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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

Feedback is an effective way to assist students in achieving learning goals. The conceptualisation of feedback is gradually moving from feedback as information to feedback as a learner-centred process. To demonstrate feedback effectiveness, feedback as a learner-centred process should be designed to provide quality feedback content and promote student learning outcomes on the subsequent task. However, it remains unclear how instructors adopt the learner-centred feedback framework for feedback provision in the teaching practice. Thus, our study made use of a comprehensive learner-centred feedback framework to analyse feedback content and identify the characteristics of feedback content among student groups with different performance changes. Specifically, we collected the instructors' feedback on two consecutive assignments offered by an introductory to data science course at the postgraduate level. On the basis of the first assignment, we used the status of student grade changes (i.e., students whose performance increased and those whose performance did not increase on the second assignment) as the proxy of the student learning outcomes. Then, we engineered and extracted features from the feedback content on the first assignment using a learner-centred feedback framework and further examined the differences of these features between different groups of student learning outcomes. Lastly, we used the features to predict student learning outcomes by using widely-used machine learning models and provided the interpretation of predicted results by using the SHapley Additive exPlanations (SHAP) framework. We found that 1) most features from the feedback content presented significant differences between the groups of student learning outcomes, 2) the gradient boost tree model could effectively predict student learning outcomes, and 3) SHAP could transparently interpret the feature importance on predictions.

Research does not demonstrate how learning analytics can support students with learning disability. It is vital to scrutinise the demographics of students with and without disabilities and their effect on performance. Machine learning algorithms can provide valuable insights by mining this expanding education data. This study aims to analyse the relationship between disabled and nondisabled students' demographic with their scores and the number of attempts to complete a module to develop prediction models. Three models were used: Adaptive Boosting, Random Forest, and K nearest neighbour. The results found that the Adaptive boosting algorithm delivered the highest prediction accuracy.

© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. Research and development in learning analytics has established viable solutions for scaling personalised feedback to all students. However, questions remain regarding how such feedback is perceived, interpreted and acted upon by stakeholders. The present study reports on the analysis of focus group data from four courses to understand students’ sensemaking of personalised, learning analytics-based feedback. Deductive analysis and epistemic network analysis were carried out to identify students’ perceptions and associated emotional responses to the feedback. The results demonstrate that students mostly held positive perceptions of their feedback–especially in regard to quality, task-focus and instructor care. While emotions to feedback were often negative, students also noted this increased their motivation to invest more effort in the learning tasks. The results of the study provide further evidence of the interpersonal communication variables influencing students’ proactive recipience of feedback. Importantly, the findings show that students were mostly actively engaged with their personalised feedback, and that the instructor-mediated approach to feedback based on learning analytics is aligned to the principles of effective feedback.

While online learning offers much flexibility for students in terms of time and place, students’ experiences of online learning have been less than positive. A sense of belonging is key to students’ success and retention in any learning environment. Teaching or teacher presence is significant for setting a climate for belonging. However, few studies have documented how teachers can foster students’ sense of belonging in online settings and students’ experience of these initiatives. This paper presents an exploratory case study of a teacher’s initiatives to foster students’ belonging in an online subject by amplifying teacher presence through technology-mediated, personalised feedback embedded within interactive, engaging subject design. Students’ experiences of the subject and perceptions of their personalised feedback were captured through surveys and focus groups. Together, the findings indicate that the primary role of personalised feedback in fostering belonging was building connectedness between the teacher and students, thereby enhancing students’ motivation to learn in the subject. The findings also resonate with other research highlighting the importance of teacher presence throughout the subject, not just during synchronous classes.

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

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

This paper reports the findings of a study that proposed a novel learning analytic methodology that combines process mining with cluster analysis to study time management in the context of blended and online learning. The study was conducted with first-year students (N = 241) who were enrolled in blended learning of a health science course. The study identified four distinct time management tactics and three strategies. The tactics and strategies were interpreted according to the established theoretical framework of self-regulated learning in terms of student decisions about what to study, how long to study, and how to study. The study also identified significant differences in academic performance among students who followed different time management strategies.