2013 — 2015 |
Burleson, Winslow [⬀] Mcnamara, Danielle Muldner, Kasia |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Hcc: Small: Modeling and Supporting Creativity During Collaborative Stem Activities @ Arizona State University
This research will advance a novel technological approach that relies on machine learning techniques in general and Natural Language Processing (NLP) in particular to develop models and support for creativity during collaborative science, technology, engineering, and mathematics (STEM) educational activities. We will extend existing educational software with NLP capabilities to automatically assess and subsequently support creativity during collaborative tasks. The research questions are: (1) Which factors influence moment-by-moment creativity during collaborative problem solving activities? (2) How can NLP be used to build student models that detect those factors? (3) How can an ITS use this information to create personalized interventions to support creativity?
The first phase in this research will collect data from students solving problems in pairs with an educational application to identify factors that are relevant to creativity processes and outcomes. These data will be used to derive computational student models for automatically assessing student creativity in terms of both moment-to-moment processes and outcomes through machine learning methodologies focusing on an NLP approach. In addition to providing automatic assessment, the models will also inform factors that influence creativity during collaboration through educational data mining techniques. The final phase of the work will design and test a set of interventions to foster creativity during collaborative activities.
Using data corresponding to pairs of students solving open-ended STEM-based problems, this research will develop a rich and nuanced understanding of creativity processes and outcomes in collaborative contexts, and how these relate to knowledge, affect and creative thinking styles. Relying on that understanding, it will develop and evaluate novel student models that recognize salient, creativity-related events through NLP techniques, as well as personalized support for creativity during collaborative activities and evaluating that support through an experiment with university students. This project will pave the way for a new class of collaborative cyberlearning technologies to both assess and foster creativity, through just-in-time personalized support based on easily deployed NLP-based student models.
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0.915 |
2014 — 2017 |
Mcnamara, Danielle |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Collaborative Research: Learning Linkages: Integrating Data Streams of Multiple Modalities and Timescales @ Arizona State University
This Research on Education and Learning (REAL) project arises from an October 2014 Ideas Lab on Data-intensive Research to Improve Teaching and Learning. The intentions of that effort were to (1) bring together researchers from across disciplines to foster novel, transformative, multidisciplinary approaches to using the data in large education-related data sets to create actionable knowledge for improving STEM teaching and learning environments in the medium term; and (2) revolutionize learning in the longer term. In this project, researchers from Carnegie-Mellon University, Wested, Arizona State University, and Northwestern University will collaborate to enhance understanding of influences on learning, and improve teaching and learning in high school and middle school STEM classes. To accomplish this, they will leverage the latest tools for data processing and many different streams of data that can be collected in technology-rich classrooms to (1) identify classroom factors that affect learning and (2) explore how to use that data to automatically track development of students' understanding and capabilities over time.
Two forces are poised to transform research on learning. First, more and more student work is conducted on computers and online, producing vast amounts of learning-related data. At the same time, advances in computing, data mining, and learning analytics are providing new tools for the collection, analysis, and representation of these data. Together, the available data and analytical tools enable smart and responsive systems that personalize learning experiences for individual learners. The PIs aim to collect highly enriched data that go far beyond typical computer data capture, leveraging the latest tools for data processing to generate new insights about STEM teaching and learning. Working to maximize the potential while mitigating the risks of automated data collection and analysis, they will: (1) collect and integrate diverse sources of data including log files, videos, and written artifacts from across eight different two-week enactments of two different computer supported learning environments (one used in middle school math and one in high school science); and (2) compare analyses of log-file data with analyses of integrated datasets to understand the possibilities and limitations in using log-file data for assessment of student learning and proficiency. The collaborators expect their findings will inform both theories and practical recommendations applicable across a wide range of disciplines and settings.
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0.915 |
2014 — 2017 |
Mcnamara, Danielle |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Collaborative Research: Modeling Social Interaction and Performance in Stem Learning @ Arizona State University
This Research on Education and Learning (REAL) project arises from an October 2014 Ideas Lab on Data-intensive Research to Improve Teaching and Learning. The intentions of that effort were to: (1) bring together researchers from across disciplines to foster novel, transformative, multidisciplinary approaches to using the data in large education-related data sets to create actionable knowledge for improving STEM teaching and learning environments in the medium term; and (2) revolutionize learning in the longer term. In this project, researchers from the Educational Testing Service, Columbia University Teachers' College, Arizona State University, and North Carolina State University will conduct data-driven, exploratory analyses to identify key places where social interactions impact learning outcomes in specific learning environments, with the goal of improving teaching and learning in large-scale STEM courses.
