Adaptive learning, also known as adaptive teaching, is an educational method
which uses computer algorithms to orchestrate the interaction with the
learner and deliver customized resources and learning activities to
address the unique needs of each learner. In professional learning
contexts, individuals may "test out" of some training to ensure they
engage with novel instruction. Computers adapt the presentation of
educational material according to students' learning needs, as indicated
by their responses to questions, tasks and experiences. The technology
encompasses aspects derived from various fields of study including
computer science, AI, psychometrics, education, psychology, and brain science.
Adaptive learning has been partially driven by a realization that tailored learning cannot be achieved on a large-scale using traditional, non-adaptive approaches. Adaptive learning systems endeavor to transform the learner from passive receptor of information to collaborator in the educational process. Adaptive learning systems' primary application is in education, but another popular application is business training. They have been designed as desktop computer applications, web applications, and are now being introduced into overall curricula.
Adaptive learning has been partially driven by a realization that tailored learning cannot be achieved on a large-scale using traditional, non-adaptive approaches. Adaptive learning systems endeavor to transform the learner from passive receptor of information to collaborator in the educational process. Adaptive learning systems' primary application is in education, but another popular application is business training. They have been designed as desktop computer applications, web applications, and are now being introduced into overall curricula.
History
Adaptive
learning or intelligent tutoring has its origins in the
artificial-intelligence movement and began gaining popularity in the
1970s. At that time, it was commonly accepted that computers would
eventually achieve the human ability of adaptivity. In adaptive
learning, the basic premise is that the tool or system will be able to
adjust to the student/user's learning method, which results in a better
and more effective learning experience for the user. Back in the 70's
the main barrier was the cost and size of the computers, rendering the
widespread application impractical. Another hurdle in the adoption of
early intelligent systems was that the user interfaces were not
conducive to the learning process. The start of the work on adaptive and
intelligent learning systems is usually traced back to the SCHOLAR
system that offered adaptive learning for the topic of geography of
South America.
A number of other innovative systems appeared within five years. A good
account of the early work on adaptive learning and intelligent tutoring
systems can be found in the classic book "Intelligent Tutoring
Systems".
Technology and methodology
Adaptive
learning systems have traditionally been divided into separate
components or 'models'. While different model groups have been
presented, most systems include some or all of the following models
(occasionally with different names):
- Expert model – The model with the information which is to be taught
- Student model – The model which tracks and learns about the student
- Instructional model – The model which actually conveys the information
- Instructional environment – The user interface for interacting with the system
Expert model
The
expert model stores information about the material which is being
taught. This can be as simple as the solutions for the question set but
it can also include lessons and tutorials and, in more sophisticated
systems, even expert methodologies to illustrate approaches to the
questions.
Adaptive learning systems which do not include an expert model
will typically incorporate these functions in the instructional model.
Student model
The simplest means of determining a student's skill level is the method employed in CAT (computerized adaptive testing).
In CAT, the subject is presented with questions that are selected
based on their level of difficulty in relation to the presumed skill
level of the subject. As the test proceeds, the computer adjusts the
subject's score based on their answers, continuously fine-tuning the
score by selecting questions from a narrower range of difficulty.
An algorithm for a CAT-style assessment is simple to implement. A
large pool of questions is amassed and rated according to difficulty,
through expert analysis, experimentation, or a combination of the two.
The computer then performs what is essentially a binary search, always
giving the subject a question which is halfway between what the computer
has already determined to be the subject's maximum and minimum possible
skill levels. These levels are then adjusted to the level of the
difficulty of the question, reassigning the minimum if the subject
answered correctly, and the maximum if the subject answered incorrectly.
Obviously, a certain margin for error has to be built in to allow for
scenarios where the subject's answer is not indicative of their true
skill level but simply coincidental. Asking multiple questions from one
level of difficulty greatly reduces the probability of a misleading
answer, and allowing the range to grow beyond the assumed skill level
can compensate for possible misevaluations.
A further extension of identifying weaknesses in terms of
concepts is to program the student model to analyze incorrect answers.
This is especially applicable for multiple choice questions. Consider
the following example:
- Q. Simplify:
- a) Can't be simplified
- b)
- c) ...
- d) ...
Clearly, a student who answers (b) is adding the exponents and
failing to grasp the concept of like terms. In this case, the incorrect
answer provides additional insight beyond the simple fact that it is
incorrect.
