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Organizational information systems cannot produce any positive outcome unless the endusers accept, adopt and use it. In this paper, we report the results of an investigation of the perception gap between end-users and software developers regarding end-user participation in software development. Our analysis shows the existence of perception gap, which could affect the level of end-user participation in software development. The implication of this is poor acceptance of software system applications and failure of some software system projects.

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Abstract-Computer Mediated Communication (CMC)platforms are incorporated in e-learning systems to actualize effective learning activities. Visualization techniques are been used to communicate the unstructured complex dataset of learners' activities in the log files to the actors. The effectiveness of this approach as bases for teaching-learning support and learning analytics however relies on the commitment of the teacher. The teacher being human can become overwhelmed when the enrolment is large and/or when the Internet access is a problem. This paper presents a technique for capturing the teacher's knowledge for monitoring learners' activities in Neuro-fuzzy model for online automatic monitoring. The model intelligently provides inbuilt competence assessment and promptly takes decisions. The IEEE-LTSA is modified to reflect the initiative. Similarly, the integration of the model in an architecture based on Actuator-Indicator Models was demonstrated.Index Terms-Actuator-indicator model, learning activities, log file visualization and neuro-fuzzy model.

Character Recognition has been one of the most intensive research during the last few decades because of its potential applications. However, most existing classifiers used in recognizing online handwritten characters suffer from poor feature selection and slow convergence which affect training time and recognition accuracy. Hence, this paper focused on integrating an optimization (genetic algorithm) into modified backpropagation neural network to enhance the performance of character recognition. This paper proposed a methodology that is based on extraction of features using stroke number, invariant moments, projection and zoning. Genetic algorithm was use as feature selection to optimize the subset of the character for classification. A Modified Genetic Algorithm (MGA) was modified to reduce character recognition errors using fitness function and genetic operators. However, an integration of optimization algorithm (modified genetic algorithm) into an existing modified backpropagation (MOBP) learning algorithm was employed as classifier. For further enhancement of classifier, three classifiers (C1, C2 and C3) were formulated from MGA-MOBP model and evaluated using training time and correct recognition accuracy. C3 performed better than C1 and C2 in terms of convergence rate, correct recognition accuracy and feature selection (its ability to remove irrelevant features of character images). The results of the developed system achieved a false recognition of 0.56% and 99.44% overall recognition accuracy compared with existing models. General TermsPattern Recognition