Academic literature on the topic 'Computational Learning Sciences'
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Journal articles on the topic "Computational Learning Sciences":
Willcox, Karen. "Scientific Machine Learning." Aerospace Testing International 2020, no. 2 (June 2020): 14. http://dx.doi.org/10.12968/s1478-2774(22)50190-8.
Frank, Michael, Dimitris Drikakis, and Vassilis Charissis. "Machine-Learning Methods for Computational Science and Engineering." Computation 8, no. 1 (March 3, 2020): 15. http://dx.doi.org/10.3390/computation8010015.
Birhane, Abeba, and Olivia Guest. "Towards Decolonising Computational Sciences." Kvinder, Køn & Forskning, no. 2 (February 8, 2021): 60–73. http://dx.doi.org/10.7146/kkf.v29i2.124899.
Nick, Mitchel Res. "Learning Through Computational Modeling." Computers in the Schools 14, no. 1-2 (December 4, 1997): 143–52. http://dx.doi.org/10.1300/j025v14n01_11.
Dodig-Crnkovic, G. "Natural morphological computation as foundation of learning to learn in humans, other living organisms, and intelligent machines." Philosophical Problems of Information Technologies and Cyberspace, no. 1 (July 14, 2021): 4–34. http://dx.doi.org/10.17726/philit.2021.1.1.
Dodig-Crnkovic, Gordana. "Natural Morphological Computation as Foundation of Learning to Learn in Humans, Other Living Organisms, and Intelligent Machines." Philosophies 5, no. 3 (September 1, 2020): 17. http://dx.doi.org/10.3390/philosophies5030017.
Thiessen, Erik D. "What's statistical about learning? Insights from modelling statistical learning as a set of memory processes." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1711 (January 5, 2017): 20160056. http://dx.doi.org/10.1098/rstb.2016.0056.
Schaal, Stefan, Auke Ijspeert, and Aude Billard. "Computational approaches to motor learning by imitation." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 358, no. 1431 (February 17, 2003): 537–47. http://dx.doi.org/10.1098/rstb.2002.1258.
Chen, Jiming, and Diwakar Shukla. "Integration of machine learning with computational structural biology of plants." Biochemical Journal 479, no. 8 (April 29, 2022): 921–28. http://dx.doi.org/10.1042/bcj20200942.
Rundo, Leonardo, Andrea Tangherloni, and Carmelo Militello. "Artificial Intelligence Applied to Medical Imaging and Computational Biology." Applied Sciences 12, no. 18 (September 8, 2022): 9052. http://dx.doi.org/10.3390/app12189052.
Dissertations / Theses on the topic "Computational Learning Sciences":
Grover, Ishaan. "A semantics based computational model for word learning." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120694.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 73-77).
Studies have shown that children's early literacy skills can impact their ability to achieve academic success, attain higher education and secure employment later in life. However, lack of resources and limited access to educational content causes a "knowledge gap" between children that come from different socio-economic backgrounds. To solve this problem, there has been a recent surge in the development of Intelligent Tutoring Systems (ITS) to provide learning benefits to children. However, before providing new content, an ITS must assess a child's existing knowledge. Several studies have shown that children learn new words by forming semantic relationships with words they already know. Human tutors often implicitly use semantics to assess a tutee's word knowledge from partial and noisy data. In this thesis, I present a cognitively inspired model that uses word semantics (semantics-based model) to make inferences about a child's vocabulary from partial information about their existing vocabulary. Using data from a one-to-one learning intervention between a robotic tutor and 59 children, I show that the proposed semantics-based model outperforms (on average) models that do not use word semantics (semantics-free models). A subject level analysis of results reveals that different models perform well for different children, thus motivating the need to combine predictions. To this end, I present two methods to combine predictions from semantics-based and semantics-free models and show that these methods yield better predictions of a child's vocabulary knowledge. Finally, I present an application of the semantics-based model to evaluate if a learning intervention was successful in teaching children new words while enhancing their semantic understanding. More concretely, I show that a personalized word learning intervention with a robotic tutor is better suited to enhance children's vocabulary when compared to a non-personalized intervention. These results motivate the use of semantics-based models to assess children's knowledge and build ITS that maximize children's semantic understanding of words.
