Abstract:

Research has indicated that education is faced with two key challenges when moving from a face-to-face environment to an online environment. First, the challenge to sustain the learner's engagement, improving their comprehension of information, achieving a positive experience, and obtain skills in an online environment. Second, the diversification of the tutor's ability in their education techniques and learning objects, leading to additional active involvement, interest and attention improvement, and an increase in motivation by the learner.

Research by Bachok et al. (2000) revealed that a learner has different learning styles, in which inapt lessons and tutoring styles can result in a decrease in learner’s outcome. Prithard (2005) proposed that effective learning does not solely depend on the learner's learning style but also the teaching material. Learning style is the concept that reflects on the cognitive, affective, and behavioral characteristics of a learner, based on seeing, interacting, and responding to methods in tutoring.

This paper recommends that bridging these two challenges can be done by creating a matrix of learning styles, tutoring styles, and teaching materials, which will present a solution that will be learner-centered and improve the overall learning outcome. It investigates the practical implementation of automated matching of a tutor’s educational approach to learner's learning styles. The matching is achieved, via a custom matrix algorithm, Matchingalgorithm for an Effective Teaching Intelligent System ( Metos). The algorithm suggests to the student a tutor that uses an academic approach, which offers better motivation and education outcome, based on a combination of learning styles that suites the student best. The research paper tested the hypothesis that a learner does not have just one learning style but a set of learning styles.

In Metos, the learning style is a supportive variable offering the best matching of a tutor to the
learner. Additional variables with demographics factors, environmental variables, special educational needs, teaching strategies, and teaching methods form part of the algorithm.

Keywords:

Learner style, tutor style, algorithm, online, learner material, matching, effective teaching.