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

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

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

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

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

Evidence -Based Learning: Futures. Using learning design and learning analytics to empower teachers to meet students’ diverse needs

With the introduction of learning design in early 2000 and learning analytics in 2012, the OU has led the way in how teachers make complex decisions to design interactive courses, and how students can maximise their learning potential. The next obvious steps would be to include AI, personalisation, and student-led learning analytics to provide learning opportunities that meet the unique needs of each learner, but whether this would be technically feasible and pedagogically desirable will be discussed. In this chapter we will look at recent and future developments concerning the “holy trinity” of learning design, learning analytics, and how teachers can help institutions like the OU to ensure that our current and future students’ needs are met. Furthermore, we will reflect on the affordances and limitations of learning design and learning analytics to help teachers to adapt their teaching and learning practices to meet learners’ needs

#Design4Learning: Designing for the Future of Higher Education

Technology enhanced learning has the potential to develop and deliver innovative learning opportunities to improve the student learning experience (Conole, 2013; Sharples et al., 2015). There is now a wide range of learning trajectories from which curricula are currently being developed and for many this raises the question of how higher education educators can ensure that they choose appropriate, robust yet innovative learning designs. This is because a good learning design needs to assist with the delivery of course materials, learning support, and appropriate assessment strategies which will meet the learning outcomes demanded by educational institutions and employers

Unravelling the dynamics of instructional practice: a longitudinal study on learning design and VLE activities

Substantial progress has been made in understanding how teachers design for learning. However, there remains a paucity of evidence of the actual students' response towards leaning designs. Learning analytics has the power to provide just-in-time support, especially when predictive analytics is married with the way teachers have designed their course, or so-called a learning design. This study investigates how learning designs are configured over time and their impact on student activities by analyzing longitudinal data of 38 modules with a total of 43,099 registered students over 30 weeks at the Open University UK, using social network analysis and panel data analysis. Our analysis unpacked dynamic configurations of learning designs between modules over time, which allows teachers to reflect on their practice in order to anticipate problems and make informed interventions. Furthermore, by controlling for the heterogeneity between modules, our results indicated that learning designs were able to explain up to 60% of the variability in student online activities, which reinforced the importance of pedagogical context in learning analytics

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 Mixed-Method Study of How Instructors Design for Learning in Online and Distance Education

The use of analytical methods from learning analytics (LA) research combined with visualizations of learning activities using learning design (LD) tools and frameworks has provided important insight into how instructors design for learning. Nonetheless, there are many subtle nuances in instructors’ design decisions that might not easily be captured using LA tools. Therefore, this study sets out to explore how and why instructors design for learning in an online and distance higher education setting by employing a mixed-method approach, which combined semi-structured interviews of 12 instructors with network analyses of their LDs. Our findings uncovered several underlying factors that influenced how instructors designed their modules and highlighted some discrepancies between instructors’ pedagogical beliefs and their actual LD as captured by the Open University Learning Design Initiative (OULDI). This study showcases the potential of combining LA with qualitative insights for a better understanding of the complex design process in online distance higher education