"This research was supported by NSF IIP-1717362 and NSF IIS-1523118"--Page 10.
by Ishaan Grover.
S.M.
Kim, Richard S. M. Massachusetts Institute of Technology. "A computational model of moral learning for autonomous vehicles." Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/122897.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 75-81).
We face a future of delegating many important decision making tasks to artificial intelligence (AI) systems as we anticipate widespread adoption of autonomous systems such as autonomous vehicles (AV). However, recent string of fatal accidents involving AV reminds us that delegating certain decisions making tasks have deep ethical complications. As a result, building ethical AI agent that makes decisions in line with human moral values has surfaced as a key challenge for Al researchers. While recent advances in deep learning in many domains of human intelligence suggests that deep learning models will also pave the way for moral learning and ethical decision making, training a deep learning model usually encompasses use of large quantities of human-labeled training data. In contrast to deep learning models, research in human cognition of moral learning theorizes that the human mind is capable of learning moral values from a few, limited observations of moral judgments of other individuals and apply those values to make ethical decisions in a new and unique moral dilemma. How can we leverage the insights that we have about human moral learning to design AI agents that can rapidly infer moral values of human it interacts with? In this work, I explore three cognitive mechanisms - abstraction, society-individual dynamics, and response time analysis - to demonstrate how these mechanisms contribute to rapid inference of moral values from limited number of observed data. I propose two Bayesian cognitive models to express these mechanisms using hierarchical Bayesian modeling framework and use large-scale ethical judgments from Moral Machine to empirically demonstrate the contributions of these mechanisms to rapid inference of individual preferences and biases in ethical decision making.
by Richard Kim.
S.M.
S.M. Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences
Fusté, Lleixà Anna. "Hypercubes : learning computational thinking through embodied spatial programming in augmented reality." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120690.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 116-120).
Computational thinking has been described as a basic skill that should be included in the educational curriculum. Several online screen-based platforms for learning computational thinking have been developed during the past decades. In this thesis we propose the concept of Embodied Spatial Programming as a new and potentially improved programming paradigm for learning computational thinking in space. We have developed HyperCubes, an example Augmented Reality authoring platform that makes use of this paradigm. With a set of qualitative user studies we have assessed the engagement levels and the potential learning outcomes of the application. Through space, the physical environment, creativity and play the user is able to tinker with basic programming concepts that can lead to a better adoption of computational thinking skills.
by Anna Fusté Lleixà.
S.M.
Dasgupta, Sayamindu. "Learning with data : a toolkit to democratize the computational exploration of data." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/78203.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 93-95).
This thesis explores the space of programming with data, focusing on the data-ecosystem opened up by the Internet and Cloud technologies. The central argument of this thesis is that the act of democratizing programmatic access to online data can further unleash the generative powers of this emerging ecosystem, and enable explorations of a new set of concepts and powerful ideas. To establish the validity of this argument, this thesis introduces a learning framework for the computational exploration of online data, a system that enables children to program with online data, and then finally describes a study of children using the system to explore wide variety of creative possibilities, as well as important computational concepts and powerful ideas around data.
by Sayamindu Dasgupta.
S.M.
Roque, Ricarose Vallarta. "Family creative learning : designing structures to engage kids and parents as computational creators." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107577.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 127-132).
The ability to create, design, and express oneself with technology is an important fluency for full participation in today's digitally mediated society. Social support can play a major role in engaging and deepening what young people can learn and do with technology. In particular, parents can play many roles, such as being collaborators, resource providers, and co-learners with their kids. In this dissertation, I explore the possibilities of engaging kids and their families as computational creators - providing opportunities and support to enable them to create things they care about with computing, to see themselves as creators, and to imagine the ways they can shape their world. I especially focus on families with limited access to resources and social support around computing. I describe the design of a community-based outreach program called Family Creative Learning, which invites kids, their families, and other families in their community to create and learn together using creative technologies. I use a qualitative approach to document the complex and diverse learning experiences of families. Through studies of family participation, I examine how kids and their parents supported one another and how the Family Creative Learning environment, activities, tools, and facilitation supported families in their development as computational creators. As families built projects, they also built perspectives in how they saw themselves, each other, and computing - developing identities as computational creators.
by Ricarose Roque.
