1. U.S. Army Research, Development and Engineering Command
Use of Evidence-based Strategies to Enhance the Extensibility of Adaptive Tutoring Technologies
Benjamin Goldberg, Ron Tarr, Dr. Deborah Billings, Naomi Malone, Keith Brawner, and
Dr. Robert Sottilare

2. Push for Tailored Training
Computer-based tutoring systems (CBTS) have demonstrated significant promise in tutoring individuals in well-defined domains, but…
Fifty years of research have been unsuccessful in making CBTS ubiquitous in military training… Why?
CBTS are expensive to author and are insufficiently adaptable to support the tailored, self-regulated , individual & small unit tutoring experiences required to support:
U.S. Army Learning Model (ALM) for 2015 (TRADOC, 2011)
U.S. Air Force (AETC, 2008)
U.S. Navy STEM Grand Challenge (ONR, 2012)
OSD R&T Vision for PAL
NATO HFM RTG 237 (Advanced ITS)
TTCP HUM TP-2 (Training Panel)
Few open-source components, methods or standards for CBTS
Few authoring tools or standards to promote reuse
CBTS do not support small unit training/tutoring experiences
CBTS do not translate well beyond desktop learning (e.g., mobile learning, mixed reality or live training) and thereby do not support ALM
U.S. Air Education and Training Command (2008). On Learning: The Future of Air Force Education and Training. Randolph AFB, TX.

3. Computer-Based Tutoring Systems (CBTS)
ITSs apply Artificial Intelligence tools and methods to individualize instruction
Based on benefits associated with one-on-one expert tutoring
(2-Sigma Problem; Bloom, 1984)
Mediates learning by providing feedback when appropriate and adjusting difficulty levels to maintain desired challenge
Facilitated by 4 common components

4. Generalized Intelligent Framework for Tutoring (GIFT)

5. Pedagogical Modeling
Designed to balance the level of guidance a learner needs with the goal of maintaining engagement and motivation

6. Macro-Adaptive Strategies
Organized and sequential set of tactics to be implemented online
What to adapt and how to adapt
Addresses four instructional design areas:
Selection
Sequencing
Synthesizing
Summarizing
Based on metrics collected prior to the commencement of instruction

7. Macro-Adaptation: Sources of Adaptation
Source of Adaptation
(Individual Differences & Task Characteristics)
Student Performance/ Achievement Levels
Working Memory Capacity (WMC)
Knowledge Type
MATERIAL: Task Difficulty/ Complexity
Learner’s Prior Knowledge/ Expertise
Learner’s Traits/Ability/ Attributes
Learning Styles/Cognitive Styles
IMI Levels
Task Learning Category: Cognitive, Affective, Psychomotor, Social
“Sources of adaptation” refer to the factors that prompt, or trigger, adaptation to occur, namely the characteristics of learners that elicit specific instructional tactics to be implemented.

8. Macro-Adaptation Targets of Adaptation
Target of Adaptation
(Instructional Tactics)
Sequence of Instruction
Presentation of Information (Graphics, Animations, etc.)
Degree of Learner Control
Feedback (Frequency, Content)
Problem Difficulty
Pace of Instruction
Different sources of adaptation appear to be linked with different targets of adaptation (as shown) in the literature.
Based on:
- The instructional elements that are adapted
- Elements are adapted according to the specific sources of adaptation

9. To start a search, click on the Strategies tab at the top.
Home
Tutorial
Instructor
Training Developer
The Instructional Strategies Indicator (ISI) is a computer-based searchable tool for selecting individualized instructional strategies based on the type of knowledge being learned, the domain in which the learning occurs, the expertise level of the learner, and the size of the group being taught.
Our goal is to inform the selection of instructional strategies in order to improve learning effectiveness and efficiency across a wide range of domains. Ultimately, the ISI is intended to provide recommendations regarding how, what, and when to embed instructional components into simulation-based training systems.
To start a search, click on the Strategies tab at the top.
Log in: User name ______________ Password _________________
The following slides show the ISI prototype, which will be demonstrated to the SMEs.
This is the home page from which the user logs in and can select either the Tutorial, the Instructor Search or the Developer Search.

10. Application in GIFT: Model Development

11. Illustrative Example
PRE-TRAINING
[Human]
Instructor Input
Trainee Characteristics
Assessment of Trainee
Goal Orientation/Motivation
Expertise Level
Self-Efficacy
Journeyman
Expert
Novice
Conceptual
Procedural
Declarative
Integrative
Instructional Content
Knowledge Type
Learning Objectives
GIFT’s Pedagogical Model
Higher Level Instructional Tactic
EXAMPLE:
Increase Scenario Complexity
Increase “fog” in Scenario
Remake this slide: Highlight information that comes from learner and domain modules…Helps distinguish information that is dependent to the tasks being trained.

12. Extensibility
The need for Empirical Evaluation of a Generalized Pedagogical Model to determine its utility across multiple domains and training platforms
Goal of Experimentation
Determine best practices for “Best-in-Class” model to be incorporated in GIFT
Determine influence Individual Differences have on training outcomes
Determine how instructors and trainers will use prescribed outputs from model implementation

13. Road-Ahead
Development of a complementary Micro-Adaptive Pedagogical Model for use in GIFT
Used to inform ‘in-situ’ strategies and tactics on a general level (e.g., provide hint, adjust difficulty, perform assessment, etc.)
Requires techniques to monitor reactive states (i.e., cognitive and affective) and strategies to mitigate negative states
Soldier Centered Army Learning Environment (SCALE)
Utilize GIFT as SCALE’s pedagogical management engine
Provide mechanisms of personalized/tailored instruction with distributable training content

14. QUESTIONS