This innovative practice WIP paper describes how a sense of belonging is a crucial element in closing the participation gap in Innovation Competitions and Programs (ICPs) among underrepresented students. Apart from fostering student innovations and career readiness, ICPs, such as hackathons, startup incubator competitions, and customer discovery labs, are essential to college entrepreneurship ecosystems. However, there is a participation gap in ICPs among traditionally underrepresented students (e.g., students identifying as women, ethnically or racially diverse, low socio-economic status, or individuals with disabilities) compared with their counterparts. In addition, employers now look for distinctive innovation-focused skills on resumes and consider ICPs as a differentiating attribute. Therefore, low ICP participation among underrepresented students places them at a disadvantage regarding their career development. Using an interview research methodology, this paper investigates two research questions: (i) what are the dimensions of belonging in the context of ICPs? (ii) how does belonging manifest in various student groups?

In smart computing, addressing combinatorial optimization game theory problems is essential for resolving intricate challenges involving limited resources and significant strategic interactions among decision-makers. In this paper, we solved sequential combinatorial optimization game theory problems using an evolutionary algorithm called the Game-Theoretic Genetic Algorithm (GTGA), where it is computationally infeasible to enumerate all players' decision options. The computational experiments show that the proposed fitness evaluation methods enable the GTGA to converge to true Stackelberg equilibria. Our research highlights the practical applicability of the GTGA in addressing real-world problems characterized by dynamic decision-making and strategic interactions.

Engineering and computing education have always embraced student Innovation Competitions and Programs (ICPs), such as design challenges, hackathons, startup competitions, and boot camps. These programs are typically organized to increase interest in STEM fields, achieve the broader objective of forming well-rounded engineers and encourage students to bring their innovative ideas into real life. In addition, all ICPs also aim to advance students' innovative thinking skills. With the increased focus on entrepreneurship and innovation in STEM programs, many higher education institutions now organize some form of ICPs. This increased popularity of ICPs bears the questions of (i) whether ICPs achieve their intended objectives, (ii) what program components are most effective, and (iii) how to design ICPs for recruiting diverse student groups. Although these questions are highly relevant to advancing the educational benefits of ICPs, the literature lacks holistic studies focusing on the best practices of ICPs. In this paper, we present the findings of a qualitative research study to investigate ICP types and attributes that make the most impact on fostering an innovation mindset. We interviewed the organizers of ICPs to understand their objectives for organizing their events and rationales for specific program elements. Besides, we asked questions about how they promote their events, the best ways to reach out to students, team selection and forming, their assessment and judging procedures, during and after competition support, and the best practices and challenges. These interview scripts were transcribed, coded, and analyzed using qualitative data analysis software. An analysis of extracted thematic concepts was performed to identify the best practices and strategies that ICP organizers utilize to increase the Impact of their programs. The paper presents the preliminary results of this thematic analysis of the codes. Overall, findings suggest that incorporating more entrepreneurial elements, innovation training in ICPs, and effective mentoring may improve the learning outcomes related to innovative thinking skills.

Student innovation competitions and programs, hereafter called ICPs, such as hackathons, start-up incubator competitions, design challenges, boot camps, and customer discovery labs, have emerged as pipeline-builders and transformative for higher education entrepreneurial ecosystems. Moreover, ICPs foster students' STEM-based experiences and serve as a gateway for career readiness. There is a disparity in ICP participation of students underrepresented in Science, Technology, Engineering, and Math (STEM) compared to student groups dominating STEM fields. While research supports the importance and benefits of STEM students' participation in these programs, literature discussing the students' perceptions of these programs remains limited. In order to increase diverse students' motivations for participating in ICPs and make ICPs more inclusive learning experiences for all students, this paper will answer two research questions: (i) what are the barriers that discourage student participation in ICPs, and (ii) what are the costs that associate with participation in ICPs? The primary research methodology in this paper is semi-structured interviews. Thirty-eight students (25 females vs. 13 males and 21 participants vs. 17 non-participants) were interviewed after a recruitment survey. The interview questions were crafted and analyzed based on the known scales and theories in the literature, namely the Expectancy-Value-Cost Scale and the Theory of Planned Behavior. Through student interviews, student perceptions of fitting in these co-curricular activities, i.e., ICPs, were explored, with a particular interest being students who are underrepresented in STEM. The complete recordings of the interviewee responses to interview questions were transcribed into text and analyzed using a grounded theory approach. The research findings will contribute to making the innovation ecosystem more inclusive and diverse by uncovering factors that discourage students from engaging in ICPs. Two levels of barriers were identified, institutional and individual. Institutional-level barriers include “low program awareness“ and ”lack of diversity and inclusiveness,” whereas individual barriers include “not matching self-identity” and “low expectancy of success.” As for costs, apart from “opportunity cost,” “teamwork cost” has emerged to be another important cost dimension that associates with ICP participation. The ongoing research direction is to share the findings with the STEM educators and ICP organizers so that they are aware of these barriers and costs of participating in ICPs, which might be a basis for designing and testing interventions.

