Instructors’ Epistemic Intervention Strategies in MOOC Discussion Forums

Facilitating students’ learning in a massive open online context is challenging for instructors in online teaching. The instructors should enact their professional (epistemic) feedback-giving skills to understand when, how, and why to address learning problems. In this study, we address this issue in terms of agency and suggest strategies that teachers can use to address these problems constructively. This study examines how instructors’ professional agency comes into play in selecting how to intervene to assist students in solving problems in course discussion forums (Facebook group and Canvas discussion forums), which we refer to as an epistemic intervention strategy (EIS). By analyzing discussion forums’ dialogical posts using thematic analysis and epistemic network analysis, we found that instructors adopted five different EISs to address students’ learning. The EISs emerged during the processes of facilitating students’ learning and were influenced by the complexity of students’ questions and positioning in learning in the discussion forums. The findings of this study can inform practitioners that facilitating learning in online discussion forums may demand that instructors go beyond their feedback-giving skills to enact professional agency.publishedVersio

Federated Bayesian optimization XGBoost model for cyberattack detection in internet of medical things

Background Hospitals and medical facilities are increasingly concerned about network security and patient data privacy as the Internet of Medical Things (IoMT) infrastructures continue to develop. Researchers have studied customized network security frameworks and cyberattack detection tools driven by Artificial Intelligence (AI) to counter different types of attacks, such as spoofing, data alteration, and botnet attacks. However, carrying out routine IoMT services and tasks during an under-attack scenario is challenging. Machine Learning has been extensively suggested for detecting cyberattacks in IoMT and IoT infrastructures. However, the conventional centralized approach in ML cannot effectively detect newly emerging attacks without compromising patient data privacy and network flow data confidentiality. Aim This study discusses a Federated Bayesian Optimization XGBoost framework that employs multimodal sensory signals from patient vital signs and network flow data to detect attack patterns and malicious network traffic in IoMT infrastructure while ensuring data privacy and detecting previously unknown attacks. Methodology The proposed model employs a Federated Bayesian Optimisation XGBoost approach, which allows us to search the parameter space quickly and find an optimal solution from each local server while aggregating the model parameters from each local server to the centralised server. The XGBoost algorithm generates a new tree by taking into account the previously estimated value for the tree's input data and then optimizing the prediction gain. This study used a dataset with 44 attributes and 16 318 instances. During the preprocessing phase, 10 features were dropped, and the remaining 34 features were used to evaluate the network flows and biometric data (patient vital signs). Results The performance evaluation reveals that the proposed model predicts data alteration, malware, and spoofing attacks in patients' vital signs and network flow data with a prediction accuracy of 0.96. The results obtained from the experiment demonstrate that both the centralized and federated models are synchronized, with the latter occasionally being slightly reduced. Conclusion The findings indicate that the suggested model can be incorporated into the IoMT domain to detect malicious patterns while maintaining data privacy and confidentiality efficiently.publishedVersio

Nye retningslinjer for syndromet relativ energimangel i idrett (REDs)

Lav energitilgjengelighet gir dårligere prestasjoner og flere skader.publishedVersio

Profiling teacher educators’ strategies for professional digital competence development

The present study investigates the variety among teacher educators (TEds) related to the use of digital resources in teaching as well as the strategies they use to develop digital competence. A person-centred approach was applied to identify meaningful patterns among TEds having different levels of self-reported digital expertise, at five teacher education institutions. Survey data from TEds (N = 389) was subjected to structural equation modelling. With latent class analysis, we identified three distinct profiles based on probability of engagement in different digital competence development (DCD) activities: 1) The restrictive user—characterised by sporadic and narrow use of DCD strategies, prefers peer-restricted collaboration, 2) The moderate user—regular user of DCD strategies, prefers peer-restricted collaboration, and 3) The extensive user—frequent and comprehensive user DCD strategies, engages in broad collaboration. The extensive users also use digital resources more frequently in their teaching compared to the moderate and especially the restricted users. This is the case for individual interactions with students, to make teaching more relevant and applicable, as well as to make teaching more student active. Based on the knowledge on TEds profiles emerging from this study, we propose recommendations for better tailoring of DCD initiatives.publishedVersio

Evaluation of the Erector Spinae Plane Block for postoperative analgesia in laparoscopic ventral hernia repair: a randomized placebo controlled trial

