Previous studies have convincingly shown that traditional, content-centered, and didactic teaching methods are not effective for developing a deep understanding and knowledge transfer. Nor does it adequately address the development of critical problem-solving skills. Active and collaborative instruction, coupled with effective means to encourage student engagement, invariably leads to better student learning outcomes irrespective of academic discipline. Despite these findings, the existing construction engineering programs, for the most part, consist of a series of fragmented courses that mainly focus on procedural skills rather than on the fundamental and conceptual knowledge that helps students become innovative problem-solvers. In addition, these courses are heavily dependent on traditional lecture-based teaching methods focused on well-structured and closed-ended problems that prepare students to plug variables into equations to get the answer. Existing programs rarely offer a systematic approach to allow students to develop a deep understanding of the engineering core concepts and discover systematic solutions for fundamental problems. Without properly understanding these core concepts, contextualized in domain-specific settings, students are not able to develop a holistic view that will help them to recognize the big picture and think outside the box to come up with creative solutions for arising problems. The long history of empirical learning in the field of construction engineering shows the significant potential of cognitive development through direct experience and reflection on what works in particular situations. Of course, the complex nature of the construction industry in the twenty-first century cannot afford an education through trial and error in the real environment. However, recent advances in computer science can help educators develop virtual environments and gamification platforms that allow students to explore various scenarios and learn from their experiences. This study aims to address this need by assessing the effectiveness of guided active exploration in a digital game environment on students’ ability to discover systematic solutions for fundamental problems in construction engineering. To address this objective, through a research project funded by the NSF Division of Engineering Education and Centers (EEC), we designed and developed a scenario-based interactive digital game, called Zebel, to guide students solve fundamental problems in construction scheduling. The proposed gamified pedagogical approach was designed based on the Constructivism learning theory and a framework that consists of six essential elements: (1) modeling; (2) reflection; (3) strategy formation; (4) scaffolded exploration; (5) debriefing; and (6) articulation. We also designed a series of pre- and post-assessment instruments for empirical data collection to assess the effectiveness of the proposed approach. The proposed gamified method was implemented in a graduate-level construction planning and scheduling course. The outcomes indicated that students with no prior knowledge of construction scheduling methods were able to discover systematic solutions for fundamental scheduling problems through their experience with the proposed gamified learning method.
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NeTra: A Neuro-Symbolic System to Discover Strategies in Math Learning
Understanding how students with varying capabilities think about problem solving can greatly help in improving personalized education which can have significantly better learning outcomes. Here, we present the details of a system we call NeTra that we developed for discovering strategies that students follow in the context of Math learning. Specifically, we developed this system from large-scale data from MATHia that contains millions of student-tutor interactions. The goal of this system is to provide a visual interface for educators to understand the likely strategy the student will follow for problems that students are yet to attempt. This predictive interface can help educators/tutors to develop interventions that are personalized for students. Underlying the system is a powerful AI model based on Neuro-Symbolic learning that has shown promising results in predicting both strategies and the mastery over concepts used in the strategy.
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- Award ID(s):
- 1934745
- NSF-PAR ID:
- 10353238
- Date Published:
- Journal Name:
- Proceedings of The Third Workshop of the Learner Data Institute , The 15th International Conference on Educational Data Mining (EDM 2022)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Evaluation Methods and Results Using the theory of planned behavior, a pre/post-test summative evaluation was developed. Preliminary results indicate that pump technicians no longer use lead in pumps unless specifically requested by the pump owners. These results indicate a positive shift towards the use of lead-free components with project follow-up and analysis ongoing.
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