This research takes advantage of data traces left in large-scale blended and online learning environments (including massively open online courses, or MOOCs). The researchers will develop a comprehensive model for social learning in the context of such courses that will enable assessment of both the collaborative needs of individuals within the context of a class, and the quality of collaborations they are carrying out. Such diagnoses will allow both instructors and automated systems to provide advice to learners about the peers they might work with to enhance their learning (e.g., regarding the kinds of social interactions that will foster better understanding and development of important disciplinary capabilities). An interdisciplinary team of investigators with expertise in theory-driven educational data mining, natural-language processing, psychometrics, social-network analysis, and computer support for collaborative learning will collaborate to explore when learners in blended and online classes benefit from social interactions, and to understand how to identify more and less productive collaborative interactions. The researchers will use data from three blended and online classes (e.g., log files capturing collaborative discussions, individual and collaborative interactions around well-instrumented examples, peer tutoring sessions, pair programming labs, paired projects) and a variety of data analysis approaches (e.g., text analysis, machine learning) to determine: (1) which cognitive, social, and affective dimensions of need and interaction can be identified from available data; (2) which analyses are useful in providing action-oriented collaboration advice; and (3) what additional types of data may be needed for making such recommendations. This exploration will be grounded in theories of social interactions for learning (e.g., self-explanation, dialectic with oneself and others, zone of proximal development, social learning theory of Bandura, peripheral and centripetal participation).
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0.915 |
2022 — 2025 |
Mcnamara, Danielle |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Collaborative Research: Stem Learning Embedded in a Machine-in-the-Loopcollaborative Story Writing Game @ Arizona State University
Developing “21st century skills” such as collaboration, communication, critical thinking, and creativity (the 4Cs) has become increasingly important for students to keep up with the ever-evolving labor market of the future. Teaching the 4Cs effectively and efficiently requires deeply intertwining them with core content knowledge areas, since the acquisition of domain knowledge can bolster students’ development of these soft skills. In this project, the investigators take a step towards combining 4C skill development with STEM education by developing a collaborative writing game in which multiple students work together to craft a narrative around embedded STEM education elements. As a key innovation, the investigators will embed this collaborative writing game with natural language processing and artificial intelligence (AI)-based tools to automate fact-checking, feedback, knowledge tracing, and narrative story arc suggestions, which will facilitate students’ progress toward mastery while reducing teacher workload. Overall, this project has the potential to increase student engagement in STEM learning activities and improve learning outcomes. The project will be grounded in StoriumEdu, a collaborative story writing platform, therefore directly benefiting its user base of 2,000 K-12 classrooms with over 27,000 students and potentially an even larger number of students through the dissemination of the team’s research findings. <br/><br/>This major technical goals of this project are intended to augment scientific writing instruction with AI-based tools. To achieve these goals, the project will develop novel technologies that automatically provide writing assistance and feedback, and these tools will be deployed into K-12 classrooms via the StoriumEdu platform in order to evaluate their effectiveness. A core technical challenge is to assess the factuality of student writing by building machine learning models for fact-checking. The team proposes to design retrieval-augmented neural networks that can localize spans within student-written text that exhibit scientific misunderstandings. These spans will then be connected with relevant passages from textbooks or online articles to enable students to easily correct their errors. After developing fact-checking methods, the team will also focus on knowledge tracing, which allows measuring student progress over time in terms of which concepts they have mastered or are still struggling with. The knowledge tracing models will be developed with feedback from scientific literacy experts. The output of these models informs the final aspect of this project, which aims to generate narrative progressions associated with conceptual misunderstandings. This will allow students to engage more strongly with concepts that they have yet to master, which maximizes the writing platform’s pedagogical potential. Taken as a whole, this project’s research contributions synthesize novel NLP methods with educational progress tracking and feedback systems in an effort to improve STEM learning.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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0.915 |