Unravelling the dynamics of learning design within and between disciplines in higher education using learning analytics

Designing effective learning experience in virtual learning environment (VLE) can be supported by learning analytics (LA) through explicit feedback on how learning design (LD) influences students’ engagement, satisfaction and performance. Marrying LA with LD not only puts existing pedagogical theories in instructional design to the test with actual learning data, but also provides the context of learning which helps educators translate established LA findings to direct interventions. My dissertation aims at unpacking the complexity of LD and its impact on students’ engagement, satisfaction and performance on VLE using LA. The context of this study is 400+ online and blended learning modules at the Open University (OU) UK. This research combines multiple sources of data from the OU Learning Design Initiative (OULDI), system log data, self-reported surveys, and performance data. Given the scope of this study, a wide range of visualization techniques, social network analysis, multi-level modelling, and machine learning will be used

Transition, Decoding and Heutagogy; A strategy for improving undergraduate learning in sport, health and exercise.

Heutagogy, an established concept in educational literature, puts an emphasis on the development of a student’s ability to understand how they learn certain skills and abilities. To gain a clearer understanding on the implementation of heutagogy within the higher education environment, the present study considered the adoption of heutagogical approaches with students at University. A review of the literature was conducted to understand the use of pedagogy and andragogy in higher education and how a heutagogical approach could create a self-directed learning experience. Contemporary research has evidenced that the implementation of heutagogy at higher education encourages students to develop highly employable skills such as determination and initiative. In contrast, it has been discovered that students find heutagogy to be challenging, therefore a progressive development from pedagogy to andragogy to heutagogy is required. Nevertheless, the beneficial outcomes are apparent to educators and students, and increase employability rates. The beneficial learning outcomes of heutagogical learning such as employability and self-directed learning is discussed

Learning Online with International Politics

The project reported on students' experiences of online learning during the Covid-19 pandemic. It identified several areas that impact upon students' active engagement with online learning resources and asynchronous learning activities. We made a number of recommendations for staff teaching online including: the need for clarity when setting up and explaining learning activities to students; the importance of referring to any asynchronous tasks in live teaching sessions and providing feedback ; the importance of building a sense of community in teaching, by creating icebreaker and informal learning opportunities for students to get to know their peers and tutors; the benefits of quizzes or polls in live teaching to test students’ understanding of key concepts; to introduce more authentic online learning based on scenarios and examples, problem- based learning alongside more innovative approaches drawing on playful learning

The impact of 151 learning designs on student satisfaction and performance: social learning (analytics) matters

An increasing number of researchers are taking learning design into consideration when predicting learning behavior and outcomes across different modules. This study builds on preliminary learning design work that was presented at LAK2015 by the Open University UK. In this study we linked 151 modules and 111.256 students with students' satisfaction and performance using multiple regression models. Our findings strongly indicate the importance of learning design in predicting and understanding performance of students in blended and online environments. In line with proponents of social learning analytics, our primary predictor for academic retention was the amount of communication activities, controlling for various institutional and disciplinary factors. Where possible, appropriate communication tasks that align with the learning objectives of the course may be a way forward to enhance academic retention

The Digital Scholar Revisited

The book The Digital Scholar was published in 2011, and used Boyer’s framework of scholarship to examine the possible impact of digital, networked technology on scholarly practice. In 2011 the general attitude towards digital scholarship was cautious, although areas of innovative practice were emerging. Using this book as a basis, the author considers changes in digital scholarship since its publication. Five key themes are identified: mainstreaming of digital scholarship, so that it is a widely accepted and encouraged practice; the shift to open, with the emphasis on the benefits that open practice brings rather than the digital or networked aspects; policy implementation, particularly in areas of educational technology platforms, open access policies and open educational resources; network identity, emphasising the development of academic identity through social media and other tools; criticality of digital scholarship, which examines the negative issues associated with online abuse, privacy and data usage. Each of these themes is explored, and their impact in terms of Boyer’s original framing of scholarly activity considered. Boyer’s four scholarly activities of discovery, integration, application and teaching can be viewed from the perspective of these five themes. In conclusion what has been realised does not constitute a revolution in academic practice, but rather a gradual acceptance and utilisation of digital scholarship techniques, practices and values. It is simultaneously true that both radical change has taken place, and nothing has fundamentally altered. Much of the increased adoption in academia mirrors the wider penetration of social media tools amongst society in general, so academics are more likely to have an identity in such places that mixes professional and personal