Ph. D.
Vosoughi, Soroush. "Interactions of caregiver speech and early word learning in the Speechome corpus : computational explorations." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62082.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 107-110).
How do characteristics of caregiver speech contribute to a child's early word learning? What is the relationship between a child's language development and caregivers' speech? Motivated by these general questions, this thesis comprises a series of computational studies on the fined-grained interactions of caregiver speech and one child's early linguistic development, using the naturalistic, high-density longitudinal corpus collected for the Human Speechome Project. The child's first productive use of a word was observed at about 11 months, totaling 517 words by his second birthday. Why did he learn those 517 words at the precise ages that he did? To address this specific question, we examined the relationship of the child's vocabulary growth to prosodic and distributional features of the naturally occurring caregiver speech to which the child was exposed. We measured fundamental frequency, intensity, phoneme duration, word usage frequency, word recurrence and mean length of utterances (MLU) for over one million words of caregivers' speech. We found significant correlations between all 6 variables and the child's age of acquisition (AoA) for individual words, with the best linear combination of these variables producing a correlation of r = -. 55(p < .001). We then used these variables to obtain a model of word acquisition as a function of caregiver input speech. This model was able to accurately predict the AoA of individual words within 55 days of their true AoA. We next looked at the temporal relationships between caregivers' speech and the child's lexical development. This was done by generating time-series for each variables for each caregiver, for each word. These time-series were then time-aligned by AoA. This analysis allowed us to see whether there is a consistent change in caregiver behavior for each of the six variables before and after the AoA of individual words. The six variables in caregiver speech all showed significant temporal relationships with the child's lexical development, suggesting that caregivers tune the prosodic and distributional characteristics of their speech to the linguistic ability of the child. This tuning behavior involves the caregivers progressively shortening their utterance lengths, becoming more redundant and exaggerating prosody more when uttering particular words as the child gets closer to the AoA of those words and reversing this trend as the child moves beyond the AoA. This "tuning" behavior was remarkably consistent across caregivers and variables, all following a very similar pattern. We found significant correlations between the patterns of change in caregiver behavior for each of the 6 variables and the AoA for individual words, with their best linear combination producing a correlation of r = -. 91(p < .001). Though the underlying cause of this strong correlation will require further study, it provides evidence of a new kind for fine-grained adaptive behavior by the caregivers in the context of child language development.
by Soroush Vosoughi.
S.M.
Hooper, Paula Kay 1961. "They have their own thoughts : children's learning of computational ideas from a cultural perspective." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/41022.
Wagner, Alex Handler. "Computational methods for identification of disease-associated variations in exome sequencing." Diss., University of Iowa, 2014. https://ir.uiowa.edu/etd/1513.
Bodily, Paul Mark. "Machine Learning for Inspired, Structured, Lyrical Music Composition." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/6930.
Bhattacharya, Sanmitra. "Computational methods for mining health communications in web 2.0." Diss., University of Iowa, 2014. https://ir.uiowa.edu/etd/4576.
Books on the topic "Computational Learning Sciences":
Ashwin, Ram, and Leake David B, eds. Goal-driven learning. Cambridge, Mass: MIT Press, 1995.
SEAL 2008 (2008 Melbourne, Vic.). Simulated evolution and learning: 7th international conference, SEAL 2008, Melbourne, Australia, December 7-10, 2008 : proceedings. Berlin: Springer, 2008.
A, Rosenbaum David, and Collyer Charles E, eds. Timing of behavior: Neural, psychological, and computational perspectives. Cambridge, Mass: MIT Press, 1998.
Baldi, Pierre. Bioinformatics: The machine learning approach. 2nd ed. Cambridge, Mass: MIT Press, 2001.
Judd, J. Stephen. Neural network design and the complexity of learning. Cambridge, Mass: MIT Press, 1990.
