In Germany, public transport organizations are mainly owned by public authorities. Procurement in Hamburg involves the buses and infrastructure instead of transport services. The procurement process for buses and infrastructure is performed by the transport companies. Such processes must meet German and European public regulations. Therefore, public tender and procurement procedures for buying buses by German Public Transport Operators (PTOs) can be more complex and lengthier than procurement by private PTOs in other countries. As a result, the public transport companies are not primarily driven by profitability, but also by obligations towards the public and political aims. Obligations can comprise to provide affordable, environmentallyfriendly transport services for the citizens. In Hamburg, the public authority incorporates obligations (requirements) for the e-buses in their tendering documents. In Utrecht, as well as most of the rest of the Netherlands, public transport is carried out by private companies, under an operating contract (concession) with a public transport authority. In Utrecht, this authority is the province of Utrecht. The e-buses are the operators’ private property and they are obliged to account to the province of Utrecht for their implementation of public transport. When the province of Utrecht procures the operation of public transport services by means of a European tendering process, private transport companies can offer a bid for this tender. Both, the authority and operators, want to provide good public transport for their customers, but they both have different goals. The operators want to earn a reasonable profit margin on public transport, while the authority wants to fulfil certain public policy goals. The tendering process is where these two come together. It is a strong mechanism to get the best ‘value for money’ out of the market – for example, the most public transport, or the highest number of e-buses running in the area, within the available budget of the public transport authority.
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This paper reports on an experiment comparing students’ results on image-rich numeracy problems and on equivalent word problems. Given the well reported problematic nature of word problems, the hypothesis is that students score better on image-rich numeracy problems than on comparable word problems. To test the hypothesis a randomized controlled trial was conducted with 31,842 students from primary, secondary, and vocational education. The trial consisted of 21 numeracy problems in two versions: word problems and image-rich problems. The hypothesis was confirmed for the problems used in this experiment. With the insights gained we intend to improve the assessment of students’ abilities in solving quantitative problems from daily life. Numeracy, word problem, image-rich problem, randomized controlled trial, assessment
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Many students persistently misinterpret histograms. This calls for closer inspection of students’ strategies when interpreting histograms and case-value plots (which look similar but are diferent). Using students’ gaze data, we ask: How and how well do upper secondary pre-university school students estimate and compare arithmetic means of histograms and case-value plots? We designed four item types: two requiring mean estimation and two requiring means comparison. Analysis of gaze data of 50 students (15–19 years old) solving these items was triangulated with data from cued recall. We found five strategies. Two hypothesized most common strategies for estimating means were confirmed: a strategy associated with horizontal gazes and a strategy associated with vertical gazes. A third, new, count-and-compute strategy was found. Two more strategies emerged for comparing means that take specific features of the distribution into account. In about half of the histogram tasks, students used correct strategies. Surprisingly, when comparing two case-value plots, some students used distribution features that are only relevant for histograms, such as symmetry. As several incorrect strategies related to how and where the data and the distribution of these data are depicted in histograms, future interventions should aim at supporting students in understanding these concepts in histograms. A methodological advantage of eye-tracking data collection is that it reveals more details about students’ problem-solving processes than thinking-aloud protocols. We speculate that spatial gaze data can be re-used to substantiate ideas about the sensorimotor origin of learning mathematics.
