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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]

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]

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]

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]

With the rapid development of multimedia technology, the student centered flipped classroom model (FCM) and massive open online courses (MOOCs) have been increasingly introduced and implemented in higher medical education. However, comparative analyses of the offline face‐to‐face FCM and completely online FCM have been rarely reported. In this study, we focused specifically on a set of flipped classrooms in which prerecorded videos were provided before class. Using the Zhihuishu platform as the major online course platform, our team built a MOOC and evaluated the teaching effectiveness of the FCM in both the offline face‐to‐face class and the online electronic live class for medical genetics education. Questionnaires, paper‐based and oral exams were used to collect data on the teaching effects of the different teaching methods. We found that student satisfaction and overall student performance in the offline FCM group was significantly higher than that in the completely online teaching group. Although online FCM allowed students to play back and review anywhere and anytime after class, students taught in offline FCM had a significantly higher degree of knowledge mastery, had a deeper understanding of theoretical knowledge, and were better at knowledge comprehensive application. The effects of their training on genetic disease clinical diagnosis and treatment skills were significantly better, and their capacity for scientific research was also significantly improved. Our research discussed the advantages of the online courses and the problems brought about by using these technologies, and it provided insight into online teaching practices in the era of internet‐based medical education. [ABSTRACT FROM AUTHOR]

Background: Investigating emotion sequence patterns in the posts of discussion forums in massive open online courses (MOOCs) holds a vital role in shaping online interactions and impacting learning achievement. While the majority of research focuses on the relationship between emotions and interactions in MOOC forum discussions, research on identifying the crucial difference in emotion sequence patterns among different interaction groups remains in its infancy. Objectives: This research utilizes deep learning and sequential pattern mining to investigate whether there are differences in emotion sequence patterns across different groups of learners who exhibit various types of interactions in online discussion forums. Methods: Data from a comprehensive array of sources, including log files, discussion texts and scores from 498 learners in online discussion forums, were collected for this study. The agglomerative hierarchical algorithm is used to classify learners into groups with different levels of interactions. Additionally, we implement and evaluate multiple deep learning models for detecting different emotions from online discussions. Relevant emotion sequence patterns were identified using sequence pattern analysis and the identified emotion sequence patterns were compared across different groups with different levels of interactions. Results and Conclusions: Using an agglomerative hierarchical algorithm, we classified learners into three distinct groups characterized by different levels of interactions: high, average and low level. Leveraging the bi‐directional long short‐term memory model for emotion detection yielded the highest predictive performance, with an impressive F‐measure of 94.01%, a recall rate of 93.83% and an accuracy score of 95.01%. The results also revealed that learners in the low‐level interaction group experienced more emotion transition from boredom to frustration than the other two groups. Therefore, the aggregation of students into groups and the utilization of their MOOC log data offer educators the capability to provide adaptive emotional feedback, customize assessments and offer more personalized attention as needed. Lay Description: What is currently known about the subject matter: Emotions are dynamic over time when learners experience cognitive disequilibrium/equilibrium.Online interactions are critical components, which influence learners' emotional state, cognitive processes and learning achievement.It is not clear what are differences in emotion sequence patterns across the groups with different interaction types. What the paper adds: An agglomerative hierarchical algorithm was implemented to cluster learners into three groups by analysing behavioural data.We explore possibilities for automated classification of emotions using deep learning approaches.Learners in the low‐level interaction group experienced more emotion transition from boredom to frustration. Implications for practitioners: A considerable amount of effort should be expended to identify and respond to learners who experience boredom and frustration emotions.Designing interventions or scaffolding to facilitate learners' interaction and promote favourable emotions.Educators could provide more personalized support based on learners' online interaction cluster. [ABSTRACT FROM AUTHOR]

The dynamic map is applied instead of traditional static maps in the teaching of geographic information science (GIS)‐related majors so as to meet the need for teaching animations and teaching interaction in new teaching methods such as mobile personalized learning, massive open online course, and Flipped Classroom. The research introduces the concept and the making tools of dynamic maps, studies the making principles and key technologies of dynamic maps, and constructs the relevant theories such as the "frame" model based on time granularity, the dynamic interpolation algorithm based on spatial characteristics, dynamic symbolic logic model, and algorithm. Based on the above principles and theories, taking a dynamic map of the change in the population of each city in Shandong province as an example, the research demonstrates the making method and process of the dynamic maps for teaching with Flash MX. At last, it analyzes the role of dynamic map in GIS‐related courses teaching and evaluates the teaching effect of dynamic maps. [ABSTRACT FROM AUTHOR]

Background: Given 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. Objectives: This 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. Methods: The 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 Conclusion: The 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. Lay Description: What is already known about this topic: There are a large number of K12 MOOC implementations, which are (i) STEM‐related and computer science courses, and (ii) from Europe and the USA.Limited studies provided different learning strategies for MOOC learning from the perspective of K12 schools, which are not MOOC developers.There is a need to explore MOOC initiatives encompassing a wide variety of subjects, including computer science MOOCs and non‐computer science MOOCs, in regions beyond Europe and the USA.There is also a need for further scrutiny of the effectiveness of learning strategies provided by the K12 school. What this paper adds: Our study launched the first MOOC implementation in K12, encompassing several subject areas beyond computer science in the region of Asia.Our study provided different learning strategies adopted by the K12 school, and the analysis revealed that MOOC learners took positive attitudes towards overall school support with mentoring enjoying great popularity with these students.Our study examined the effectiveness of school support and identified that mentoring and reimbursement are more effective than university training and the learning guide. Implications for practitioners: The insights gained from the study enable other K12 schools and educators to implement MOOC into existing school infrastructures successfully.These strategies provided in the MOOC implementation program can be helpful in many different online learning contexts. [ABSTRACT FROM AUTHOR]

A new mode of knowledge production, known as the 'organic growth pattern', has emerged within the online learning environment. It exhibits several unique features, such as iterative growth, fuelled by collective intelligence, fuzzy complexity, etc. It is more valuable and adaptable to support knowledge innovation within rapidly changing fields. To reveal its new evolutional production pattern and the interaction mechanism of different generative topics, We take a dual perspective of content and behaviour into consideration. A total of 1855 interactive data points were collected from a connectivist massive open online course. The study employed methods, such as latent Dirichlet allocation, content analysis and lag sequential analysis. Results showed that (1) the paths of knowledge innovation were complex and diverse. The process typically began with an ill‐structured question and was driven by collective contribution and cognitive conflict. (2) Knowledge production involved interactive evolution and collaborative growth of multiple generative topics. (3) The four stages of knowledge production were cyclic, with sense‐making acting as a bridge between way‐finding and innovation. Several implications of the findings are recommended to enhance the quality of knowledge growth, such as designing ill‐structured questions, providing discussion activities from easy to difficult, introducing relevant topics, taking advantage of various platforms and mobilizing learners' or participants' quality attention‐related behaviours. [ABSTRACT FROM AUTHOR]