Kearns, Michael J. An introduction to computational learning theory. Cambridge, Mass: MIT Press, 1994.
ISICA, 2008 (2008 Wuhan China). Advances in computation and intelligence: Third international symposium, ISICA 2008 : Wuhan, China, December 19-21, 2008 : proceedings. Berlin: Springer, 2008.
ISICA 2007 (2007 Wuhan, China). Advances in computation and intelligence: Second international symposium, ISICA 2007, Wuhan, China, September 21-23, 2007 ; proceedings. Berlin: Springer, 2007.
ISICA 2008 (2008 Wuhan, China). Advances in computation and intelligence: Third international symposium, ISICA 2008 : Wuhan, China, December 19-21, 2008 : proceedings. Berlin: Springer, 2008.
ISICA 2008 (2008 Wuhan, China). Advances in computation and intelligence: Third international symposium, ISICA 2008 : Wuhan, China, December 19-21, 2008 : proceedings. Berlin: Springer, 2008.
Book chapters on the topic "Computational Learning Sciences":
Verguts, Tom. "Computational Models of Human Learning." In Encyclopedia of the Sciences of Learning, 707–10. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_417.
Schmajuk, Nestor A. "Computational Models of Classical Conditioning." In Encyclopedia of the Sciences of Learning, 700–707. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_528.
Yagawa, Genki, and Atsuya Oishi. "Deep Learning for Computational Mechanics." In Lecture Notes on Numerical Methods in Engineering and Sciences, 199–208. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66111-3_16.
Inceoglu, Mustafa Murat, and Burak Galip Aslan. "Computational Sciences Learning Project for Pre-university Students." In Computational Science – ICCS 2007, 607–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72588-6_104.
Lu, Xiaofei, and Berlin Chen. "Computational and Corpus Approaches to Chinese Language Learning: An Introduction." In Chinese Language Learning Sciences, 3–11. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3570-9_1.
Vaidehi Nayantara, P., Surekha Kamath, K. N. Manjunath, and Rajagopal Kadavigere. "Comparison of machine learning and deep learning methods for detection of liver abnormality." In Recent Trends in Computational Sciences, 21–28. London: CRC Press, 2023. http://dx.doi.org/10.1201/9781003363781-4.
Shankar, S. N. Baba, B. Karthik Reddy, B. Koushik Reddy, Venuthurla Venkata Pradeep Reddy, and H. B. Mahesh. "Smart Driving Assistance Using Deep Learning." In Computational Sciences and Sustainable Technologies, 402–18. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-50993-3_32.
Dalmeida, Sonal Patreena, and Surekha Kamath. "Soil micronutrient detection using machine learning." In Recent Trends in Computational Sciences, 29–35. London: CRC Press, 2023. http://dx.doi.org/10.1201/9781003363781-5.
Jing-Schmidt, Zhuo. "Corpus and Computational Methods for Usage-Based Chinese Language Learning: Toward a Professional Multilingualism." In Chinese Language Learning Sciences, 13–31. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3570-9_2.
Thakur, Gour Sundar Mitra, Subhayu Dutta, and Bratajit Das. "Diabetes Prediction Using Machine Learning: A Detailed Insight." In Computational Sciences and Sustainable Technologies, 159–73. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-50993-3_13.
Conference papers on the topic "Computational Learning Sciences":
Rasul, Injila, Danielle Crabtree, Francisco Castro, Allison Poh, Sai Satish Gattupalli, Krishna Chaitanya Rao Kathala, and Ivon Arroyo. "WearableLearning: Developing Computational Thinking Through Modeling, Simulation, and Computational Problem Solving." In 17th International Conference of the Learning Sciences (ICLS) 2023. International Society of the Learning Sciences, 2023. http://dx.doi.org/10.22318/icls2023.829827.
Arce, Gonzalo R., Andres Ramirez, and Nestor Porras. "High altitude computational lidar emulation and machine learning reconstruction for Earth sciences." In Big Data VI: Learning, Analytics, and Applications, edited by Panos P. Markopoulos. SPIE, 2024. http://dx.doi.org/10.1117/12.3025299.
