonal connection as a major factor in this disjunct. Interviews with students support the conclusion that lecturers' interpersonal tone is crucial to shortening psychological distance in pre-recorded videos. However, it is unclear how the interpersonal tone preferred by students can be realised discursively. This paper captures discursive instances by modelling an engagement framework and identifying particularly useful strategies through comparison of a high-rated and a low-rated engineering MOOC course delivered on Coursera. Findings show that in the high rated course the lecturer consistently anticipated students' state of mind and in response constructed a discourse that performed several interpersonal functions. On the other hand, in the low-rated course the lecturer mostly anticipated and addressed knowledge gaps and issues of difficulty, but not students' emotional responses, and so deployed far fewer interpersonal strategies in the lecture discourse. These findings shed light on how lecturers can use interpersonal language in pre-recorded lectures to improve student engagement. [ABSTRACT FROM AUTHOR]