Deep Learning APIs

AI Powered Human Performance Improvement

Machine Learning that automatically learns from your top human experts

What if you could model human performance at an individual level and then multiply that to scale across the organization?​

We built the human-machine AI training technology so you can.​

Now you can know how trainees are performing inside your simulations, scenarios and performance tasks. Automatically compare their performances to your selected performance criteria and experts. Build in realtime feedback to improve performance at scale.

Learning Analytics Cloud Platform

21st Century IoT Skills Assessment API Data Platform. Measure what people CAN DO - not just what they KNOW.

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Authentic digital performance assessment

Embed Machine Intelligence into Your Products

Use our APIs to beat the competition. Build and sell SMART predictive and prescriptive products.

Metacog APIs
Understand learner affect Automated Diagnostics Reporting

AI to Identify and Improve Human Potential

Connect to our Process Analytics Cloud Platform to provide direct assessment, prove skills and close skills gaps.

Use Cases
AI Scoring with IRR on open ended items Learner Path Patterns feeding Analytics

Assessment that IMPROVES Performance

Don't just MEASURE it. Top performers train ML models to optimize Human Capital.

Invisible Integrated Assessment
What are learners thinking Learner Analytics

Award Winning AI Technology

Metacog ML measures higher order skills like complex problem solving, collaboration and knowledge synthesis.

ATP Industry Gamechanging Award Winner

The Award-Winning Metacog AI API Products

Metacog is pleased to announce that it has been acquired by CompTIA, the Computing Technology Industry Association. Metacog as a Service is now supported by CompTIA. Please see PRESS RELEASE for further details.

Build powerful data model driven capabilities into your products that know how your users are critically thinking and that continuously evaluates what they can do to improve


Problem Solving

Metacog is a learning analytics, human-computer interface and EDM rooted startup - uniquely leveraging streaming big-data technology to measure an individual’s ability to demonstrate in-demand skills.

Metacog evaluates HOW people think, and their DEMONSTRATED competencies - not just what they have memorized. Hard to assess skills such as complex problem solving, knowledge synthesis and collaboration are now measurable.

The key is unique instrumentation libraries to collect atomic level interaction data - not just clickstream and activity levels.


Performance Capture

The Metacog API data platform supports delivery, analysis, and reporting of authentic, performance-based assessment.

Metacog’s trainable machine learning models and real-time data analytics observe the user (learner, candidate, employee) as they work through a digital task, capturing not just their final answers, but also their behavior, process, and problem-solving approaches. All in real-time.

Metacog goes far beyond assessing and auto-scoring simple written or selected responses that primarily assess ability to memorize, not to do the job.


Performance Improvement

Metacog brings AI performance scoring to real-world scenarios.

The rubric trainable scoring engine offers multi-dimensional insights into the knowledge, skills and abilities proven more credible predictors of an individual’s competence and future performance than conventional multiple choice assessments and traditional hiring methods.

Metacog is built on an IoT platform that harnesses unique streaming analytics, and real-time continuous feedback that can be individualized and automated for providing intelligence - at the point of performance.

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Integrate the power of IoT Realtime process data streams, Machine Learning and Artificial Intelligence TODAY

  • Step 1 Begin emitting process data from your new and existing products

    Metacog Instrumentation API

    • Feature
      Semantic level instrumentation JavaScript Client Library enables realtime continuous streaming of all user’s interaction process data that has technology that automatically caches data for spotty networks (such as schools) to ensure data integrity.
    • Use Cases
      Since this atomic level data is timestamped, it is granular enough to be fed to machine learning models to visualize behavior at scale, playback through data, and generate data science rooted inferences about activity that indicate both cognitive and non-cognitive path patterns (affect) that understand not only how a user is thinking and what their competencies are but also how they are feeling (boredom, frustration, confusion, etc.). Some uses for these capabilities include adoption, social-emotional learning, collaboration, and complex problem solving assessment.
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  • Step 2 Store & Retrieve Process Data

    Metacog Data Storage API

    • Feature
      DataRequest API allows users to retrieve the original event streams as they were logged by the widget for a set of filtered sessions.The validated events are stored in the main database, without any restrictions on the amount or age of the records. Once all the data is stored in the main database, it can be retrieved from, and processed by different services: from raw data extraction to complex Educational Data Mining processes and visualizations tools, to API's, metacog offers you a wide range of options for extracting meaningful information from your data.
    • Use Cases
      The retrieval-data API is a JSON-based REST API that allows users of the metacog platform to request data originated in learning objects instrumented using the instrumentation input API.

