Explore the vast range of titles available.

Drawing on data from a recent research international research project, this article focuses on the challenges faced by teachers of English to young learners against the backdrop of the global rise of English. A mixed-methods approach was used to obtain the data, including a survey, which was completed by 4,459 teachers worldwide, and case studies, including observations and interviews with teachers, in five different primary schools in five different countries. A number of challenges emerged as affecting large numbers of teachers in different educational contexts, namely, teaching speaking, motivation, differentiating learning, teaching large classes, discipline, teaching writing, and teaching grammar. Importantly, some of these challenges have not been highlighted in the literature on young learner teaching to date. Other challenges are more localised, such as developing teachers' English competence. The article argues that teacher education should focus less on introducing teachers to general approaches to English language teaching and more on supporting teachers to meet the challenges that they have identified.

Blended learning combines online digital resources with traditional classroom activities and enables students to attain higher learning performance through well-defined interactive strategies involving online and traditional learning activities. Learning analytics is a conceptual framework and as a part of our Precision education used to analyze and predict students' performance and provide timely interventions based on student learning profiles. This study applied learning analytics and educational big data approaches for the early prediction of students' final academic performance in a blended Calculus course. Real data with 21 variables were collected from the proposed course, consisting of video-viewing behaviors, out-of-class practice behaviors, homework and quiz scores, and after-school tutoring. This study applied principal component regression to predict students' final academic performance. The experimental results show that students' final academic performance could be predicted when only one-third of the semester had elapsed. In addition, we identified seven critical factors that affect students' academic performance, consisting of four online factors and three traditional factors. The results showed that the blended data set combining online and traditional critical factors had the highest predictive performance.

In massive open online learning courses (MOOCs) with a low instructor-student ratio, students are expected to have self-directed learning abilities. This study investigated the relationship among motivation, self-monitoring, self-management, and MOOC learners’ use of learning strategies. An online survey was embedded at the end of three MOOCs with large enrollments asking for learners’ voluntary participation in the study. The survey results from 470 participants indicated that motivation positively influenced self-monitoring, self-management, and learning strategies. In addition, self-monitoring and self-management did not affect the utilization of learning strategies. This underscores learners’ motivation and the need to encourage them to adopt appropriate learning strategies for successful learning. The results also revealed that self-monitoring positively affected self-management. The findings highlight the critical need to enhance self-monitoring skills to further promote self-management skills in MOOCs. In addition, self-monitoring and self-management did not encourage learners to use related learning strategies in this study. This study should be extended to investigate practical ways to encourage MOOC learners to adopt learning strategies.

This research used the qualitative multiple case study and phenomenological designs to explore how, without training, university teachers in Ghana managed large student numbers in the virtual environment during COVID-19. The study examined further the challenges the teachers faced in their virtual instructional delivery. Twelve participants drawn purposively from four large Ghanaian universities participated in individual interviews and follow-up virtual class observations. The findings revealed that the participants employed two management techniques in their virtual teaching—regulating the behaviour of learners and controlling instructional content. The research further uncovered that, although the teachers’ complaints generally centred on environmental constraints and inadequate institutional support, those whose difficulties included using virtual tools did not have virtual teaching experience before the COVID period. The study supports the clarion call on university teachers involved in virtual teaching to personally seek a continual update of skills and competency in virtual delivery because it is an approach hinged on evolving technology.

Why do over 90% of the learners in Massive Open Online Courses (MOOCs) never finish the course? There is a need for further studies focusing on the learners’ experiences of participating in MOOCs and factors that influence the decision to complete or drop out of the course. To deepen our understanding of why learners complete or drop out of MOOCs, we report on a qualitative case study based on in-depth interviews with 34 learners with different degrees of course completion for two MOOCs. A qualitative analysis of the interviews led to the identification of four main factors influencing dropout: (1) the learner’s perception of the course content, (2) the learner’s perception of the course design, (3) the learner’s social situation and characteristics, and (4) the learner’s ability to find and manage time effectively. How the learners conceptualized a MOOC had a strong impact on how they engaged with the contents. We discuss the implications of our results for MOOC practice in terms of time, openness and accessibility and provide recommendations for future research.

