Online learning is currently adopted by educational institutions worldwide to provide students with ongoing education during the COVID‐19 pandemic. Even though online learning research has been advancing in uncovering student experiences in various settings (i.e., tertiary, adult, and professional education), very little progress has been achieved in understanding the experience of the K‐12 student population, especially when narrowed down to different school‐year segments (i.e., primary and secondary school students). This study explores how students at different stages of their K‐12 education reacted to the mandatory full‐time online learning during the COVID‐19 pandemic. For this purpose, we conducted a province‐wide survey study in which the online learning experience of 1,170,769 Chinese students was collected from the Guangdong Province of China. We performed cross‐tabulation and Chi‐square analysis to compare students’ online learning conditions, experiences, and expectations. Results from this survey study provide evidence that students’ online learning experiences are significantly different across school years. Foremost, policy implications were made to advise government authorises and schools on improving the delivery of online learning, and potential directions were identified for future research into K‐12 online learning. Practitioner notes What is already known about this topic Online learning has been widely adopted during the COVID‐19 pandemic to ensure the continuation of K‐12 education. Student success in K‐12 online education is substantially lower than in conventional schools. Students experienced various difficulties related to the delivery of online learning. What this paper adds Provide empirical evidence for the online learning experience of students in different school years. Identify the different needs of students in primary, middle, and high school. Identify the challenges of delivering online learning to students of different age. Implications for practice and/or policy Authority and schools need to provide sufficient technical support to students in online learning. The delivery of online learning needs to be customised for students in different school years.

Schools are increasingly becoming into complex learning spaces where students interact with various physical and digital resources, educators, and peers. Although the field of learning analytics has advanced in analysing logs captured from digital tools, less progress has been made in understanding the social dynamics that unfold in physical learning spaces. Among the various rapidly emerging sensing technologies, position tracking may hold promises to reveal salient aspects of activities in physical learning spaces such as the formation of interpersonal ties among students. This paper explores how granular x-y physical positioning data can be analysed to model social interactions among students and teachers. We conducted an 8-week longitudinal study in which positioning traces of 98 students and six teachers were automatically captured every day in an open-plan public primary school. Positioning traces were analysed using social network analytics (SNA) to extract a set of metrics to characterise students' positioning behaviours and social ties at cohort and individual levels. Results illustrate how analysing positioning traces through the lens of SNA can enable the identification of certain pedagogical approaches that may be either promoting or discouraging in-class social interaction, and students who may be socially isolated. CCS CONCEPTS• Applied computing → Collaborative learning; Computer-assisted instruction; Learning management systems.

Multimodal Learning Analytics (MMLA) innovations are commonly aimed at supporting learners in physical learning spaces through state-of-the-art sensing technologies and analysis techniques. Although a growing body of MMLA research has demonstrated the potential benefits of sensor-based technologies in education, whether their use can be scalable, sustainable, and ethical remains questionable. Such uncertainty can limit future research and the potential adoption of MMLA by educational stakeholders in authentic learning situations. To address this, we systematically reviewed the methodological, operational, and ethical challenges faced by current MMLA works that can affect the scalability and sustainability of future MMLA innovations. A total of 96 peer-reviewed articles published after 2010 were included. The findings were summarised into three recommendations, including i) improving reporting standards by including sufficient details about sensors, analysis techniques, and the full disclosure of evaluation metrics, ii) fostering interdisciplinary collaborations among experts in learning analytics, software, and hardware engineering to develop affordable sensors and upgrade MMLA innovations that used discontinued technologies, and iii) developing ethical guidelines to address the potential risks of bias, privacy, and equality concerns with using MMLA innovations. Through these future research directions, MMLA can remain relevant and eventually have actual impacts on educational practices.

Simulation-based learning provides students with unique opportunities to develop key procedural and teamwork skills in close-to-authentic physical learning and training environments. Yet, assessing students' performance in such situations can be challenging and mentally exhausting for teachers. Multimodal learning analytics can support the assessment of simulation-based learning by making salient aspects of students' activities visible for evaluation. Although descriptive analytics have been used to study students' motor behaviours in simulation-based learning, their validity and utility for assessing performance remain unclear. This study aims at addressing this knowledge gap by investigating how indoor positioning analytics can be used to generate meaningful insights about students' tasks and collaboration performance in simulation-based learning. We collected and analysed the positioning data of 304 healthcare students, organised in 76 teams, through correlation, predictive and epistemic network analyses. The primary findings were (1) large correlations between students' spatial-procedural behaviours and their group performances; (2) predictive learning analytics that achieved an acceptable level (0.74 AUC) in distinguishing between low-performing and high-performing teams regarding collaboration performance; and (3) epistemic networks that can be used for assessing the behavioural differences across multiple teams. We also present the teachers' qualitative evaluation of the utility of these analytics