Education is crucial for a productive life and providing necessary resources. With the advent of technology like artificial intelligence, higher education institutions are incorporating technology into traditional teaching methods. Predicting academic success has gained interest in education as a strong academic record improves a university's ranking and increases student employment opportunities. Modern learning institutions face challenges in analyzing performance, providing high-quality education, formulating strategies for evaluating students' performance, and identifying future needs. E-learning is a rapidly growing and advanced form of education, where students enroll in online courses. Platforms like Intelligent Tutoring Systems (ITS), learning management systems (LMS), and massive open online courses (MOOC) use educational data mining (EDM) to develop automatic grading systems, recommenders, and adaptative systems. However, e-learning is still considered a challenging learning environment due to the lack of direct interaction between students and course instructors. Machine learning (ML) is used in developing adaptive intelligent systems that can perform complex tasks beyond human abilities. Some areas of applications of ML algorithms include cluster analysis, pattern recognition, image processing, natural language processing, and medical diagnostics. In this research work, K-means, a clustering data mining technique using Davies' Bouldin method, obtains clusters to find important features affecting students' performance. The study found that the SVM algorithm had the best prediction results after parameter adjustment, with a 96% accuracy rate. In this paper, the researchers have examined the functions of the Support Vector Machine, Decision Tree, naive Bayes, and KNN classifiers. The outcomes of parameter adjustment greatly increased the accuracy of the four prediction models. Naïve Bayes model's prediction accuracy is the lowest when compared to other prediction methods, as it assumes a strong independent relationship between features. [ABSTRACT FROM AUTHOR]

This article discusses the importance of STEM education, which stands for science, technology, engineering, and mathematics, and its impact on a nation's capacity to thrive. It highlights the need for equitable access and diversity in STEM education and explores the intersection of adult education and STEM education. The article presents various examples of how adult education can inform and be informed by STEM disciplines, including digital literacy for older adults, workforce development in the agriculture retail industry, educator professional development through massive open online courses (MOOCs), mathematics instruction, the implications of artificial intelligence (AI) in healthcare, and the role of social capital in informal learning in STEM contexts. The article concludes by discussing the benefits and challenges of collaborations between adult education and STEM disciplines. [Extracted from the article]

This article explores the design and development of a Massive Open Online Course (MOOC) on inclusive teaching. Released in the Fall of 2023, the MOOC is particularly unique because of its intentional use of ethnically diverse voices and multimodal content to include art, music, spoken word, and storytelling. Modules range from Self and Empathy to Classroom Climate and Networks. MOOC production was graciously funded by a grant from the Howard Hughes Medical Institute. At its core, the article will share reflections on the rationale and process, describe how STEM and Adult Education scholar‐practitioners collaborated to infuse adult education and interdisciplinary concepts in MOOC development and outline meaningful implications for the field. [ABSTRACT FROM AUTHOR]

Background: Collaborative learning can effectively promote the development of higher‐order competencies, where learners collaborate via social interactions to explore and solve complex problems. It has been found that the effective occurrence of collaborative learning requires appropriate external guidance and support. In particular, collaborative interactions in open learning environments are more complex and difficult, which makes it important to provide dynamic interventions for learners according to different collaborative situations. However, the role of facilitators and the characteristics and mechanisms of their interventions have yet to be thoroughly investigated. Objectives: The study aims to uncover the characteristics and patterns of interaction among groups with different collaborative performance and facilitator performance in an open learning environment, and to explore the differentiated role of facilitators in this process. Method: Groups are categorised as high or low based on their collaborative performance and facilitator performance. This study used lag sequential analysis (LSA) and content analysis to explore the interaction patterns of groups with different collaborative performance and facilitator performance. Also, the different roles of facilitators were analysed by comparing the differences in interaction patterns of different collaborative performance groups. Results: The study found that groups with different collaborative performances and facilitator performances showed significant differences in interaction patterns, and the two had crossover effects. There is also variability in the role of facilitators in groups with different collaborative performance profiles. Patterns of transition between different layers of interaction can indicate the level of groups' collaborative engagement. Specific interaction patterns exist that are conducive to enhancing collaborative learning. Conclusions: The results of the study pointed out that groups with different collaborative performance and facilitator performance showed different characteristics of interaction patterns. The role of the facilitator in online collaborative learning is characterised by compensatory and merit‐promoting. High‐quality online collaboration is the result of multiple factors, including learners and facilitators working together. This study has practical implications for the design of instructional interventions for group collaboration in online learning communities. Lay Description: What is already known about this topic: Deep instructional interactions help stimulate group intelligence and promote knowledge creation and deep connections.The facilitation strategies of the instructional implementer in online collaborative learning contexts play an important role in guiding students to engage in interactions and promoting efficient learning.Facilitators need to adopt different intervention strategies for different learners and learning stages to advance collaborative task completion. What this paper adds: Group collaborative performance is closely related to the strength of the connection and the depth of interaction of group members.Roles of facilitator in online collaborative learning can be characterised by strong support, compensation, and merit promotion.High‐quality online collaboration is the result of multifactor interaction between learners and facilitators. Implications for practice and/or policy: Course organisers' rating scale proposed in this study can help screen and train facilitators with excellent attributes.Examining different roles of facilitators can help curriculum designers rationalise the design and organisation of collaborative instruction.When group collaboration is active, learners should be guided through activities for deeper interaction and exploration.When group collaboration is low‐spirited, learners should be encouraged to focus on the stage tasks and explore the tasks step by step. [ABSTRACT FROM AUTHOR]

Issue addressed: Evaluated the impact of the Understanding Multiple Sclerosis (MS) massive open online course, which was intended to increase understanding and awareness about MS, on self‐reported health behaviour change 6 months after course completion. Methods: Observational cohort study evaluating precourse(baseline) and postcourse (immediately postcourse and six‐month follow‐up) survey data. The main study outcomes were self‐reported health behaviour change; change type; and measurable improvement. We also collected participant characteristic data (eg, age, physical activity). We compared participants who reported health behaviour change at follow‐up to those who did not and compared those who improved to those who did not using χ2 and t tests. Participant characteristics, change types and change improvement were described descriptively. Consistency between changes reported immediately postcourse and at the 6‐month follow‐up was assessed using χ2 tests and textual analysis. Results: N = 303 course completers were included in this study. The study cohort included MS community members (eg, people with MS, healthcare providers) and nonmembers. N = 127 (41.9%) reported behaviour change in ≥1 area at follow‐up. Of these, 90 (70.9%) reported a measured change, and of these, 57 (63.3%) showed improvement. The most reported change types were knowledge, exercise/physical activity and diet. N = 81 (63.8% of those reporting a change) reported a change in both immediately and 6 months after course completion, with 72.0% of those that described both changes giving similar responses each time. Conclusion: Understanding MS encourages health behaviour change among course completers up to 6 months after course completion. So what?: An online education intervention can effectively encourage health behaviour change over a 6‐month follow‐up period, suggesting a transition from acute change to maintenance. The primary mechanisms underpinning this effect are information provision, including both scientific evidence and lived experience, and goal‐setting activities and discussions. [ABSTRACT FROM AUTHOR]

