NSF Award Title: DIP: Improving Collaborative Learning in Engineering Classes Through Integrated Tools
National Science Foundation, Division of Information and Intelligent Systems
Award # 1628976
Investigators:
Project Page: CoLearn Lab: Improving Collaborative Learning in Engineering Classes Through Integrated Tools
NSF Abstract:
The Cyberlearning and Future Learning Technologies Program funds efforts that support envisioning the future of learning technologies and advance what we know about how people learn in technology-rich environments. Development and Implementation (DIP) Projects build on proof-of-concept work that shows the possibilities of the proposed new type of learning technology to build and refine a minimally-viable example of their proposed innovation that allows them to understand how such technology should be designed and used in the future and that allows them to answer questions about how people learn, how to foster or assess learning, and/or how to design for learning. This project is focused on the teaching of collaborative problem solving activities in introductory engineering courses and builds on a prior project to design tools for collaborative sketching in these courses. The project is based on a recognition of the importance of collaborating in engineering, the need for student to learn this skill, the value of collaborative learning tasks for engaging students in authentic problem solving activities, and the difficulty that graduate student teaching assistants (TAs) encounter when trying to teach in this way. There are two parts to the technology innovation. The first part is a set of tools for the teaching assistants, to help them manage the classroom technologies, and to help them understand how to intervene in groups who are struggling with the content or collaborative processes. The second part is a set of tools for the students. Building on the collaborative sketch software previously developed, prompts to support their collaborative processes will be embedded in the software students will use, based on analysis of the logfiles that help determine who needs what prompts when. Research goals include understanding how receiving prompts changes the nature of students' collaborative activity, and how receiving insight into the difficulties students are having helps TAs learn about to foster collaborative learning in their classes.
The PIs are addressing the difficulties encountered implementing collaborative learning activities in engineering courses by designing and studying tools for TAs and students in these classes. Through an iterative design approach, the PIs will design and study tools for TAs to orchestrate the classroom and collaboration activities and to tools for students which support their collaborative problem solving processes. The PIs will investigate the use of learning analytics in evaluating the collaborative practices of students using these tools; in particular, logfiles will be examined for collaborative indicators based on prior research on collaborative processes, then clustered to look for patterns of engagement, and finally used to create regression models of successful collaboration processes using machine learning techniques. Cross-validation of the models will be done with both logfile and video data to avoid overfitting. These insights will be provided to TAs to examine whether such information is helpful in determining how and when to intervene in groups. Findings from the research will provide insight into: 1) The knowledge that TAs need in order to successfully implement collaborative problem solving in undergraduate courses; 2) Whether TAs can learn more about collaborative problem solving with the support of tools aimed at helping them implement this form of pedagogy; 3) Whether students can learn collaborative problem solving skills through embedded prompts during multi-week collaborative activities and 4) The potential of analytics in determining when and how to reduce the collaboration supports from groups.
NSF Award Title: Fostering Collaborative Drawing and Problem Solving through Digital Sketch and Touch
National Science Foundation, Division Of Information & Intelligent Systems
Award #1441149
Investigators:
Project Page: C-STEPS
Video: IDEALL C-STEPS
data-sf-ec-immutable="">NSF Abstract:
The Cyberlearning and Future Learning Technologies Program funds efforts that will help envision the next generation of learning technologies and advance what we know about how people learn in technology-rich environments. Cyberlearning Exploration (EXP) Projects explore the viability of new kinds of learning technologies by designing and building new kinds of learning technologies and studying their possibilities for fostering learning and challenges to using them effectively. This project’s technology innovation is a set of sketching tools for multi-touch tablets and tables to allow collaborative sketching and sharing between small groups and a whole class. Sketching is often a means of working out and expressing one’s understanding of how a mechanism works or how a process develops over time. Collaborative sketching supports learners collaboratively making sense of a mechanisms or processes. While technology exists for collaboratively annotating sketches, support for collaboratively making sense requires other mechanisms that are not available in current tools. Such sense making is important in developing domain expertise across many different STEM fields. Research goals include designing and exploring the benefits of different sketching and sharing tools and better understanding the benefits of using drawing and sketching for communicating and understanding.
