Socos is an inovative educational technology & data mining company.

We specialise in research and development of new technologies combining machine learning and learning sciences, providing Cognitive Analytics, Adaptive Learning and Educational Data Mining services.

About Socos

Socos develops web applications both for individuals, students and educators, and large companies and academic institutions. In addition, we offer direct consulting services for educational data mining research. Our team of exceptional researchers has unique expertise in machine learning, theoretical neuroscience, cognitive psychology and the learning sciences. The promise of technology to revolutionize education is finally being realized. Intelligent systems from Socos will power the change.

The team at Socos can advance your development projects with individually focused research in Text Mining Cognitive Modeling Multi-modality data analysis Our consulting services offer our years of expertise for any large-scale analysis of your education-related data.

Cognitive Analytics

One of the most fundamental challenges in teaching is peering inside students' heads and figuring out what they're thinking. Our Cognitive Analytics dashboard can plug into any LMS or other data sources. It gives teachers immediate insight into the current understanding of their students directly from the work the students already generate. The value of specific lessons and interventions can be assessed in the context of the actual learning environment, from the level of individual students to complex subpopulations.

Adaptive Learning

The dream of educational technolgy Adaptive Learning Everyone wants a system which can automatically tailor itself to the specific needs of each individual student. Our solution to this is the Cognitive Graph, a unique approach to modeling the connections between ideas uniquely for each learner. The graph can map any conceptual domain for both individuals and larger populations of student, allowing instructions to be dynamically targeted to a student's current misconceptions.

Educational Data Mining

While education is a field rich with data, obtaining high-quality data and processing them meaningfully and efficiently remains difficult. The team at Socos can advance your development projects with individually focused research in Text Mining Cognitive Modeling Multi-modality data analysis We love data!

Want to know more about Socos', reserach and development?

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Services

One of the most fundamental challenges in teaching is peering inside students' heads and figuring out what they're thinking. While education is a field rich with data, obtaining high-quality data and processing them meaningfully and efficiently remains difficult. Whether in formal classes, individualized tutoring, or casual web queries, learners continually generate questions, comments, proposals, discussions and a multitude of other assessable work.

These constitute valuable assessment data for informing instructors’ professional judgment, but systematically analyzing them across multiple students and time-points demands attention and resources beyond what most teachers can spare. The quantity of possible data to track defies ambition. The vast majority is lost to any broader perspective for instructors, educational leaders, and decision-makers. Lessons go untried, assessments unvalidated, population trends undetected and teaching opportunities missed. Rather than constantly designing and administering new tests, education needs tools which can actually make intelligent use of existing data.

Cognitive Analytics.

Using our Cognitive AnalyticsTM dashboard, a teacher can assign a lesson in-class or online, collect real-time analytics on student work, and use its intuitive visualizations to facilitate assessment and instruction. The dashboard displays learning trajectories for individual students over time and across domains by analyzing entire bodies of work. Student populations can be tracked across time and across classes. The value of specific lessons and interventions can be assessed in the context of the actual learning environment, from the level of individual students

Adaptive Learning

The three co-founders of Socos have combined their expertise in Cognition, Education and Machine Learning to develop our core Cognitive AnalyticsTM platform. It combines a set of proprietary algorithms for discovering underlying causes for student judgments with our unique Cognitive GraphTM Technology for capturing relationships connecting ideas and thinkers. The graph infers core conceptual factors underlying domains from normative sources as well as the student's own work. These factors power the analytics, providing metrics of the actual semantic content of students' work, not just word counts or time on task. Using the Cognitive Graph, instructors can quickly and easily perceive unique patterns in the understanding of individual students on specific assignments, across assignments, across domains and across time. The Graph easily extends to groups of students, allowing automated discovery of trends within and across populations.

Educational Data Mining

While education is a field rich with data, obtaining high-quality data and processing them meaningfully and efficiently remains difficult.

