IDEAS home Printed from https://ideas.repec.org/p/sek/iacpro/4106705.html
   My bibliography  Save this paper

Intelligent Coaching Systems in Higher-Order Applications: Lessons from Automated Content Creation Bottlenecks

Author

Listed:
  • Christian Greuel

    (SRI International)

  • John Murray

    (SRI International)

  • Cindy Ziker

    (SRI International)

  • Louise Yarnall

    (SRI International)

  • Alexander Kernbaum

    (SRI International)

Abstract

Intelligent virtual environments hold promise for improving learner-directed instruction in context. These systems trace the progress of learners performing tasks and can insert immediate coaching to focus learner attention, link knowledge to activity, and accelerate the shift from abstract to concrete learning. Such technology has been used to improve self-directed learning of hands-on procedures, but also shows promise for higher-order applied fields, such as engineering. To realize this vision, research must address the formidable bottlenecks around content creation and build understanding of the types of reusable content libraries relevant to the subject domains. This presentation describes two projects for interactive training that developed prototypes for automated content creation. A third project is presented that illustrates a suite of learning object libraries to support engineering instruction.The first project, SAVE, uses a 3D browser-based simulation environment not only for hands-on training in equipment maintenance, but also for automating the generation of instructional exercise solutions. SAVE allows a subject matter expert to use the interactive simulation for modeling the correct steps of a procedure, thus providing a rapid way to extract their knowledge. The system collects a trace of the expert?s activity, which becomes the reference against which learner activity is compared in automated assessment. The second project, AR Mentor, delivers augmented reality overlays in head-mounted displays worn by student mechanics while learning to maintain terrestrial vehicles. An automated speech system interacts with the students as they perform equipment adjustments and troubleshoot system faults. To deliver audible step-by-step guidance, a prototype text-to-speech translator was developed to convert steps as written in the technical manual into the voice of a virtual coach. The third project, SiMPLE, developed a library of engineering computation objects to allow learners to construct electromechanical simulations, and provides an intelligent coaching system to allow novice engineers to iteratively refine their design specifications. When a working simulation is achieved, the system is linked to a 3D printer for physical prototype production.The first two projects demonstrate methods of using virtual intelligent technologies to accelerate training content production in hands-on domains: expert model tracing and technical manual translation. The third project provides the tools needed to support engineering instruction: object libraries with embedded computations, as well as scripts for design coaching, design testing, and physical prototyping. Together, these projects illustrate the wide range of available, reusable libraries and the extensive opportunities for automating content creation in many socio-technical fields.

Suggested Citation

  • Christian Greuel & John Murray & Cindy Ziker & Louise Yarnall & Alexander Kernbaum, 2016. "Intelligent Coaching Systems in Higher-Order Applications: Lessons from Automated Content Creation Bottlenecks," Proceedings of International Academic Conferences 4106705, International Institute of Social and Economic Sciences.
  • Handle: RePEc:sek:iacpro:4106705
    as

    Download full text from publisher

    File URL: https://iises.net/proceedings/25th-international-academic-conference-oecd-paris/table-of-content/detail?cid=41&iid=027&rid=6705
    File Function: First version, 2016
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Intelligent coaching systems; Augmented reality; Interactive sociotechnical training; Automated educational content creation;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sek:iacpro:4106705. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Klara Cermakova (email available below). General contact details of provider: https://iises.net/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.