Adaptivity in advanced learning technologies offer the possibility to adapt to different student backgrounds, which is difficult to do in a traditional classroom setting. However, there are mixed results on the effectiveness of adaptivity based on different implementations and contexts. In this paper, we introduce AI adaptivity in the context of a new genre of Intelligent Science Stations that bring intelligent tutoring into the physical world. Intelligent Science Stations are mixed-reality systems that bridge the physical and virtual worlds to improve children’s inquiry-based STEM learning. Automated reactive guidance is made possible by a specialized AI computer vision algorithm, providing personalized interactive feedback to children as they experiment and make discoveries in their physical environment. We report on a randomized controlled experiment where we compare learning outcomes of children interacting with the Intelligent Science Station that has task-loop adaptivity incorporated, compared to another version that provides tasks randomly without adaptivity. Our results show that adaptivity using Bayesian Knowledge Tracing in the context of a mixed-reality system leads to better learning of scientific principles, without sacrificing enjoyment. These results demonstrate benefits of adaptivity in a mixed-reality setting to improve children’s science learning.
Multi-Party Mixed Reality Interaction for Earth Sciences Education
Collaborative learning has been shown to be beneficial for children's learning performance, increasing the curiosity and intensity of the ability of cooperation. Mixed-Reality with collaborative learning is the trending research topic in the Human-Computer Interaction (HCI) area. Additionally, with the rise of attention to global warming which brings in more extreme weather and climate conditions, the earth science education would be one of the crucial topics for the next generation. Moreover, there are few augmented reality and mixed reality applications on earth science subject. In this paper, we propose a Mixed Reality Tornado Simulator which offers an earth science education in a collaborative setting. Students and the instructor can cooperate on learning the knowledge of the formation and its damage cause on human-built structures, farming, and vegetation by using our mixed reality application with the Microsoft HoloLens. Also, for evaluating the learning performance in this mixed reality setting, we propose to study the cognitive load while the student is learning the abstract knowledge in Earth Science. We will separate the student into a control group and experimental groups and use different teaching instruments to test the difference of cognitive load.
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- Award ID(s):
- 1919375
- NSF-PAR ID:
- 10156504
- Date Published:
- Journal Name:
- Thirteenth International Conference on Tangible, Embedded, and Embodied Interaction (TEI ’19).
- Page Range / eLocation ID:
- 719 to 722
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
In civil and construction engineering education research, a focus has been on using 3D models to support students’ design comprehension. Despite this trend, the predominant mode of design communication in the industry relies on 2D plans and specifications, which typically supersede other modes of communication. Rather than focusing on the presentation of less common 3D content as an input to support students’ understanding of a design, this paper explores more common 2D inputs, but compares different visualization formats of student output in two educational interventions. In the first intervention, students document a construction sequence for wood-framed elements in a 2D worksheet format. In the second, students work with the same wood-framed design, but document their sequence through an augmented reality (AR) format where their physical interactions move full-scale virtual elements as if they were physically constructing the wood frame. Student approaches and performance were analyzed using qualitative attribute coding of video, audio, and written documentation of the student experience. Overall, results showed that the 2D worksheet format was simple to implement and was not mentally demanding to complete, but often corresponded with a lack of critical checks and a lack of mistake recognition from the students. The AR approach challenged students more in terms of cognitive load and completion rates but showed the potential for facilitating mistake recognition and self-remediation through visualization. These results suggest that when students are tasked with conceptualizing construction sequences from 2D documentation, the cognitive challenges associated with documenting a sequence in AR may support their recognition of their own mistakes in ways that may not be effectively supported through 2D documentation as an output for documenting and planning a construction sequence. The results presented in this paper provide insights on student tendencies, behaviors, and perceptions related to defining construction sequences from 2D documentation in order for educators to make informed decisions regarding the use of similar learning activities to prepare their students for understanding the 2D design documents used in industry.more » « less
