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The widespread adoption of digital tools for learning enables extensive data collection about employee learning activities. This study analyzes how corporations use data to inform and support their learning needs. This study presents a qualitative analysis of what managers and executives overseeing learning and development initiatives in their respective organizations value in learning analytics (LA) at workplace. The interviews were analyzed through a framework that combined lenses of value-sensitive design and value creation to uncover the values underpinning current applications of LA in the corporate sector. We found that corporations vary in their strategic LA orientations when it comes to organizational learning and development. However, although aspirational narratives of executives are diverse, their activities are not. The implications of the rich qualitative findings suggest that learning sciences can support organizational learning by creating diagnostic tools to identify organizational orientations for learning analytics. Computer scientists can also support decision making and research on corporate learning analytics by creating tools that can help map organizational aspirations and activities in relation to the various data sources, stemming from different organizational units. We conclude outlining these recommendations Refereed/Peer-reviewed

Self-regulated learning (SRL) is associated with university student academic success outcomes, however students often need support to develop these skills. Technology-mediated feedback is one strategy that may aid educators in supporting students’ SRL development. This study aims to explore whether a technology-mediated feedback strategy targeting tutorial preparation for flipped classrooms enhances first-year students’ self-report SRL and observed implementation of the strategy. Self-report SRL was measured using the Motivated Strategies for Learning Questionnaire (MSLQ); strategy implementation profiles were based on lecture video access patterns. First-year psychology students (n = 99) were sent technology-mediated feedback emails aimed at developing their SRL. Paired-samples t-tests revealed significant increases in post-intervention self-reported motivational SRL subscales; self-reported and observed learning strategies implementation did not improve. Future research could build upon this exploratory work to form a multi-pronged strategy to increase understanding of the role of technology-mediated feedback in first-year students’ SRL development for flipped classroom learning. Refereed/Peer-reviewed

Learning analytics (LA) has been presented as a viable solution for scaling timely and personalised feedback to support students' self-regulated learning (SRL). Research is emerging that shows some positive associations between personalised feedback with students' learning tactics and strategies as well as time management strategies, both important aspects of SRL. However, the definitive role of feedback on students' SRL adaptations is under-researched; this requires an examination of students' recalled experiences with their personalised feedback. Furthermore, an important consideration in feedback impact is the course context, comprised of the learning design and delivery modality. This mixed-methods study triangulates learner trace data from two different course contexts, with students' qualitative data collected from focus group discussions, to more fully understand the impact of their personalised feedback and to explicate the role of this feedback on students' SRL adaptations. The quantitative analysis showed the contextualised impact of the feedback on students' learning and time management strategies in the different courses, while the qualitative analysis highlighted specific ways in which students used their feedback to adjust these and other SRL processes Refereed/Peer-reviewed

Recent developments in educational technologies have provided a viable solution to the challenges associated with scaling personalised feedback to students. However, there is currently little empirical evidence about the impact such scaled feedback has on student learning progress and study behaviour. This paper presents the findings of a study that looked at the impact of a learning analytics (LA)-based feedback system on students' self-regulated learning and academic achievement in a large, first-year undergraduate course. Using the COPES model of self-regulated learning (SRL), we analysed the learning operations of students, by way of log data from the learning management system and e-book, as well as the products of SRL, namely, performance on course assessments, from three years of course offerings. The latest course offering involved an intervention condition that made use of an educational technology to provide LA-based process feedback. Propensity score matching was employed to match a control group to the student cohort enrolled in the latest course offering, creating two equal-sized groups of students who received the feedback (the experimental group) and those who did not (the control group). Growth mixture modelling and mixed between-within ANOVA were also employed to identify differences in the patterns of online self-regulated learning operations over the course of the semester. The results showed that the experimental group showed significantly different patterns in their learning operations and performed better in terms of final grades. Moreover, there was no difference in the effect of feedback on final grades among students with different prior academic achievement scores, indicating that the LA-based feedback deployed in this course is able to support students’ learning, regardless of prior academic standing Refereed/Peer-reviewed

Video in education has become pervasive. Globally, educators are recording instructional videos to augment their students’ learning and, in many contexts, replace face-to-face lectures. However, the mere act of watching a video is primarily a passive learning experience likely leading to lack of student engagement hindering learning. Active learning strategies such as video annotations and in-video questions have the potential to shift the passive experience of watching an instructional video to a more active one by engaging students with learning strategies designed to promote self-regulated learning and improve content knowledge. This experimental study investigates the impact of in-video questions compared to video annotations on learning and self-efficacy in an experimental setting. Findings revealed that learners who annotated videos had higher self-efficacy than those who completed in-video questions likely due to the immediate feedback received from the in-video questions. The study further concluded that prior knowledge plays a critical role in selecting appropriate active learning strategies, suggesting that video annotations be considered when students have prior knowledge about a topic whereas in-video questions with immediate feedback be interspersed in videos when students do not have prior knowledge about a topic. Refereed/Peer-reviewed

Thesis (PhD(Education)Curriculum, Education Studies)(DUCIER)--University of South Australia, 2020. Includes bibliographical references (pages 179-199) Learner data collected as a by-product of digital learning environments have created new opportunities for providing personalised feedback and support to students. Learning analytics (LA), an area of research that uses digital learner traces to enable insights into students’ learning processes, has garnered much interest in the development of technologies to scale personalised feedback. However, empirical research is limited regarding the impact of such feedback systems on students’ learning and performance. This doctoral thesis examines the impact of LA-based feedback systems on students’ performance and self-regulated learning (SRL) through four studies. Collectively, the results contribute to theory and practice of feedback based on LA data and provide empirical support to feedback theories explicitly linking feedback with SRL.

This paper reports on the findings of a study that proposed a novel learning analytics methodology that combines three complimentary techniques - agglomerative hierarchical clustering, epistemic network analysis, and process mining. The methodology allows for identification and interpretation of self-regulated learning in terms of the use of learning strategies. The main advantage of the new technique over the existing ones is that it combines the time management and learning tactic dimensions of learning strategies, which are typically studied in isolation. The new technique allows for novel insights into learning strategies by studying the frequency of, strength of connections between, and ordering and time of execution of time management and learning tactics. The technique was validated in a study that was conducted on the trace data of first-year undergraduate students who were enrolled into two consecutive offerings (N2017 = 250 and N2018 = 232) of a course at an Australian university. The application of the proposed technique identified four strategy groups derived from three distinct time management tactics and five learning tactics. The tactics and strategies identified with the technique were correlated with academic performance and were interpreted according to the established theories and practices of self-regulated learning. Refereed/Peer-reviewed

Students entering enabling programs as an alternative pathway to University tend to have higher rates of attrition than their peers admitted via more traditional pathways. Students in enabling programs require high levels of personalised feedback to support their transition to study.However, the size and diversity of the enabling student cohort presents formidable challenges for instructors. The field of learning analytics offers a viable solution for scaling the communication of personalised, data-informed feedback to support student learning. This study describes the use of a novel learning analytics-based feedback system called On Task, to provide personalised feedback and support to students in an enabling course at one Australian higher education institution. An end-of-course student survey (N=41; 17% response rate) was employed to gain insights into their perceptions of personalised, data-informed feedback messages. Using importance-performance analysis (IPA), the survey results indicated that this technology-mediated form of feedback exceeded students’ expectations of learning support, as well as the enhancement of their overall course experience. The implications for using learning analytics and data-informedfeedback mechanisms in teaching and learning are discussed. Refereed/Peer-reviewed