Engineering education can be improved through the implementation of a flipped classroom, an instructional approach that allows students to learn theory online and practice it offline. However, the flipped classroom approach does not directly solve the challenge of the high dropout rate in online distance education, such as massive open online courses (MOOCs). Actually, distance education lacks the convenience of face‐to‐face communication and supervision. This study considers a deep learning model based on an attention mechanism in addition to some other common traditional machine learning models in an engineering MOOC with a flipped classroom design. The aim is to predict students' future performance and assist teachers in discovering the important learning stages that affect the prediction, thus improving teaching and learning performance. Our model performs better than other commonly used models. It is found that the middle and final learning stages, where specific knowledge is taught, are important, offering a new approach to analyzing and promoting distance engineering education. [ABSTRACT FROM AUTHOR]

The absence of counterfactual outcomes presents a fundamental challenge in causal inference. However, existing work typically does not apply to multiple learning behaviors of Massive Open Online Courses. This paper proposes a counterfactual representation learning model based on multitask learning, applicable to any dimension, and any type of treatment. The model consists of a potential outcome network and a propensity score encoder, which shares feature information from the base layer. The propensity scores calculated by the encoder are then utilized in the potential outcome network to mitigate selection bias. Experiments based on real‐world data sets demonstrate the superior performance of our model compared with baselines. [ABSTRACT FROM AUTHOR]

Background: Australian university students are at risk of experiencing poor mental health, being vulnerable to self‐harm and suicidal ideation. Aim: "Talk‐to‐Me" is a suicide ideation prevention Massive open online course (MOOC) previously showing it can support Western Australian university students' knowledge of identifying and responding to suicide ideation in themselves and others. Methods: A multi‐site one‐group pre‐test/post‐test design with a 12‐week follow‐up explored the efficacy of "Talk‐to‐Me" for university students Australia‐wide, evaluating the influence of COVID‐19 and location. Overall, 217 students (55% female; mage = 24.93 years [18, 60]) enrolled in this study from 2020 to 2021. Participants' responses to suicidal statements, mental health literacy, generalized self‐efficacy, help‐seeking behavior, and overall utility of the program were collected at baseline, post‐MOOC (10 weeks from baseline) and 12‐week follow‐up. The effect of time and location interaction was explored using a random‐effects regression model. Results: Findings indicated significant improvement in participants' knowledge of positive mental health support strategies (ES = 0.42, p < 0.001) and recognizing appropriate responses to suicidal statements (ES = 0.37, p < 0.001) at 10‐weeks, with further improvement at 12 weeks follow‐up (ES = 0.47 and 0.46, p < 0.001). Students reported higher generalized self‐efficacy at the 12‐week follow‐up compared to baseline (ES = 0.19, p = 0.03) and an increased tendency to seek professional help for mental health issues (ES = 0.22, p = 0.02). Conclusion: These findings provide preliminary evidence of the efficacy of the "Talk‐to‐Me" program in supporting ‎university students across Australia to increase their suicide‐related knowledge and skills, ‎general self‐efficacy, and overall mental fitness. [ABSTRACT FROM AUTHOR]

Background: Providing feedback in massive open online courses (MOOCs) is challenging due to the massiveness and heterogeneity of learners' population. Learning analytics (LA) solutions aim at scaling up feedback interventions and supporting instructors in this endeavour. Paper Objectives: This paper focuses on instructor‐led feedback mediated by LA tools in MOOCs. Our goal is to answer how, to what extent data‐driven feedback is provided to learners, and what its impact is. Methods: We conducted a systematic literature review on the state‐of‐the‐art LA‐informed instructor‐led feedback in MOOCs. From a pool of 227 publications, we selected 38 articles that address the topic of LA‐informed feedback in MOOCs mediated by instructors. We applied etic content analysis to the collected data. Results and Conclusions: The results revealed a lack of empirical studies exploring LA to deliver feedback, and limited attention on pedagogy to inform feedback practices. Our findings suggest the need for systematization and evaluation of feedback. Additionally, there is a need for conceptual tools to guide instructors' in the design of LA‐based feedback. Takeaways: We point out the need for systematization and evaluation of feedback. We envision that this research can support the design of LA‐based feedback, thus contributing to bridge the gap between pedagogy and data‐driven practice in MOOCs. Lay Description: What is known about this topic: Learners' scale and diversity hinder timely feedback in massive open online courses (MOOCs).Learning analytics (LA) can optimise learners' support by scaling the feedback interventions. What this paper adds: We present a literature review on LA‐based feedback in MOOCs in the last 12 years.We focused on LA approaches that support instructors in providing feedback.Results show a scarcity of empirical studies related to these LA‐based tools.Results show that limited attention is paid to pedagogical and contextual aspects. Implications of the study findings for practitioners: We address knowledge gaps related to LA‐based tools for feedback in MOOCs.There is a need of LA‐based interventions to consider the course context.There is a need to evaluate the impact of LA‐based feedback in MOOCs empirically. [ABSTRACT FROM AUTHOR]

Background: The COVID‐19 lockdown forced students and teachers to adjust to remote lecturers and digital learning material and design criteria for online classes became the centre of discussion. Objectives: The purpose of this empirical study was to investigate the relationship between design principles of educational online practices in higher education and students' active engagement in the online learning process. Methods: A total of 127 students participated in the study after their first semester of fully online classes resulting from the COVID‐19 lockdown. The online classes enabled real‐time communication and allowed for the creation of professional learning communities or the collaborative creation of content, for example. To evaluate the classes and to indicate perceived student learning, the study applied scales for measuring academic student engagement, as well as the four distinct factors for assessing online teaching effectiveness. Results and Conclusions: Bivariate correlational analysis suggested that the two design elements Time on Task (TT) and Active Learning (AL) are related to emotional engagement at a meaningful level thus influencing student commitment and class activities. The results further indicated that TT and AL are significant design elements in relation to perceived usefulness and students' perceived learning. Implications: The results may identify effective means to support and enhance student engagement in online settings, thus improving the quality of online teaching and learning. Lay Description: What is already known about this topic?: Student Engagement is one of the main factors that influence student achievement and student motivation to learn. It is usually associated positively with desired learning outcomes.Active learning strategies are an effective means to engage students.Research on the quality of online learning is frequently directed at student learning outcomes or the attitudes of teachers and students towards online learning. What this paper adds?: Investigations on levels of student engagement in online learning (Student‐Faculty Interaction, Time on Task, Active Learning and Cooperation Among Students) instead of levels of achievement (learning outcomes).Relationships between levels of student engagement and design elements of online classes to inform good educational practice.Design criteria for online classes that students value as useful and that positively impact student learning. Implications for practice and/or policy: Time On Task and Active Learning share a strong relationship with emotional engagement. They influence student commitment and class activities.Time On Task and Active Learning are significant design elements in relation to perceived usefulness and students' perceived learning.The weakest correlations show for Cooperation Among Students and each of the three factors of academic engagement (behavioural, emotional and cognitive engagement). [ABSTRACT FROM AUTHOR]

