EU–originated MOOCs, with focus on multi- and single-institution platforms

No abstract available

Cross-cultural MOOCs: designing MOOCs for Chinese students

Advocates of Massive Open Online Courses (MOOCs), a cross-cultural phenomenon that has attracted public attention throughout the world, portray them as an equalizing force in international higher education; but researchers have noted discrepancies in how learners from different countries have engaged with them. The number of MOOC learners in China is growing rapidly, and Chinese learners are enthusiastic about the unprecedented freedom they now have in selecting courses and accessing resources from the best international universities. However, they have a significantly low completion rate and may experience unique challenges about which little is known. This study took into account the diversity of MOOC learners and proposed changes to its course design to make it more inclusive for Chinese students. I used a mixed method—including document analysis, surveys, and interviews—to investigate the Chinese experience of taking Western MOOCs and also to explore the educational theories and design principles of MOOCs that have been discussed in the Western and Chinese literature. My analysis of the literature revealed issues of contextualization that may play a critical role in improving the MOOC experience for Chinese students. Drawing on theoretical educational frameworks—including motivation, community of inquiry, self-regulated learning, and social identity—my analysis of surveys and interviews identified common themes in the Chinese experience of Western MOOCs. In accordance with the results of my analysis, and also in line with interaction equivalency and situational principles, this study provided suggestions for adapting MOOCs to Chinese learners, such as enhancing content quality, improving learner–learner and learner–instructor interactions, providing social support, and collaborating with local universities and agencies in providing technical and credentialing support

Thriving in a Pandemic: Lessons Learned from a Resilient University Program Seen Through the CoI Lens

In March 2020, college campuses underwent a sudden transformation to online learning due to the COVID-19 outbreak. To understand the impact of COVID-19 on students' expectations, this study conducted a three-year survey from ten core courses within the Project Management Center for Excellence at the University of Maryland. The study involved two main steps: 1) a statistical analysis to evaluate students' expectations regarding "student," "class," "instructor," and "effort;" and 2) a lexical salience-valence analysis (LSVA) through the lens of the Community of Inquiry (CoI) framework to show the changes of students' expectations. The results revealed that students' overall evaluations maintained relatively consistent amid the COVID-19 teaching period. However, there were significant shifts of the student expectations toward Cognitive, Social and Teaching Presence course elements based on LSVA results. Also, clear differences emerged between under-graduates and graduates in their expectations and preferences in course design and delivery. These insights provide practical recommendations for course instructors in designing effective online courses

Online Instruction in Higher Education: Promising, Research-based, and Evidence-based Practices

The purpose of this study was to review the research literature on online learning to identify effective instructional practices. We narrowed our scope to empirical studies published 2013-2019 given that studies earlier than 2013 had become quickly outdated because of changes in online pedagogies and technologies. We also limited our search to studies with undergraduate and graduate students, application of an empirical methodological design, and descriptions of methodology, data analysis, and results with sufficient detail to assure verifiability of data collection and analysis. Our analysis of the patterns and trends in the corpus of 104 research studies led to identification of five themes: course design factors, student support, faculty pedagogy, student engagement, and student success factors. Most of the strategies with promising effectiveness in the online environment are the same ones that are considered to be effective in face-to-face classrooms including the use of multiple pedagogies and learning resources to address different student learning needs, high instructor presence, quality of faculty-student interaction, academic support outside of class, and promotion of classroom cohesion and trust. Unique to the online environment are user-friendly technology tools, orientation to online instruction, opportunities for synchronous class sessions, and incorporation of social media. Given the few studies utilizing methodological designs from which claims of causality can be made or meta-analyses could be conducted, we identified only faculty feedback as an evidence-based practice and no specific intervention that we could identify as research-based in online instruction

Online Instruction in Higher Education: Promising, Research-based, and Evidence-based Practices

The purpose of this study was to review the research literature on online learning to identify effective instructional practices. We narrowed our scope to empirical studies published 2013-2019 given that studies earlier than 2013 had become quickly outdated because of changes in online pedagogies and technologies. We also limited our search to studies with undergraduate and graduate students, application of an empirical methodological design, and descriptions of methodology, data analysis, and results with sufficient detail to assure verifiability of data collection and analysis. Our analysis of the patterns and trends in the corpus of 104 research studies led to identification of five themes: course design factors, student support, faculty pedagogy, student engagement, and student success factors. Most of the strategies with promising effectiveness in the online environment are the same ones that are considered to be effective in face-to-face classrooms including the use of multiple pedagogies and learning resources to address different student learning needs, high instructor presence, quality of faculty-student interaction, academic support outside of class, and promotion of classroom cohesion and trust. Unique to the online environment are user-friendly technology tools, orientation to online instruction, opportunities for synchronous class sessions, and incorporation of social media. Given the few studies utilizing methodological designs from which claims of causality can be made or meta-analyses could be conducted, we identified only faculty feedback as an evidence-based practice and no specific intervention that we could identify as research-based in online instruction