  • Cognitive Analytics

    Using our Cognitive AnalyticsTM dashboard, a teacher can assign a lesson in-class or online, collect real-time analytics on student work, and use its intuitive visualizations to facilitate assessment and instruction. The dashboard displays learning trajectories for individual students over time and across domains by analyzing entire bodies of work. Student populations can be tracked across time and across classes. The value of specific lessons and interventions can be assessed in the context of the actual learning environment, from the level of individual students to complex subpopulations.

  • Adaptive Learning

    The three co-founders of Socos have combined their expertise in Cognition, Education and Machine Learning to develop our core Cognitive AnalyticsTM platform. It combines a set of proprietary algorithms for discovering underlying causes for student judgments with our unique Cognitive GraphTM Technology for capturing relationships connecting ideas and thinkers. The graph infers core conceptual factors underlying domains from normative sources as well as the student's own work. These factors power the analytics, providing metrics of the actual semantic content of students' work, not just word counts or time on task. Using the Cognitive Graph, instructors can quickly and easily perceive unique patterns in the understanding of individual students on specific assignments, across assignments, across domains and across time. The Graph easily extends to groups of students, allowing automated discovery of trends within and across populations.

  • Educational Mining

    It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites.

 

SXSWedu 2014 Video Highlights:

Keeping the Promise of Educational Technology

Research

  • circle
    Ming & Ming

    (2012)

    Modeling student conceptual knowledge from unstructured data using a hierarchical generative model. NIPS2012 Workshop: Personalizing Education With Machine Learning. South Lake Tahoe, CA.

    circle
    Ming & Ming

    (2012)

    Predicting Student Outcomes from Unstructured Data. IARIA, Data Analytics.

    circle
    Carlson, Ming & DeWeese

    (2012)

    Sparse codes for speech predict spectrotemporal receptive fields in the inferior colliculus. PLoS CompBio

    circle
    Ming & Ming

    (2012)

    Predicting Student Outcomes from Unstructured Data. UMAP2012.

  • circle
    Bumbacher & Ming

    (2012)

    Heirachical coding of natural signals in a dynamical system model. Cosyne2012

    circle
    Bumbacher & Ming

    (2012)

    Pitch-sensitive components emerge from hierarchical sparse coding natural sounds. ICPRAM2012

    circle
    Ming, N.C. & Baumer, E.P.S.

    (2011)

    Using text mining to characterize online discussion facilitation. Journal of Asynchronous Learning Networks, 15(2).

    circle
    Carlson, N., Ming, V.L. & DeWeese, M.R.

    (2010)

    A Sparse Representation of Speech Data. Sensory Coding & the Natural Environment, Gordon Research Conference.

  • circle
    Ming, V.L. & Holt

    (2009)

    Evidence of efficient coding in human speech perception. JASA 129, Num. 3: 1312-1321.

    circle
    Ming, N.C.

    (2009)

    Analogies vs. contrasts: A comparison of their learning benefits. In B. Kokinov, D. Gentner, & K. Holyoak (Eds.), New frontiers in analogy research: Proceedings of the second international conference on analogy (pp. 338-347). Sofia, Bulgaria: New Bulgarian University.

    circle
    Smith & Lewicki

    (2006)

    Efficient auditory coding. Nature 439, Num. 7079.

    circle
    Chang, N.M.

    (2006)

    Learning to Discriminate and Generalize through Problem Comparisons. Unpublished doctoral dissertation, Carnegie Mellon University, Pittsburgh PA.

  • circle
    Ming & Ming

    (2012)

    Modeling student conceptual knowledge from unstructured data using a hierarchical generative model. NIPS2012 Workshop: Personalizing Education With Machine Learning. South Lake Tahoe, CA.

    circle
    Ming & Ming

    (2012)

    Predicting Student Outcomes from Unstructured Data. IARIA, Data Analytics.

    circle
    Carlson, Ming & DeWeese

    (2012)

    Sparse codes for speech predict spectrotemporal receptive fields in the inferior colliculus. PLoS CompBio

    circle
    Ming & Ming

    (2012)

    Predicting Student Outcomes from Unstructured Data. UMAP2012.

