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

Application of Internet of Things in Health Care

The paper focuses on the continuously growing area of Internet of Things and its application to health care. We discuss several important aspects, namely quality, and relevance of data acquired. We illustrate IoT by a case study of diabetes mellitus personalised treatment. Modern type 1 diabetes mellitus therapy is now unimaginable without intensive glycaemia monitoring. In the last decade the possibility of real time continuous glucose monitoring (RT-CGMS) was realised along with integration to some types of insulin pump. Currently the research focuses on continuous glucose monitoring systems that have following advantages: non-invasiveness, high customer acceptance; comfort in use; ease in use; accuracy; long-term measurement up to 4 weeks; calibrating unit integrated; alerts for low or highs of glucose level; enabling higher lifestyle flexibility, e.g. physical activity, food, medication; wireless data and energy transmission; infection risk is minimised. Obviously several sensors are necessary to acquire the contextual data, in particular vital parameters, physical activity, and stress. All measured data must be collected and evaluated in parallel. The aim is to identify the mutual relations in measured parameters, the differences among patients and finally the most important parameters for development of personalised data models

Measures for recommendations based on past students' activity

This paper introduces two measures for the recommendation of study materials based on students' past study activity. We use records from the Virtual Learning Environment (VLE) and analyse the activity of previous students. We assume that the activity of past students represents patterns, which can be used as a basis for recommendations to current students.The measures we define are Relevance, for description of a supposed VLE activity derived from previous students of the course, and Effort, that represents the actual effort of individual current students. Based on these measures, we propose a composite measure, which we call Importance.We use data from the previous course presentations to evaluate of the consistency of students' behaviour. We use correlation of the defined measures Relevance and Average Effort to evaluate the behaviour of two different student cohorts and the Root Mean Square Error to measure the deviation of Average Effort and individual student Effort

Using Temporal Analytics to Detect Inconsistencies between Learning Design and Student Behaviours

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

Investigating Influence of Demographic Factors on Study Recommenders

Recommender systems in e-learning platforms, can utilise various data about learners in order to provide them with the next best material to study. We build on our previous work, which defines the recommendations in terms of two measures (i.e. relevance and effort) calculated from data of successful students in the previous runs of the courses. In this paper we investigate the impact of students’ socio-demographic factors and analyse how these factors improved the recommendation. It has been shown that education and age were found to have a significant impact on engagement with materials

Maternal body mass index and external cephalic version success rate — are they related?

Objectives: External cephalic version (ECV) is a useful method helping to reduce the incidence of planned caesarean deliveries for fetal malpresentation. There is an effort to look for the best predictors for a successful ECV, the effect of maternal weight is still unclear. The aim of our study is to determine maternal body mass index (BMI) in association with the ECV success rate and the risk of complications.Material and methods: A retrospective observational cohort study in 981 women after the 36th week of gestation with a fetus in a breech presentation who had undergone an ECV attempt. We evaluated the success rate and complications of ECV in association with BMI categories according to the WHO classification of obesity.Results: ECV was successful in 478 cases (48.7%). In the category of overweight patients (BMI > 25; n = 484), ECV was successful in 51% and unsuccessful in 49% (p = 0.28) of cases. In obese patients (BMI > 30; n = 187), ECV was successful in 44.8% and unsuccessful in 55.2% (p = 0.28) of cases. The effect of BMI on the success rate of ECV for the category of overweight and obesity was not proven by statistical analysis. Serious complications occurred in seven cases in similar numbers in all three subgroups according to BMI.Conclusions: BMI in the categories of overweight and obesity is not a factor influencing the success rate and risk of complications of ECV. These results can be helpful when consulting pregnant women the chance of successful ECV

Serum lactate in refractory out-of-hospital cardiac arrest:Post-hoc analysis of the Prague OHCA study

Background: The severity of tissue hypoxia is routinely assessed by serum lactate. We aimed to determine whether early lactate levels predict outcomes in refractory out-of-hospital cardiac arrest (OHCA) treated by conventional and extracorporeal cardiopulmonary resuscitation (ECPR). Methods: This study is a post-hoc analysis of a randomized Prague OHCA study (NCT01511666) assessing serum lactate levels in refractory OHCA treated by ECPR (the ECPR group) or conventional resuscitation with prehospital achieved return of spontaneous circulation (the ROSC group). Lactate concentrations measured on admission and every 4 hours (h) during the first 24 h were used to determine their relationship with the neurological outcome (the best Cerebral Performance Category score within 180 days post-cardiac arrest). Results:In the ECPR group (92 patients, median age 58.5 years, 83% male) 26% attained a favorable neurological outcome. In the ROSC group (82 patients, median age 55 years, 83% male) 59% achieved a favorable neurological outcome. In ECPR patients lactate concentrations could discriminate favorable outcome patients, but not consistently in the ROSC group. On admission, serum lactate &gt;14.0 mmol/L for ECPR (specificity 87.5%, sensitivity 54.4%) and &gt;10.8 mmol/L for the ROSC group (specificity 83%, sensitivity 41.2%) predicted an unfavorable outcome. Conclusion: In refractory OHCA serum lactate concentrations measured anytime during the first 24 h after admission to the hospital were found to correlate with the outcome in patients treated by ECPR but not in patients with prehospital ROSC. A single lactate measurement is not enough for a reliable outcome prediction and cannot be used alone to guide treatment.</p

Mapování elektrických potenciálů z povrchu hrudníku - předzpracování a vizualizace

The aim of the paper is to present current research activity in the area of computer supported ECG processing. Analysis of heart electric field based on standard 12lead system is at present the most frequently used method of heart disease diagnostics. However body surface potential mapping (BSPM) that measures electric potentials from several tens to hundreds of electrodes placed on thorax surface has in certain cases higher diagnostic value given by data collection in areas that are inaccessible for standard 12lead ECG. For preprocessing, wavelet transform is used; it allows detect significant values on the ECG signal. Several types of maps, namely immediate potential, integral, isochronous, and differential