Adaptive learning

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Adaptive learning is a relatively new educational method which uses computers as interactive teaching devices. Computers adapt the presentation of educational material according to students' weaknesses, as indicated by their responses to questions. The motivation is to allow electronic education to incorporate the value of the interactivity afforded to a student by an actual human teacher or tutor. The technology encompasses aspects derived from various fields of study including computer science, education, and psychology.

Adaptive learning systems' primary application is in education, but another popular application is business training. They have been designed as both desktop computer applications and web applications.

Technology/Methedology

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 which retains 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 has to hold information about the material which is being taught. This can be as simple as the correct answers to the questions but it can also include lessons and tutorials and, in more sophisticated adaptive learning systems, even expert methodologies for approaching the questions can be a part of the expert model.

Adaptive learning systems which do not include an expert model will incorporate these functions in the insturctional model.

Student Model

Student model algorithms have been a rich area for researches over the past twenty years. The simplest means of determining a student's skill level is the method employed in CAT (Computer Adaptive Testing). In CAT, the subject is presented with questions which 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 by difficulty, either 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 half way 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 skill level but simply coincidental. Asking multiple questions from one difficulty level greatly reduces the probability of a misleading answer, and allowing the range to grow beyond the assumed skill level should compensate for possible misevaluations.

Richer student model algorithms look to determine causality and provide a more extensive diagnosis of student's weaknesses by linking 'concepts' to questions and determining strengths and weaknesses in terms of concepts rather than simple 'levels' of ability. Because a number of concepts can influence a single question, questions have to be linked to all concepts in terms of relevance. For example, a matrix can list binary values (or even scores) for the intersection of every concept and every question. Then, conditional probability values have to be calculated to reflect the likelihood that a student who is weak in a particular concept will fail to correctly answer a particular question. After a student has taken a full test, the probabilities of weakness in all concepts conditional on inccorect answers in all questions can be calculated using Bayes' Law (these adaptive learning methods are often called bayesian algorithms).

A further extension of identifying weaknesses in terms of concepts is to program the student model to analyze the incorrect answers which the students submit. This is especially applicable for multiple choice questions. Consider the following question:

Simplify: 2x² + x³

a) Can't be simplified b) 3x⁵ 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 gives additional insight beyond the 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 how lessons should be presented. 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 ranked in some order which corresponds to the levels of difficulty which were used to rate the question pool. When the student's level has been satisfactorily determined, the instructional model can determine the appropriate lesson. The more advanced student models which provide assessments 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 can again 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 there is really an overlap between the student, instructional, and expert models.

Implementations

Classroom Implementation

Adaptive learning which is implemented in the classroom environment using information technology is ofter referred to as an Intelligent Tutoring System or an Adaptive Learning System. Intelligent Tutoring Systems operate upon three basic principals :

• Systems need to be able to dynamically adapt to the skills and abilities of a student.
  • Environments utilize cognitive modeling to provide feedback to the student while assessing student abilities and adapting the curriculum based upon past student performance.
  • Inductive logic programming (ILP) is a way to bring together inductive learning and logic programming to an Adaptive Learning System. Systems using ILP are able to create hypothesis from examples demonstrated to it by the programmer or educator and then use those experiences to develop new knowledge to guide the student down paths to correct answers.
• Systems must have the ability to be flexible and allow for easy addition of new content.
  • Cost of developing new Adaptive Learning Systems is often prohibitive to educational institutions so re-usability is essential.
  • School districts have specific curriculum which the system needs to be to utilize to be effective for the district. Algorithms and cognitive models should be broad enough to teach mathematics, science, and language.
• Systems need to also adapt to the skill level of the educators.
  • Many educators and domain experts are not skilled in programming or simply do not have enough time to demonstrate complex examples to the system so it should adapt to the abilities of educators.

Web Implementation

Adaptive Learning systems can be implemented on the Internet for use in distance learning applications, group collaboration, and for web personalization.

References

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