The PIs are designing and developing the next generation of collaborative sketching tools, focusing on the functions those tools need to support collaborative sketching and sharing in the context of sense-making. There is also a focus on the introduction of such tools, i.e., the kinds of activities that need to be embedded into classroom activities to foster joint engagement and learning to be collaborative around sketching. The tools will allow sketch creation, reversion to earlier versions, zooming for refinement, selecting and changing sketch size, choosing pieces for elaborating, reference, and combination with other parts; the tools will support part-whole relationships of engineered products and allow both per-part and system-level analyses, moving, and recombining. Three interaction conditions are being trialed and their affordances for fostering learning and challenges to effective learning compared: many users around one tablet with one input at a time, one user per tablet working with others who also have tablets, and many users working at a table that allows multiple inputs. Foundations for this work are in the literatures around spatial reasoning, sketching for development of domain expertise, representational fluency, collaborative learning, and computer support for collaborative learning (CSCL). Important to understanding how to best design collaborative sketching tools and the roles collaborative sketching plays in joint meaning making is analysis of the discourse as learners are using the tools. The team will analyze that discourse along with the sketching behaviors of learners as they investigate how sketching tools impact learning outcomes of group members, how they support collaborative problem solving and sketching interactions, effects of collaborative sketching on understanding of the potential breadth of solutions and solution paths while solving problems, and principles for design of tools that support collaborative sketching.
National Science Foundation, Division Of Undergraduate Education
Award #1432424
Investigator(s):
Project Page: GRASP
NSF Abstract:
The project will research how student body movements support their reasoning and understanding about scientific concepts that involve hidden structures or unobservable mechanisms. Student and scientist descriptions and explanations often involve embodied expressions, such as body movements used to represent or symbolically manipulate components of scientific systems (e.g., turning the hands to an object's rotation, tilting the upper torso to demonstrate some type of imbalance, etc.). Exploring the embodied foundations of science reasoning is also timely because of recent advances in human-computer interfaces that allow people to interact with computers using expressive, natural movements as opposed to keystrokes and mouse-clicks. The project will initially research what types of body motion that support causal explanations for observable phenomena, which are called Embodied Explanatory Expressions (EEEs). Building on the research, the project will then explore whether identified EEEs can be integrated into the control structures of online simulations utilizing newly available motion sensing input devices (e.g., Microsoft Kinect, Leap Motion). The research findings will enrich our basic understanding of science learning and have practical implications in curriculum design and in the development of new learning technologies.
The project will research student gestures and body motion during explanation of such scientific concepts by recording and analyzed these gestures and motion not only to identify the embodiment of such knowledge, but also to design interventions and simulations based on such motions that could facilitate learning. Using interviews, available motion sensing input devices, and other methods, the project will validate the approach, investigate how students reason using body movements, and how their expressions change with interventions. This project will identify core EEEs for supporting scientific reasoning in three critical areas of science involving unobservable mechanisms: Molecular Interactions, Heat Transfer, and Earth Systems. The project will focus specifically on the types of embodied interactions that support students' construction of explanations, and explore whether these body movements can be integrated into the design of a new generation of online learning simulations that elevates the level of science reasoning exhibited by students who use them.
National Science Foundation, Division of Research on Learning
Award #1441563
Investigator(s):
Project Page: ELASTIC3S
NSF Abstract:
The Cyberlearning and Future Learning Technologies Program funds efforts that will help envision the next generation of learning technologies and advance what we know about how people learn in technology-rich environments. Development and Implementation (DIP) Projects build on proof-of-concept work that showed the possibilities of the proposed new type of learning technology, and project teams build and refine a minimally-viable example of their proposed innovation that allows them to understand how such technology should be designed and used in the future and that allows them to answer questions about how people learn, how to foster or assess learning, and/or how to design for learning. This proposal uses advances in multimodal immersive interfaces, such as sensors that allow motion detection (like the Microsoft Kinect) to examine questions about how learners think with their bodies as they make sense of science concepts like 'scale' or 'rates of change'. The project will help create "simulation theatres for embodied learning," or rooms with immersive technology that allow students to interact with science simulations and simultaneously express ideas by moving their bodies. Research studies will examine whether gestures students use carry over from one science discipline to the next, and whether this type of interaction helps them transfer what they know in one science domain to others. At the end of the project, we should have 1. a technology platform that can be used to help research how students use gesture to understand science concepts, 2. information about how well this tool supports learning across disciplines, and 3. novel psychology research about how people think with their bodies.
