Adaptive Learning: A Dynamic Methodolgy for Effective Online Learning

Article excerpt


The genesis of adaptive learning systems is from the artificial intelligence (AI) research. In the early 1980s there was significant development of systems to provide intelligent response to user interacting with the computers. The early AI research developed into three overlapping streams, namely, knowledge-based expert systems, neural networks, and genetic algorithms. These technologies were used primarily in adaptive control systems that managed the difficult task of controlling electromechanical actuators to adapt to the given situation and respond accordingly.

The artificial intelligence systems were based on strategies to learn users' behavior and respond accordingly. The conceptual and philosophical differences of theses approaches led to the learning systems that were either influenced by the connectionists model that created supervised neural nets or unsupervised self-organizing maps or reduction of the knowledge domain into set-of symbolic representations leading to knowledge-based expert rules that can be fired to resolve a decision for the given situation.

Unfortunately, the learning management systems (LMS), learning content management systems (LCMS) or even the course management systems (CMS) completely have been completely void of any tool that allowed intelligent tutoring system to become part of the learning system to help individual learners to learn.

The learning systems developed by the author combine the elements of pedagogical learning framework with the intelligent systems to develop adaptive learning systems.


Adaptive learning systems can be defined as the intelligent systems that are dynamically organized based on the observation of the learning preferences of an individual for the best learning performance.

The definition above illustrates following important characteristics of adaptive learning systems:

1. The adaptive systems needs to have a well defined pedagogical framework to identify and differentiate individual learning preferences

2. The systems needs to have a well defined quantification of learning performance and learning preference inference model and

3. The system needs to have a dynamic content sequencing engine to organize learning assets to match the individual learning


The three-dimensional learning cube provides a logical framework to identify individual learning preferences based on the learning styles that define distinctive learning pathway. Three dimensions of the learning cube represent media, models and interactivity.

The proposed learning cube depicted in Figure 1 is composed of three dimensions -learning media, learning models/strategies and interactivities. The media elements are the modes of collecting information through text, graphics, audio, video, animations and simulations based on visual, auditory and kinesthetic preferences, the learning models refers to the process preferred by a learner to understand the information and turn it into useful knowledge, such as apprenticeship, incidental, inductive, deductive, and discovery, and the interactivity is used to provide feedback for confirmation, reinforcement and discussions. The learning cube is useful to map the individual learning preferences based on media, learning models and interaction.

For adaptive learning we define the five functional leaning styles/strategies as:

1. Apprenticeship. A "building block" approach for presenting concepts in a step-by-step procedural learning style similar to mentor-student interaction. …