The document discusses three generations of personalized learning approaches: longitudinal student data, adaptive assessments, and adaptive instruction. Longitudinal data provides performance over time but does not diagnose skills gaps. Adaptive assessments identify skills gaps but taking the additional steps of diagnosis and reteaching is time-consuming. Adaptive instruction, which uses artificial intelligence to assess learning styles and performance, customize instruction, and integrate assessment, shows promising preliminary results in truly personalizing learning. The variations in personalized learning approaches have caused confusion, demonstrating the need for clarity on what constitutes different types of personalized learning.

1. The State of Personalized Learning
An Ever Evolving Concept
Dr. Philip W.V. Hickman, Superintendent, Columbus (MS) Municipal School
District; Dr. Kenneth Eastwood, Superintendent, Enlarged City School District of
Middletown (NY); and Dr. Eliot Levinson Ph.D., President, The BLEgroup a PCG
company — Friday, December 09, 2016
Personalized learning is the ed tech phrase of the year and a key concept
for how digitally delivered teaching and learning will make the most
significant impact on teaching and learning, but what is it? The variety of
products and practices that claim to personalize learning differ vastly,
making the term hard to understand.
Schools want to provide personalized learning, a plethora of vendors
are selling it, and it is required for ESSA beginning in 2018. A few
BLEgroup superintendents, staff, and vendors held a discussion to
see if we could define a way to understand the reality of the concept.
We also conversed with vendors who believe they are providing
personalized learning products and services to K-12 schools and
with school districts who believe that they are far down the path of
providing personalized teaching and learning.
All personalized learning is not the same: Before data collection
and discussion, all stakeholders held a common definition of
personalized learning as “customized curriculum targeted to each
student’s level of subject knowledge and his/her learning style.” After
our discussion and observation of several districts’ practices and
conversations with vendors of personalized learning platforms, we
came to the conclusion that there needed to be an easy-to-use
taxonomy for the types of PL products and practices so that school
systems can clearly understand product and practice variation.
The current use of the term “personalized learning” varies from:
• small group instruction based on performance levels to
• longitudinal history of all assessments students ever took to
provide them with knowledge of what to assign to

2. • artificial intelligence based products that assess the cognitive
level and learning style of a student and provide a variety of
resources based on the student’s learning style, current
performance, and understanding of a subject.
We concluded that rapid new developments in the areas of adaptive
assessment, data analytics, customized content, and adaptive
instruction in recent years are the reason why “personalized
learning” is not a well understood concept. There are currently at
least three generations of personalized learning on the market.
The purpose of our piece is to provide a structure for defining the
levels of personalized instruction, comments on the advantages and
disadvantages of each, and recommendations to vendors and
schools about where the market may be heading.
Generation I: Many school districts provide teachers
with longitudinal data on individual students, including classroom
tests, standards-based tests, and formative assessments. The
reason is that the assessments will be able to identify exactly which
standards and cognitive level a student is achieving and find
appropriate resources for that student. This approach also provides
performance data on the classes, which provide data on teachers
and can focus the schools on particular problems. The longitudinal
data approach has been around longer than any other
personalization approach.
• The advantage of the longitudinal data is that it provides
teachers with performance levels of students on each standard
over time.
• The disadvantage is that it is difficult for teachers to locate
appropriate materials for every student, and the longitudinal data
shows performance but does not diagnose missing skills that a
student did not learn in earlier years and may be preventing her
from mastering a standard now. In reality many teachers don’t
have the bandwidth to individualize for a class of 30. The best
they can master is small group instruction.
Generation II: Adaptive assessment is done a few times
throughout the year, which identifies the exact level at which a
student is performing. For example, a first grader could be reading at