Instructional model
The
instructional model generally looks to incorporate the best educational
tools that technology has to offer (such as multimedia presentations)
with expert teacher advice for presentation methods. The level of
sophistication of the instructional model depends greatly on the level
of sophistication of the student model. In a CAT-style student model,
the instructional model will simply rank lessons in correspondence with
the ranks for the question pool. When the student's level has been
satisfactorily determined, the instructional model provides the
appropriate lesson. The more advanced student models which assess based
on concepts need an instructional model which organizes its lessons by
concept as well. The instructional model can be designed to analyze the
collection of weaknesses and tailor a lesson plan accordingly.
When the incorrect answers are being evaluated by the student
model, some systems look to provide feedback to the actual questions in
the form of 'hints'. As the student makes mistakes, useful suggestions
pop up such as "look carefully at the sign of the number". This too can
fall in the domain of the instructional model, with generic
concept-based hints being offered based on concept weaknesses, or the
hints can be question-specific in which case the student, instructional,
and expert models all overlap.
Implementations
Learning management system
Many learning management systems have incorporated various adaptive learning features. A learning management system
(LMS) is a software application for the administration, documentation,
tracking, reporting and delivery of educational courses, training
programs, or learning and development programs.
Distance learning
Adaptive learning systems can be implemented on the Internet for use in distance learning and group collaboration.
The field of distance learning is now incorporating aspects of
adaptive learning. Initial systems without adaptive learning were able
to provide automated feedback to students who are presented questions
from a preselected question bank. That approach however lacks the
guidance which teachers in the classroom can provide. Current trends in
distance learning call for the use of adaptive learning to implement
intelligent dynamic behavior in the learning environment.
During the time a student spends learning a new concept they are
tested on their abilities and databases track their progress using one
of the models. The latest generation of distance learning systems take
into account the students' answers and adapt themselves to the student's
cognitive abilities using a concept called 'cognitive scaffolding'. Cognitive scaffolding
is the ability of an automated learning system to create a cognitive
path of assessment from lowest to highest based on the demonstrated
cognitive abilities.
A current successful implementation of adaptive learning in
web-based distance learning is the Maple engine of WebLearn by RMIT
university.
WebLearn is advanced enough that it can provide assessment of questions
posed to students even if those questions have no unique answer like
those in the Mathematics field.
Adaptive learning can be incorporated to facilitate group
collaboration within distance learning environments like forums or
resource sharing services.
Some examples of how adaptive learning can help with collaboration
include automated grouping of users with the same interests, and
personalization of links to information sources based on the user's
stated interests or the user's surfing habits.
Educational game design
In 2014, an educational researcher concluded a multi-year study of
adaptive learning for educational game design. The research developed
and validated the ALGAE (Adaptive Learning GAme dEsign) model, a
comprehensive adaptive learning model based on game design theories and
practices, instructional strategies, and adaptive models. The research
extended previous researching in game design, instructional strategies,
and adaptive learning, combining those three components into a single
complex model.
The study resulted in the development of an adaptive educational
game design model to serve as a guide for game designers, instructional
designers, and educators with the goal of increasing learning outcomes.
Survey participants validated the value of the ALGAE model and provided
specific insights on the model's construction, use, benefits, and
challenges. The current ALGAE model is based on these insights. The
model now serves as a guideline for the design and development of
educational computer games.
The model's applicability is assessed as being cross-industry
including government and military agencies/units, game industry, and
academia. The model's actual value and the appropriate implementation
approach (focused or unfocused) will be fully realized as the ALGAE
model's adoption becomes more widespread.
Development tools
While
adaptive learning features are often mentioned in the marketing
materials of tools, the range of adaptivity can be dramatically
different.
Entry-level tools tend to focus on determining the learner's
pathway based on simplistic criteria such as the learner's answer to a
multiple choice question. A correct answer may take the learner to Path
A, whereas an incorrect answer may take them to Path B. While these
tools provide an adequate method for basic branching, they are often
based on an underlying linear model whereby the learner is simply being
redirected to a point somewhere along a predefined line. Due to this,
their capabilities fall short of true adaptivity.
At the other end of the spectrum, there are advanced tools which
enable the creation of very complex adaptions based on any number of
complex conditions. These conditions may relate to what the learner is
currently doing, prior decisions, behavioral tracking, interactive and
external activities to name a few. These higher end tools generally
have no underlying navigation as they tend to utilize AI methods such as
an inference engine. Due to the fundamental design difference advanced
tools are able to provide rich assessment capabilities. Rather than
taking a simple multiple choice question, the learner may be presented
with a complex simulation where a number of factors are considered to
determine how the learner should adapt.
