Indian Society of Geomatics (ISG) Room No. 6202, Space Applications Centre (ISRO), Ahmedabad

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Indian Society of Geomatics (ISG) Room No. 6202, Space Applications Centre (ISRO), Ahmedabad

DECEMBER 5, 2020

big data use cases in education

As a result, our teams were able to easily construct visuals like Figure 4 within minutes. Some of us are good at it, some of us not. We wanted to capture the maximum semantic complexity inherent within the data we store so that we can address most, if not all, future questions about the data. We are a random collection of buildings held together by parking lots. Early on, some staff in one key unit resisted strongly and lobbied for its own alternate architecture, leading us to chalk up the fight to the "information is power" bias. big data is represented by different types, including raw, unstructured, structured, and verified data. Think of it as just-in-time predictive tutoring. As the community improves its knowledge and skill with the data, the university can improve accordingly. Dr. Kellen brings a rare combination of…, Adam Recktenwald is the chief innovation architect at the University of Kentucky. We separated out visualization of the data as a separate activity and have good information designers working on good visualizations. 43-45% of small, mid-sized and large organizations (fewer than 5,000 employees) already use big data, and all the segments are similarly open to the future use. It allows the instructors to create assignments and tests using the information that is already online using automation. Furthermore how big mounts of unused data can benefit and improve education. Specify interaction history with various applications, including learning management system, clickers, course capture and playback, academic alerts. Besides, such amounts of information bring many opportunities for analysis, allowing you to take a glance at a specific concept from many different perspectives. Universities, especially research universities, tend to have faculty with excellent quantitative skills who serve in administrative positions. so that these analysts could use whatever tool they wished. These three data scientists, our IR director, and the leader of our old BI group now serve as the nucleus for a new IR office and our old BI group leader now leads an advanced analytics (AA) group that supports IR and the rest of the university. Why would a committee be any better? However, with further development, big data analysis can be effectively put to use and bring even more benefits for students and educators. Should we switch gears from an always-in beta, informal marketing and release model with a more formal product release method? Integrate data from public social media and analyze for student involvement and success. Having such a flat model also frees the analyst from joining tables together and the issues that joins have, especially if the analyst isn't aware of the ramifications of certain types of joins. Hope springs eternal, even in the face of new challenges. Since HANA compresses data so well, exploding the data out like this doesn't really impose a database speed penalty. Big data analytics allows companies to track leads through the entire sales conversion process, from a click on an adword ad to the final transaction, in order to uncover insights on how the conversion process can be improved. We also avoided public pronouncements and fanfare around the project. Las Vegas, NV 89147, Lori Jones is a professional writer, passionate about technology and how it influences different aspects of our lives. Micro-surveys and personalization technology represent opportunities to have analytics working for students, one at a time. We are smack-dab in the middle of an analytics revolution. All these people produce tons and tons of data, passing over this information to other Internet users. We didn't immediately have a lot of ideas on how having a super-fast data analysis system might help. Fortunately, this particular tool, HANA, has a strong relational component and knowledge of SQL and relational data modeling was a critical and transferrable skill. Our approach consisted of the following concepts: Design valuable views for capable analysts. All systems today that try to calculate where a student is in his or her degree run in batch mode, which can take many seconds or minutes to complete. HANA was a rewrite of a full-functioning, totally in-memory database environment. We are also in the middle of decommissioning the old IR data warehouse so that both the AA and IR units will rely on one source of truth. We will be developing performance dashboards for each college and are prepared to customize them for each college. They will challenge organizations at different levels. 1. Where appropriate and helpful, analyze their social media interactions and provide recommendations to improve their likelihood of retaining and graduating. How can analytics infuse many processes?" All this creates questions that we have been pondering: How would we measure this technology's ability to impact our advising and teaching processes? Here are just a few. Her blog posts always contain in-depth research and bring valuable insights to the reader. We learned that no matter how fast the speed of improvement, we will always need more, forcing us to keep pushing toward the fastest designs possible. Students lack essential competencies that would allow them to use big data for their benefit, . Our work has produced several analytic models to date. Specify class enrollment, midterm and final grades, credit hours attempted and earned, instructor teaching the class. In our case, we are partnering with Coursera, a leading MOOC (massive open online course) provider and hope to someday provide personalized learning interactions in these kinds of environments. Unless the IT team understands the disruptive technology's innards and can understand the technology market, the IT team will depend mostly on vendors in explaining the technology. First, audio can be converted automatically to text (we have tested this out with Microsoft services, but YouTube and other service providers can do this as well). 