This paper addresses the economic emission dispatch (EED) problem where the goal is to allocate the power output of power generation units to satisfy power demand and minimize the cost and emissions simultaneously. We propose a multiobjective differential evolution algorithm and a reinforcement learning technique to adaptively control the parameters of differential evolution. Moreover, the proposed approach utilizes mating restriction and preferences in mating selection to improve search effectiveness and a dynamically controlled mutation to increase the exploration ability. The proposed ideas and algorithm were examined using four EED test cases. Experimental results showed positive effects of our proposed methods and the competitive performance of our algorithm.

We introduce a regret-based fitness assignment strategy for evolutionary algorithms to find Nash equilibria in noncooperative simultaneous combinatorial game theory problems where it is computationally intractable to enumerate all decision options of the players involved in the game. Applications of evolutionary algorithms to non-cooperative simultaneous games have been limited due to challenges in guiding the evolutionary search toward equilibria, which are usually inferior points in the objective space. We propose a regret-based approach to select candidate decision options of the players for the next generation in a multipopulation genetic algorithm called Regret-Based Nash Equilibrium Sorting Genetic Algorithm (RNESGA). We show that RNESGA can converge to multiple Nash equilibria in a single run using two- and three- player competitive knapsack games and other games from the literature. We also show that pure payoff-based fitness assignment strategies perform poorly in three-player games.

In the last decade, virtual computer laboratories have been successfully used to provide students with hands-on experimentation in information technology fields in a cost effective and secure manner. In this paper, we study the effect of collaborative learning on student perception and acceptance of virtual computing as a learning medium. Virtual computing is a relatively new technology and can be intimidating for students at times with limited information technology background. Students can feel overwhelmed as they follow voluminous and tedious step-by-step information technology activities that are not very familiar to them. Collaborative learning can help to alleviate such problems and enhance the student experience of using a virtual computer laboratory in different ways. To investigate the effect of collaborative work on student experience in virtual computer laboratories and their acceptance of this new technology, we conducted empirical studies where two groups of students (with technology and non-technology backgrounds) performed two types of hands-on activities. These types were individual and collaborative in nature and were conducted in a virtual computer laboratory. Then, we compared the experiences of the two groups across the types of hands-on exercises. The preliminary findings show that novice students benefited more from collaborative learning when compared to more experienced, and probably more technologically competent, students.

Engineers and information technology professionals often work in international settings or teams, as well as across diverse cultures in today's global economy. Ever expanding globalized society has required updates in organizational procedures and practices, especially for engineering and information technology organizations, to keep up with increasingly competitive and technology-oriented markets. Professional accreditation organizations (e.g., ABET, Inc.) also recognized that it is necessary to develop programs to prepare globally competent graduates. In this paper, our primary emphasis is on understanding students' interest in global awareness. As the theoretical framework, we use Model of Domain Learning (MDL) - a learning theory that has a three-pronged approach to the conceptualization of learning in a domain, covering interest, knowledge and strategic processing. Students' interest in a domain changes from situational to individual as they develop from novice to proficient in the domain. Therefore, we propose interest as an additional construct to measure students' global awareness. We developed an instrument to effectively measure students' global awareness development and engagement throughout their education. In this paper, we present our preliminary findings for the proposed global awareness interest assessment framework and also analyze factors affecting engineering and technology students' global awareness interest.

The complex and multidisciplinary nature of today's engineering problems demands that new graduates excel in not only technical knowledge but also teamwork skills. In fact, the lack of effective teamwork has been identified among the most important factors contributing to the high failure rate of complex engineering projects. In this paper, we focus on engineering students' attitudes toward teamwork, their self-efficacy and interest in teamwork knowledge, skills, and abilities. Self-efficacy in a domain is an important construct that can predict whether or not someone is willing to undertake a challenge in that domain. Research suggests that the sufficient level of self-efficacy can encourage personal growth and skill development. The relevant research also points out that interest is a construct that can predict students' professional development in a domain. For example, as someone becomes an expert in a domain, his/her interest in the domain becomes individual, which means there is a long-term personal connection resulting in further exploration of the domain. In this paper, we postulate that the development of students in teamwork knowledge, skills and abilities can be tracked by the progress in their teamwork interest. In addition, we argue that interest development should be measured as a part of the assessment efforts to evaluate the professional skills development of students. We have developed and validated an instrument to measure teamwork efficacy and interest. The instrument was used to collect data in a geographically distributed university. The collected data were analyzed to identify the factors affecting students' attitudes toward interest and self-efficacy in teamwork as well as their relationships. The preliminary results indicated that students had a high level of self-efficacy and a low level of interest, which makes it challenging to improve students' teamwork skills.

