The education system is becoming increasingly dependent on information technology to maintain its competitiveness and adapt to the ever-changing economic environment. The industry that is essentially becoming a higher order service industry must rely on technology to keep pace with the global economy that technology has opened up. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay How Data and Analytics Can Improve Education George Siemens on the Applications and Challenges of Educational Data. Universities have long accumulated data: tracking grades, attendance, textbook purchases, test scores, cafeteria meals, and the like. But little has actually been done with this information – whether due to privacy concerns or technical capabilities – to improve student learning. As more universities adopt technology and push for more open government data, there are clearly many opportunities for better data collection and analysis in education. But what will it be like? This is undoubtedly a politically challenging issue, as some states are turning to standardized test score data to evaluate teacher effectiveness and, consequently, retention and promotion. What kind of data do colleges traditionally track? Colleges and universities have long tracked a wide range of student data, often from applications (universities) or enrollment forms (universities). This data includes any combination of: location, prior learning activities, health issues (physical and emotional/mental), attendance, grades, socioeconomic data (parental income), parental status, and so on. Most universities will store and aggregate this data under the umbrella of institutional statistics. Privacy laws differ from country to country, but generally prohibit academics from accessing data that is not relevant to a particular class, course or program. Unfortunately, most colleges and universities do little with this wealth of data, other than possibly produce an annual institutional profile report. Even a simple analysis of existing institutional data could raise the profile of potential at-risk students or reveal patterns of attendance or homework delivery that indicate the need for additional support. What new types of educational data can now be captured and mined? In terms of learning analytics or educational data mining, the increasing externalization of the learning activity (i.e. the acquisition of the way in which students interact with the contents and the discourse they have on the learning materials, as well as on the social networks that form in the process) is driven by greater attention to online teaching. For example, a learning management system like Moodle or Desire2Learn captures a significant amount of data, including time spent on a resource, posting frequency, number of hits, etc. This data is quite similar to what Google Analytics or Piwik collect about website traffic. . A new generation of tools, such as SNAPP, uses this data to analyze social networks, connectivity levels and peripheral students. Discourse analysis tools, such as those developed at the Knowledge Media Institute of the Open University, UK, are also effective in assessing the qualitative attributes of discourse and discussions and in evaluating each student's contributions based onto depth and substance in relation to the topic of discussion. Day after day, mountains of data are produced directly as a result of training activities and as a by-product of various procedures. A great deal of information concerns their students. However, most of this data remains locked within storage systems that must be coupled with operational systems to generate insights needed to support strategic decision making. A variety of approaches for computer-aided decision systems have appeared over time with different terms such as Management Information Systems (MIS), Executive Information Systems (EIS), and Decision Support Systems (DSS). The term Management Information System is not new in the education sector. Colleges and universities use management information systems for the process of generating various reports which are used for analysis in admissions, exams, results, etc. for their decision-making for their own use and for transmission to regulatory authorities. These reports are often computer generated and can be generated at any time. However, the use of the terms Data Warehousing and Data Mining is relatively new. These terms have gained meaning with the increasing sophistication of technology and the need for predictive analytics with what-if simulations. Finally, data warehousing and data mining tools are essential components in the education sector. Data Warehousing and Data Mining Needs for Colleges and Universities The development of management support systems is characterized by the cyclical up and down of buzzwords. Model-based decision support and executive information systems have always been limited by a lack of consistent data. Nowadays the data warehouse tries to fill this gap by providing real and decision-relevant information to enable the control of critical success factors. A data warehouse integrates large amounts of business data from multiple, independent data sources made up of operational databases into a common repository for query and analysis. Data warehousing will gain critical importance in the presence of data mining and generation of different types of analytical reports that are usually not available in original transaction processing systems. Education being an information-intensive industry, building a management information system is a mammoth task. This is even more so for public sector colleges and universities, which have a large network of colleges or universities, with branches spread across the country. This becomes more difficult due to the prevalence of varying degrees of computerization. Currently, colleges and universities generate MIS reports largely from periodic paper reports/statements submitted by branches and regional/zonal offices. Except for a few colleges and universities, which have used the technology in a big way, MIS reports are available with a substantial time tag. The reports thus generated also have a high margin of error due to data entry being carried out at various levels and the likelihood of different interpretations at different levels. Although the computerization of college or university branches is progressing at a good pace, the MIS requirements have not been fully satisfied A. It is due to the fact that most of the Total Branch Computerization (TBC) software packages are geared towards processing transactions. Most colleges and universities operate large databases for normal day-to-day transactions. In most cases, these databases are not operational.