As a team of practitioners with backgrounds across various academic disciplines, we identified the use of chi-square tests as being particularly accessible and useful when analyzing ordinal variables to better understand subgroup differences. 1 Table Statistical findings, including p-values for example variables' chi-square tests comparing rating distributions for subgroups. Many contemporary innovations in higher education aim to improve educational equity, and analyses of subgroup differences are often crucial for understanding the needs of learners. [Extracted from the article]

Issue addressed: Encouraging people to adopt life‐long habits that reduce dementia risk is necessary to manage the growing global prevalence of this condition and is, therefore, a global health priority. Current initiatives promoting risk‐reducing behaviour primarily attract participants from a limited range of backgrounds, even if widely available. This may inadvertently increase health inequities, as the people who are most likely to develop dementia are the people who are least involved in risk‐reduction initiatives. Interpersonal communication can effectively disseminate health messages to demographically diverse populations and may, therefore, broaden the reach of dementia risk‐reduction information. Methods: Coding reliability thematic analysis was used to categorise reports of information sharing provided by participants from one global online dementia risk education initiative, the Preventing Dementia Massive Open Online Course, or MOOC. These reports of information sharing were provided in response to the feedback question: "If you have already applied your MOOC learning, please tell us how." Results: Information was reportedly shared with a wide range of people, including those from demographic groups that are under‐represented among Preventing Dementia MOOC participants. Information about specific risk factors was shared, along with general information about the course and/or dementia risk reduction. Some participants also reported that the people they shared information with were initiating risk‐reducing behaviours. Conclusion: Interpersonal communication has the potential to disseminate dementia risk reduction information to, and promote behaviour change among, a broad group of people at risk of dementia, thereby increasing equity in dementia risk education. [ABSTRACT FROM AUTHOR]

The drivers for the internationalization of business teaching and research were multinational corporations, the Cold War, and the relative price advantage of non‐America business schools. Yet, the internationalization largely failed to reflect local contexts. The consequent paucity of original local business thought, international or not, exposes non‐American business schools to the raid of American‐based massive open online courses which will prune off the mainstay courses at emerging market business schools. Deprived of a substantial share of their fee‐paying students, the remaining docents at those schools will be less likely to publish altogether, including in fields that might sustain independent thought. Therefore, American‐like international business research is likely to have a longer life, more narrowly focused on English‐speaking countries and Europe, with even less of a contribution from emerging markets. [ABSTRACT FROM AUTHOR]

Since their beginning, Massive Open Online Courses (MOOC) have known great success and have managed to establish themselves with significant enrollment rates. However, this success was quickly disrupted by the drop‐out phenomenon observed in the majority of MOOCs, which reaches 90% in some courses. Studying and understanding this phenomenon, and consequently determining the relevance of the efforts made to develop MOOCs, has led several researchers to propose predictive models of learners at risk of dropping out. On one hand, these models have been made relying on machine learning and the massive data generated by learners' navigation. On the other hand, these models only provide weekly predictions and do not give clear visibility about the overall course progress. We present in this paper a framework based on the recurrent neural networks' strengths which uses generator and predictor modules. Our framework allows not only the prediction of dropouts but also the generation of each learners' behaviors during the whole course since its first week. Besides, an OLAP analytical module proved great support for MOOC moderators to report on the learners' behavior at‐risk to target their interventions and guide their support. [ABSTRACT FROM AUTHOR]

Background: Empathy is an essential part of patient‐centred health care, which positively benefits both patients and clinicians. There is little agreement regarding how best to design and deliver training for healthcare trainees to impart the skills and behaviours of clinical empathy. The study aimed to inform the field by sharing an educational intervention where we aimed to improve empathy amongst dental undergraduate students in Trinity College Dublin using a virtual learning module. Methods: Adopting pre–post‐repeat pre‐experimental design, dental professional students completed the Jefferson Scale of Empathy (JSE) for Health Professional Students immediately prior to and after a three‐week virtual programme designed to increase clinical empathy. Using a three‐factor model described for the JSE in the literature, scores were evaluated for internal consistency and paired tests were performed on scores appropriate to their distributions. Seven‐point Likert scales were scored to record student experience of training and technology, which are reported descriptively. Results: Most of the 37 participants were female (76%) and represented dental science (N = 27) and dental hygiene roles (N = 7). Results revealed a mean JSE‐HPS scale score rise from 110.0 (SD = 10.4) to 116.4 (SD = 11.1), which represented a rise of 5.8% (t (36) = 3.6, p = 0.001). The three factors associated with cognitive empathy, namely perspective‐taking (T(36) = 3.931, p < 0.001; walking in the patient's shoes T(36) = 2.093, p = 0.043); and compassionate care (Z = 2.469, p = 0.014) were all found to have increased after the intervention. Students reported a positive experience of discipline‐specific and generic videos as part of the module. Conclusion: The study demonstrated that a virtual educational module was associated with an increase in empathy amongst dental undergraduate students. The design of a blended module incorporating the Massive Open Online Course (MOOC) and virtual learning are beneficial and have a promising future. [ABSTRACT FROM AUTHOR]

Background: Select and enact appropriate learning tactics that advance learning has been considered a critical set of skills to successfully complete highly flexible online courses, such as Massive open online courses (MOOCs). However, limited by analytic methods that have been used in the past, such as frequency distribution, sequence mining and process mining, we lack a deep, complete and detailed understanding of the learning tactics used by MOOC learners. Objectives: In the present study, we proposed four major dimensions to better interpret and understand learning tactics, which are frequency, continuity, sequentiality and role of learning actions within tactics. The aim of this study was to examine to what extent can a new analytic technique, the ordered network analysis (ONA), deepen the understanding of MOOC learning tactics compared to using other methods. Methods: In particular, we performed a fine‐grained analysis of learning tactics detected from more than 4 million learning events in the behavioural trace data of 8788 learners who participated in a large‐scale MOOC 'Flipped Classroom'. Results and Conclusions: We detected eight learning tactics, and then chose one typical tactic as an example to demonstrate how the ONA technique revealed all four dimensions and provided deeper insights into this MOOC learning tactic. Most importantly, based on the comparison with different methods such as process mining, we found that the ONA method provided a unique opportunity and novel insight into the roles of different learning actions in tactics which was neglected in the past. Takeaway: In summary, we conclude that ONA is a promising technique that can benefit the research on learning tactics, and ultimately benefit MOOC learners by strengthening the strategic support. Lay Description: What is already known about this topic?: Since external support from instructors or peers are usually limited in MOOCs, the ability to self‐regulate learning (SRL), that is, select and enact goal‐oriented learning tactics that advance learning comes to be a critical set of skills to successfully complete coursework.Researchers have utilised analytic techniques that go beyond frequency‐based approaches, for example, process mining and sequence mining, to detect and understand learning tactics.However, limited by previous analytic methods that have been used in the past, we still lack a deep, complete and detailed understanding of the learning tactics used by MOOC learners. What this paper adds?: We proposed four major dimensions to better interpret and understand learning tactics, which are frequency, continuity, sequentiality and role of learning actions within tactics.We examined to what extent can a new analytic technique, the ordered network analysis (ONA), deepen the understanding of learning tactics compared to other methods.We found that the ONA method provided a unique opportunity and novel insight into the roles of different learning actions in tactics which was neglected in the past. Implications for practice and/or policy: Researchers who study learning tactics unpacked using the ONA technique can gain a deeper insight into a theorised learning processes that interplay within and across learning tactics and, in that way, improve their understanding of how self‐regulated learners enact and monitor learning tactics.The analytics on learning tactics use in a form of ONA graphs has the potential to provide learners with a detailed overview of their learning engagement over the selected learning period in MOOCs.The ONA graphs of learning tactics, combined with the information about learning performance and course requirements, instructors or learners (prompted by instructors) can evaluate whether the way learners have studied in a MOOC was beneficial to learners' learning success. [ABSTRACT FROM AUTHOR]