  • circle
    Bumbacher & Ming

    (2012)

    Heirachical coding of natural signals in a dynamical system model. Cosyne2012

    circle
    Bumbacher & Ming

    (2012)

    Pitch-sensitive components emerge from hierarchical sparse coding natural sounds. ICPRAM2012

    circle
    Ming, N.C. & Baumer, E.P.S.

    (2011)

    Using text mining to characterize online discussion facilitation. Journal of Asynchronous Learning Networks, 15(2).

    circle
    Carlson, N., Ming, V.L. & DeWeese, M.R.

    (2010)

    A Sparse Representation of Speech Data. Sensory Coding & the Natural Environment, Gordon Research Conference.

  • circle
    Ming, V.L. & Holt

    (2009)

    Evidence of efficient coding in human speech perception. JASA 129, Num. 3: 1312-1321.

    circle
    Ming, N.C.

    (2009)

    Analogies vs. contrasts: A comparison of their learning benefits. In B. Kokinov, D. Gentner, & K. Holyoak (Eds.), New frontiers in analogy research: Proceedings of the second international conference on analogy (pp. 338-347). Sofia, Bulgaria: New Bulgarian University.

    circle
    Smith & Lewicki

    (2006)

    Efficient auditory coding. Nature 439, Num. 7079.

    circle
    Chang, N.M.

    (2006)

    Learning to Discriminate and Generalize through Problem Comparisons. Unpublished doctoral dissertation, Carnegie Mellon University, Pittsburgh PA.

  • circle
    Ming & Ming

    (2012)

    Modeling student conceptual knowledge from unstructured data using a hierarchical generative model. NIPS2012 Workshop: Personalizing Education With Machine Learning. South Lake Tahoe, CA.

    circle
    Ming & Ming

    (2012)

    Predicting Student Outcomes from Unstructured Data. IARIA, Data Analytics.

    circle
    Carlson, Ming & DeWeese

    (2012)

    Sparse codes for speech predict spectrotemporal receptive fields in the inferior colliculus. PLoS CompBio

    circle
    Ming & Ming

    (2012)

    Predicting Student Outcomes from Unstructured Data. UMAP2012.

  • circle
    Bumbacher & Ming

    (2012)

    Heirachical coding of natural signals in a dynamical system model. Cosyne2012

    circle
    Bumbacher & Ming

    (2012)

    Pitch-sensitive components emerge from hierarchical sparse coding natural sounds. ICPRAM2012

    circle
    Ming, N.C. & Baumer, E.P.S.

    (2011)

    Using text mining to characterize online discussion facilitation. Journal of Asynchronous Learning Networks, 15(2).

    circle
    Carlson, N., Ming, V.L. & DeWeese, M.R.

    (2010)

    A Sparse Representation of Speech Data. Sensory Coding & the Natural Environment, Gordon Research Conference.

  • circle
    Ming, V.L. & Holt

    (2009)

    Evidence of efficient coding in human speech perception. JASA 129, Num. 3: 1312-1321.

    circle
    Ming, N.C.

    (2009)

    Analogies vs. contrasts: A comparison of their learning benefits. In B. Kokinov, D. Gentner, & K. Holyoak (Eds.), New frontiers in analogy research: Proceedings of the second international conference on analogy (pp. 338-347). Sofia, Bulgaria: New Bulgarian University.

    circle
    Smith & Lewicki

    (2006)

    Efficient auditory coding. Nature 439, Num. 7079.

    circle
    Chang, N.M.

    (2006)

    Learning to Discriminate and Generalize through Problem Comparisons. Unpublished doctoral dissertation, Carnegie Mellon University, Pittsburgh PA.

