We developed an application which allows learners to construct qualitative representations of dynamic systems to aid them in learning subject content knowledge and system thinking skills simultaneously. Within this application, we implemented a lightweight support function which automatically generates help from a norm-representation to aid learners as they construct these qualitative representations. This support can be expected to improve learning. Using this function it is not necessary to define in advance possible errors that learners may make and the subsequent feedback. Also, no data from (previous) learners is required. Such a lightweight support function is ideal for situations where lessons are designed for a wide variety of topics for small groups of learners. Here, we report on the use and impact of this support function in two lessons: Star Formation and Neolithic Age. A total of 63 ninth-grade learners from secondary school participated. The study used a pretest/intervention/post-test design with two conditions (no support vs. support) for both lessons. Learners with access to the support create better representations, learn more subject content knowledge, and improve their system thinking skills. Learners use the support throughout the lessons, more often than they would use support from the teacher. We also found no evidence for misuse, i.e., 'gaming the system', of the support function.
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The rising rate of preprints and publications, combined with persistent inadequate reporting practices and problems with study design and execution, have strained the traditional peer review system. Automated screening tools could potentially enhance peer review by helping authors, journal editors, and reviewers to identify beneficial practices and common problems in preprints or submitted manuscripts. Tools can screen many papers quickly, and may be particularly helpful in assessing compliance with journal policies and with straightforward items in reporting guidelines. However, existing tools cannot understand or interpret the paper in the context of the scientific literature. Tools cannot yet determine whether the methods used are suitable to answer the research question, or whether the data support the authors’ conclusions. Editors and peer reviewers are essential for assessing journal fit and the overall quality of a paper, including the experimental design, the soundness of the study’s conclusions, potential impact and innovation. Automated screening tools cannot replace peer review, but may aid authors, reviewers, and editors in improving scientific papers. Strategies for responsible use of automated tools in peer review may include setting performance criteria for tools, transparently reporting tool performance and use, and training users to interpret reports.
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To cope with changing demands from society, higher education institutes are developing adaptive curricula in which a suitable integration of workplace learning is an important factor. Automated feedback can be used as part of formative assessment strategies to enhance student learning in the workplace. However due to the complex and diverse nature of workplace learning processes, it is difficult to align automated feedback to the needs of the individual student. The main research question we aim to answer in this design-based study is: ‘How can we support higher education students’ reflective learning in the workplace by providing automated feedback while learning in the workplace?’. Iterative development yielded 1) a framework for automated feedback in workplace learning, 2) design principles and guidelines and 3) an application prototype implemented according to this framework and design knowledge. In the near future, we plan to evaluate and improve these tentative products in pilot studies. https://link.springer.com/chapter/10.1007/978-3-030-25264-9_6
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Current methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not consistent with process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to very limited application of energy performance diagnosis in practice. In a previous paper, a generic reference architecture – hereafter referred to as the 4S3F (four symptoms and three faults) framework – was developed. Because it is closely related to the way HVAC experts diagnose problems in HVAC installations, 4S3F largely overcomes the problem of limited application. The present article addresses the fault diagnosis process using automated fault identification (AFI) based on symptoms detected with a diagnostic Bayesian network (DBN). It demonstrates that possible faults can be extracted from P&IDs at different levels and that P&IDs form the basis for setting up effective DBNs. The process was applied to real sensor data for a whole year. In a case study for a thermal energy plant, control faults were successfully isolated using balance, energy performance and operational state symptoms. Correction of the isolated faults led to annual primary energy savings of 25%. An analysis showed that the values of set probabilities in the DBN model are not outcome-sensitive. Link to the formal publication via its DOI https://doi.org/10.1016/j.enbuild.2020.110289
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Students often struggle with constructing models of system behaviour, particularly in open modelling tasks where there is no single correct answer. The challenge lies in providing effective support that helps students develop high quality models while maintaining their autonomy in the modelling process. This study presents a procedure for assessing the quality of student-generated qualitative models in open modelling tasks, based on three characteristics: correctness, parsimony, and completeness. The procedure was developed and refined using student-generated models from two secondary school tasks on thermoregulation and sound properties. The findings contribute to the development of automated support systems that guide students through open modelling tasks by focusing on quality characteristics rather than adherence to a predefined norm model.
