Learning Outside of the Box: Three Innovations That Can Benefit the Military Learner Now

Online technology has enabled a large number of military students access to a post-secondary education that is equal to a face-to-face education. As technology advances and supports our educational outreach to the military, we will see that this technology will allow for increased quality with scale and will be embedded not only in online courses but in the traditional classroom as well. [see http://jolt.merlot.org/vol7no3/stone_0911.htm ]  The day will soon come when no learning experience will be conducted without a technological presence to support learning, because the potential to increase learning outcomes with relevant technology is immense. Today’s presentation will introduce to you to some truly powerful and potentially disruptive augmentations to higher education that will be “business as usual” in 5-10 years, but that are available now and being explored by education researchers and progressive institutions.

URL. https://sites.google.com/site/3learninginnovations

Learning Analytics

Analytics begins with data collection - determining what data to collect, tracking over time, and careful review of quality of data

Determining What to Collect
  • Standard demographic data commonly collected in applications
  • Data on prior colleges and schools - with associated grade information
  • Other relevant data about the student
  • Note that FERPA rules set restrictions on which data may be collected for non-administrative uses - so there are limits on publication of results
Tracking over Time
  • This is may be more difficult that it seems at first
  • New information categories are added and categories are changed over time (e.g. degrees change, grading systems change, "semester" term length changes)
    • These changes need to be applied to data to support a meaningful track
Careful Review of Data Quality over Time
  • Validating data is important to assure quality of the entire process ("garbage in : garbage out" phenomenon)

Tracking learner data at UMUC, at each community college, and between community colleges and UMUC.

Examples of findings to date:

1. Behavior in the online classroom of new students attending first courses are strongly predictive of student success and re-enrollment in the following term:

a.       Early Access to the online classroom

b.      Early awareness of syllabus, course expectations, and class members associated with success

2. Patterns of course sequences are differentially effective by discipline—advising will find these useful.

An example of a tool that can provide meaningful results is CHAID (Chi Squared Automatic Interaction Detection) decision tree analysis
In a broad study conducted under the auspices of WCET with data from six universities and colleges, a clear trend emerged:
Those new online students who took the heaviest loads of credit hours in their first year were significantly less likely to succeed than those who began with lighter loads.  

The application of this tool has resulted in some interesting results specific to the University of Illinois Springfield:
For example - if an entering first year student takes at least six credit hours online in the summer his/her degree program - he/she is twice as likely to complete the program in less than four years compared to those who do not.  
Another example - students who take at least one course from another institution concurrent with enrollment at UIS and transfer that course to the university show persistence rate that is significantly higher than those who do not.


Adaptive Learning

A revolution is taking place - creating tools that will adapt to the individual learner as s/he progresses through learning material.  With a series of questions and quizzes, adaptive learning engines assess the learning of the student at each point in the process and create a customize "next step" in the process.  That step is different depending up on the specific learning that has taken place.

An aside:  Sebastian Thrun quotes Salman Kahn in describing one of the failures of our current system of learning.  We put students through a standardized learning experience (course) in which they are graded, but not encouraged to improve as they progress - if a student fails or get s a D on module one s/he moves with the class to section two and so on.  Having failed to fully learn the cornerstone information, the student is doomed to fail in the following scaffold of instruction. Outside of academe we recognize that just doesn't work.  For example, if your child has difficulty in learning how to ride a bicycle in an afternoon or two, you don't simply push him/her forward to the unicycle the next week.  

The Rise of Adaptive Learning  http://bigthink.com/ideas/41046  (begin video 3:15 in - run to 6:15)

EDUCAUSE ECAR:  Adaptive Learning Technologies: from One-Size-Fits-All to Individualization


Adaptive Learning - UMUC /  Grant Study

UMUC used the statistics course developed by the Open Learning Initiative at CMU. Results have consistently showed increased achievement across our pilots. 

Recently received a Carnegie Corporation Grant to transform three STEM-related courses to the  OLI model. Partnered with Prince George’s Community College to pilot courses there as well: statistics (psychology), intro biology, intro computing.


Adaptive Learning Technology: An Introduction

Adaptive learning systems have a range of features and functions that work together to provide not only the subject matter content, but also support and guidance as the learner progresses through the adaptive learning modules or courses:

  • Pre-test: Some systems begin with an assessment of current knowledge and skills. These assessments gather information about individual learner characteristics, including prior knowledge.
  • Pacing and control: Adaptive learning systems are usually self-paced and learner-centered, meaning that the learner controls the speed at which the content is delivered and has some choices within the system.
  • Feedback and assessment: Adaptive systems continuously assess student progress and often provide feedback as students work through the interactions presented. This feedback can include more than just correct/incorrect responses – suggesting resources and providing opportunities for additional practice.
  • Progress tracking and reports: Systems that include learner profiles are able to track individual progress and allow users to return to where they left off, breaking their work into multiple sessions. The system can also generate periodic progress reports and also communicate these with instructors who can then provide further guidance to students based on their individual performance.
  • Motivation and reward: Interactive online games are an interesting example of adaptive technology. Players advance based on past choices and performance in the game itself. Games also often motivate players to continue with rewards for reaching higher levels, such as points or badges. These kinds of rewards are also appearing in learning systems motivating students to continue striving for their own "high score," advancing through levels of study and skill achievement.

Immersive Learning/Gaming/Simulations

Education is no longer confined to the classroom - on campus or online.  Development of new learning environments has accelerated in the past decade.  Games and simulations are offering effective new technologies for learning that create real-life and entirely new realities that promote deep learning, creative thinking and cultivate effective decision-making skills.  Development tools are making the process easier and faster.  

Serious Military Games (via WCET)

The use of games in military education and training applications in recent years has become a more widely applied technology. As using games becomes more mainstream, many researchers have taken on the task of performing in-depth evaluations into games, game mechanics and game characteristics. The increase in research has and will continue to guide the application, implementation and evaluation of games within the educational and training realm by providing deeper insight into the potential of games. 

Source: Government Elearning


Here is an example of a virtual augmented environment:


NMC / EDUCAUSE Annual Horizon Report 2012 - technologies on the horizon:

Time-to-Adoption Horizon: One Year or Less
> Mobile Apps 
> Tablet Computing 
Time-to-Adoption Horizon: Two to Three Years
> Game-Based Learning 
> Learning Analytics 
Time-to-Adoption Horizon: Four to Five Years
> Gesture-Based Computing 
> Internet of Things 



Ray Schroeder Daily Blogs

The gadget spec URL could not be found


Contact information:

Marie Cini
Vice President for Undergraduate Studies
University of Maryland University College 
3501 University Blvd. East
Adelphi, MD 20783


Ray Schroeder
Associate Vice Chancellor of Academic Affairs
University of Illinois Springfield
One University Plaza
Springfield, IL 62703
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