This project seeks to extend and refine emerging theories of embodied learning and embodied design. Embodied interactions have shown promise for increasing learning in specific STEM concepts, but there is less known about how body movement and gesture promote understanding abstract and crosscutting ideas that may facilitate learning transfer. This project examines explicitly whether persistent schemes of embodied interactions with computer simulations make it easier for learners to engage with, and learn from, new simulations of novel STEM topics. This project will also make intellectual advances in computational gesture recognition and processing, for instance through single-instance machine learning algorithms, real-time training, and modeling of paraterized gestures to capture full-body gestures to create a highly flexible gesture-learning environment that will enable training based on individual subjects without having to build a large database of gestures in order to achieve reliable recognition. By developing (1) an easy to use, low cost, and highly reconfigurable system for recognizing learning gestures and (2) an integrated set of learning simulations that rely on embodied interactions to investigate a broad range of STEM topics using consistent interface schemes, the project will be able to investigate how gestural congruency can be used to support learners' conceptions of STEM disciplines. Research studies will use 12-15 middle-school students in the initial phases to help identify candidate gestures for cross-disciplinary gestural metaphors. Three later iterations will use approximately 50 students per iteration to examine whether interacting with the system can engage embodied metaphors that support transfer of learning from the domain of a STEM simulation to other domains, including development of instruments for assessing transfer.
National Science Foundation, Division of Research on Learning
Award # 1417966
Investigators:
Video: Energize
NSF Abstract:
This research project, in collaboration with The University of Central Florida and the Museum of Science and Industry, investigates a three-cycle research and development process where middle school student learners will be immersed in a mixed reality environment while interacting with functional metaphors to determine the effects of conceptual change, motivation and scientific habits of mind while engaged in learning physics content. The project is guided by the following research questions: How does the opportunity to embody elements of an immersive simulation affect a learner's propensity to experience conceptual change and develop scientific habits of mind? What design features of missed reality environments best support metaphors? What metrics are most effective for assessing learning through body-based metaphors? What are the practical considerations to creating immersive metaphor-based learning experiences in ISE institutions such as a Science Center?
The investigators will use a between subjects mixed method approach with middle school students (N = 360) involving three research cycles that are performed in controlled conditions. The multiple iterations will allow modifications to the study's design to dig deeper into the data and afford more careful analysis, revisions and modifications to simulation content, protocol and data collection instruments and the technology installation.
Middle school students will be recruited from local schools and the Museum of Science and Industry visitors. The evaluation plan includes the assessment of perceived values of using whole-body metaphors within mixed reality environments to learn physics. Professional audiences, educators and ISE practitioners will assess the impact, design and content associated with research on learning, mixed reality design, science and physics education.
Research on understanding the process of using whole-body interactions in a mixed reality environments will help educational researchers and practitioners in the field understand the effectiveness of metaphor based learning of scientific concepts with whole body interactions. This project contributes knowledge about how people learn within informal settings. This theory-driven design approach has the potential for broad implementation in both formal and informal environments.
National Science Foundation, Division of Undergraduate Education, STEM-C Partnerships
Award #1542828
Investigator(s):
Project Page: LTEC
NSF Abstract:
The STEM+Computing Partnership (STEM+C) program seeks to advance multidisciplinary integration of computing in STEM teaching and learning through applied research and development across one or more domains. The goal of this project is to define the concepts and skills needed for computer science education. The project integrates the knowledge and skills for computer science with mathematics learning goals. This project presents a unique opportunity to integrate such practices with an existing, well-supported and well-designed elementary mathematics curriculum. This addresses the practical needs of schools and teachers in terms of time constraints. The concepts will be organized into a learning trajectory for K-5 computer science and mathematics that examines areas of overlap such as abstract thinking. This project will synthesize prior work in the field and use assessments of students' work with computational thinking and mathematics activities in targeted concepts to develop a framework for understanding students' learning. The project also includes piloting activities connected to the learning trajectory to integrate mathematics and computational thinking practices and concepts.
The research plan begins with a literature review to understand the major goals of existing computer science materials and findings from research on teaching and learning in the early elementary grades. The review will identify points of compatibility for mathematics and computer science in elementary settings and build possible learning trajectories with accompanying learning materials. Learning module design includes participation from elementary teachers at the school sites. Selected learning trajectories will then be tested in classrooms with integrated mathematics and computational thinking activities and lessons. Classroom observations, teacher interviews, measures of students' knowledge, and surveys of attitudes about computer science will be used to support the learning trajectory design and analysis.