Driving examiners’ views on data-driven assessment of test candidates:An interview study

Vehicles are increasingly equipped with sensors that capture the state of the driver, the vehicle, and the environment. These developments are relevant to formal driver testing, but little is known about the extent to which driving examiners would support the use of sensor data in their job. This semi-structured interview study examined the opinions of 37 driving examiners about datadriven assessment of test candidates. The results showed that the examiners were supportive of using data to explain their pass/fail verdict to the candidate. According to the examiners, data in an easily accessible form such as graphs of eye movements, headway, speed, or braking behaviour, and colour-coded scores, supplemented with camera images, would allow them to eliminate doubt or help them convince disagreeing test-takers. The examiners were sceptical about higher levels of decision support, noting that forming an overall picture of the candidate’s abilities requires integrating multiple context-dependent sources of information. The interviews yielded other possible applications of data collection and sharing, such as selecting optimal routes, improving standardization, and training and pre-selecting candidates before they are allowed to take the driving test. Finally, the interviews focused on an increasingly viable form of data collection: simulator-based driver testing. This yielded a divided picture, with about half of the examiners being positive and half negative about using simulators in driver testing. In conclusion, this study has provided important insights regarding the use of data as an explanation aid for examiners. Future research should consider the views of test candidates and experimentally evaluate different forms of data-driven support in the driving test

Five-Point Likert Items: t Test versus Mann-Whitney-Wilcoxon

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Human subject research for engineers: a practical guide

This Brief introduces engineers to the main principles in ethics, research design, statistics, and publishing of human subject research. In recent years, engineering has become strongly connected to disciplines such as biology, medicine, and psychology. Often, engineers (and engineering students) are expected to perform human subject research. Typical human subject research topics conducted by engineers include human-computer interaction (e.g., evaluating the usability of software), exoskeletons, virtual reality, teleoperation, modelling of human behaviour and decision making (often within the framework of ‘big data’ research), product evaluation, biometrics, behavioural tracking (e.g., of work and travel patterns, or mobile phone use), transport and planning (e.g., an analysis of flows or safety issues), etc. Thus, it can be said that knowledge on how to do human subject research is indispensable for a substantial portion of engineers. Engineers are generally well trained in calculus and mechanics, but may lack the appropriate knowledge on how to do research with human participants. In order to do high-quality human subject research in an ethical manner, several guidelines have to be followed and pitfalls have to be avoided. This book discusses these guidelines and pitfalls. The aim is to prepare engineers and engineering students to carry out independent research in a responsible manner

Supplementary materials for the article: Five-Point Likert Items: t test versus Mann-Whitney-Wilcoxon

Supplementary materials for the article: De Winter, J. C.F., & Dodou, D. (2010). Five-point Likert items: t test versus Mann-Whitney-Wilcoxon. Practical Assessment, Research & Evaluation, 15, 1-12

Soft Robotic Grippers for Crop Handling or Harvesting: A Review

Nowadays, harvesting delicate and high-value fruits, vegetables, and edible fungi requires a large input of manual human labor. The relatively low wages and many health problems the workforce faces make this profession increasingly unpopular. Meanwhile, robotic systems that selectively harvest crops are being developed. Whilst the moving platform, manipulator, and image recognition systems of such robots have been studied the past few decades, research on the gripping end of such robots is only since recently growing. This study analyzes the state-of-the-art of soft grippers for crop handling and harvesting, reporting on their quantitative and qualitative characteristics. Seventy-eight grippers are retrieved from the academic literature and compared with each other in terms of their design and reported performance, more specifically grasping and detachment methods, materials used, type of actuators and sensors employed, and the control of the gripping procedure. In addition, the identified grippers are classified into 13 distinct soft grasping technology categories. Moreover, the retrieved papers are analyzed with respect to their publication date and country of origin to observe trends in the recent growth in the field. Furthermore, a subset of soft grippers is identified that was tested on the task of selectively harvesting crops, where grip and detachment success rates and plant and crop damage are compared

Supplementary materials for the article: Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size.

Supplementary materials for the article: De Winter, J. C. F., & Dodou, D. (2012). Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size. Journal of Applied Statistics, 39, 695-710. https://doi.org/10.1080/02664763.2011.61044

Common Factor Analysis versus Principal Component Analysis: A Comparison of Loadings by Means of Simulations

<div><p>Common factor analysis (CFA) and principal component analysis (PCA) are widely used multivariate techniques. Using simulations, we compared CFA with PCA loadings for distortions of a perfect cluster configuration. Results showed that nonzero PCA loadings were higher and more stable than nonzero CFA loadings. Compared to CFA loadings, PCA loadings correlated weakly with the true factor loadings for underextraction, overextraction, and heterogeneous loadings within factors. The pattern of differences between CFA and PCA was consistent across sample sizes, levels of loadings, principal axis factoring versus maximum likelihood factor analysis, and blind versus target rotation.</p></div