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Learning models used for prediction purposes are mostly developed without paying much cognizance to the size of datasets that can produce models of high accuracy and better generalization. Although, the general believe is that, large dataset is needed to construct a predictive learning model. To describe a data set as large in size, perhaps, is circumstance dependent, thus, what constitutes a dataset to be considered as being big or small is vague. In this paper, the ability of the predictive model to generalize with respect to a particular size of data when simulated with new untrained input is examined. The study experiments on three different sizes of data using Matlab program to create predictive models with a view to establishing if the size of data has any effect on the accuracy of a model. The simulated output of each model is measured using the Mean Absolute Error (MAE) and comparisons are made. Findings from this study reveals that, the quantity of data partitioned for the purpose of training must be of good representation of the entire sets and sufficient enough to span through the input space. The results of simulating the three network models also shows that, the learning model with the largest size of training sets appears to be the most accurate and consistently delivers a much better and stable results.

Technology enhancement learning is a process that leads to deep point of learning and adds knowledge of technologies. Various studies shed light on technology development and its effect in educational sector. The aim of this integrative review is to examine the current evidence of the impact of technology learning on student learning and academic performance in courses requiring collaborative or activities. The authors searched electronic databases for relevant articles, with different learning techniques. 24 articles met the requirement of paper, it's collected from (2011)(2012)(2013)(2014)(2015)(2016)(2017). Three themes of techniques used for student learning outcomes, includes technology enhanced learning, assessment method and faculty experience on academic performance in universities with technology use. The final results of this paper show the relationship between what has been done and the factors used by the authors. Also the future work needs more use of technologies in different phases of learning process.

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