Background: Small private online courses (SPOCs) are one of the strategies to introduce the massive open online courses (MOOCs) within the university environment and to have these courses validates for academic credit. However, numerous researchers have highlighted that academic dishonesty is greatly facilitated by the online context in which SPOCs are offered. And while numerous algorithms have already been proposed, no research has been performed on how to transfer this information to instructors, so that they can intervene and decrease the prevalence of this issue. Objectives: In this article, we present a qualitative evaluation of a tool for detecting and monitoring students suspected of academic dishonesty in SPOCs in Selene, a Colombian instance of Open edX. Methods: The evaluation was carried out through semi‐structured interviews with four instructors who taught SPOCs with academic recognition at the University of Cauca. Results: The evaluation results indicated that participants found the dashboard reliable and appropriate to detect academic dishonesty behaviours in order to intervene in these cases. Implications: But interventions are difficult to systematise, need an institutional policy, and there is uncertainty about whether these interventions can actually contribute to decreasing academic dishonesty. Lay Description: What is currently known about the subject matter: There are studies that detect academic dishonesty behaviour through webcams in massive open online courses (MOOCs).There are studies based on Learning Analytics techniques for the detection of academic dishonesty behaviour in MOOCs.There are dashboards for monitoring students' learning activities in online courses. What their paper adds to this: An algorithm for real‐time detection of academic dishonesty behaviour in small private online courses (SPOCs) with academic recognition is presented.A dashboard for the visualisation of students' academic dishonesty behaviour in SPOCs is presented.A qualitative evaluation of a dashboard for the detection of academic dishonesty in SPOCs with academic recognition is presented. Implications of study findings for practitioners: Our work opens an avenue for exploration of different ways to detect and follow up on academic dishonesty in online courses.Implies that trainers should have a basic knowledge of data analysis. [ABSTRACT FROM AUTHOR]

Technological affordances have shown promising potential in advancing the delivery of corporate learning programmes designed for professional leadership development. However, there is a considerable challenge in evaluating learners' skill acquisition, with most of the past research relying on pre‐ and post‐tests or other forms of self‐reports to measure leadership development. In that sense, these approaches measure leadership development before and after the programme, while being inefficient for measuring the development during the learning process. This study collected self‐reflection answers from a professional development MOOC that allows learners to express their stepwise learning and reflect on their professional experience on leadership fronts. We developed a novel methodology and an automated system for the evaluation of leadership skills' mastery based on the depth of reflection exhibited during the learning process. We identified four groups of learners based on their course content mastery and explored the differences within groups. The results also highlight relevant insights about instructional design and provide promising avenues for future research. Practitioner notesWhat is already known about this topicProfessional leadership programmes have become increasingly common in workplace learning.Programmes mainly use manual/introspective measures to assess skill acquisition.What this paper addsAn automated assessment system to evaluate leadership skill mastery.Evidence‐based and leadership driven inferences about skill acquisition.Use of a novel multidisciplinary methodology for complex skills assessment.Implications of practice and/or policyAssessing leadership development should include more than course grades.Assessing differences in content mastery requires evaluation of various skills.Developed assessment system provides promise for other similar domains. [ABSTRACT FROM AUTHOR]

Flipped classroom based on active learning is becoming an increasingly popular pedagogical method in higher education capable of increasing student performance in higher‐order learning outcomes including application, analysis, evaluation, and creation. However, the success of a flipped classroom model relies on various supporting elements such as the accessibility of technology, and it may not be appropriate for all students and courses. In this study, a new blended biochemistry classroom model based on massive open online courses and a "semi‐flipped" environment was applied to the students enrolled in three majors (stomatology, pharmacy, and preventive medicine) at Cheeloo College of Medicine, Shandong University, China. To assess the improvement of the students' perception of self‐cognition in the blended biochemistry classes, surveys were conducted before and after undertaking the biochemistry course. Survey responses and total (final) score for the biochemistry course were analyzed using appropriate statistical methods. Compared to students who received traditional classroom instruction, students who participated in the blended classroom model achieved higher academic performance (p < 0.01) and reported a significant improvement in their perception of self‐cognition (p < 0.01, or p < 0.05). More than 80% of participants preferred the blended classroom model to that of traditional classroom instruction. [ABSTRACT FROM AUTHOR]

Background: Learners in a given massive open online course (MOOC) are usually provided with the same learning materials, guided by the same syllabus, and assessed in the same format. This "one‐size‐fits‐all" approach constrains learners' ability to reap the optimal benefits from online learning. Objectives: This study aims to characterize learners' differences in MOOC learning. Specifically, it examines how learners might vary in their enrolment motivation and the development of continuance intention to learn in a MOOC because of their gender and employment status. Methods: Data were collected via a questionnaire survey. Quantitative and qualitative methods were used to analyse data from 664 learners in a Chinese MOOC. Results and Conclusion: The research revealed significant differences in learners' enrolment motivation across groups defined by employment status, but not for gender groups. Learner groups defined by gender and employment status experienced variant psychological processes when deciding to continue to learn in the MOOC. Major Takeaways: Working adults stressed the instrumental values derived from MOOC learning; therefore, it is vital to design and integrate additional features into the MOOC to satisfy their needs. Besides, it would be critical to understand female learners' and working adults' expectations of MOOC learning, as they are more sensitive to confirmation in determining their attitudes toward learning in a MOOC. A short pre‐course survey of learners' expectations would serve the purpose. Lay Description: What is already known about this topic?: Gender and employment status are associated with varied learning outcomes in MOOCs.Motivation and continuance intention are two critical psychological mechanisms.Confirmation, satisfaction, and attitude are determinants of continuance intention. What this paper adds?: Students' enrolment motivation is significantly different from that of working adults.No significant difference was found in motivation across gender.Working adults' and female learners' attitudes were more determined by confirmation.Students and male learners depended more on satisfaction in forming their attitudes. Implications for practitioners: Working adults stressed the instrumental value derived from MOOC learning.Extra features should be integrated into MOOCs to satisfy working adults' needs.It is critical to understand female learners' and working adults' expectations.A short pre‐course survey could shed light on learners' expectations [ABSTRACT FROM AUTHOR]