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In practice, faults in building installations are seldom noticed because automated systems to diagnose such faults are not common use, despite many proposed methods: they are cumbersome to apply and not matching the way of thinking of HVAC engineers. Additionally, fault diagnosis and energy performance diagnosis are seldom combined, while energy wastage is mostly a consequence of component, sensors or control faults. In this paper new advances on the 4S3F diagnose framework for automated diagnostic of energy waste in HVAC systems are presented. The architecture of HVAC systems can be derived from a process and instrumentation diagram (P&ID) usually set up by HVAC designers. The paper demonstrates how all possible faults and symptoms can be extracted on a very structured way from the P&ID, and classified in 4 types of symptoms (deviations from balance equations, operational states, energy performances or additional information) and 3 types of faults (component, control and model faults). Symptoms and faults are related to each other through Diagnostic Bayesian Networks (DBNs) which work as an expert system. During operation of the HVAC system the data from the BMS is converted to symptoms, which are fed to the DBN. The DBN analyses the symptoms and determines the probability of faults. Generic indicators are proposed for the 4 types of symptoms. Standard DBN models for common components, controls and models are developed and it is demonstrated how to combine them in order to represent the complete HVAC system. Both the symptom and the fault identification parts are tested on historical BMS data of an ATES system including heat pump, boiler, solar panels, and hydronic systems. The energy savings resulting from fault corrections are estimated and amount 25%. Finally, the 4S3F method is extended to hard and soft sensor faults. Sensors are the core of any FDD system and any control system. Automated diagnostic of sensor faults is therefore essential. By considering hard sensors as components and soft sensors as models, they can be integrated into the 4S3F method.
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Background:An eHealth tool that coaches employees through the process of reflection has the potential to support employees with moderate levels of stress to increase their capacity for resilience. Most eHealth tools that include self-tracking summarize the collected data for the users. However, users need to gain a deeper understanding of the data and decide upon the next step to take through self-reflection.Objective:In this study, we aimed to examine the perceived effectiveness of the guidance offered by an automated e-Coach during employees’ self-reflection process in gaining insights into their situation and on their perceived stress and resilience capacities and the usefulness of the design elements of the e-Coach during this process.Methods:Of the 28 participants, 14 (50%) completed the 6-week BringBalance program that allowed participants to perform reflection via four phases: identification, strategy generation, experimentation, and evaluation. Data collection consisted of log data, ecological momentary assessment (EMA) questionnaires for reflection provided by the e-Coach, in-depth interviews, and a pre- and posttest survey (including the Brief Resilience Scale and the Perceived Stress Scale). The posttest survey also asked about the utility of the elements of the e-Coach for reflection. A mixed methods approach was followed.Results:Pre- and posttest scores on perceived stress and resilience were not much different among completers (no statistical test performed). The automated e-Coach did enable users to gain an understanding of factors that influenced their stress levels and capacity for resilience (identification phase) and to learn the principles of useful strategies to improve their capacity for resilience (strategy generation phase). Design elements of the e-Coach reduced the reflection process into smaller steps to re-evaluate situations and helped them to observe a trend (identification phase). However, users experienced difficulties integrating the chosen strategies into their daily life (experimentation phase). Moreover, the identified events related to stress and resilience were too specific through the guidance offered by the e-Coach (identification phase), and the events did not recur, which consequently left users unable to sufficiently practice (strategy generation phase), experiment (experimentation phase), and evaluate (evaluation phase) the techniques during meaningful events.Conclusions:Participants were able to perform self-reflection under the guidance of the automated e-Coach, which often led toward gaining new insights. To improve the reflection process, more guidance should be offered by the e-Coach that would aid employees to identify events that recur in daily life. Future research could study the effects of the suggested improvements on the quality of reflection via an automated e-Coach.