Related Research

Tom Griffiths, Comptnl Cognitive Science Josh Tenenbaum, Comptnl Mdls of Learning Kenneth R. Koedinger, Human-Computer Interaction


Areas of Interest

Cognitive Development Computer-Mediated Learning Curriculum Development Educational Media Experimental Design In Education Learner-centered Education Mathematics Education Professional Development for Educators Science Education Simulation Learning Environments Teacher Education and Certification Technology and Schools

The three co-founders of Socos have combined their expertise

in Cognition, Education and Machine Learning.

About Us

Engin Bumbacher
Engin Bumbacher

Director of Research

Engin is devoted to the development of the company’s core cognitive modeling and predictive analytics technology. He did his master’s thesis project at the Redwood Center for Theoretical Neuroscience at UC Berkeley under the supervision of Dr. Vivienne Ming, applying and further developing elaborate models of information processing to human speech and music. Engin earned his master’s degree with honors in Neural Systems and Computation from the Swiss Federal Institute of Technology Zurich and the Institute of Neuroinformatics, both researching in the field of theoretical neuroscience and exploring models of collective intelligence through implementation of interactive flocking algorithms to control computer sound synthesis and 3D sound positioning. Prior to that, he finished his B.S. with honors in Physics at the same university.

Vivien Ming
Vivienne Ming

Executive Director

Dr. Vivienne Ming, named one of 10 Women to Watch in Tech in 2013 by Inc. Magazine, is a theoretical neuroscientist, technologist and entrepreneur. She is chief scientist at Gild, an innovative startup that applies machine learning to predict optimal candidates for technology jobs, and to bring meritocracy to job markets. She joined Gild in 2012 to oversee R&D and IP development, solving problems in data mining, text analysis, cognitive modeling and algorithm development. Dr. Ming also co-founded her own cutting-edge edtech startup, Socos, with her wife, Norma. She is a visiting scholar at UC Berkeley's Redwood Center for Theoretical Neuroscience pursuing her research in neuroprosthetics. In her free time, Dr. Ming also explores augmented cognition using technology like Google Glass and has been developing a predictive model of diabetes to better manage blood glucose levels. She sits on the boards of StartOut and Our Family Coalition and speaks on issues of LGBT inclusion and gender in technology. Previously, she was a junior fellow at Stanford’s Mind, Brain & Computation Center and earned her Ph.D. from Carnegie Mellon. Her work and research has received extensive media attention including the New York Times, NPR, Nature, O Magazine, Forbes, and The Atlantic.

Robert Doe
Norma Ming

Director of Learning Design

Dr. Norma Ming is a learning scientist and educational technology thought leader who works at the intersection of research and development, policy, and practice. She is a co-founder and the Director of Learning Design at Socos, which applies cognitive modeling to create adaptive, personalized educational technology. Dr. Ming merges a pragmatic understanding of the teaching enterprise with a long-term, systemic vision of how research can illuminate and policy can facilitate better learning. Her experience in teaching, professional development, assessment design, and curriculum evaluation crosses multiple disciplines and spans elementary through postgraduate students, teachers, administrators, and faculty trainers. Research projects have explored relationships among predictors, processes, and outcomes across a range of student populations and instructional models, from case studies to massive scale, individual or collaborative, with and without technology. Her policy advocacy highlights issues of equity in creating flexible paths and innovative resources to enable all learners to meet high expectations. Previously, she worked as Senior Research Scientist at the Nexus Research and Policy Center and taught as a lecturer in Education in Math, Science, and Technology at UC Berkeley’s Graduate School of Education, where she is now a visiting scholar. She earned an A.B. with honors in chemistry at Harvard University and a Ph.D. in cognitive psychology in the Program for Interdisciplinary Educational Research at Carnegie Mellon University.

CONTACT US

You can find the Socos offices in downtown Berkeley, California.

Learn more about Consulting services for educational datamining Developing a Cognitive Analytics plug-in for your course or LMS Designing adaptive learning systems

The team at Socos

can advance your development

projects with individually focused research in Text Mining Cognitive Modeling Multi-modality data analysis Our consulting services offer our years of expertise for any large-scale analysis of your education-related data.