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The Education system is becoming increasingly dependent on Information Technology to retain its competitiveness and adapt with the ever-evolving business environment. The industry which is essentially becoming a service industry of a higher order has to rely on technology to keep abreast with global economy that technology has thrown open.
How data and analytics can improve education George Siemens on the applications and challenges of education data. Colleges have long amassed 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 issues or technical capacities — to enhance students’ learning. With the adoption of technology in more colleges and with a push for more open government data, there are clearly a lot of opportunities for better data gathering and analysis in education. But what will that look like? It’s a politically charged question, no doubt, as some states are turning to things like standardized test score data in order to gauge teacher effectiveness and, in turn, retention and promotion.
What kinds of data have colleges traditionally tracked?
Colleges and universities have long tracked a broad range of learner data — often drawn from applications (universities) or enrollment forms (colleges). This data includes any combination of: location, previous learning activities, health concerns (physical and emotional/mental), attendance, grades, socio-economic 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 will prohibit academics from accessing data that is not relevant to a particular class, course, or program. Unfortunately, most colleges and universities do very little with this wealth of data, other than possibly producing an annual institutional profile report. Even a simple analysis of existing institutional data could raise the profile of potential at-risk students or reveal attendance or assignment submission patterns 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 growing externalization of learning activity (i.e. capturing how learners interact with content and the discourse they have around learning materials as well as the social networks they form in the process) is driven by the increased attention to online learning. For example, a learning management system like Moodle or Desire2Learn captures a significant amount of data, including time spent on a resource, frequency of posting, number of logins, etc. This data is fairly similar to what Google Analytics or Piwik collects regarding website traffic. A new generation of tools, such as SNAPP, uses this data to analyze social networks, degrees of connectivity, and peripheral learners. Discourse analysis tools, such as those being developed at the Knowledge Media Institute at the Open University, UK, are also effective at evaluating the qualitative attributes of discourse and discussions and rate each learner’s contributions by depth and substance in relation to the topic of discussion.
Day after Day Mountains of data is produced directly as a result of Education activities, and as a by-product of various procedures. A vast amount of information is about their students. Yet, most of these data remains locked within archival systems that must be coupled with operational systems to generate information necessary to support strategic decision-making.
A variety of approaches for computer-aided decision-making systems have appeared over time under different terms like Management Information Systems (MIS), Executive Information Systems (EIS), and Decision Support Systems (DSS).
The term Management Information System is not new to the Education sector. Colleges and universities have been using the Management Information Systems to the process of generation various reports which are used for analysis at the Admissions, Examinations, Result, etc for their decision making for own use as well as for conveyance to authorities in charge of regulation. Often, these reports are generated through computers and can be generated at any point of time. However, the usage of the terms Data Warehousing and Data Mining are relatively new. These terms have gained significance with the growing sophistication of the technology and the need for predictive analysis with What-if simulations.
Finally Data warehousing and Data mining tools are essential components in Education sector.
Need of Data Warehousing and Data Mining for Colleges and universities
The development of management support systems is characterized by the cyclic up and down of buzzwords. Model based decision support and executive information systems were always restricted by the lack of consisted data. Now-a-days data warehouse tries to cover this gap by providing actual and decision relevant information to allow the control of critical success factors. A data warehouse integrates large amounts of enterprise data from multiple and independent data sources consisting of operational databases into a common repository for querying and analyzing. Data warehousing will gain critical importance in the presence of data mining and generating several types of analytical reports which are usually not available in the original transaction processing systems.
Education being an information intensive industry, building a Management Information System is a gigantic a task. It is more so for the public sector colleges and universities, which have a wide network of college or university, branches spread all over the country. It becomes more difficult due to prevalence of varying degrees of computerization. At present, colleges and universities generate MIS reports largely from periodic paper reports/statements submitted by the branches and regional/zonal offices. Except for a few colleges and universities, which have been using technology in a big way, MIS reports are available with a substantial time tag. Reports so generated have also a high margin of error due to data entry being done at various levels and likelihood of varying interpretations at different levels.
Though computerization of college or university branches has been going at a good pace, MIS requirements have not been fully addressed to. It is on account of the fact that most of the Total Branch Computerization (TBC) software packages are transaction processing oriented. In most colleges and universities large databases are in operation for normal daily transactions. In most cases, these operational databases have not been designed to store historical data or to respond to queries but simply to support all the applications for day-to-day transactions.
Data Warehouse & Data Mining Applications in Education Systems
The Warehouse infrastructure can support a wide range of applications and reports to meet exact business needs. In Education, the most important of data warehousing is building Risk Management Systems. Risk Management System will identify the risks associated with a given set of assets. This means understanding the way in which the market is likely to move in the future, based on past performance.
Competitions, rising colleges and universities are exploring ways to use their data assets to ain a competitive advantage. This paper analyses how, in practice, data warehouse applications fits in with various different problems at Education sector and also demonstrates how the college-wide enterprise data warehouse can be implemented to provide atomic level information on all Education sectors, The possibility of setting up a data warehouse, seems more remote when compared to the setting up of data marts, which can later be integrated into a college or university-wide enterprise data warehouse. The integrated data store can be used to uncover a huge potential loss of stream, which can be averted and which will further guide how to approach courses and syllabus grouping well into the future. An area of data gathering that universities and colleges are largely overlooking relates to the distributed social interactions learners engage in on a daily basis through Face book, blogs, Twitter, and similar tools. Of course, privacy issues are significant here. However, as we are researching at Athabasca University, social networks can provide valuable insight into how connected learners are to each other and to the university. Potential models are already being developed on the web that would translate well to college settings.
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