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BACKGROUND:Endotracheal suctioning causes discomfort, is associated with adverse effects, and is resource-demanding. An artificial secretion removal method, known as an automated cough, has been developed, which applies rapid, automated deflation, and inflation of the endotracheal tube cuff during the inspiratory phase of mechanical ventilation. This method has been evaluated in the hands of researchers but not when used by attending nurses. The aim of this study was to explore the efficacy of the method over the course of patient management as part of routine care.METHODS:This prospective, longitudinal, interventional study recruited 28 subjects who were intubated and mechanically ventilated. For a maximum of 7 d and on clinical need for endotracheal suctioning, the automatic cough procedure was applied. The subjects were placed in a pressure-regulated ventilation mode with elevated inspiratory pressure, and automated cuff deflation and inflation were performed 3 times, with this repeated if deemed necessary. Success was determined by resolution of the clinical need for suctioning as determined by the attending nurse. Adverse effects were recorded.RESULTS:A total of 84 procedures were performed. In 54% of the subjects, the artificial cough procedure was successful on > 70% of occasions, with 56% of all procedures considered successful. Ninety percent of all the procedures were performed in subjects who were spontaneously breathing and on pressure-support ventilation with peak inspiratory pressures of 20 cm H2O. Rates of adverse events were similar to those seen in the application of endotracheal suctioning.CONCLUSIONS:This study solely evaluated the efficacy of an automated artificial cough procedure, which illustrated the potential for reducing the need for endotracheal suctioning when applied by attending nurses in routine care.
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The aim of this research is to explore the potential of Mixed Reality (MR) technologies for Operator Support in order to progress towards Industry 4.0 (I4.0) particularly for SMEs. Through a series of interventions and interviews conducted with local SMEs, potential use cases and their drawbacks have been identified. From this, insights were derived that serve as a starting point for conducting further experiments with MR technology in the smart manufacturing laboratory at the THUAS in Delft. The intervention consisted of a free form workshop in which the participants get ‘tinkering’ time to explore MR in their own work environment. The various levels of awareness were assessed in three stages: during an introductory interview, and after an instruction meeting and some ‘tinkering’. The study took place in the period from January 2022 to July 2022 with 10 local SMEs in the Netherlands. The results show that for all SMEs the awareness and understanding increased. The use cases identified by operators themselves concerned Quality Control, Diagnostics, Instruction, Specification and Improvement of Operations. Drawbacks foreseen related to Ergonomic Concerns, Resistance from operators, Technical considerations, Unavailability of MR device and an insufficient digital infrastructure to support MR in full extent. The use case most promising to the participants was further developed into a physical prototype for an ‘assisted assembly cell’ by which the aspects of ergonomics and the mentioned technical considerations could be analysed.
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Cybersecurity threat and incident managers in large organizations, especially in the financial sector, are confronted more and more with an increase in volume and complexity of threats and incidents. At the same time, these managers have to deal with many internal processes and criteria, in addition to requirements from external parties, such as regulators that pose an additional challenge to handling threats and incidents. Little research has been carried out to understand to what extent decision support can aid these professionals in managing threats and incidents. The purpose of this research was to develop decision support for cybersecurity threat and incident managers in the financial sector. To this end, we carried out a cognitive task analysis and the first two phases of a cognitive work analysis, based on two rounds of in-depth interviews with ten professionals from three financial institutions. Our results show that decision support should address the problem of balancing the bigger picture with details. That is, being able to simultaneously keep the broader operational context in mind as well as adequately investigating, containing and remediating a cyberattack. In close consultation with the three financial institutions involved, we developed a critical-thinking memory aid that follows typical incident response process steps, but adds big picture elements and critical thinking steps. This should make cybersecurity threat and incident managers more aware of the broader operational implications of threats and incidents while keeping a critical mindset. Although a summative evaluation was beyond the scope of the present research, we conducted iterative formative evaluations of the memory aid that show its potential.
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