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Data mining (DM) is a class of database application that look for the hidden patterns in a collection of data and their relationships. DM is used in developing methods for discovering facts from data which come from educational environment and it becomes educational data mining (EDM). The educational institutions can use classification for complete analysis of students' characteristics. This paper details the Iterative Dichotomiser (ID3) algorithm in classification technique. The ID3 algorithm builds a decision tree from a dataset. This action we accumulates Teaching Assistant Evaluation's (TAE) dataset from UCI machine learning repository. This paper demonstrates the ID3 algorithm to construction of decision tree (DT). The implementation of this algorithm is useful to study of teaching performance over three regular semesters and two summer semesters of 151 Teaching Assistant (TA).In this work various kinds of impurities measures and discovery the maximum information gain at various iterations levels. This task is to extract the knowledge that describes TA performance over summer and regular semester. This exertion will help the institute to growth the performance.

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Design plays a key role in the development of software. The quality of design is crucial and is a fundamental decision element in assessing the software product. The early availability of design quality evaluation provides a better way to decide the quality of the final product. This avoids presumption in the quality evaluation process. Hence Software Metrics provide a valuable and objective insight of enhancing each of the software quality characteristics. This paper proposes a quality model to assess the design phase of any object-oriented system based on the works of Chidamber, Kemrer and Basili and suggests two new metrics. The research focuses on analyzing a set of metrics, which has direct influence on the quality of the software and creating a metrics tool based on Java that can be used to validate the object-oriented projects against these metrics. The analysis is carried out on a set of real world projects designed using Unified Modeling Language, which are used as test cases. These metrics and models are proposed to add more quality information in refining any object-oriented system during the early stages of design itself.

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The growth of information technology led to the Internet development that in turn helped people in many ways. The major one is to express their views about the products and services through reviews, blogs, feedback, and comments on the website and in social media. The buyers are forced to go through investigation on these reviews/blogs, before choosing any product or service. Out of all online services, Mobile learning app places a vital role to increase the thirst for knowledge. But to identify the suitable mobile learning app, the opinions of the existing customers need to be mined. This research paper analyzes the mobile learning reviews which are available in the corpus. A novel preprocessing framework is proposed in this paper to improve classification accuracy in the dataset - mobile learning app review dataset. The corpus dimension is reduced using SVD through which, the data is prepared for mining. The classification accuracy is evaluated by applying Multinomial Naïve Bayes, Random Forest data mining algorithms and Learning Vector Quantization (LVQ), Elman Neural Network (ENN), Feed Forward Neural Network (FFNN) algorithms with the dataset obtained by the proposed processing method.

Designing the high-quality software is a difficult one due to the high complexity and fault prone class. To reduce the complexity and predict the fault-prone class in the object orient software design proposed a new empirical approach. This proposed approach concentrates more on to increase the software quality in the object oriented programming structures. This technique will collect the dataset and metric values from CK-based metrics. And then complexity will be calculated based on the weighted approach. The fault prediction will be done, based on the low usage of the dataset and high complexity dataset. This helps to increase the software quality. In simulation section, the proposed approach has performed and analysed the parameters such as accuracy, fairness, recall, prediction rate and efficiency. The experimental results have shown that the proposed approach increases the prediction rate, accuracy and efficiency.