Search

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.

Learning analytics has been increasingly outlined as a powerful tool for measuring, analysing, and predicting learning experiences and behaviours. The rising use of learning analytics means that many educational researchers now require new ranges of technical analytical skills to contribute to an increasingly data-heavy field. However, it has been argued that educational data scientists are a 'scarce breed' (Buckingham Shum et al., 2013) and that more resources are needed to support the next generation of early career researchers in the education field. At the same time, little is known about how early career education researchers feel towards learning analytics and whether it is important to their current and future research practices. Using a thematic analysis of a participatory learning analytics workshop discussions with 25 early career education researchers, we outline in this article their ambitions, challenges and anxieties towards learning analytics. In doing so, we have provided a roadmap for how the learning analytics field might evolve and practical implications for supporting early career researchers' development.

Extensive research in learning science has established the importance of time management in online learning. Recently, learning analytics (LA) has shed further lights on the temporal characteristics of learning by allowing researchers to capture authentic digital footprints of student learning behaviours. Nonetheless, students' timing of engagement and its relation to learning design (LD) and academic performance have received limited attention. This study investigates to what extent students' timing of engagement aligned with instructor learning design, and how engagement varied across different levels of performance. Our findings revealed a mismatch between how instructors designed for learning and how students study. In most weeks, students spent less time studying the assigned materials on the virtual learning environment (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. By incorporating the pedagogical context into learning analytics, not only we can understand what, why, and when students engage, but also how their behaviours are influenced by the way instructors design for learning.

Although the attainment gap between black and minority ethnic (BME) students and white students has persisted for decades, the potential causes of these disparities are highly debated. The emergence of learning analytics allows researchers to understand how students engage in learning activities based on their digital traces in a naturalistic setting. This study investigates the attainment gap by analysing the differences in behavioural engagement between different ethnic groups. Using multilevel models of academic performance, demographics, and online traces of 149,672 students enrolled in 401 modules in a distance learning setting, we confirmed the existing attainment gap. After controlling for other demographics, module characteristics and engagement, BME students were between 19% and 79% less likely to complete, pass or achieve an excellent grade compared to white students. Given the same academic performance, BME students spent 4-12% more time on studying than white students. While the attainment gap remained persistent after controlling for academic engagement, our study further highlighted the inequality of attainment between BME and white students.

There has been a growing interest in how teaching might be informed by "learning design" (LD), with a promising method for investigating LD being offered by the emerging field of "learning analytics" (LA). In this study, we used a novel LA for LD methodology to investigate the implementation of LD in an online distance learning context. A key innovation is the focus on "patterns" of LD. Using data from the virtual learning environment, outcomes data, and self-reports, for 47,784 students, we investigated the impact of those patterns on student behaviour, pass rates and satisfaction. A second innovation involves "social network analysis." Our study revealed that different patterns of LD were associated with statistically significant differences in behaviour, but not in pass rates or satisfaction. Nonetheless, the study highlights that applying LA to LD might, in a virtuous circle, contribute to the validity and effectiveness of both, and to the enhancement of online distance learning.

Language education has a rich history of research and scholarship focusing on the effectiveness of learning activities and the impact these have on student behaviour and outcomes. One of the basic assumptions in foreign language pedagogy and CALL in particular is that learners want to be able to communicate effectively with native speakers of their chosen language. Combining principles of learning analytics and Big Data with learning design, this study used a student activity based taxonomy adopted by the Open University UK to inform module design. The learning designs of four introductory and intermediary language education modules and online engagement of 2111 learners were contrasted using weekly learning design data. In this study, we aimed to explore how learning design decisions made by language teachers influenced students' engagement in the VLE. Using fixed effect models, our findings indicated that 55% of variance of weekly online engagement in these four modules was explained by the way language teachers designed weekly learning design activities. Our learning analytics study highlights the potential affordances for CALL researchers to use the power of learning design and big data to explore and understand the complexities and dynamics of language learning for students and teachers.

Although the attainment gap between black and minority ethnic (BME) students and white students has persisted for decades, the potential causes of these disparities are highly debated. The emergence of learning analytics allows researchers to understand how students engage in learning activities based on their digital traces in a naturalistic setting. This study investigates the attainment gap by analysing the differences in behavioural engagement between different ethnic groups. Using multilevel models of academic performance, demographics, and online traces of 149,672 students enrolled in 401 modules in a distance learning setting, we confirmed the existing attainment gap. After controlling for other demographics, module characteristics and engagement, BME students were between 19% and 79% less likely to complete, pass or achieve an excellent grade compared to white students. Given the same academic performance, BME students spent 4-12% more time on studying than white students. While the attainment gap remained persistent after controlling for academic engagement, our study further highlighted the inequality of attainment between BME and white students. [ABSTRACT FROM AUTHOR]

Purpose: To compare the diagnostic test accuracy (DTA) of trained nurses, mid‐level and senior ophthalmologists in Vietnam against a reference standard from the UK, and assess the impact of further training on graders. Methods: Fundus photographs were taken and graded at two different time points by trained nurses, mid‐level ophthalmologists and senior ophthalmologists in Vietnam using the UK National Screening Committee classification system. A reference standard from the UK regraded all images in masked fashion and DTA was calculated using sensitivity and specificity (with 95% CIs), and positive and negative predictive values. Results: Between September and November 2018, 431 people with diabetes attended diabetic retinopathy (DR) screening in Southern Vietnam. The sensitivity for detecting any DR for all grader groups in Vietnam was low (<43%) against the reference standard in the UK. The sensitivity for detecting referable DR and referrable diabetic macular oedema was particularly low (<20%) for all grader groups. Subsequently, an additional subset of images from 138 consecutive patients screened from March to April 2021 was regraded in the UK to evaluate graders' performance after additional training was delivered. A significant improvement in DTA among all grader groups in Vietnam was identified, with sensitivity for detecting any DR exceeding 85%. Nurses achieved the highest sensitivity for grading referable DR (76.9%, 60.3–88.3%) and mid‐level ophthalmologists attained the highest sensitivity for detecting referable maculopathy (77.5%, 61.2–88.6). Conclusions: Grading accuracy was low in Vietnam in the first six months of implementing a training programme. Many factors may have contributed to poor grader performance, including inadequate training and feedback, insufficient time to participate in quality assurance testing and competing work responsibilities. After further training was delivered, DTA among all graders in Vietnam increased. Findings from this study are particularly important for DR programme planners in low‐ and middle‐income countries. [ABSTRACT FROM AUTHOR]