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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Designing and Developing Interactive Narratives for Collaborative Problem-Based Learning
Narrative and collaboration are two core features of rich interactive learning. Narrative-centered learning environments offer significant potential for supporting student learning. By contextualizing learning within interactive narratives, these environments leverage students’ innate facilities for developing understandings through stories. Computer-supported collaborative learning environments offer students rich, collaborative learning experiences in which small groups of students engage in constructing artifacts, addressing disciplinary challenges, and solving problems. Narrative and collaboration have distinct affordances for learning, but combining them poses significant challenges. In this paper, we present initial work on solving this problem by introducing collaborative narrative-centered learning environments. These environments will enable small groups of students to collaboratively solve problems in rich multi-participant storyworlds. We propose a novel framework for designing and developing these environments, which we are using to create a collaborative narrative-centered learning environment for middle school ecosystems education. In the learning environment, students work on problem-solving scenarios centered on how to support optimal fish health in aquatic environments. Results from pilot testing the learning environment with 45 students suggest it supports the creation of engaging and effective collaborative narrative-centered learning experiences.
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- NSF-PAR ID:
- 10162222
- Date Published:
- Journal Name:
- Proceedings of the Twelfth International Conference on Interactive Digital Storytelling
- Page Range / eLocation ID:
- 86-100
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The EngageAI Institute focuses on AI‐driven narrative‐centered learning environments that create engaging story‐based problem‐solving experiences to support collaborative learning. The institute's research has three complementary strands. First, the institute creates narrative‐centered learning environments that generate interactive story‐based problem scenarios to elicit rich communication, encourage coordination, and spark collaborative creativity. Second, the institute creates virtual embodied conversational agent technologies with multiple modalities for communication (speech, facial expression, gesture, gaze, and posture) to support student learning. Embodied conversational agents are driven by advances in natural language understanding, natural language generation, and computer vision. Third, the institute is creating an innovative multimodal learning analytics framework that analyzes parallel streams of multimodal data derived from students’ conversations, gaze, facial expressions, gesture, and posture as they interact with each other, with teachers, and with embodied conversational agents. Woven throughout the institute's activities is a strong focus on ethics, with an emphasis on creating AI‐augmented learning that is deeply informed by considerations of fairness, accountability, transparency, trust, and privacy. The institute emphasizes broad participation and diverse perspectives to ensure that advances in AI‐augmented learning address inequities in STEM. The institute brings together a multistate network of universities, diverse K‐12 school systems, science museums, and nonprofit partners. Key to all of these endeavors is an emphasis on diversity, equity, and inclusion.
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null (Ed.)Developing narrative and computational thinking skills is crucial for K-12 student learning. A growing number of K-12 teachers are utilizing digital storytelling, where students create short narratives around a topic, as a means of creating motivating problem-solving activities for a variety of domains, including history and science. At the same time, there is increasing awareness of the need to engage K-12 students in computational thinking, including elementary school students. Given the challenges that the syntax of text-based programming languages poses for even novice university-level learners, block-based programming languages have emerged as an effective tool for introducing computational thinking to elementary-level students. Leveraging the unique affordances of narrative and computational thinking offers significant potential for student learning; however, integrating them presents significant challenges. In this paper, we describe initial work toward solving this problem by introducing an approach to block-based programming for interactive storytelling to engage upper elementary students (ages 9 to 11) in computational thinking and narrative skill development. Leveraging design principles and best practices from prior research on elementary-grade block-based programming and digital storytelling, we propose a set of custom blocks enabling learners to create interactive narratives. We describe both the process used to derive the custom blocks, including their alignment with elements of interactive narrative and with specific computational thinking curricular goals, as well as lessons learned from students interacting with a prototype learning environment utilizing the block-based programming approach.more » « less
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Abstract Our career-forward approach to general chemistry laboratory for engineers involves the use of design challenges (DCs), an innovation that employs authentic professional context and practice to transform traditional tasks into developmentally appropriate career experiences. These challenges are scaled-down engineering problems related to the US National Academy of Engineering’s Grand Challenges that engage students in collaborative problem solving via the modeling process. With task features aligned with professional engineering practice, DCs are hypothesized to support student motivation for the task as well as for the profession. As an evaluation of our curriculum design process, we use expectancy–value theory to test our hypotheses by investigating the association between students’ task value beliefs and self-confidence with their user experience, gender and URM status. Using stepwise multiple regression analysis, the results reveal that students find value in completing a DC (F(5,2430) = 534.96,
p < .001) and are self-confident (F(8,2427) = 154.86,p < .001) when they feel like an engineer, are satisfied, perceive collaboration, are provided help from a teaching assistant, and the tasks are not too difficult. We highlight that although female and URM students felt less self-confidence in completing a DC, these feelings were moderated by their perceptions of feeling like an engineer and collaboration in the learning process (F(10,2425) = 127.06,p < .001). When female students felt like they were engineers (gender x feel like an engineer), their self-confidence increased (β = .288) and when URM students perceived tasks as collaborative (URM status x collaboration), their self-confidence increased (β = .302). Given the lack of representation for certain groups in engineering, this study suggests that providing an opportunity for collaboration and promoting a sense of professional identity afford a more inclusive learning experience. -
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Pedagogical planners can provide adaptive support to students in narrative-centered learning environments by dynamically scaffolding student learning and tailoring problem scenarios. Reinforcement learning (RL) is frequently used for pedagogical planning in narrative-centered learning environments. However, RL-based pedagogical planning raises significant challenges due to the scarcity of data for training RL policies. Most prior work has relied on limited-size datasets and offline RL techniques for policy learning. Unfortunately, offline RL techniques do not support on-demand exploration and evaluation, which can adversely impact the quality of induced policies. To address the limitation of data scarcity and offline RL, we propose INSIGHT, an online RL framework for training data-driven pedagogical policies that optimize student learning in narrative-centered learning environments. The INSIGHT framework consists of three components: a narrative-centered learning environment simulator, a simulated student agent, and an RL-based pedagogical planner agent, which uses a reward metric that is associated with effective student learning processes. The framework enables the generation of synthetic data for on-demand exploration and evaluation of RL-based pedagogical planning. We have implemented INSIGHT with OpenAI Gym for a narrative-centered learning environment testbed with rule-based simulated student agents and a deep Q-learning-based pedagogical planner. Our results show that online deep RL algorithms can induce near-optimal pedagogical policies in the INSIGHT framework, while offline deep RL algorithms only find suboptimal policies even with large amounts of data.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-33894-7_10
