Maximizing Human Potential

There exists a disconnect between what is assessed in education and what is relevant throughout life. There are few resources - in or out of the classroom - for connecting assessments with feedback that can describe, predict, or improve relevant life-outcomes. Tests are often intrusive, over-generalize, exaggerate the impact of a limited view, provide delayed feedback, and are poorly aligned with students’ and teachers’ needs.

The Socos Solution

There exists an enormous amount of data on how students are learning and workers performing. While there is an increasing awareness of the importance of skills like collaboration and creative problem solving, there is little done to reinforce these traits. Socos takes naturalistic data as the basis for our assessments. We close the educational loop by providing relevant feedback to educators on what they can do to improve life outcomes.

There is an extremely important change that occurs when an individual becomes an autodidact – in control of his own learning process instead of passively absorbing another’s teachings. Student exhibit increases in metrics actually predictive of life outcomes that range from motivation and creativity to self-regulation and metacognitive ability (Roberts et al., 2007; Luhmann et al., 2012).


Delayed Gratification

In the oft-cited marshmallow study, a child’s early-life ability to delay gratification has been shown to be predictive of life-long measures of success (Mischel, Ayduk, et al., 2010). However, such interventions, when poorly designed, can have contrary effects: In a lesser-known variant of the same study, prior to being given their first marshmallow the children were promised crayons or similar enticement by an adult who did not deliver on the promise. In each case of this reneging on a promise, children ate their first marshmallow right way (Kidd, Palmeri, and Aslin, 2013). Children were trained to take what was available because they could not rely on a future promise, which has implications for the future success of those children.

Lasting Change in Socio-Economic Status

It has been repeatedly demonstrated in scientific studies that small adjustments can create life-long changes. A famous example is a study of families of severely underprivileged toddlers in Kingston, Jamaica. Families were identified in a government program, and once each month for three years the parents were educated in simple nutrition, social and motivational skills. Twenty years later the children from those families were studied by a group of economists and found to be indistinguishable from more wealthy populations (Gertler, Heckman, et al., 2014). Those simple interventions were able to effectively erase the fact that those children came from impoverished backgrounds.

Health Implications of Minor Interventions

Similar impacts from low-grade interventions were found in addressing health problems of children from families of low socioeconomic status in the United States. Eight years after the interventions, the youth who participated had significantly fewer health problems than controls (Miller, Chen, et al., 2013). Socioeconomic status and health have been demonstrably impacted as the result of small interventions. There is even more potential for impact when processes are triggered that are relevant for successful learning. In fact, the reason why these interventions are so effective is that they target personality characteristics such as persistence, motivation, and mindset, which prove to be key to development and life-outcomes (Yeager, Walton, and Cohen, 2013).








21st Century Skills

Characteristics like the ability to delay gratification in the marshmallow study are relevant to “21st Century Skills.” These learnable skills include creative problem solving, critical thinking, and collaboration, and are important to aspects of life (Pellegrino & Hilton, 2013; Binkley, Erstad, et al., 2012). Students and professionals would benefit if these complex skills were studied in schools and taught in the workplace. Unfortunately, educational systems fail to teach to these goals, instead focusing on training domain-specific knowledge under the assumptions that these more complex skills will come about naturally. Specific knowledge is valuable, but in the modern era information is abundant and relevant knowledge changes very quickly. What actually matters most is the ability to learn whatever new information may be important, and to use it productively.

Human Capital

Education requires direct human participation. Parents, caregivers, colleagues, and teachers all play an important role in a learner’s development. This includes education in nurtuant parenting (Gertler, Heckman, et al., 2014), implicit education which fosters a growth mindset (Dweck, 2006), and positive framing and affirmation (Cohen & Sherman, 2014). Socos is building on pre-existing research, but mainly in the realm of "hand analysis" of complex data. One study that demonstrates that how students take notes over several weeks of a course is highly reflective of self-regulated learning aspects (Glogger, Schwonke, et al., 2012). Socos will be using the demonstrated importance of human participation in education to further the goal of lifetime outcomes.