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This paper reports on the first stage of a research project1) that aims to incorporate objective measures of physical activity into health and lifestyle surveys. Physical activity is typically measured with questionnaires that are known to have measurement issues, and specifically, overestimate the amount of physical activity of the population. In a lab setting, 40 participants wore four different sensors on five different body parts, while performing various activities (sitting, standing, stepping with two intensities, bicycling with two intensities, walking stairs and jumping). During the first four activities, energy expenditure was measured by monitoring heart rate and the gas volume of in‐ and expired O2 and CO2. Participants subsequently wore two sensor systems (the ActivPAL on the thigh and the UKK on the waist) for a week. They also kept a diary keeping track of their physical activities, work and travel hours. Machine learning algorithms were trained with different methods to determine which sensor and which method was best able to differentiate the various activities and the intensity with which they were performed. It was found that the ActivPAL had the highest overall accuracy, possibly because the data generated on the upper tigh seems to be best distinguishing between different types of activities and therefore led to the highest accuracy. Accuracy could be slightly increased by including measures of heartrate. For recognizing intensity, three different measures were compared: allocation of MET values to activities (used by ActivPAL), median absolute deviation, and heart rate. It turns out that each method has merits and disadvantages, but median absolute deviation seems to be the most promishing metric. The search for the best method of gauging intensity is still ongoing. Subsequently, the algorithms developed for the lab data were used to determine physical activity in the week people wore the devices during their everyday activities. It quickly turned out that the models are far from ready to be used on free living data. Two approaches are suggested to remedy this: additional research with meticulously labelled free living data, e.g., by combining a Time Use Survey with accelerometer measurements. The second is to focus on better determining intensity of movement, e.g., with the help of unsupervised pattern recognition techniques. Accuracy was but one of the requirements for choosing a sensor system for subsequent research and ultimate implementation of sensor measurement in health surveys. Sensor position on the body, wearability, costs, usability, flexibility of analysis, response, and adherence to protocol equally determine the choice for a sensor. Also from these additional points of view, the activPAL is our sensor of choice.
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Crime scene investigations are highly complex environments that require the CSI to engage in complex decision-making. CSIs must rely on personal experience, context information, and scientific knowledge about the fundamental principles of forensic science to both find and correctly interpret ambiguous traces and accurately reconstruct a scene. Differences in CSI decision making can arise in multiple stages of a crime scene investigation. Given its crucial role in forensic investigation, CSI decision-making must be further studied to understand how differences may arise during the stages of a crime scene investigation. The following exploratory research project is a first step at comparing how crime scene investigations of violent robberies are conducted between 25 crime scene investigators from nine countries across the world.Through a mock crime scene and semi-structured interview, we observed that CSIs have adopted a variety of investigation approaches. The results show that CSIs have different working strategies and make different decisions when it comes to the construction of relevant hypotheses, their search strategy, and the collection of traces. These different decisions may, amongst other factors, be due to the use of prior information, a CSI’s knowledge and experience, and the perceived goal of their investigation. We suggest the development of more practical guidelines to aid CSIs through a hypothetico-deductive reasoning process, where (a) CSIs are supported in the correct use of contextual information, (b) outside knowledge and expertise are integrated into this process, and (c) CSIs are guided in the evaluation of the utility of their traces.
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Understanding how experiences unfold requires measuring participants' emotions, especially as they move from location to location. Measuring and mapping emotions over space is technically challenging, however. While a number of technologies to record and spatially resolve emotion data exist, they have not been systematically compared. We present emotion data collected at a natural and military heritage site in the Netherlands using three different methods, namely retrospective self report, experience reconstruction, and physiology. These data are applied to three corresponding mapping methods. The resulting maps lead to divergent findings, demonstrating that spatial mapping of emotion data accentuates differences between distinct dimensions of emotions.
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Understanding how experiences unfold requires measuring participants' emotions, especially as they move from location to location. Measuring and mapping emotions over space is technically challenging, however. While a number of technologies to record and spatially resolve emotion data exist, they have not been systematically compared. We present emotion data collected at a natural and military heritage site in the Netherlands using three different methods, namely retrospective self report, experience reconstruction, and physiology. These data are applied to three corresponding mapping methods. The resulting maps lead to divergent findings, demonstrating that spatial mapping of emotion data accentuates differences between distinct dimensions of emotions.
