Playware Research – Methodological Considerations

Several sub-disciplines of engineering are driven by the researchers’ aim of providing positive change to the society through their engineering. These researchers are challenged by the traditional research method of experimental research with a waterfall model which demands clearly defined project definition and functional requirements, and impose a sequential processes leading to the final system evaluation, which may lead to solutions which work in the lab, but have little impact in the messy real world. Based on two decades research in developing engineering systems with a societal impact (e.g. in robotics, embodied AI, and playware), in this paper we suggest a cyclic research method based on a mix between participatory and experimental processes. In particular, inspiration from the action research method applied to interdisciplinary technology development becomes a participatory approach characterized by rapid prototyping cycles which allow iterative technology specification and development together with people in their real world environment

Knowmad Society

The emergence of Knowmad Society impacts everybody. It is a product of the changes in a world driven by exponential accelerating technological and social change, globalization, and a push for more creative and context-driven innovations. It is both exciting and frightening. It presents us with new opportunities, challenges, and responsibilities. And, we recognize that in a world of accelerating change, the future is uncertain. This prompts a key question: In a world consumed with uncertainty, how can we ensure the success of ourselves as individuals, our communities, and the planet

Deep learning and Internet of Things for tourist attraction recommendations in smart cities

The version of record is available online at: http://dx.doi.org/10.1007/s00521-021-06872-0We propose a tourist attraction IoT-enabled deep learning-based recommendation system to enhance tourist experience in a smart city. Travelers will enter details about their travels (traveling alone or with a companion, type of companion such as partner or family with kids, traveling for business or leisure, etc.) as well as user side information (age of the traveler/s, hobbies, etc.) into the smart city app/website. Our proposed deep learning-based recommendation system will process this personal set of input features to recommend the tourist activities/attractions that best fit his/her profile. Furthermore, when the tourists are in the smart city, content-based information (already visited attractions) and context-related information (location, weather, time of day, etc.) are obtained in real time using IoT devices; this information will allow our proposed deep learning-based tourist attraction recommendation system to suggest additional activities and/or attractions in real time. Our proposed multi-label deep learning classifier outperforms other models (decision tree, extra tree, k-nearest neighbor and random forest) and can successfully recommend tourist attractions for the first case [(a) searching for and planning activities before traveling] with the loss, accuracy, precision, recall and F1-score of 0.5%, 99.7%, 99.9%, 99.9% and 99.8%, respectively. It can also successfully recommend tourist attractions for the second case [(b) looking for activities within the smart city] with the loss, accuracy, precision, recall and F1-score of 3.7%, 99.5%, 99.8%, 99.7% and 99.8%, respectively.This work has been supported by the Agencia Estatal de Investigación of Spain under project PID2019-108713RB-C51/AEI/10.13039/501100011033.Peer ReviewedPostprint (published version

Engineering Outreach to Danish Students

Due to a recent decline in mathematics and science performance among primary school students in Denmark and waning interest in engineering, the Engineering College of Copenhagen (IHK) sponsored this project to increase engineering enrollment. The project created six short-term, high-interest, hands-on programs allowing Danish students to experience the everyday activities of engineers. Quantitative and qualitative analysis of programs revealed success at increasing engineering interest and awareness. Program materials were left with IHK for future program implementation at other Danish schools

The strategic impacts of Intelligent Automation for knowledge and service work : An interdisciplinary review

We would like to thank Professor Jarvenpaa and the review team for all the constructive comments and suggestions that were most helpful in revising the paper and in offering a stronger contribution. We would also like to thank Professor Guy Fitzgerald for his constructive comments on earlier versions of the paper. This study was funded by the Chartered Institute of Professional Development (CIPD). The views expressed are those of the authors and not necessarily those of the CIPD.Peer reviewedPublisher PD

A Case Study: Motivational Attributes of 4-H participants engaged in Robotics

Robotics has gained a great deal of popularity across the United States as a means to engage youth in science, technology, engineering, and math. Understanding what motivates youth and adults to participate in a robotics project is critical to understanding how to engage others. By developing a robotics program built on a proper understanding of the motivational influences, the program can be built on a foundation that addresses these influences. By engaging more youth in the robotics program, they will be able to envision a future for themselves as a high-school or college graduate, in addition to a viable employee with marketable skills in tough economy. The purpose of this research was to evaluate the underlying motivational attributes or factors that influenced 4-H youth, parents, volunteers, and agents to participate in the Mississippi 4-H robotics project. Specifically, this research focuses on two unique counties in Mississippi with very diverse populations. Interviews with participants, observation, and document analysis which took place occurred over the course of a robotics year – October to July. This study sought to identify motivational attributes of participants in the robotics project. Once identified these attributes could be used when developing new program curricula or expanding into new counties in Mississippi. Data analysis revealed that there are many unique motivational factors that influence participants. Among these factors, (1) the desire to build and construct a robot, (2) competition and recognition, (3) desire for future success and security, (4) safe place to participate and build relationships, (5) teamwork, (6) positive role models, and (7) encouragement

Experiential education: Understanding the impact of remotely operated vehicles on at-risk student learning

How do educators engage students in the curriculum? State and Common Core Standards determine what to teach; how those standards are taught varies. This research examined a group of at-risk elementary and secondary students using underwater Remotely Operated Vehicles (ROVs) as part of an experiential education program in a rural Michigan school district. With a foundation of grounded theory, qualitative methods are used in this case study of the experience of at-risk learners whose mission is to solve a problem. In particular, the story evolves from nine emergent themes identified in the experiences of students and adults: attendance, communication, confidence, engagement, fun, problem-solving, responsibility, teamwork, and time. Common across the nine themes is the conclusion that student motivation strongly impacts learning that is perceived to be hands-on and relevant

The Utah Statesman, April 23, 2010

Weekly student newspaper of Utah State University in Logan.https://digitalcommons.usu.edu/newspapers/1397/thumbnail.jp