Mehta, Shalin B. "4D computational imaging and deep learning." In Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, edited by Liang Gao, Guoan Zheng, and Seung Ah Lee. SPIE, 2024. http://dx.doi.org/10.1117/12.3012549.
Basu, Satabdi, Arif Rachmatullah, Kevin McElhaney, Nonye Alozie, Hui Yang, Nicole Hutchins, Gautam Biswas, and Kelly Mills. "A Comparison of Computational Practices and Student Challenges Across Three Types of Computational Modeling Activities Integrating Science and Engineering." In 18th International Conference of the Learning Sciences (ICLS) 2024. International Society of the Learning Sciences, 2024. http://dx.doi.org/10.22318/icls2024.549121.
Asif, Ali D., Hamza Malik, Chandra Orrill, Ramprasad Balasubramanian, and Shakhnoza Kayumova. "Computational Thinking: Teachers’ Practice of Abstraction." In 18th International Conference of the Learning Sciences (ICLS) 2024. International Society of the Learning Sciences, 2024. http://dx.doi.org/10.22318/icls2024.877800.
Asif, Ali D., Hamza Malik, Chandra Orrill, Stephen B. Witzig, Ramprasad Balasubramanian, and Shakhnoza Kayumova. "Computational Thinking: A Tale of Debugging." In 18th International Conference of the Learning Sciences (ICLS) 2024. International Society of the Learning Sciences, 2024. http://dx.doi.org/10.22318/icls2024.426571.
Ocak, Ceren, Aman Yadav, and Victoria Macann. "Using Computational Thinking as a Metacognitive Tool in the Context of Plugged Vs. Unplugged Computational Activities." In 17th International Conference of the Learning Sciences (ICLS) 2023. International Society of the Learning Sciences, 2023. http://dx.doi.org/10.22318/icls2023.474441.
Conlin, Luke D., Jennifer Elisabeth Mesiner, and Aditi Wagh. "Exploring the Affective Dimension of Integrating Computational Modeling with Science Learning." In 18th International Conference of the Learning Sciences (ICLS) 2024. International Society of the Learning Sciences, 2024. http://dx.doi.org/10.22318/icls2024.791611.
Tang, Xiaoyu, and Matthew Lira. "Drawing Upon Computational Experiences to Navigate Ontologies." In 17th International Conference of the Learning Sciences (ICLS) 2023. International Society of the Learning Sciences, 2023. http://dx.doi.org/10.22318/icls2023.605594.
Chichekian, Tanya, Joel Trudeau, Tawfiq Jawhar, and Yi-Mei Zhang. "Computational Thinking and Robotics in Mixed Environments." In 17th International Conference of the Learning Sciences (ICLS) 2023. International Society of the Learning Sciences, 2023. http://dx.doi.org/10.22318/icls2023.354637.
Reports on the topic "Computational Learning Sciences":
Luke, Christina, and Viki M. Young. Integrating Micro-credentials into Professional Learning: Lessons from Five Districts. Digital Promise, October 2020. http://dx.doi.org/10.51388/20.500.12265/103.
Danylchuk, Hanna B., and Serhiy O. Semerikov. Advances in machine learning for the innovation economy: in the shadow of war. Криворізький державний педагогічний університет, August 2023. http://dx.doi.org/10.31812/123456789/7732.
Mills, Kelly, Merijke Coenraad, Pati Ruiz, Quinn Burke, and Josh Weisgrau. Computational Thinking for an Inclusive World: A Resource for Educators to Learn and Lead. Digital Promise, December 2021. http://dx.doi.org/10.51388/20.500.12265/138.
Beckman, Ivan. Development of alternative air filtration materials and methods of analysis. Engineer Research and Development Center (U.S.), June 2023. http://dx.doi.org/10.21079/11681/47188.
Striuk, Andrii M., and Serhiy O. Semerikov. The Dawn of Software Engineering Education. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3671.
Nurturing an Innovative District: Inclusive Computing Pathways in Talladega County Schools. Digital Promise, 2021. http://dx.doi.org/10.51388/20.500.12265/132.