Research Interests

Technology supported learning: e- and m-learning, instructional design, monitoring and evaluation of technology supported learning projects, adaptive user interfaces for learning, technologies for informal learning and knowledge management such as for small scale farmers

Work Experience



Mogire, AM, Oboko RO.  2013.  Context Aware Framework to Support Formal Ubiquitous Learning. International Journal of Societal Applications of Computer Science. 2(3):248-254.
Oluoch, FO &, Oboko RO.  2013.  Designing an M-learning system for Community Education and Information on HIV and AIDS in Kenya. International Journal of Societal Applications of Computer Science. 2(3):238-247.
3) Adede, CO &, Oboko RO.  2013.  Model for Predicting the Probability of Event Occurrence Using Logistic Regression: The Case of Credit Scoring for a Kenyan Commercial Bank. International Journal of Societal Applications of Computer Science. 2(3):216-223.
Ayoma, M, Oboko RO.  2013.  M-Learning Support Services for Corporate Learning. International Journal of Societal Applications of Computer Science. 2(3):210-215.


Musumba, GW, R.O. O, E.T.O. O.  2012.  Agent Based Adaptive Learning Model for Intermittent Internet Connection Conditions. Journal of Continuing, Open and Distance Education.
Oboko, RO, Wagacha PW.  2012.  Using Adaptive Link Hiding to Provide Learners with Additional Learning Materials in a Web-Based System for Teaching Object Oriented Programming. . Journal of the Research Center for Educational Technology. 8(1):11-25.


  2011.  Use of Concept Map Scaffolds to Promote Adaptive E-Learning in Web-Based System. International Journal of computing and ICT Research. 5(2):59-66.
Awuor, Y, Oboko R.  2011.  Automatic Assessment of Online Discussions Using Text Mining. International Journal of Machine Learning & Applications. 1(1):2-11.


  2010.  Understanding Intention to Use Computer Assisted Audit Tools and Techniques (CAATTs) Using UTAUT Model: Perspectives of Auditors in Kenya National Audit Offi ce (KENAO). Abstract

Adoption of computer assisted audit tools and techniques (CAATTs) has become fundamental in many audit methodologies owing to rapid advances in clients' information system usage. Audit standards encourage auditors to adopt CAATTs to improve audit efficiency and effectiveness. However, the pace of adoption has been slow among auditors. We employed a well validated information technology (IT) model, the unifi ed theory of acceptance and use of technology (UTAUT) to model the voluntary adoption of technology in auditing. A survey instrument to collect quantitative data on the model’s predictors, intention to use CAATTs and individual characteristics was used. Data was obtained from 70 auditors of Kenya National Audit Offi ce (KENAO). Results indicate that performance expectancy, effort expectancy, facilitating conditions and professional influence, affect the probability that auditors will adopt and use CAATTs. The model explains 69 percent of the variance of the auditors’ behavioral intention to use CAATTs. Though age, gender and experience are moderating influences to many UTAUT predictors, none had a signiicant effect on intention for auditors. These results suggest UTAUT to be a valid model for studying technology adoption decisions among auditors, but other individual characteristics need to be explored. This paper contributes to literature and research on technology acceptance in general, and is also important to auditing research and practice. To increase CAATTs usage, audit firm’s management needs to develop training programs to increase auditors’ degree of ease and enhance their organizational and computer technical support for CAATTs. Regulators need to make a stronger recommendation; and a more direct regulatory intervention in adoption decisions.


Oboko, RO, Wagacha PW, Omwenga EI, Odotte Z.  2009.  Non-Obtrusive Determination of Learning styles in Adaptive Web-Based Learning..
OBWOCHA, MROBOKOROBERT, I DROMWENGAELIJAH, W DRWAGACHAPETER.  2009.  Using Adaptive Link Hiding to Provide Learners with Additional Learning Materials in a Web-Based System for Teaching Object Oriented Programming. VLIR-IUC-UoN International Conference. : Journal of School of Continuous and Distance Education Abstract
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Oboko, RO, Wagacha PW, Masinde EM, Omwenga E, Libotton A.  2008.  Value Difference Metric for Student Knowledge Level initialization in a Learner Model-based Adaptive e-Learning System. AbstractValue Difference Metric for Student Knowledge Level initialization in a Learner Model-based Adaptive e-Learning System

Web-based learning systems give students the freedom to determine what to study based on each individual learner’s learning goals. These systems support learners in constructing their own knowledge for solving problems at hand. However, in the absence of instructors, learners often need to be supported as they learn in ways that are tailored to suit a specific learner. Adaptive web-based learning systems fit in such situations. In order for an adaptive learning system to be able to provide learning support, it needs to build a model of each individual learner and then to use the attribute values for each learner as stored in the model to determining the kind of learning support that is suitable for each learner. Examples of such attributes are learner knowledge level, learning styles and learner errors committed by learners during learning. There are two important issues about the use of learner models. Firstly, how to initialize the attributes in the learner models and secondly, how to update the attribute values of the learner model as learners interact with the learning system. With regard to initialization of learner models, one of the approaches used is to input into a machine learning algorithm attribute values of learners who are already using the system and who are similar (hence called neighbors) to the learner whose model is being initialized. The algorithm will use these values to predict initial values for the attributes of a new learner. Similarity among learners is often expressed as the distance from one learner to another. This distance is often determined using a heterogeneous function of Euclidean and Overlap measures (HOEM). This paper reports the results of an investigation on how HOEM compares to two different variations of Value Difference Metric (VDM) combined with the Euclidean measure (HVDM) using different numbers of neighbors. An adaptive web-based learning system teaching object oriented programming was used. HOEM was found to be more accurate than the two variations of HVDM

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