Naturalistic Technology

Technology is sufficiently advanced that algorithms can accurately predict lifetime outcomes. When that information is provided recursively to the students and teachers it becomes actionable. However, the current integration of technological assessments occur in tightly regulated environments. While they might provide flexibility in their assessment strategies, they can only do so by offloading standardization onto the environment. The solution proposed by Socos is to take naturalistic student experiences and perform predictive assessment on lifelong outcome. In the case studies previously described, lifelong outcomes of test subjects were changes by slight alterations to their environment. Children denied crayons immediately ate their first marshmallow and parents given weekly interventions in Jamaica raised children indistinguishable from less socioeconomically limited families. Minor real-world interventions have a major impact.






"Dr. Vivienne Ming at SXSWEDU 2014"

Keeping the Promise of Educational Technology

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.


Kindersight

Assessing the linguistic environment of kindergarteners

Interest in improving early childhood learning across school and home settings is colliding with movements to increase standardized testing at ever younger ages. While testing proponents are rightfully concerned about measuring children’s learning, their design and use carry many problems. Tests are valid only for the population and purpose for which they were designed; eliminating cultural bias from tests is extremely difficult; and tests are often designed as sequestered experiences stripped from authentic contexts.

Read More
 


College Learners

Innovative Competency-Based Online College

Socos is in the early stages of a project with a new, competency-based online college, which Wired Magazine has named among the most innovative organizations in the world. This organization has taken an entirely new approach to self-directed learning. There are no grades or professors. Instead, progression through the curriculum is driven entirely by competency. Students work digitally with coaches who support students for the duration of their projects. Students’ work are then given a pass or fail by an unbiased panel of assessors...

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Online Students

Online Student Discussion Predictive of Grades

Socos has conducted and published research based on online class discussion data at one of the world’s largest universities. Socos partnered with this university and subsequently published multiple journal articles describing how grades in a course could be successfully predicted. In academic studies, Socos has successfully predicted final grades by analyzing unstructured student text in online discussion forums, which also yielded preliminary topic maps that can be used in student thinking (Ming & Ming, 2012).

Read More

"People want answers, not more data."

Research

Over the last 5 years, Socos has researched and developed technology capable of predicting outcomes based on naturalistic data, and is now specifically focused on turning commonplace learning experiences directly into assessments aligned with life outcomes through a combination of non-invasive technological interventions and human capital guidance.





Publications

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    Binkley, M., Erstad, O., Herman, J., Raizen, S., Ripley, M., Miller-Ricci, M., & Rumble, M.

    (2012)

    Defining twenty-first century skills. In Assessment and teaching of 21st century skills (pp. 17-66). Springer Netherlands.

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    Chen, E., & Miller, G. E.

    (2012)

    “Shift-and-Persist” Strategies Why Low Socioeconomic Status Isn’t Always Bad for Health. Perspectives on Psychological Science, 7(2), 135-158.

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    Cohen, G. L., & Sherman, D. K.

    (2014)

    The psychology of change: Self-affirmation and social psychological intervention. Annual review of psychology, 65, 333-371.

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    Ming & Ming

    (2012)

    Predicting Student Outcomes from Unstructured Data. UMAP2012.

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    Dweck, C.

    (2006)

    Mindset: The new psychology of success. Random House LLC.

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    Bumbacher & Ming

    (2012)

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

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    Geiser, S., & Studley, W. R.

    (2002)

    UC and the SAT: Predictive validity and differential impact of the SAT I and SAT II at the University of California. Educational Assessment, 8(1), 1-26.

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    Gertler, P., Heckman, J., Pinto, R., Zanolini, A., Vermeersch, C., Walker, S., ... & Grantham-McGregor, S.

    (2014)

    Labor market returns to an early childhood stimulation intervention in Jamaica. Science, 344(6187), 998-1001.