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The MIs are shorter, flexibly scheduled throughout the year, easily accessible, and participation in multiple MI is encouraged. ESTEEM also establishes a sustainable and collaborative model, working with partners from BSCS Science Education, for MI’s mentor, training, compliance, and building capacity, with shared values and practices to maximize the improvement of student outcomes. New Knowledge (e.g., hypothesis, research questions) Research indicates that REU/internship experiences can be particularly powerful for students from Latinx and underrepresented groups in STEM. However, those experiences are difficult to access for many HSI-community college students (85% of our students hold off-campus jobs), and lack of confidence is a barrier for a majority of our students. The gap between those who can and those who cannot is the “internship access gap.” This project is at a central California Community College (CCC) and HSI, the only affordable post-secondary option in a region serving a historically underrepresented population in STEM, including 75% Hispanic, and 87% have not completed college. MI is designed to reduce inequalities inherent in the internship paradigm by providing access to professional and research skills for those underserved students. The MI has been designed to reduce barriers by offering: shorter duration (25 contact hours); flexible timing (one week to once a week over many weeks); open access/large group; and proximal location (on-campus). MI mentors participate in week-long summer workshops and ongoing monthly community of practice with the goal of co-constructing a shared vision, engaging in conversations about pedagogy and learning, and sustaining the MI program going forward. 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Abstract Despite theoretical benefits of collaborative robots, disappointing outcomes are well documented by clinical studies, spanning rehabilitation, prostheses, and surgery. Cognitive load theory provides a possible explanation for why humans in the real world are not realizing the benefits of collaborative robots: high cognitive loads may be impeding human performance. Measuring cognitive availability using an electrocardiogram, we ask 25 participants to complete a virtual-reality task alongside an invisible agent that determines optimal performance by iteratively updating the Bellman equation. Three robots assist by providing environmental information relevant to task performance. By enabling the robots to act more autonomously—managing more of their own behavior with fewer instructions from the human—here we show that robots can augment participants’ cognitive availability and decision-making. The way in which robots describe and achieve their objective can improve the human’s cognitive ability to reason about the task and contribute to human–robot collaboration outcomes. Augmenting human cognition provides a path to improve the efficacy of collaborative robots. By demonstrating how robots can improve human cognition, this work paves the way for improving the cognitive capabilities of first responders, manufacturing workers, surgeons, and other future users of collaborative autonomy systems.
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Major challenges in engineering education include retention of undergraduate engineering students (UESs) and continued engagement after the first year when concepts increase in difficulty. Additionally, employers, as well as ABET, look for students to demonstrate non-technical skills, including the ability to work successfully in groups, the ability to communicate both within and outside their discipline, and the ability to find information that will help them solve problems and contribute to lifelong learning. Teacher education is also facing challenges given the recent incorporation of engineering practices and core ideas into the Next Generation Science Standards (NGSS) and state level standards of learning. To help teachers meet these standards in their classrooms, education courses for preservice teachers (PSTs) must provide resources and opportunities to increase science and engineering knowledge, and the associated pedagogies. To address these challenges, Ed+gineering, an NSF-funded multidisciplinary collaborative service learning project, was implemented into two sets of paired-classes in engineering and education: a 100 level mechanical engineering class (n = 42) and a foundations class in education (n = 17), and a fluid mechanics class in mechanical engineering technology (n = 23) and a science methods class (n = 15). The paired classes collaborated in multidisciplinary teams of 5-8 undergraduate students to plan and teach engineering lessons to local elementary school students. Teams completed a series of previously tested, scaffolded activities to guide their collaboration. Designing and delivering lessons engaged university students in collaborative processes that promoted social learning, including researching and planning, peer mentoring, teaching and receiving feedback, and reflecting and revising their engineering lesson. The research questions examined in this pilot, mixed-methods research study include: (1) How did PSTs’ Ed+gineering experiences influence their engineering and science knowledge?; (2) How did PSTs’ and UESs’ Ed+gineering experiences influence their pedagogical understanding?; and (3) What were PSTs’ and UESs’ overall perceptions of their Ed+gineering experiences? Both quantitative (e.g., Engineering Design Process assessment, Science Content Knowledge assessment) and qualitative (student reflections) data were used to assess knowledge gains and project perceptions following the semester-long intervention. Findings suggest that the PSTs were more aware and comfortable with the engineering field following lesson development and delivery, and often better able to explain particular science/engineering concepts. Both PSTs and UESs, but especially the latter, came to realize the importance of planning and preparing lessons to be taught to an audience. UESs reported greater appreciation for the work of educators. PSTs and UESs expressed how they learned to work in groups with multidisciplinary members—this is a valuable lesson for their respective professional careers. Yearly, the Ed+gineering research team will also request and review student retention reports in their respective programs to assess project impact.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3294109.3302933
