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As maker-centered learning grows rapidly in school environments, there is an urgent need for new forms of assessment. The purpose of this paper is to report on the development and implementation of tools to support embedded assessment of maker competencies within school-based maker programs and describes alternative assessment approaches to rubrics and portfolios.

This study used a design-based research (DBR) method, with researchers collaborating with US middle school teachers to iteratively design a set of tools that support implementation of embedded assessment. Based on teacher and student interviews, classroom observations, journal notes and post-implementation interviews, the authors report on the final phase of DBR, highlighting how teachers can implement embedded assessment in maker classrooms as well as the challenges that teachers face with assessment.

This study showed that embedded assessment can be implemented in a variety of ways, and that flexible and adaptable assessment tools can play a crucial role in supporting teachers in this process. Additionally, though teachers expressed a strong desire for student involvement in the assessment process, we observed minimal student agency during implementation. Further study is needed to investigate how establishing classroom culture and norms around assessment may enable students to fully participate in assessment processes.

Due to the dynamic and collaborative nature of maker-centered learning, teachers may find it difficult to provide on-the-fly feedback. By employing an embedded assessment approach, this study explored a new form of assessment that is flexible and adaptable, allowing teachers to formally plan ahead while also adjusting in the moment.

This paper aims to identify computational thinking (CT) in 4th to 6th grade students in the context of project-based problem-solving while engaged in an after-school program.

This case study approach was selected due to its suitability for answering “how” or “why” questions about real-world phenomena within a set context (Creswell and Poth, 2018; Yin, 2018). This was an appropriate fit given the context of an after-school program and the research question asked how to identify learners’ demonstrated CT through project-based learning hands-on activities and problem-solving in a naturalistic environment.

Results show that heuristics, algorithms and conditional logic were observed more than other components of CT such as data collection, simulations and modeling. Descriptions of common activities in a naturalistic learning environment are presented to illustrate how the students practiced CT over time, which could help readers develop an understanding of CT in conjunction with hands-on problem-solving activities in elementary students. Identifying and classifying CT in this study focused on students’ learning process.

This study contributes to the challenging field of evaluating CT while focusing on observable behaviors and problem-solving activities with various degrees of teacher’s facilitation instead of final artifacts. Implications for researchers and educators interested in integrating CT in K-12 learning and its assessment are discussed.

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.

This research seeks to examine the influence of sponsorship on spectators' consideration sets by investigating, in a naturalistic setting, whether sport sponsorship adds a prominent brand to spectators' consideration sets, with and without the explicit memory that the brand is a sponsor.

A field study involved 1,084 visitors to a tennis tournament. For the control group (n=276), the interviews took place before the spectators entered the stadium; interviews with the exposed group (n=808) were conducted after they had attended at least one match. Three hypotheses related to consumer status and consideration set conditions were tested.

Sponsorship can influence the likelihood that a prominent brand becomes part of the consideration set in a naturalistic setting, even without an explicit memory that the brand is a sponsor. This implicit sponsorship effect was limited to the memory-based consideration set of non-consumers of the brand.

This study establishes an implicit sponsorship effect for prominent brands in naturalistic environments and contributes to a better understanding of moderating (boundary) conditions.

An individual’s driving style significantly affects overall traffic safety. However, driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data. As such, the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies, improving traffic safety and reducing fuel consumption. This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions (DOCs) using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system (ADAS).

Specifically, a driving style recognition framework based on longitudinal DOCs was established. To train the model, a real-world driving experiment was conducted. First, the driving styles of 44 drivers were preliminarily identified through natural driving data and video data; drivers were categorized through a subjective evaluation as conservative, moderate or aggressive. Then, based on the ADAS driving data, a criterion for extracting longitudinal DOCs was developed. Third, taking the ADAS data from 47 Kms of the two test expressways as the research object, six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed. Finally, four machine learning classification (MLC) models were used to classify and predict driving style based on the natural driving data.

The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion. Cautious drivers undertook the largest proportion of the free cruise condition (FCC), while aggressive drivers primarily undertook the FCC, following steady condition and relative approximation condition. Compared with cautious and moderate drivers, aggressive drivers adopted a smaller time headway (THW) and distance headway (DHW). THW, time-to-collision (TTC) and DHW showed highly significant differences in driving style identification, while longitudinal acceleration (LA) showed no significant difference in driving style identification. Speed and TTC showed no significant difference between moderate and aggressive drivers. In consideration of the cross-validation results and model prediction results, the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting > multi-layer perceptron > logistic regression > support vector machine.

The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models. This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment, such as ADAS.