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In this paper we analyse the way students tag recorded lectures. We compare their tagging strategy and the tags that they create with tagging done by an expert. We look at the quality of the tags students add, and we introduce a method of measuring how similar the tags are, using vector space modelling and cosine similarity. We show that the quality of tagging by students is high enough to be useful. We also show that there is no generic vocabulary gap between the expert and the students. Our study shows no statistically significant correlation between the tag similarity and the indicated interest in the course, the perceived importance of the course, the number of lectures attended, the indicated difficulty of the course, the number of recorded lectures viewed, the indicated ease of finding the needed parts of a recorded lecture, or the number of tags used by the student.
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The recently introduced Risk SituatiOn Awareness Provision (RiskSOAP) methodology suggested an indicator to measure the distance between the configuration of a real system and its ideal version or between various system versions. It considers the (in)existence or (mal)functioning of system components, processes and connections based on a binary approach. However, in practice safety requirements can be fulfilled to some degree and each system component might have a different impact on system outcomes. This work suggests the Comparing System Configurations (COSYCO) indicator which introduces (1) the use of continuous values for the behaviour of system components, (2) the inclusion of weights according to the hierarchal system level to which each component belongs, and (3) the consideration of the outgoing connections of each component with other system components. Both RiskSOAP and COSYCO are based on the STPA hazard analysis which is a systematic technique used to define the components and the requirements that the system should ideally meet to achieve its objectives. To demonstrate the applicability and sensitivity of COSYCO, we applied it to a published case for small drones. Drones with same overall differences in the satisfaction of requirements concluded to different values when applying COSYCO, indicating the increased sensitivity of the specific indicator when compared to the RiskSOAP. We envisage that the metric proposed in this work is a first practical and realistic approach to the quantification of the distance between the optimal design and current system states as well amongst various systems and their versions over time.
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Background:Tobacco consumption is a leading cause of death and disease, killing >8 million people each year. Smoking cessation significantly reduces the risk of developing smoking-related diseases. Although combined treatment for addiction is promising, evidence of its effectiveness is still emerging. Currently, there is no published research comparing the effectiveness of blended smoking cessation treatments (BSCTs) with face-to-face (F2F) treatments, where web-based components replace 50% of the F2F components in blended treatment.Objective:The primary objective of this 2-arm noninferiority randomized controlled trial was to determine whether a BSCT is noninferior to an F2F treatment with identical ingredients in achieving abstinence rates.Methods:This study included 344 individuals who smoke (at least 1 cigarette per day) attending an outpatient smoking cessation clinic in the Netherlands. The participants received either a blended 50% F2F and 50% web-based BSCT or only F2F treatment with similar content and intensity. The primary outcome measure was cotinine-validated abstinence rates from all smoking products at 3 and 15 months after treatment initiation. Additional measures included carbon monoxide–validated point prevalence abstinence; self-reported point prevalence abstinence; and self-reported continuous abstinence rates at 3, 6, 9, and 15 months after treatment initiation.Results:None of the 13 outcomes showed statistically confirmed noninferiority of the BSCT, whereas 4 outcomes showed significantly (P<.001) inferior abstinence rates of the BSCT: cotinine-validated point prevalence abstinence rate at 3 months (difference 12.7, 95% CI 6.2-19.4), self-reported point prevalence abstinence rate at 6 months (difference 19.3, 95% CI 11.5-27.0) and at 15 months (difference 11.7, 95% CI 5.8-17.9), and self-reported continuous abstinence rate at 6 months (difference 13.8, 95% CI 6.8-20.8). The remaining 9 outcomes, including the cotinine-validated point prevalence abstinence rate at 15 months, were inconclusive.Conclusions:In this high-intensity outpatient smoking cessation trial, the blended mode was predominantly less effective than the traditional F2F mode. The results contradict the widely assumed potential benefits of blended treatment and suggest that further research is needed to identify the critical factors in the design of blended interventions.Trial Registration:Netherlands Trial Register 27150; https://onderzoekmetmensen.nl/nl/trial/27150
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