Massive open online courses (MOOCs) continue to remain in the spotlight as a promising future education environment. However, more than 80% of learners stop learning before attending one-third of the course. Despite a continuous spread of MOOC and high dropout rate, little has examined the antecedent factors that influence student engagement in technology enhanced MOOC learning environment from the Job Demand-Resources (JD-R) model. The purpose of this study was to empirically identify the effects of individuals' basic psychological needs and the task–technology fit on MOOC learners' continuance intention to use, as well as the mediating effect of student engagement in MOOCs. Based on survey data from 201 Korean-MOOC learners, structural equation modeling was employed to assess the model. The findings are as follows: The basic psychological needs in MOOCs did not directly affect continuance intention to use, but did affect student engagement; the task–technology fit of MOOCs directly affected continuance intention to use and student engagement; and student engagement in MOOCs mediated between the basic psychological needs and task–technology fit, and continuance intention to use. It directly affected continuance intention to use. Implications were suggested for designing courses in MOOCs to increase student engagement for continuance intention to use. [ABSTRACT FROM AUTHOR]

Massively open online courses (MOOCs) offer learners opportunities for self‐improvement and knowledge development. Linking cognitive processes and learning outcomes is helpful in supporting students learning with the help of MOOCs. Based on the cognitive presence of the Community of Inquiry framework, this study quantified the effect of cognitive presence on students' learning performance by analysing more than 400,000 posts of 13 MOOCs. First, the study built a highly predictive classification model using a machine learning algorithm to automatically identify the phases of cognitive presence in MOOC forum posts. Subsequently, multilevel modelling was used to analyse the influence of learners' cognitive presence on their learning performance. The results showed that different phases of cognitive presence influenced students' learning differently. The findings help us understand the phases and depth of students' cognitive presence and can be used as the basis for MOOCs to automatically provide appropriate cognitive feedback and support to students. Practitioner notesWhat is already known about this topic Massively open online courses (MOOCs) offer learners opportunities for self‐improvement and knowledge development.Linking cognitive processes and learning outcomes is helpful in supporting student learning in MOOCs.Little is known so far about the role of cognitive presence in influencing students' learning performance.What this paper adds This study built a highly predictive classification model using a machine learning algorithm to automatically identify the phases of cognitive presence in MOOC forum posts.Multilevel modelling was used to quantify the influence of learners' cognitive presence on their learning performance.The results showed that different phases of cognitive presence influenced students' learning differently.Implications for practice and/or policy This study built an effective, high‐performance machine learning model with easy feature extraction, which could be used to automatically identify students' cognitive processes in MOOC forums and beyond.The combination of cognitive presence and learning performance helps us understand the phases and depth of students' cognitive presence.The findings can be used as the basis for MOOCs to automatically provide appropriate cognitive feedback and support to students. [ABSTRACT FROM AUTHOR]

The systematic training of metacognitive strategies in teaching Chinese as a foreign language, cultivating learners' autonomous learning ability, and improving the effectiveness of teaching Chinese as a foreign language is of great significance for realizing the overall goal of teaching Chinese as a foreign language. Therefore, this paper designs a model based on CDIO to guide the teaching of data structures and algorithms, which emphasizes students' hands-on ability, advocates learning in use, students' autonomous learning, and teamwork. Taking massive online open courses(MOOC) and small private online courses (SPOC) learning data as a sample, hidden Markov algorithm and data mining technology are used to establish a student learning behavior evaluation model to evaluate students' learning behavior in real-time. Meanwhile, teachers adjust the teaching content according to the evaluation results and enhance students' learning performance and improve teaching quality. [ABSTRACT FROM AUTHOR]

Recent articles reported a massive increase in frustration among weak students due to the outbreak of COVID-19 and Massive Open Online Courses (MOOCs). These students need to be evaluated to detect possible psychological counseling and extra attention. On the one hand, the literature reports many optimization techniques focusing on existing students' performance prediction systems. On the other hand, psychological works provide insights into massive research findings focusing on various students' emotions, including frustration. However, the synchronization among these contributions is still a black box, which delays the mathematical modeling of students' frustration. Also, the literature is still limited in using insights of psychology and assumption-based datasets to provide an in-house iterative procedure for modeling students' frustration severity. This paper proposes an optimization technique called the iterative model of frustration severity (IMFS) to explore the black box. It analyzes students' performance via two modules. First, frustration is divided into four outer layers. Second, the students' performance outcome is split into 34 inner layers. The prediction results are iteratively optimized under the umbrella of frustration severity layers through the outer and inner iterations. During validation, the IMFS achieves promising results with various evaluation measures. [ABSTRACT FROM AUTHOR]

Background: Learners in Massive Open Online Courses (MOOCs) are presented with great autonomy over their learning process. Learners must engage in self‐regulated learning (SRL) to handle this autonomy. It is assumed that learners' SRL, through monitoring and control, influences learners' behaviour within the MOOC environment (e.g., watching videos). The exact relationship between SRL and learner behaviour has however not been investigated. Objectives: We explored whether differences in SRL are related to differences in learner behaviour in a MOOC. As insight in this relationship could improve our understanding of the influence of SRL on behaviour, could help explain the variety in online learner behaviour, and could be useful for the development of successful SRL support for learners. Methods: MOOC learners were grouped based on their self‐reported SRL. Next, we used process mining to create process models of learners' activities. These process models were compared between groups of learners. Results and conclusions: Four clusters emerged: average regulators, help seekers, self‐regulators, and weak regulators. Learners in all clusters closely followed the designed course structure. However, the process models also showed differences which could be linked to differences in the SRL scores between clusters. Takeaways: The study shows that SRL may explain part of the variability in online learner behaviour. Implications for the design of SRL interventions include the necessity to integrate support for weak regulators in the course structure. Lay Description: Practitioners Notes: What is currently known:Learners' self‐regulated learning (SRL) is related to learner behaviour.Inadequate SRL may result in learner dropout in MOOCs.Learners in open online education behave in a variety of ways.What this paper adds:Most learners follow the designed course structure.Learners' SRL influences learner behaviour in a MOOC.Low SRL activity is related to adherence to the course structure.High SRL activity is related to more variety in learner behaviour.Implications for practitioners:SRL support should be integrated in the course structure.Results of process mining can inform instructional design. [ABSTRACT FROM AUTHOR]

Learning analytics research presents challenges for researchers embracing the principles of open science. Protecting student privacy is paramount, but progress in increasing scientific understanding and improving educational outcomes depends upon open, scalable and replicable research. Findings have repeatedly been shown to be contextually dependent on personal and demographic variables, so how can we use this data in a manner that is ethical and secure for all involved? This paper presents ongoing work on the MOOC Replication Framework (MORF), a big data repository and analysis environment for Massive Open Online Courses (MOOCs). We discuss MORF's approach to protecting student privacy, which allows researchers to use data without having direct access. Through an open API, documentation and tightly controlled outputs, this framework provides researchers with the opportunity to perform secure, scalable research and facilitates collaboration, replication, and novel research. We also highlight ways in which MORF represents a solution template to issues surrounding privacy and security in the age of big data in education and key challenges still to be tackled. Practitioner notesWhat is already known about this topic Personal Identifying Information (PII) has many valid and important research uses in education.The ability to replicate or build on analyses is important to modern educational research, and is usually enabled through sharing data.Data sharing generally does not involve PII in order to protect student privacy.MOOCs present a rich data source for education researchers to better understand online learning.What this paper adds The MOOC replication framework (MORF) 2.1 is a new infrastructure that enables researchers to conduct analyses on student data without having direct access to the data, thus protecting student privacy.Detail of the MORF 2.1 structure and workflow.Implications for practice and/or policy MORF 2.1 is available for use by practitioners and research with policy implications.The infrastructure and approach in MORF could be applied to other types of educational data. [ABSTRACT FROM AUTHOR]

The editors discuss reports published within the issue on topics including the Massive Open Online Course Replication Framework, a safety-aware model for artificial intelligence (AI)-enhanced conversational agents and learning analytics and AI-based methods in peer assessment processes.

