Learning System and a Comparative Study on Learning Style of Students in Africa(Nigeria) and those in Asia (Japan).


In different parts of the world especially African and Asian continents, students attend classes and taught in groups using the same method of teaching applied to all students irrespective of whether or not they have the same method of taking and assimilating information (learning style).

This contributed negatively towards the development of educations in some countries of those continents. To tackle such problems, an intelligent and adaptive learning system should be the focal point in order to ensure faster and better performances in the learning process.

One of its advantages is to help learners and teachers to discover students’ learning preferences. Learning Style is the various ways or approaches of learning. They involve educating method, particular to an individual that are presumed to allow that individual to learn best.

Learning preferences can then help learners to find their most effective way to learn conveniently. It can also help teachers to adopt/prepare suitable learning materials for efficient learning process.

This paper is concerned with the study, implementation, and application of a web-based learning style index. In this paper we examine the learning style of students in Asia and those in Africa, Nigeria and Japan as a case study.

However, this paper also compares the learning preferences of students in those two above mentioned continents. Based on the study, we also give some recommendation to enhance the learning processes in those countries.



  • Introduction—————– 9
  • Aims and Objective——- 10
  • Scope and Limitation—— 10
  • Methodology——- 10


  • SQL———— 11
  • Web Server——- 12
  • Apache Web Server———– 12
  • Apache Tomcat———– 12
  • Learning Style——————– 13
    • Dimensions of Learning Style————- 13
  • Related Works on Learning Style——————– 15
  • Learning Style Index————- 18


  • Enhanced Learning style Index—————- 20
  • Implementation—— 22
  • Web Application—– 24
    • Students Modules———— 25
    • Teachers Module—————- 26
    • Administrator Module——————————————- 28
  • Integration in to Intelligent and Adaptive e-learning System— 29


  • Learning Style of Japanese students———— 31
  • Learning Style of Nigerian Students———- 34
  • The Comparism———————- 38
  • Global Result and Implications——— 43


  • Summary——– 45
  • Conclusion——————- 45
  • Suggestion and Recommendation———- 46
  • Challenges———– 46
  • Future work—— 47

References——— 47

Appendix———- 48


During the past decade, educational research has identified a number of factors that account for some of the differences in how students learn.

One of these factors, learning styles, is broadly described as cognitive, effective and physiological traits that are relatively stable indicators of how learners perceive, interact with, and respond to learning environment.

This research work uses an online learning style index (LSI) to determine the learning preferences of students based on their responses to answering some questions with multiple choice answers on a questionnaire to the best of their ability.

The system will automatically categorize the students as Active/Reflective, Sensing/Intuitive, Visual/Verbal, Sequential/Global or Social/Emotional learners.

Learning style index can help learners to know their learning preferences to enable them select/find out suitable learning materials in order to boost their

learning process. Moreover, teachers can take this advantage to measure their students’ learning preferences, which they can use to setup/adjust their teaching styles and to find out the necessary materials or equipment to match their students’ learning capabilities.

It is very important to know the learning preferences of an individual or group of students so as to avoid mismatch between leaner’s learning style and the available learning or teaching materials.


The Apache Software Foundation, Web site: 2006.

The Advanced Distance Learning group, Website: 2007.

Felder and L. Silverman, Learning and teaching styles in engineering education. Engineering Education, Vol. 78, No. 7, pp. 674-681, 1988.

Hamada, An Integrated Virtual Environment for Active and Collaborative e-Learning in Theory of Computation. IEEE Transactionson Learning Technologies, Vol. 1, No. 2, pp. 1-14, 2008.

Herrmann, The Creative Brain. Lake Lure, NC: Brain Books, 1990. [6]. Java2D of Sun Microsystems 2007.

Kolb, Experiential Learning: Experience as the Source of Learning and Development. Englewood Cliffs, NJ: Prentice-Hall, 1984.

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