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    Ming, V.L. & Holt

    (2009)

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

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    Glogger, I., Schwonke, R., Holzäpfel, L., Nückles, M., & Renkl, A.

    (2012)

    Learning strategies assessed by journal writing: Prediction of learning outcomes by quantity, quality, and combinations of learning strategies. Journal of educational psychology, 104(2), 452.

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    Grant, A. M.

    (2008)

    Does intrinsic motivation fuel the prosocial fire? Motivational synergy in predicting persistence, performance, and productivity. Journal of applied psychology, 93(1), 48.

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    Karoly, L. A., Kilburn, M. R., & Cannon, J. S.

    (2006)

    Early childhood interventions: Proven results, future promise (Vol. 341). Rand Corporation.

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    Kidd, C., Palmeri, H., & Aslin, R. N.

    (2013)

    Rational snacking: Young children’s decision-making on the marshmallow task is moderated by beliefs about environmental reliability. Cognition, 126(1), 109-114.

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    Luhmann, M., Hofmann, W., Eid, M., & Lucas, R. E.

    (2012)

    Subjective well-being and adaptation to life events: a meta-analysis. Journal of personality and social psychology, 102(3), 592.

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    Ming, N. C., & Ming, V.

    (2012 September)

    Automated predictive assessment from unstructured student writing. In DATA ANALYTICS 2012, The First International Conference on Data Analytics (pp. 57-60).

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    Mischel, W., Ayduk, O., Berman, M. G., Casey, B. J., Gotlib, I. H., Jonides, J., ... & Shoda, Y.

    (2010)

    ‘Willpower’over the life span: decomposing self-regulation. Social Cognitive and Affective Neuroscience.

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    Pellegrino, J. W., & Hilton, M. L. (Eds.).

    (2013)

    Education for life and work: Developing transferable knowledge and skills in the 21st century. National Academies Press.

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    Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A., & Goldberg, L. R.

    (2007)

    The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspectives on Psychological Science, 2(4), 313-345.

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    Rothstein, J. M.

    class="italic">(2004)

    College performance predictions and the SAT. Journal of Econometrics, 121(1), 297-317.

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    Wiliam, D.

    (2011)

    What is assessment for learning?. Studies in Educational Evaluation, 37(1), 3-14.

The three co-founders of Socos have combined their expertise

in Cognition, Education and Machine Learning.

About Us

Everyone at Socos has a very personal relationship with educational technology, research, and teaching. Co-founder Dr. Vivienne Ming, PhD, named one of 10 Women to Watch in Tech in 2013 by Inc. Magazine, is a theoretical neuroscientist, technologist and entrepreneur. She is a visiting scholar at UC Berkeley's Redwood Center for Theoretical Neuroscience. 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.

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 inovative 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.

Ming
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. A former high school and university educator, she is now Supervisor of Research in the San Francisco Unified School Didstrict’s Research, Planning, and Accountability department, where she coordinates results-oriented research to help the district implement its strategic plan. She 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. Previously, she taught as a lecturer in Education in Math, Science, and Technology at UC Berkeley’s Graduate School of Education. 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.

"At Socos we are developing

and implementing technology which turns learning
experiences directly into predictive analytics focused on
developing autodidactic learning focused on 21st
century skills".

CONTACT US

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

Recent Talks

* Lawrence Berkeley Labs
* LinkedIn
* SXSWEdu2014 keynote
* Pearson Foundation's "Be the Source" interviews
* EdLab Groundbreakers vialogue

Current Projects

* Bridging the Word Gap (US Prez.)
* KinderSight
* College Learners
* UT Austin
* Washington College

Socos currently has two main areas of focus. Though our project Kindersight we are focused on assessing and improving the linguistic environment of kindergarteners. In our work with several different colleges and online universities we are implementing technology to predict outcomes and providing relevant feedback that can maximize learning potential.