The cognitive and social benefits of peer teaching for both teacher and learner are well established.1,2 To achieve its full impact, however, peer teaching must be driven by a deliberate effort to balance teaching the basic sciences with teaching the "how" of learning, that is, creating a culture of learning how to learn by translating decades of research from the learning sciences into practical study strategies. Assessment of specific programme activities in an MSAT programme, it must be noted, are more meaningful when carried out as a subset of a broader assessment programme which, in turn, can be used to drive instruction. RECOMMENDATION 5: SET CLEAR PROGRAMME GOALS AND EXPECTATIONS In developing an MSAT programme driven by the science of learning, the stated goals should distinguish it from or complement other peer teaching programmes centred around content tutoring. ACKNOWLEDGEMENTS Our most heartfelt gratitude to all 56 peer teachers of our Medical Students as Teachers programme, both past and present, for their invaluable work on programme development and implementation. [Extracted from the article]

In the process of continuous reform and development of education and teaching, some traditional teaching modes are gradually eliminated, whereas new teaching modes are gradually recognized by teachers and students. These modes are widely used in education and teaching due to their overwhelming characteristics. Among these emerging teaching modes, massive open online course (MOOC) is a relatively advanced teaching mode with a better application effect. Quickly and accurately detecting the cheating behavior of MOOC learners is of great significance for maintaining the development of the MOOC platform and English education counseling. This paper studies a deep-learning-based hybrid model for MOOC cheating detection. The model greatly improves the detection performance of a single model by integrating CNN, a bidirectional gated recurrent unit, and an attention mechanism. The proposed model selects the English learning behavior data of a MOOC platform to verify the performance of the algorithm. Simulation results show that the proposed scheme can greatly help MOOC-based English education tutoring. [ABSTRACT FROM AUTHOR]

Massive open online courses (MOOCS) are increasingly used by higher education providers to promote their fee‐paying courses. But while multiple studies have examined the reasons why individuals enrol into MOOCs, little is understood about the effects of course engagement on their later educational goals. The current work addresses this gap by investigating the impact of MOOC engagement on participants' future study goals and educational brand choices. This research uses a mixed methods approach to examine the goals and selection preferences of students following their engagement with a MOOC. This is accomplished through two separate longitudinal studies of MOOC participants. The first study consists of two sets of individual interviews (pre and post‐MOOC). The second involves the use of pre‐ and post MOOC surveys of participants enroled into courses on project management and cyber security. The findings show that engagement with a MOOC can positively influence the future academic goals of students, as well as their choice of academic provider. These results highlight the marketing potential of Massive Open Online Courses as a pathway into higher education. The goal framework used in this study is also likely to have broader applicability for understanding goal‐oriented behaviour in other consumer domains. [ABSTRACT FROM AUTHOR]

Int J Intell Syst. doi:10.1002/int.22609 9 Ahlim WSAW, Kamis NH, Ahmad SAS, Chiclana F. Similarity-trust network for clustering-based consensus group decision-making model. CI techniques also play a key role in measuring diversity, spread, inequality and other relevant indicators for several sectors, including health and economics. Computational Intelligence (CI), originally represented by the three subjects of Evolutionary Computation (EC), Fuzzy Logic (FL) and Neural Networks (NNs), has significantly evolved to date and is ever more embedded in both software platforms and hardware devices forming intelligent systems capable of self-adaptation, decision-making and problem-solving. [Extracted from the article]

In online learning, the dropout phenomenon is a relevant issue to address with practical solutions. Several data sets stimulate original, and resolutive data analysis approaches, demonstrating the importance of the dropout phenomenon. This study proposes a novel approach to predicting massive online open course (MOOC) students at risk of dropout stressing the need to consider the temporal dimension in the data log. The proposal aims to build a data‐driven decision support system able to identify students at risk of dropout based on the conceptualization of such students' behavior and its evolution along the time dimension. The primary theoretical model behind the proposed method is the formal concept analysis, and its temporal extension (i.e., temporal concept analysis) for analyzing timestamped data and carrying out a timed lattice. The main result of the paper is a method to extract behavioral patterns of MOOC students at risk of dropout. Such patterns are defined as Time‐based Behavior Rules extracted from the aforementioned timed lattice obtained through the preprocessing of MOOC platform log files. The resulting rule set can be easily integrated for implementing educational DSS, as shown in the last part of the paper. The conducted experiments reveal promising results in terms of F‐score and students' monitoring time. [ABSTRACT FROM AUTHOR]

This research is to investigate the impact of massive open online course (MOOC) learning platforms on teacher development, promote the development and innovation of online learning models under the "Double First-Rate," and especially expand the application of the MOOC platforms combining language learning with professional development paths. The MOOC online learning platform has a problem of a high abandonment rate. This paper first proposes a MOOC learning recommendation algorithm based on the learning sequence and similarity distance analysis as well as evaluates its accuracy. Then, the reliability test and the MOOC learning recommendation algorithm are used to evaluate the quality evaluation system of English teaching in the MOOC utilizing the structural equation model. Finally, the strengths, weaknesses, opportunities, and threats (SWOT) are determined to analyze the impact of the MOOC regarding the English teaching platform on teachers' comprehensive development. The results show that the MOOC platform-based learning recommendation algorithm has higher recommendation accuracy and efficiency, improving the learning effect with the utilization of the MOOC. Also, it can effectively reduce the abandonment rate and has a positive effect of resolving the interaction problem pertinent to characteristic differences and sequences in the learning recommendation. The quality evaluation system of online English teaching in the MOOC has higher reliability and convergent validity, which shows better stability and consistency in all dimensions. If teachers can actively learn from the resources of the MOOC platform, then they continuously update teaching concepts, improve online teaching, give full play to their language advantages, accurately locate student needs, and develop unique courses. Therefore, it will promote the overall development of their careers and improve innovation. [ABSTRACT FROM AUTHOR]