Identifying students facing difficulties and providing them with timely support is one of the educator's key responsibilities. Yet, this task is becoming increasingly challenging as the complexity of physical learning spaces grows, along with the emergence of novel educational technologies and classroom designs. There has been substantial research and development work focused on identifying student social behaviours in digital platforms (eg, the learning management system) as predictors of academic progression. However, little work has investigated such relationships in physical learning spaces. This study explores the potential of using wearable trackers for the early detection of low‐progress students based on their social and spatial (socio‐spatial) behaviours at the school. Positioning data from 98 primary school students and six teachers were automatically captured over a period of eight weeks. Fourteen socio‐spatial behavioural features were extracted and processed using a set of machine learning classifiers to model students’ learning progression. Results illustrate the potential of prospectively identifying low‐progress students from these features and the importance of adapting classroom learning analytics to differences in pedagogical designs. What is already known about this topic Learning analytics research on predicting students’ academic progression is emerging in both digital and physical learning spaces. Students’ social behaviours in learning activities is a key factor in predicting their academic progression. Emerging sensing technologies can provide opportunities to study students’ real‐time social behaviours in physical learning spaces. What this paper adds Fourteen progression‐related socio‐spatial behavioural features are extracted from students’ physical (x‐y) positioning traces. Predictive learning analytics that achieved 81% accuracy in prospectively identifying low‐progress students from their real‐time socio‐spatial behaviours. Empirical evidence to support the need for classroom learning analytics to have instructional sensitivity (ie, be calibrated according to the learning design). Implications for practice and/or policy Sensing technologies and machine learning algorithms can be used to capture and generate valuable insights about higher‐order learning constructs (eg, performance and collaboration) from students' physical positioning traces in classrooms. Researchers and practitioners should be cautious with generalised classification algorithms and predictive learning analytics that do not account for the pedagogical differences between different subjects or learning designs. Researchers and practitioners should consider the potentially unforeseen ethical issues that can emerge in using sensing technologies and predictive learning analytics in authentic, physical classroom settings.

BackgroundEvidence of reduced cardiovascular morbidity and mortality as well as cost support thiazide diuretics as the first-line choice for treatment of hypertension. The purpose of this study was to determine the proportion of senior hypertensives that received thiazide diuretics as first-line treatment, and to determine if cardiovascular and other potentially relevant comorbidities predict the choice of first-line therapy.Methods and FindingsBritish Columbia PharmaCare data were used to determine the cohort of seniors (residents aged 65 or older) who received their first reimbursed hypertension drug during the period 1993 to 2000. These individual records were linked to medical and hospital claims data using the British Columbia Linked Health Database to find the subset that had diagnoses indicating the presence of hypertension as well as cardiovascular and other relevant comorbidities. Rates of first-line thiazide prescribing as proportion of all first-line treatment were analysed, accounting for patient age, sex, overall clinical complexity, and potentially relevant comorbidities. For the period 1993 to 2000, 82,824 seniors who had diagnoses of hypertension were identified as new users of hypertension drugs. The overall rate at which thiazides were used as first-line treatment varied from 38% among senior hypertensives without any potentially relevant comorbidity to 9% among hypertensives with previous acute myocardial infarction. The rate of first-line thiazide diuretic prescribing for patients with and without potentially relevant comorbidities increased over the study period. Women were more likely than men, and older patients were more likely than younger, to receive first-line thiazide therapy.ConclusionsFindings indicate that first-line prescribing practices for hypertension are not consistent with the evidence from randomized control trials measuring morbidity and mortality. The health and financial cost of not selecting the most effective and least costly therapeutic options are significant.

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[2001][2002][2003][2004]. These data were then graphed to assess (using concentration curves) changes in the progressivity of private and public pharmaceutical financing. Results: Overall, the move to Fair PharmaCare resulted in larger but slightly less regressive private payments and smaller but slightly more progressive public subsidies. Because total drug spending increased while the total subsidy available decreased, average private household spending as a proportion of household income increased across virtually all age and income levels. Discussion: The PharmaCare Program redistributed public subsidies in a manner that was more progressive than previous programs; this reduced the regressivity of private pharmaceutical payments. However, total public subsidy decreased, and private spending increased by a commensurate amount. This makes the program' s overall financial impact on BC households somewhat ambiguous. Income-based pharmacare could improve financial equity unambiguously if public shares of drug spending are expanded.