In massive open online courses (MOOCs), low barriers to registration attract large numbers of students with diverse interests and backgrounds, and student use of course content is asynchronous and unconstrained. The authors argue that MOOC data are not only plentiful and different in kind but require reconceptualization—new educational variables or different interpretations of existing variables. The authors illustrate this by demonstrating the inadequacy or insufficiency of conventional interpretations of four variables for quantitative analysis and reporting: enrollment, participation, curriculum, and achievement. Drawing from 230 million clicks from 154,763 registrants for a prototypical MOOC offering in 2012, the authors present new approaches to describing and understanding user behavior in this emerging educational context.

BackgroundOver the past decade, the prevalence of anxiety has increased among college-aged students and college counseling centers have become increasingly concerned about the negative impact of anxiety on students. While college in general can be stressful, college science classrooms have the potential to be especially anxiety-inducing because of the sometimes chilly and competitive environment of the class. Further, college science courses are increasingly being transitioned from traditional lecture to active learning where students take an active role in their learning, often through participating in activities such as clicker questions and group work. There is emerging evidence that suggests active learning activities may cause students to feel anxious, but no studies have thoroughly explored why active learning activities in science courses may increase students’ anxiety. Further, no studies have explored whether active learning activities can reduce students’ anxiety. In this exploratory interview study of 52 students enrolled in large-enrollment active learning college science courses, we investigate how three active learning practices, clicker questions, group work, and cold call/random call, increase and decrease students’ anxiety.ResultsStudents reported that clicker questions and group work had the potential to both increase and decrease their anxiety. The way the active learning activity is implemented and the extent to which students perceive they benefit from the activity seems to influence the effect of the activity on students’ anxiety. Conversely, students reported that cold call and random call only increased their anxiety. From our interviews, we identified the fear of negative evaluation, or the sense of dread associated with being unfavorably evaluated while participating in a social situation, as the primary construct underlying students’ high levels of anxiety associated with speaking out in front of the whole class when they do not volunteer.ConclusionThis study illustrates that active learning can both increase and decrease students’ anxiety depending on the way active learning is implemented. We hope that this study encourages instructors to create more inclusive active learning science courses by implementing active learning in ways that minimize students’ anxiety.

This study tries to propose a unified model integrating the unified theory of acceptance and use of technology (UTAUT) model, task–technology fit (TTF) model, and user satisfaction to investigate the determinants that affect university students’ continued intention of using massive open online courses (MOOCs). Based on the data of a survey on 464 respondents, structural equation modeling is adopted to assess the model. The results reveal that performance expectancy, effort expectancy, social influence, and user satisfaction are the crucial predictors of university students’ continued intention. TTF has an indirect influence on continued intention through user satisfaction. Performance expectancy is affected both by effort expectancy and TTF. Facilitating conditions do not directly influence continued intention; however, they present indirect influences in that they play a mediating role for user satisfaction. The findings help researchers and practitioners to attain a better understanding of university students’ continued usage intention of MOOCs. The implications and limitations of this study are also described.

In introducing the special issue on Large Class Pedagogy: Opportunities and Challenges of Massification the present editorial takes stock of the emerging literature on this subject. We seek to contribute to the massificaiton debate by considering one result of it: large class teaching in higher education. Here we look to large classes as a problem in promoting student learning, quality education, and consequently as a challenge to socio-economic development. That said, whilst large classes do pose very specific challenges, they also hold promise and opportunities for innovation in support of student learning. Here we consider the contributions to this special issue from a cross section of disciplines and higher education environments. (HRK / Abstract übernommen).

Participants in massive open online courses show a wide variety of motivations. This has been studied with the elaboration of classifications of the users according to their behavior throughout the course. In this study, we aimed to classify the participants in the MOOCs according to the initial motivations and intentions, before long interaction with the online device. Using a survey of 1768 participants in 6 MOOCs, we classify the participants according to: internal motives, external motives and intention of persistence. Three profiles of involvement in the course were identified: poorly motivated (16.7%), self referential (28.8%) and highly committed (54.5%). All three profiles showed significant differences in self-reported learning experiences at the end of the course. The intensity of the initial motivation was positively related to the satisfaction and perceived quality of the training experience. According to our analysis, identifying motivational profiles before starting the course allows to diagnose in advance the educational use and the diversity of individual training itineraries.