Background. The Erector spinae plane block (ESPB) reduces postoperative pain after several types of abdominal laparoscopic surgeries.. There is sparse data on the efect of ESPB in laparoscopic ventral hernia repair. The purpose of this study was to test the postoperative analgesic efcacy of an ESPB for this procedure. Methods. In this prospective, double-blind, randomized controlled study, adult patients undergoing laparoscopic ventral hernia repair were randomly assigned to either bilateral preoperative ESPB with catheters at the level of Th7 (2×30 ml of either 2.5 mg/ml ropivacaine or saline), with postoperative catheter top ups every 6 h for 24 h. The primary outcome was rescue opioid consumption during the frst hour postoperatively. Secondary outcomes were total opioid consumption at 4 h and 24 h, pain scores, nausea, sedation, as well as Quality of Recovery 15 (QoR-15) and the EuroQol-5 Dimensions (EQ-5D-5L) during the frst week. Results. In total, 64 patients were included in the primary outcome measure. There was no signifcant diference in rescue opioid consumption (oral morphine equivalents (OME)) at one hour postoperatively, with the ESPB group 26.9±17.1 mg versus 32.4±24.3 mg (mean±SD) in the placebo group (p=0.27). There were no signifcant diferences concerning the secondary outcomes during the seven-day observation period. Seven patients received a rescue block postoperatively, providing analgesia in fve patients. Conclusion. We found no diference in measured outcomes between ESPB and placebo in laparoscopic ventral hernia repair. Future studies may evaluate whether a block performed using higher concentration and/or at a diferent thoracic level provides more analgesic efcacy.publishedVersio

Major depression mistaken as frontotemporal dementia due to PET scan

Frontotemporal dementia (FTD) is associated with progressive degeneration of the frontal lobes and this leads to changes in language, motor symptoms, behavior and executive functions.1 In an early stage, patients with FTD usually have intact memory functions.2 40% of the cases of FTD are misdiagnosed,3 with delayed diagnosis compared to other dementias.4 Differentiating FTD from other psychiatric disorders poses challenges, given executive impairment is a common symptom across disorders.5 The need for diagnostic tools has led to the increased use of positron emission tomography (PET), which is regarded as the most accurate in-vivo method for investigating brain metabolism.6 We present a case where PET was central to the diagnostic process.publishedVersio

What do adults living with obesity want from a chatbot for physical activity? – a qualitative study

Background Regular physical activity helps to reduce weight and improve the general well-being of individuals living with obesity. Chatbots have shown the potential to increase physical activity among their users. We aimed to explore the preferences of individuals living with obesity for the features and functionalities of a modern chatbot based on social media, Artificial intelligence (AI) and other recent and relevant technologies. Methods In this study, we used qualitative methods. Focusing on individuals’ preferences for a chatbot to increase physical activity, we conducted both individual interviews and focus groups with nine adult patients staying at Evjeklinikken, a Norwegian rehabilitation clinic for individuals living with morbid obesity. The interviews were fully transcribed and then analysed inductively using thematic analysis. Results Participants preferred motivational features such as social support, goal setting, physical activity illustrations, monitoring of physical activity behaviour and outcomes, and feedback, prompts and reminders. They also preferred features for connecting and synchronising with smartwatches and training device apps. Participants wanted a chatbot that is easy to use and allows for human assistance when needed. Regarding personalising the chatbot, the participants wanted to choose the language, number of messages, and turn functionalities on and off. Conclusions Co-designing chatbots with potential users is essential to understand their specific needs and preferences. We gained valuable insight into a diverse set of features and functionalities relevant to designing physical activity chatbots for individuals living with obesity. Behaviour change techniques are equally important as personalisation features and the option for synchronising with third-party devices. In future work, we will consider the collected needs in the development of a physical activity chatbot to ensure acceptance and adherence to the digital health intervention.publishedVersio

A Comprehensive Review on Deep Learning-Based Motion Planning and End-To-End Learning for Self-Driving Vehicle

Self-Driving Vehicles (SDVs) are increasingly popular, with companies like Google, Uber, and Tesla investing significantly in self-driving technology. These vehicles could transform commuting, offering safer, and efficient transport. A key SDV aspect is motion planning, generating secure, and efficient routes. This ensures safe navigation and prevents collisions with obstacles, pedestrians, and other vehicles. Deep Learning (DL) could aid SDV motion planning. AI tools and algorithms, like Artificial Neural Networks (ANNs), Machine Learning (ML) and DL can learn from data to create effective driving strategies, enhancing SDV adaptability to changing conditions for improved safety and efficiency. This survey gives a DL-based motion planning overview for SDVs, covering behaviour planning, trajectory planning, and End to End Learning (E2EL). It assesses various DL-based behaviour and trajectory planning methods, comparing and summarizing them. It also reviews diverse E2EL techniques including Imitation Learning (IL) and Reinforcement Learning (RL) gaining traction lately. Additionally, this review emphasizes the significance of two crucial enablers: datasets and simulation deployment frameworks for SDVs. The survey compares strategies using multiple metrics and highlights DL-based SDV implementation challenges, including simulation and real-world use cases. This article also suggests future research directions to address E2EL and DL-based motion planning limitations. The presented article is an excellent reference for scholars, engineers, and decision-makers who have an interest in DL-based SDV motion planning.publishedVersio