Renuka Mahajan

Usage patterns discovery from a web log in an Indian e-learning site: A case study

An important research area in education and technology is how the learners use e-learning. By exploring the various factors and relationships between them, we can get an insight into the learners’ behaviors for delivering tailored e-content required by them. Although many tools exist to record detailed navigational activities, they don’t explore the learners’ usage patterns for an adaptive e-learning site. The previous web log data analyses, done so far, have been very limited in their scope as they lack detailed empirical results on the learning technology usage. This paper discusses the detailed results of a case study of web data mining in a specific e-learning application. The main objective of this study is to conduct research on usability and effectiveness of the e-content by analyzing the web log. For this, a suitable data set was retrieved from raw web log records, to which various web mining & statistical techniques could be applied. We have evaluated different features of e-content that can lead to better learning outcomes for the learners, by understanding their navigational behaviors, their interaction with system and their area of interest. We found, for example, what sequence of topics were the most liked and the least liked by the learners; we also found that these patterns, lead us to hypothesize, the correlation and regression analysis between the average time, test score and total attempts.

Review on factors affecting customer churn in telecom sector

The communications sector is emerging with new technologies, wireless and wireline services. The industrys success expects a better perception of customer requirements and superior quality of service and models. Customer churn has a huge impact on companies and is the prime focus area for the companies to remain competitive and profitable. Hence, significant research had been undertaken by researchers worldwide to understand the dynamics of customer churn. This paper provides a review of around 75 recent journal articles (starting from year 2000) to identify the various churn factors and their complex relationships, in existing telecom churn literature. It gives detailed discussion of what factors were identified in various studies, the sample sizes used and the method used for the study by different researchers. The gaps identified in the previous studies have also been discussed. A model on churn factors, identified from the study is proposed to serve as a roadmap, to build upon exciting churn management techniques.

Detecting and managing operational, transactional and auditing risk using data analytics

Companies depend on timely and accurate financial information. Businesses and investors both need current and reliable information for making the decisions that must be made every day. Much of the information that companies, investors and the government receives and incorporates in their long term business decisions is provided by a third party known as independent auditors and the process they undertake is called data analysis used for preventing and identifying risks. This paper explores the basic concepts and techniques of the set of tools used to detect and manage operational, transactional and auditing risk. It provides insights of the internal control evaluations that take place to ensure the accuracy of the information being audited and the reports that are drafted to summarize the audit findings. It also includes the reports and findings of that audit of the company and how the company can recognize the operational, transactional and auditing risks. This will help leading organizations and audit departments in leveraging data analytics for auditing purposes.