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Omwenga EI, Kinoti P. "the Development of a Framework for Open Courseware for Emerging Economies: the case of Kenyan public Universities.". In: Special Issues in Computing and ICT Research . Vol. Volume VII. Kampala: Fountain Publishers; 2011.
Ireri BN, Omwenga EI, Oboko RO, Wario R. "Developing Pedagogical Skills for Teachers: A Learner Centered Approach for Technology Supported Instruction. Accepted for publication In J. Keengwe, & G. Onchwari (Eds.)." Handbook of Research on Active Learning and the Flipped Classroom Model in the Digital Age. 2016.
Ireri BN, Omwenga EI, Oboko R, Wario R. "Developing Pedagogical Skills for Teachers: A Learner-Centered Approach ." Handbook of Research on Learner-Centered Pedagogy in Teacher Education and Professional Development. 2016:128. AbstractFull Link Text

A Learner-Centered Approach for
Technology Supported Instructions ABSTRACT Bonface Ngari Ireri Africa Nazarene
University, Kenya Elijah I. Omwenga University of Nairobi, Kenya Robert Oboko University of
Nairobi, Kenya Ruth Wario University of Free State, South Africa Since technology alone without
the instructor or teacher cannot deliver learning to learners, the presence of the teacher or instructor
is very important. For any meaningful teaching and learning to take place in a class, the teacher
must gain learner's attention. Teachers who use learner centered approaches have a strong
trust in students, they believe that students want to learn, have great faith in student ability and
offer students ownership of class activities. They are able to manage their classroom.

Oboko RO, Maina EM, Waiganjo PW, Omwenga EI, Wario RD. "Designing adaptive learning support through machine learning techniques.". In: IST-Africa Week Conference, 2016. IEEE; 2016. Abstract

The use of web 2.0 technologies in web based learning systems has made learning more learner-centered. In a learner centered environment, there is need to provide appropriate support to learners based on individual learner characteristics in order to maximize learning. This requires a Web-based learning system to have an adaptive interface to suit individual learner characteristics in order to accommodate diversity of learner needs and abilities and to maintain an appropriate context for interaction and for achieving personalized learning. The purpose of this paper is to discuss how machine learning techniques can provide adaptive learning support in a Web-based learning system. In this research, two machine learning algorithms namely: Heterogeneous Value Difference Metric (HVDM) and Naive Bayes Classifier (NBC) were used. HVDM was used to determine those learners who were similar to the current learner while NBC was used to estimate the likelihood that the learner would need to use additional materials for the current concept. To demonstrate the concept we used a course in object oriented programming (OOP).

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