This paper presents a biobjective genetic algorithm (GA) to design reliable two-node connected telecommunication networks. Because the exact calculation of the reliability of a network is NP-hard, network designers have been reluctant to use network reliability as a design criterion; however, it is clearly an important aspect. Herein, three methods of reliability assessment are developed: an exact reliability calculation method using factoring, an efficient Monte Carlo estimation procedure using the sequential construction technique and network reductions, and an upper bound for the all-terminal reliability of networks with arbitrary arc reliabilities. These three methods of reliability assessment are used collectively in a biobjective GA with specialized mutation operators that perturb solutions without disturbing two-node connectivity. Computational experiments show that the proposed approach is tractable and significantly improves upon the results found by single-objective heuristics.

In this paper, a reliable server assignment problem in networks is defined as determining a deployment of identical servers to maximize a measure of service availability, and solved using nature-inspired metaheuristic approaches, namely Ant Colony Optimization, Particle Swarm Optimization, and Clonal Selection Principle of Artificial Immune Systems. In networks, the communication between a client and a server might be interrupted because the server itself is offline or unreachable as a result of catastrophic network failures. Therefore, it is very important to deploy servers at critical network nodes so that the reliability of the system is maximized. A new reliability measure, called critical service rate, is defined to evaluate alternative server assignments with respect to the network's ability to provide services in the case of catastrophic component failures. The structure of the optimal server assignments is studied, and the performances of three nature inspired metaheuristics are investigated in a rigorous experimental study. Based on the computational studies, their advantages and disadvantages are discussed.

A new heuristic is proposed and tested for system reliability optimization. The multiple weighted objective heuristic is based on a transformation of the problem into a multiple objective optimization problem, and then ultimately, transformation into a different single objective problem. The multiple objectives are to simultaneously maximize the reliability of each individual subsystem. This is a logical approach because system reliability is the product of the subsystem reliabilities, so if they are maximized, the system reliability will also be high. This new formulation and associated heuristic are then based on solving a sequence of linear programming problems. It is one of the very few optimization approaches that allow for linear programming algorithms and software to be used for the redundancy allocation problem when mixing of functionally equivalent components is allowed. Thus, it represents an efficient solution method that relies on readily available optimization tools. The heuristic is tested on many example problems, and compared to competing solution approaches. Overall, the heuristic performance is observed to be very good on the tested problem, and superior to the max-min heuristic regarding both efficiency, and performance

As high-speed networks have proliferated across the globe, their topologies have become sparser due to the increased capacity of communication media and cost considerations. Reliability has been a traditional goal within network design optimization of sparse networks. This paper proposes a genetic approach that uses network resilience as a design criterion in order to ensure the integrity of network services in the event of component failures. Network resilience measures have been previously overlooked as a network design objective in an optimization framework because of their computational complexity - requiring estimation by simulation. This paper analyzes the effect of noise in the simulation estimator used to evaluate network resilience on the performance of the proposed optimization approach.

This paper proposes an evolutionary approach for constructing 2-edge-connected minimal Steiner graphs spanning n points given in the rectilinear plane. The 1-edge-connected version of this problem is known as the rectilinear Steiner tree problem and has been widely studied. Despite the possible application areas, the 2-edge-connected problem has not received the same attention. In this paper, some properties of an optimal solution to the problem are used to develop an encoding scheme. The proposed evolutionary approach is compared on a test problem with the optimal TSP tour of the given points.

The paper presents a hybrid approach of a genetic algorithm (GA) and local search algorithms for the backbone design of communication networks. The backbone network design problem is defined as finding the network topology minimizing the design/operating cost of a network under performance and survivability considerations. This problem is known to be NP-hard. In the hybrid approach, the local search algorithm efficiently improves the solutions in the population by using domain-specific information while the GA recombines good solutions in order to investigate different regions of the solution space. The results of the test problems show that the hybrid methodology improves upon previous approaches.