      Given the large amount of data logged by each learning object, the results of a query are not returned immediately. Instead, a data-request object is created in the server to keep track of the current request, based on the filter parameters defined by the client, while the real processing is executed as a background process.

      The API allows polling the data-request object in order to know the status of the background process. The client may also wait for an automatic e-mail notification to a prevalidated e-mail address, indicating that the process is finished, and the data can then be downloaded.
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  • Step 3 Visualize Your Process Data

    Metacog Data Visualization API

    • Feature
      This tutorial presents the VisualizationRequest API as one of the ways to retrieve information stored in the Metacog Platform by instrumented widgets, and how the Client Side Library offer helper methods to interact with the API to build aggregated reports via Javascript.
    • Use Cases
      The intended audience are both the Domain Experts who want to understand what kind of aggregated datasets are available out-of-the-box and the Front End Developers who need to build visually-appealing reports based on those datasets.

      Once you have an instrumented widget and your learners start to use it, an amazingly huge amount of rich data will begin to being stored in the Metacog Backend.

      Currently, the Metacog Platform offers two ways to retrieve information, with more services being under active development. The VisualizationRequest API allows you to retrieve aggregated datasets with different views of your data. They are useful for general data-exploration without the need to download and process the original event streams.
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  • Step 4 Record, Playback and Score models via your rubrics

    Metacog Authentic Scoring Service API

    • Feature
      Metacog Training Toolbar is used to record and playback Training Sessions, on an already instrumented widget, and in the context of a particular Learning Task. This service provides the ability to score open ended performances via rubrics.
    • Use Cases
      Target Audience is learning experts who want to create Training Sessions for Learning Tasks, in order to train the system with the goal of producing automated real-time scores from Learner Sessions. A Score is an object that represents an association between a Rubric and a Training Session, and holds specific values for each dimension in the Rubric.

      In scoring mode, the goal of the learning expert is to visually check the event stream of a Training Session and based on what he sees, make decisions about the values for each dimension. Also, he/she can mark the event stream with special markers (indicators) to enrich the scoring information.
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  • Step 5 Send your recommendation engine preprocessed scores for individualization

    Score and Forward API

    • Feature
      Sending scoring results in a format available by recommendation engines such as Knewton, etc.
    • Use Cases
      Since recommendation engines are built to probabilistically adapt to where the user is in their learning progression for a given subject matter (a directed acyclic knowledge graph) then feed content or questions to their zone of proximal development, they have been limited in the past to multiple choice questions due to complexity. Metacog enables more authentic and real-world measures of competency being assessed in digital environments to be utilized to add a much deeper and wider range of learning objects capable of providing individualized responses. This allows product differentiation through enabling much more engaging content to be utilized to improve adoption and churn rates, as well as simultaneously testing complex skills.
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  • Step 6 Non-cognitive skills assessment (such as affect, persistence, collaboration and creativity)

    Diagnostics API

    • Feature
      The ability to markup user sessions to train a machine learning model to automatically detect behavior patterns such as boredom, frustration, WTF and engagement.
    • Use Cases
      Since this atomic level data is timestamped, it is granular enough to be fed to machine learning models to visualize behavior at scale, playback through data, and generate data science rooted inferences about activity that indicate both cognitive and non-cognitive path patterns (affect) that understand not only how a user is thinking and what their competencies are but also how they are feeling. Some use cases for these capabilities include improving adoption, social-emotional learning, collaboration, and complex problem solving assessment.
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  • Step 7 Playback user behavior through data for audit purposes

    Playback API

    • Feature
      Playback through data streams.
    • Use Cases
      Since this atomic level data is timestamped, it is granular enough to be fed to play back (as a data fed “video”) when proper instrumentation is implemented. Sessions are captured in data and can be played back on demand to support how scores were generated and to prove micro-competencies/badges were earned. These sessions are also used for scoring training to teach machine learning algorithms what constitutes desirable rubric framed behaviors and scored utilizing the scoring harness mechanism.
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  • Step 8 Try the APIs for Yourself

    Visit the Developer Welcome Screen to get started!

    Simply paste the PID/AID into the fields below:

    Publisher Id:

    Application ID: c7f16e0b559f05489e2b900f52f08a99

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