37,578 research outputs found

    Hybrid pedagogy and learning design influences in a higher education context

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    After pivoting to a completely new mode of teaching and learning for much of the higher education sector, a focus on the learning design influences and networked communities sought to address a gap in current literature. The research attempted to delve into the scope of hybrid learning design in response to the changing educational landscape, forced by the Covid-19 pandemic. Thirty-eight participants from across the higher education sector participated in a qualitative survey and institutional context was derived from internal system analytics and engagement data to inform usage of specific systems and tools. Overall, hybrid learning design was limited in its prevalence across the participants learning design, with online and blended playing a key role. Furthermore, the research focuses on identification of key factors influencing learning design and possibly the neglect of a hybrid model required to meet the expectations and needs of the current scenario higher education finds itself. Possible limitations of this research and future associated research are addressed in relation to the results and analysis, with recommendations of how to improve

    NAVIGATING VIDEO-BASED LEARNING IN SCIENCE – HOW DO WE CLOSE THE GAP BETWEEN ONLINE AND PHYSICAL CLASSROOMS?

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    Online learning increases the physical distance between instructors and students and depending on the mode of delivery it can be challenging to close this gap. In the face of a global pandemic institutions are rapidly increasing their portfolio of online and blended courses, which increases the potential risk of student attrition across the sector. To ameliorate this potential for student isolation, instructors need to communicate to students in a variety of ways, blending original online resources with synchronous interactive learning activities. In 2020, 55 videos designed for lectures, tutorials, and laboratory sessions were created for 400 undergraduate microbiology students at The University of Queensland. The videos collectively received over 35,000 views, and through learning analytics it was observed that videos featuring instructor presence on screen, frequent scene transitions, and dynamic pacing increased the consistency of student engagement (>80% average audience retention). This session will outline the design principles underlying a framework for developing video-based learning resources in science to maximise their utility as part of blended and online courses, as well as the academic digital upskilling required in the current Higher Education landscape

    Predictive Analytics In Higher Education: Five Guiding Practices for Ethical Use

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    Without ethical practices, student data could be used to curtail academic success rather than help ensure it. For example, without a clear plan in place, an institution could use predictive analytics to justify using fewer resources to recruit low-income students because their chances of enrolling are less sure than for more affluent prospective students. In this report, New America lays out important questions to consider as administrators formulate how to use predictive analytics ethically

    Data Analytics in Higher Education: Key Concerns and Open Questions

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    “Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the audience for this symposium is students and faculty in higher education institutions (HEIs), and the subject of this paper is data analytics in our own backyards. Higher education learning analytics (LA) is something that most of us involved in this symposium are familiar with. Students have encountered LA in their courses, in their interactions with their law school or with their undergraduate institutions, instructors use systems that collect information about their students, and administrators use information to help understand and steer their institutions. More importantly, though, data analytics in higher education is something that those of us participating in the symposium can actually control. Students can put pressure on administrators, and faculty often participate in university governance. Moreover, the systems in place in HEIs are more easily comprehensible to many of us because we work with them on a day-to-day basis. Students use systems as part of their course work, in their residences, in their libraries, and elsewhere. Faculty deploy course management systems (CMS) such as Desire2Learn, Moodle, Blackboard, and Canvas to structure their courses, and administrators use information gleaned from analytics systems to make operational decisions. If we (the participants in the symposium) indeed care about Individual and Informational Privacy in the Age of Big Data, the topic of this paper is a pretty good place to hone our thinking and put into practice our ideas

    Guide to using Evidence in Higher Education

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    This Guide to Using Evidence has been designed to, to support and encourage students and students’ association and union staff to actively engage with data and evidence. It offers an accessible introduction to a range of key ideas and concepts and a range of activities which allow readers to develop their own thinking and confidence in key areas. The ambition of its authors, QAA Scotland and the students who reviewed early drafts, is that students and students’ association and union staff will reach for this resource as they prepare for committees, devise new campaigns, deliver services, and do all of the other things they do to enhance students’ experiences and outcomes. Underpinning all of this is a belief that students themselves, the institutions they are working with, and the sector as a whole, are better served when students are, and are seen to be, agents in the ‘data landscape’, not just subjects of it. Engaging with this Guide will help students and students’ association and union staff to develop that sense of agency in themselves and foster it in others. This Guide is a product of a student-led project coordinated by QAA Scotland as part of the Evidence for Enhancement Theme (2017-20)

    Analytics and complexity: learning and leading for the future

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    There is growing interest in the application of learning analytics to manage, inform and improve learning and teaching within higher education. In particular, learning analytics is seen as enabling data-driven decision making as universities are seeking to respond a range of significant challenges that are reshaping the higher education landscape. Experience over four years with a project exploring the use of learning analytics to improve learning and teaching at a particular university has, however, revealed a much more complex reality that potentially limits the value of some analytics-based strategies. This paper uses this experience with over 80,000 students across three learning management systems, combined with literature from complex adaptive systems and learning analytics to identify the source and nature of these limitations along with a suggested path forward

    Mapping the open education landscape: citation network analysis of historical open and distance education research

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    The term open education has recently been used to refer to topics such as Open Educational Resources (OERs) and Massive Open Online Courses (MOOCs). Historically its roots lie in civil approaches to education and open universities, but this research is rarely referenced or acknowledged in current interpretations. In this article the antecedents of the modern open educational movement are examined, as the basis for connecting the various strands of research. Using a citation analysis method the key references are extracted and their relationships mapped. This work reveals eight distinct sub-topics within the broad open education area, with relatively little overlap. The implications for this are discussed and methods of improving inter-topic research are proposed

    Big data for monitoring educational systems

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    This report considers “how advances in big data are likely to transform the context and methodology of monitoring educational systems within a long-term perspective (10-30 years) and impact the evidence based policy development in the sector”, big data are “large amounts of different types of data produced with high velocity from a high number of various types of sources.” Five independent experts were commissioned by Ecorys, responding to themes of: students' privacy, educational equity and efficiency, student tracking, assessment and skills. The experts were asked to consider the “macro perspective on governance on educational systems at all levels from primary, secondary education and tertiary – the latter covering all aspects of tertiary from further, to higher, and to VET”, prioritising primary and secondary levels of education

    Designing and Delivering a Curriculum for Data Science Education across Europe

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    Data is currently being produced at an incredible rate globally, fuelled by the increasing ubiquity of the Web, and stoked by social media, sensors, and mobile devices. However, as the amount of available data continues to increase, so does the demand for professionals who have the necessary skills to manage and manipulate this data. This paper presents the European Data Science Academy (EDSA), an initiative for bridging the data science skills gap across Europe and training a new generation of world-leading data scientists. The EDSA project has established a rigorous process and a set of best practices for the production and delivery of curricula for data science. Additionally, the project’s efforts are dedicated to linking the demand for data science skills with the supply of learning resources that offer these skills

    The moderating influence of device characteristics and usage on user acceptance of smart mobile devices

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    This study seeks to develop a comprehensive model of consumer acceptance in the context of Smart Mobile Device (SMDs). This paper proposes an adaptation of the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT2) model that can be employed to explain and predict the acceptance of SMDs. Also included in the model are a number of external and new moderating variables that can be used to explain user intentions and subsequent usage behaviour. The model holds that Activity-based Usage and Device Characteristics are posited to moderate the impact of the constructs empirically validated in the UTAUT2 model. Through an important cluster of antecedents the proposed model aims to enhance our understanding of consumer motivations for using SMDs and aid efforts to promote the adoption and diffusion of these devices
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