Performance and Professional Skills in an Online Java Programming Course for Engineering Students

The main purpose of this work is to describe the case of an online Java Programming course for engineering students to learn computer programming and to practice other non-technicalabilities: online training, self-assessment, teamwork and use of foreign languages. It is important that students develop confidence and competence in these skills, which will be required later in their professional tasks and/or in other engineering courses (life-long learning). Furthermore, this paper presents the pedagogical methodology, the results drawn from this experience and an objective performance comparison with another conventional (face-to-face) Java course

UNDERSTANDING STUDENT BEHAVIORS USING IMMEDIATE FEEDBACK FEATURES IN A BLENDED LEARNING ENVIRONMENT

Feedback serves to close the gap between learners’ current understanding and the desired understanding. Informative feedback can keep students from holding onto misconceptions, actively engage learners in knowledge acquisition, and increase confidence and motivation to learn. Yet, in the context of higher education, it is usually not possible for instructors to provide timely feedback to every individual student. This is especially difficult in first-year foundational courses due to the large number of students. Online learning platforms offer a solution by providing students immediate feedback during the course of their interactions with formative assessment tools (e.g., online homework, quizzes, embedded questions in lecture videos). However, how students choose to interact with these features and how these features influence students’ learning experiences have not been well understood. Even less is known about student behaviors with these immediate feedback features in a blended learning class

Enhancing the Performance of Automated Grade Prediction in MOOC using Graph Representation Learning

In recent years, Massive Open Online Courses (MOOCs) have gained significant traction as a rapidly growing phenomenon in online learning. Unlike traditional classrooms, MOOCs offer a unique opportunity to cater to a diverse audience from different backgrounds and geographical locations. Renowned universities and MOOC-specific providers, such as Coursera, offer MOOC courses on various subjects. Automated assessment tasks like grade and early dropout predictions are necessary due to the high enrollment and limited direct interaction between teachers and learners. However, current automated assessment approaches overlook the structural links between different entities involved in the downstream tasks, such as the students and courses. Our hypothesis suggests that these structural relationships, manifested through an interaction graph, contain valuable information that can enhance the performance of the task at hand. To validate this, we construct a unique knowledge graph for a large MOOC dataset, which will be publicly available to the research community. Furthermore, we utilize graph embedding techniques to extract latent structural information encoded in the interactions between entities in the dataset. These techniques do not require ground truth labels and can be utilized for various tasks. Finally, by combining entity-specific features, behavioral features, and extracted structural features, we enhance the performance of predictive machine learning models in student assignment grade prediction. Our experiments demonstrate that structural features can significantly improve the predictive performance of downstream assessment tasks. The code and data are available in \url{https://github.com/DSAatUSU/MOOPer_grade_prediction

Developing sustainable business models for institutions’ provision of open educational resources: Learning from OpenLearn users’ motivations and experiences

Universities across the globe have, for some time, been exploring the possibilities for achieving public benefit and generating business and visibility through releasing and sharing open educational resources (OER). Many have written about the need to develop sustainable and profitable business models around the production and release of OER. Downes (2006), for example, has questioned the financial sustainability of OER production at scale. Many of the proposed business models focus on OER’s value in generating revenue and detractors of OER have questioned whether they are in competition with formal education. This paper reports on a study intended to broaden the conversation about OER business models to consider the motivations and experiences of OER users as the basis for making a better informed decision about whether OER and formal learning are competitive or complementary with each other. The study focused on OpenLearn - the Open University’s (OU) web-based platform for OER, which hosts hundreds of online courses and videos and is accessed by over 3,000,000 users a year. A large scale survey and follow-up interviews with OpenLearn users worldwide revealed that university provided OER can offer learners a bridge to formal education, allowing them to try out a subject before registering on a formal course and to build confidence in their abilities as learners. In addition, it was found that using OER during formal paid-for study can improve learners’ performance and self-reliance, leading to increased retention and satisfaction with the learning experience