Background: Failure to effectively organize and manage learning time is an important factor influencing online learners' performance. Investigation of time‐investment patterns for online learning will provide educators with useful knowledge of how learners engage in and regulate their online learning and support them in tailoring online course design and teaching. However, understanding of how learners invest and manage their time during online learning remains limited. Objectives: This study aims to discover the typical time‐investment patterns of MOOC learners and their temporal‐learning characteristics based on a systematic time‐investment analysis framework and their relationship with learning performance. Methods: Based on a proposed time‐investment‐analysis framework, this study applied statistical, cluster and lag sequential analyses to investigate learners' time‐investment patterns and their relationships with learning performance, session time allocation, and learning sequences by analysing the learning data from 12,463 participants of a Massive Open Online Course (MOOC) in China. Results and Conclusions: Seven time‐investment patterns of MOOC learners were defined, and learning performance was found to differ among them. Further analysis shows that high performers invested time throughout the whole course and allocated time to multiple activities, exam‐takers performed better in time management and produced more behavioural sequences related to cognitive strategy and recourse use, and learners' motivation and prior knowledge affected the management and effectiveness of their time investment. Implications: The results support the recognition and evaluation of online learning time‐investment patterns and suggest relevant cues for improving MOOC design and teaching. Lay Description: What is currently known about the subject matter: Insufficient learning time affects learning persistence and effectiveness in open and online education programs.Learning time investment and management regarded as an element of self‐regulated learning is a predictor of learning performance.Some proxy behavioural variables or characteristics of online learning time management have been found in previous studies.Learning analysis can be used to dig the knowledge implicit in learning data for online teaching evaluation and improvement. What is paper adds to the subject: Based on a comprehensive analytic framework, seven time‐investment patterns of MOOC learners and their relationship with achievement are identified.Under different time frames, learners' time allocation and learning behaviour pattern are different.Learners performing better in time management across course learning allocate more time to multiple activities in session learning.Exam‐takers perform better in time management across MOOC learning, and produce more behaviour sequences related to cognitive strategy and course recourse use. The implications of study findings for practitioners: The recognition of time investment pattern can support online course design and teaching improvement.MOOC design and teaching need to consider the differences in learners' time resources and their ability of time management.MOOC design and teaching need to encourage learners lacking of performance motivation to engage in various learning activities instead of just watching videos.MOOC design need consider the differences in learners' prior knowledge, and offer personalized learning path based on assessment. [ABSTRACT FROM AUTHOR]

In the era of big data mining, educational data mining has become a principal research focus, with online education mining, such as massive open online courses' (MOOC) data analysis, representing an important source of it. Recent studies have found that learners have low passing rates on MOOCs. A number of studies have proposed prediction models for the dropout rate of learners on MOOCs. The improvement of MOOCs and the promotion of personalized education are the key points of online education. However, the selection and intervention of students with a tendency to drop out slows down the efficiency of teaching and increases the burden on teachers. This study's aim is to utilize back propagation neural networks and radar graphs in a flipped classroom based on MOOCs to predict students' future grades and to analyze the influence of teaching from various perspectives to support the promotion and reform of teaching and curriculum. Compared with the previous year, after the forecast and the adjustment, this year's student scores increase significantly. [ABSTRACT FROM AUTHOR]

This letter to the editor responds to a cross-sectional study on the willingness to receive the human papillomavirus (HPV) vaccination among male and female university students in China. The author highlights some issues with the study, including discordance in the data presented, the lack of representativeness in the university sample, and the assumption that perceptions of threat and severity of HPV are related to willingness to be vaccinated. The author suggests that further research should involve a broader range of university students and investigate the association between university level and HPV infection and vaccination willingness. [Extracted from the article]

To choose a balanced diet, consumers need to understand what is in the food they buy. However, studies indicate that food labels may be misunderstood or not used. Understanding food labels, a massive open online course (MOOC), using concise, factual and engaging information, aimed to increase consumers' awareness around food labelling information. Course engagement and participation were assessed by the end of course feedback, learning metrics and course completion rates. A pre‐ and post‐course survey was integrated into the MOOC to assess changes in food label use frequency and understanding. Furthermore, use and understanding of nutrition labels were explored by text analysis of one of the discussion forums. The initial course run attracted 2046 Joiners, of whom 65% (1320/2046) became Learners, 44% (905/2046) became Active Learners (those who marked at least one step as complete in a course) and 31% (281/905) of Active Learners completed at least 90% of course steps. The number of learners who fully completed the course was 281 of 1320 (21%). Those who completed the MOOC reported reading food labels more frequently at food purchasing (p < 0.05), specifically reading the nutrition information more regularly (p < 0.05) and reading labels more frequently when preparing food at home than before the MOOC. In addition, after completing the course, learners reported that they found food labels easier to understand (p < 0.05) than before undertaking the course. Increasing consumer awareness, understanding and trust of food labelling could help consumers in their ability to make informed and healthier food choices. [ABSTRACT FROM AUTHOR]

Rapid progress in expansion of the internet services have provided an alternative way for learning other than the traditional classroom learning. Due to the availability of multiple learning options, evaluating each option and judging the best use case plays a vital role. One of the most important characteristics that a human brain utilizes during process of learning is cognition that involves attention and retention. Student's attention span and situational interests during learning have always been a subject matter of research. Apart from classroom learning, e‐learning (MOOC based learning) is the other most preferred way of learning. Therefore, the objective of this study is to assess attention levels of a learner in MOOC (Massive Open Online Courses) learning environments and compare it with conventional classroom learning using brain signals. The proposed method captures electroencephalogram (EEG) frequency bands of different subjects while going through a short lecture in MOOC/e‐learning environment and classroom environment. The captured data points were annotated for attentiveness manually by referring to the subject's feedback and video clips. Machine learning classification model of support vector machines (SVM) was used to classify student's mental state as attentive or nonattentive. Promising results were obtained and experiments revealed that higher attention levels were maintained during MOOC learning environment in comparison to traditional learning approach. [ABSTRACT FROM AUTHOR]

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

The coronavirus disease of 2019 (COVID‐19) pandemic has severely crippled our globalized society. Despite the chaos, much of our civilization continued to function, thanks to contemporary information and communication technologies. In education, this situation required instructors and students to abandon the traditional face‐to‐face lectures and move to a fully online learning environment. Such a transition is challenging, both for the teacher tasked with creating digital educational content, and the student who needs to study in a new and isolated working environment. As educators, we have experienced these challenges when migrating our university courses to an online environment. Through this paper, we look to assist educators with building and running an online course. Before we needed to transition online, we researched and followed the best practices to establish various digital educational elements in our online classroom. We present these elements, along with guidance regarding their development and use. Next, we designed an empirical study consisting of two surveys, focus group discussions, and observations to understand the factors that influenced students' engagement with our online classroom. We used the same study to evaluate students' perceptions regarding our digital educational elements. We report the findings and define a set of recommendations from these results to help educators motivate their students and develop engaging digital educational content. Although our research is motivated by the pandemic, our findings and contributions are useful to all educators looking to establish some form of online learning. This includes developers of massive open online courses and teachers promoting blended learning in their classrooms. [ABSTRACT FROM AUTHOR]