More Like this

1. Problem Solving Personas of Civil Engineering Practitioners Using Eye Tracking Techniques

   Gestson, Sean L.; Barner, Mathew S.; Abadi, Masoud G.; Hurwitz, David S.; Brown, Shane (January 2019, International journal of engineering education)

   Engineering practitioners solve problems in various ways; it is plausible that they often rely on graphs, figures, formulas and other representations to reach a solution. How and why engineering practitioners use representations to solve problems can characterize certain problem-solving behaviors, which can be used to determine particular types of problem solvers. The purpose of this research was to determine the relationship between time spent referring to various representations and the justifications for the decisions made during the problem-solving process of engineering practitioners. A persona-based approach was used to characterize the problem-solving behavior of 16 engineering practitioners. Utilizing eye tracking and retrospective interview techniques, the problem-solving process of engineering practitioners was explored. Three unique problem-solver personas were developed that describe the behaviors of engineering practitioners; a committed problem solver, an evaluative problem, and an indecisive problem solver. The three personas suggest that there are different types of engineering practitioner problem solvers. This study contributes to engineering education research by expanding on problem-solving research to look for reasons why decisions are made during the problem-solving process. Understanding more about how the differences between problem solvers affect the way they approach a problem and engage with the material presents a more holistic view of the problem-solving process of engineering practitioners. 

   more » « less
2. Factors Contributing to the Problem-Solving Heuristics of Civil Engineering Students

   Geston, Sean L.; Brown, Shane; Barner, Matthew S.; Abadi, Masoud G.; Hurwitz, David S. (January 2019, ASEE annual conference & exposition)

   Problem solvers vary their approaches to solving problems depending on the context of the problem, the requirements of the solution, and the ways in which the problems and material to solve the problem are represented, or representations. Representations take many forms (i.e. tables, graphs, figures, images, formulas, visualizations, and other similar contexts) and are used to communicate information to a problem solver. Engagement with certain representations varies between problem solvers and can influence design and solution quality. A problem solver’s evaluation of representations and the reasons for using a representation can be considered factors in problem-solving heuristics. These factors describe unique problem-solving behaviors that can help understand problem solvers. These behaviors may lead to important relationships between a problem solver’s decisions and their ability to solve a problem and overall quality of the solution. Therefore, we pose the following research question: How do factors of problem-solving heuristics describe the unique behaviors of engineering students as they solve multiple problems? To answer this question, we interviewed 16 undergraduate engineering students studying civil engineering. The interviews consisted of a problem-solving portion that was followed immediately by a semi-structured retrospective interview with probing questions created based on the real time monitoring of the problem-solving interview using eye tracking techniques. The problem-solving portion consisted of solving three problems related to the concept of headloss in fluid flow through pipes. Each of the three problems included the same four representations that were used by the students as approaches to solving the problem. The representations are common ways to present the concept of headloss in pipe flow and included two formulas, a set of tables, and a graph. This paper presents a set of common reasons for why decisions were made during the problem-solving process that help to understand more about the problem-solving behavior of engineering students. 

   more » « less
3. Student problem solving on Textbook and YouTube Problems pertaining to Vapor Liquid Equilibrium

   Asogwa, U.; Duckett, T. R.; Liberatore, M. W. (January 2021, 2021 ASEE North Central Section Conference)

   null (Ed.)

   Faculty often utilize homework problems as a means to help students practice problem solving. Recently, with textbook solutions manuals being freely available online, students are prone to copying/cheating, which can severely limit improvements in problem solving. One hypothesis is that YouTube problems could serve as alternatives to textbook problems to significantly reduce cheating and promote better problem solving. YouTube problems are student-written problems that were inspired by events in a video publicly available online. While our previous studies have showcased positive attitudes related to engineering, high engagement, and rigor of the YouTube problems, the current study examines a subset of problems related to one major course topic, namely vapor-liquid equilibrium. The cohorts include engineering students from a public university who were assigned homework problems as part of a material and energy balance course. Two constructs were explored: problem solving and perception of problem difficulty. The study adopted an established and validated rubric to quantify performance in relevant stages of problem solving, including problem identification, representation, organization, calculation, solution completion, and solution accuracy. While problem solving can be influenced by perception of problem difficulty, the widely used NASA Task Load Index was adopted to measure the problem rigor. This paper will compare textbook and YouTube problem with respect to overall problem-solving ability as well as in each stage of problem solving. Furthermore, we will investigate whether disparities exist in students’ perceptions when solving vapor-liquid equilibrium problems. 

   more » « less
4. Planning for Aerial Robot Teams for Wide-Area Biometric and Phenotypic Data Collection

   https://doi.org/10.1109/IROS51168.2021.9636549  

   Mandal, Shashwata; Gao, Tianshuang; Bhattacharya, Sourabh (September 2021, International Conference on Intelligent Robots and Systems)

   This work presents an efficient and implementable solution to the problem of joint task allocation and path planning in a multi-UAV platform. The sensing requirement associated with the task gives rise to an uncanny variant of the traditional vehicle routing problem with coverage/sensing constraints. As is the case in several multi-robot path-planning problems, our problem reduces to an mTSP problem. In order to tame the computational challenges associated with the problem, we propose a hierarchical solution that decouples the vehicle routing problem from the target allocation problem. As a tangible solution to the allocation problem, we use a clustering-based technique that incorporates temporal uncertainty in the cardinality and position of the robots. Finally, we implement the proposed techniques on our multi-quadcopter platforms. 

   more » « less
5. In the proceeding of 24th International Conference OF Artificial Intelligence in Education (AIED)

   Shabrina, P; Mostafavi, B; Chi, M; Barnes, T (July 2023, Springer)

   Humans adopt various problem-solving strategies depending on their mastery level, problem type, and complexity. Many of these problem-solving strategies have been integrated within intelligent problem-solvers to solve structured and complex problems efficiently. One such strategy is the means-ends analysis which involves comparing the goal and the givens of a problem and iteratively setting up subgoal(s) at each step until the subgoal(s) are straightforward to derive from the givens. However, little is known about the impact of explicitly teaching novices such a strategy for structured problem-solving with tutors. In this study, we teach novices a subgoal-directed problem-solving strategy inspired by means-ends analysis using a problem-based training intervention within an intelligent logic-proof tutor. As we analyzed students’ performance and problem-solving approaches after training, we observed that the students who learned the strategy used it more when solving new problems, constructed optimal logic proofs, and outperformed those who did not learn the strategy. 

   more » « less