3. the 8th grade level, and a 4th grade math student could be
functioning at the 2nd grade level.
The adaptive assessment identifies the skills needed to achieve
different standards that a student may not have achieved because of
missing skills they did not learn earlier. Some teachers and products
can re-teach missing skills, while others do not.
The major providers of adaptive assessment are NWEA, Curriculum
Associates, and Scantron. Adaptive assessments all identify missing
skills. The tests are lengthy to deliver, and some have a 3-phase
process, including the original assessment, a diagnostic structure to
identify missing skills, and a re-teaching component that is used to
deal with missing skills.
• The advantage of adaptive assessments is that they identify
missing skills that prevent students from mastering a standard
being taught 3 years later. Some of the products have the ability
to re-teach the missing skills
• The disadvantage is that, in most cases, teachers teach the first
part of the test only to find out what a student has mastered but
don’t take the second step to diagnose and re-teach the missing
skills necessary for moving forward, as that phase is extremely
time consuming. Not doing so prevents rapid learning and
mastery of standards by the student. When teachers diagnose
the missing skills and re-teach them, the students do rapidly
master standards.
Below are examples of two school districts that are highly successful
in fully utilizing adaptive assessments. In each case they provide
extra time for the diagnosis and planning for each student and hold
teachers accountable for utilizing the data. One district has very little
technology, and the other has a vast amount of technology, but they
are both successful.
Two Eagle River School - Montana
There are interesting exceptions to the poor use of adaptive
assessments that show no results. The more effective schools focus
on professional development and time to analyze the missing skills.
At Two Eagle School, a native American alternative school in
Montana which receives throw outs from public high schools, the
superintendent provides teachers with 13 hours a month to analyze

4. the adaptive assessment to the skill level and presents plans to the
rest of the faculty. Teachers use their diagnoses to plan students’
work for the next three weeks and present and discuss them with the
whole faculty.
The Two Eagle alternative school is the only school in Montana
consistently graduating Native American Students from high school.
They have moved from being one of the worst performing high
schools in the state to one of a highly proficient level. NWEA
adaptive assessment is the only technology resource used to
personalize learning.
Enlarged City School District of Middletown – New York
Middletown, like Two Eagle, is an 80% minority district containing
Hispanic students. Middletown has a large technology budget
because they won a $20 million Race to the Top grant.
Middletown has made tremendous gains in performance and
graduation rates due to the peer professional developers in each
school that provide ongoing PD to teachers. When individual skill
performance problems are identified, the district immediately
provides the necessary teaching resources to the teacher.
Generation III: Adaptive instruction is the new generation of
personalized learning and initially appears to be the major
breakthrough for real personalized learning due to the use of artificial
intelligence on cognitive and learning style issues to truly
individualize instruction. Also it changes the value chain by
potentially being able to leapfrog assessments and Learning
Management Systems. The preliminary results on these products are
promising. The main product brands in this space are Curriculum
Associate, Dreambox Learning, Apex Learning, Fishtree, and MyOn
reader, a specialized product that is a virtual library that can be
included in the group.
The adaptive instruction products are often composed of artificial
intelligence. Metrics assess performance levels and learning style
preferences of students and also provide ongoing performance
analytics.
Adaptive instruction products provide a customized learning
experience based on the ongoing data that the system is collecting
about students. The advantage of this system is that there are

5. myriad resources that the system provides for the student. Some of
these systems use open resources that reduce instructional resource
costs for a district.
Adaptive instruction products have recently entered the market so
we can’t yet make statements about their impact. The evidence,
though positive, is insufficient to prove that these products produce
the best results of the three generations of personalized learning
products. However, we think adaptive instruction is the most
promising form of personalized instruction products.
There are several economic benefits for using adaptive instruction
products:
• Most of the products use open source curriculum, which cuts
instructional material costs.
• They eliminate the need for formative or adaptive assessment
systems as testing is integrated into the products, thereby
allowing districts to cut significant costs.
We want to be explicit that these systems increase teacher
productivity and allow time to track results and work individually with
students. The qualitative data on the third generation demonstrates
that students learn quickly and have high levels of engagement in
learning.
Wrap-up: There are three generations of personalized learning
products, which is confusing to the market. The three generations
of are:
• Longitudinal data
• Adaptive assessment
• Adaptive instruction
All three generations are “personalized learning,” but each
generation appears to provide an increasing amount of
personalization. Although early in the game, adaptive instruction
appears to provide the most personalized learning and provides
additional benefits, such as lowering instructional material, LMS, and
assessment costs, while providing pedagogical advantages of
providing teachers more time to work with individuals and
meaningful data to understand the student’s progress. However, it is
early in the third generation, and we don’t yet have all the answers.
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6. The BLEgroup, an organization of leading ed tech decision makers,
works with both schools and the industry. The BLEgroup, part of the
education practice of Public Consulting Group (PCG), produces a
quarterly piece for EdNET Insight News Alert on critical ed tech
issues. Eliot Levinson, President, may be reached
at eliot@blegroup.com.