The present paper attempts to integrate three streams of alternative approaches to provide a contingency‐based framework to understand the management control systems (MCS) that are operating in call centers. Specifically, the paper's aim is to analyse the work practices of call center employees from three different lenses: the radical approach using Braverman's labor process theory; the Foucauldian approach, which explains the governance of the self; and the naturalistic approach, which explains the governance of the built environment.

The data for the case study were collected by conducting interviews. The data were gathered to identify if the work experience of the call center operators reflects the Tayloristic principles of scientific management, whether their responses reflect evidence of internalizing norms and adjusting their selves to fit to the norm, and lastly whether their work conditions are shaped in the manner described above to facilitate control of their work lives.

Prior academic studies have relied on either one of the approaches and the author's contention is that neither of the theories in isolation can explain the complexities that characterize the contemporary call center operator's labor process.

The study of control and its resistance can add to the understanding of the modern workplace. Since the call center environment is unique, the results of this study may have limited generalizability.

Future researchers can extend the approach used in this research to areas other than call centers that may have similar overlapping paradigms. Such an endeavor will enrich the understanding of complex phenomena, where multiple theories may be espoused to explain a particular research question.

A significant contribution of this study is to recognize that there is concentration of limited control and power in the hands of those who are controlled. Such an understanding may revive the “emancipatory tradition” of labour process theory and though it may not lead to the revolutionary overthrow of capitalism, it may lead to the awareness that strategies aimed at improving the lives of workers within the capitalist political economy are desirable.

The purpose of this paper is to explore investigative decision-making processes in the context of major crimes as experienced by the law enforcement agents.

Episodic interviews were conducted with six agents from medium-sized police forces in the UK. Following the framework of naturalistic inquiry, qualitative content analysis took place with the assistance of Atlas.ti software. To ensure the validity of findings, the within method triangulation was preferred, by additionally analysing the interview transcripts with Alceste.

Findings from this study revealed a variety of internal factors at play, shaping the decision-making course into an act of balancing various desired goals. Detectives appear to assess a situation based on their experiences confirming that the naturalistic decision-making model may assist in understanding investigative decision-making.

Due to the busy schedule of law enforcement agents the number of participants was limited and availability difficult; therefore, this study can be thought of as a pilot study that will inspire researchers to use the same method for in-depth understanding of investigative decision-making.

Results captured the ill-defined goals in the police environment and provided ways of decreasing their impact on investigative decision-making thus should help detectives to understand their decision-making limitations and strengths.

This project will enhance the psychological understanding of investigative decision-making.

This project assists in understanding the psychological aspect of investigative decision-making during police duty and provides the opportunity to law enforcement agents to re-evaluate situations in order to improve the investigative decision-making process; while adds to existing literature.

The purpose of this paper is to draw on the naturalistic decision making and cognitive science literature to examine how experienced crisis managers utilize the intuitive and analytical strategies when managing complex incidents. A cognitive model that describes the interplay between strategies is presented and discussed, and the specific role that intuition plays in analytical decision making is addressed.

Designed as a conceptual paper, the extant literature is reviewed to advance discussions on the theme of intuitive and analytical decision making in the naturalistic environment. A new model of expert intuition – the information filtering and intuitive decision model – is presented and evaluated against existing cognitive models from the wider literature.

The paper suggests that experts’ ability to make intuitive decisions is strongly hinged on their information processing skills that allow irrelevant cues to be sifted out while the relevant cues are retained. The paper further revealed that experts generally employ the intuitive mode as their default strategy, drawing on the analytical mode only as conditions warrant.

Prior research has shown that experts often make important task decisions using intuitive or analytical strategies or by combining both, but the sequence these should typically follow is still unresolved. Findings from the intuition model reveal that although intuition often precedes analytical thinking in almost all cases, both strategies exist to offer significant values to decision makers if the basis of their application is well understood.

Pew and Mavor (1998) called for an integrative representation of human behavior for use in models of individual combatants and organizations. Models with integrated representation of behavior have only been achieved at rudimentary levels according to those performing the studies (e.g. Pew & Mavor, 1998; Tulving, 2002) and those building the models (e.g. Warwick et al., 2002). This chapter will address aspects of cognitive performance that are important to incorporate into models of combat based on acceptance of theory, strength of empirical data, or for other reasons such as to bridge gaps where incomplete knowledge exists about cognitive behavior and performance. As a starting point, this chapter will assess which of Pew and Mavor’s recommendations are still appropriate as determined by a review of selected literature on cognition and its representation. We will also provide some review and extensions of key literature on cognition and modeling and suggest a way ahead to close the remaining gaps. Different aspects of cognition are described with recent findings, and most are followed by an example of how they have been represented in computer models or a discussion of challenges to their representation in modeling.