Linking students' timing of engagement to learning design and academic performance

In recent years, the connection between Learning Design (LD) and Learning Analytics (LA) has been emphasized by many scholars as it could enhance our interpretation of LA findings and translate them to meaningful interventions. Together with numerous conceptual studies, a gradual accumulation of empirical evidence has indicated a strong connection between how instructors design for learning and student behaviour. Nonetheless, students' timing of engagement and its relation to LD and academic performance have received limited attention. Therefore, this study investigates to what extent students' timing of engagement aligned with instructor learning design, and how engagement varied across different levels of performance. The analysis was conducted over 28 weeks using trace data, on 387 students, and replicated over two semesters in 2015 and 2016. Our findings revealed a mismatch between how instructors designed for learning and how students studied in reality. In most weeks, students spent less time studying the assigned materials on the VLE compared to the number of hours recommended by instructors. The timing of engagement also varied, from in advance to catching up patterns. High-performing students spent more time studying in advance, while low-performing students spent a higher proportion of their time on catching-up activities. This study reinforced the importance of pedagogical context to transform analytics into actionable insights

A review of ten years of implementation and research in aligning learning design with learning analytics at the Open University UK

There is an increased recognition that learning design drives both student learning experience and quality enhancements of teaching and learning. The Open University UK (OU) has been one of few institutions that have explicitly and systematically captured the designs for learning at a large scale. By applying advanced analytical techniques on large and fine-grained datasets, the OU has been unpacking the complexity of instructional practices, as well as providing conceptual and empirical evidence of how learning design influences student behaviour, satisfaction, and performance. This study discusses the implementation of learning design at the OU in the last ten years, and critically reviews empirical evidence from eight recent large-scale studies that have linked learning design with learning analytics. Four future research themes are identified to support future adoptions of learning design approaches

Study behaviours in an increasingly digital world: Learning habits, top tips and 'study hacks' questionnaire survey

In response to recent changes in the higher education market, student performance and competitor activity, The Open University has developed strategic objectives around a shift to developing ‘digital by design’ modules and the development of new digital tools to improve student success rates. In order to design effective tools, this initial piece of research was designed to understand more about students’ current study behaviours. The survey was built on a framework with student success at the centre, and generated a great deal of rich, qualitative data about how current distance learning students approach their study. The data was analysed using a thematic analysis, and produced a number of interesting themes. These included a variety of digital personas; priorities when organising study sessions; note-taking methods and reasons for making notes; and boundaries. The practical applications of these findings are some embryonic concepts for new tools and digital spaces for students that encourage the development of successful study behaviours. These concepts are being developed in conjunction with a rigorous research plan

Estimating student workload during the learning design of online courses:Creating a student workload calculator

UK university students are expected to undertake 10 hours of work for each Credit Accumulation and Transfer Scheme (CATS) credit. With face-to-face learning, this is relatively easy to quantify as x hours of contact time and the remainder made up of independent study. For online and distance learning, this is more complex. Study materials are provided for students to work through independently, but unlike face-to-face where the class ends after an hour or two, online students could continue working indefinitely. Some students will inevitably take longer than others to complete tasks, and it is therefore more difficult to ensure student workload in online courses is proportionate to the credits awarded. This paper provides a means to calculate student workload in online courses via a workload calculator, derived from a review of the literature and available at http://bit.ly/postgradworkload. It uses Laurillard’s (2009, 2013) conversational framework activity types to categorise online course materials into task types, and provides a means of estimating the time it would take an average student to complete each task, for use in informing the design of online courses. For those task types that cannot be accurately estimated it is recommended to provide guidance on how long a student should spend on the task within the learning materials