1. For example, with social media listening tools we can find the student who tweets "My dorm roof is leaking!" Others have leveraged predictive analytic… It started making use of big data analytics much before the word Big Data came into the picture. But what we had in HANA that these software solutions did not have were two things: (1) easy and real-time integration with our core student information systems and potential integration with any other data source on students and (2) a super-fast computational environment at our disposal and under our control. Education industry is flooding with huge amounts of data related to students, faculty, courses, results, and what not. © 2020 Colocation America. The use cases cover the six industries listed below. The report also showed an increase in the effective use of data. But building these models flies in the face of traditional data warehouse construction techniques, which typically require designing a fact table of metrics (e.g., total tuition billed, total classes taken) one wishes to analyze at a predescribed level of detail (granularity) and several parsimonious dimension tables (to save on space and improve speed) that contain attributes like a student's chosen degree, home ZIP code, high school, and so on, in them. Analytic work like this takes effort that other universities might not be able to expend. We don't need to be afraid of processing 1 billion rows of data. Required fields are marked *, Managed Colocation Reportedly, we produce over 2.5 quintillion data on average every day. For the first time in four years, study results showed that a wider use of analytics and a greater focus on applications has resulted in the increased ability to use analytics to strategically innovate. Our thought was to develop a better means of providing this data so we could improve the productivity of IT resources that had been preparing data for analysis in a more ad hoc manner. Each is designed so that an analyst can work with a single view and have all attributes needed for analysis (see Table 1). Since many of the data models are now unified and all separate models will be transformed into HANA models, all models will be designed to suit multiple goals within a single, flexible architecture. We believe the cause for this is due to inadequate deep thinking about novel architectures that can dramatically speed up this iterative data modeling cycle. The K-score represents the student's rank in interactivity relative to his or her peers for each class, with a high score representing higher involvement. For instance, some instructors incorporate Social Network Adapting Pedagogical Practice (SNAPP) into the teaching process, studying students’ blogs, and measuring how much they are interested in a specific course. We are also experimenting with one of our technology vendors on more sophisticated ways of mining social media data to uncover students with interests in, or deep problems with, the university. A big vision requires partners. However, before we get into the good and the bad, let’s clarify what big data is. As part of building the business case for HANA, we noticed that when you tell someone that an incredibly fast Big Data appliance is now available, a long pause proceeds. All of us tend to fight our next battle based on lessons learned from the last one. We made these models available via direct data access (ODBC, etc.) Apparently, by 2017, the online community already reached 3.7 billion, and the numbers are still growing: The progression of online population growth 2012-2017 (Domo). We brought in two new PhD-level data scientists and moved a PhD-track statistician all into the data scientist positions. Unhappy with the high price of vendor data warehouse solutions and wary of their often excessive complexity and still unclear about SAP's emerging high-speed Big Data analytic strategy, we experimented with pulling incremental data from SAP's auditing subsystem. In the past, these rules of classification were within a particular data analyst's mind and not shared. Keep a list of majors and minors for each student and degrees awarded. Our own "sticky mental models" sometimes get in the way and prevent us from conceptualizing solutions correctly. But even after all that hard work, more often than one would like, accurate data beautifully displayed falls on deaf ears and blind eyes. Borrowing from examples like Kahn Academy but using Big Data analytic tools, we feel it is now feasible to efficiently distill this kind of useful information about one important aspect of student learning without consuming too many staff and faculty hours. With mobile technology, students leave all sorts of potentially valuable digital footprint data. As we began to talk to other universities thinking about adopting these kinds of analytic tools, we saw that this IR/BI conflict was a common theme that prevented collaboration and the application of disruptive approaches. We believe that while we can automate many activities that staff and faculty currently perform by leveraging Big Data analytics, there is so much more for faculty and staff to do that only they and not a computer can do. However, these three concepts do not adequately describe the phenomenon of big data without the fourth and fifth components, which are variability and veracity. Question 2. These two groups still offer different services but now operate as one unit. A large square means high (near 100%) utilization. When we started this process, our team was initially clueless about the real potential. According to a study, published by the Publications Office of the European Union, the most significant change brought by the big data to education, is the ability to monitor educational systems. . Improve tuition revenue forecasting and the pinpointing of financial aid. Since prior cases of missing data were filled in after the fact, the up-front data collection processes were not providing high-quality data at the source. Manufacturing big data use cases run the gamut from improved product development to optimizing spend. The phrase "increase its surface area" is apt. For example, if the student is in a physics class and is learning about entanglement in quantum mechanics, the student can search lecture and class notes and slides for all references to the concept "entanglement." The models have a base of SQL code that models smaller components (e.g., classes a student takes, a student's basic demographic data elements) that are then used by higher-level SQL code, bringing these together into a large collection of fragments into a "super-query." big data is collected from millions of different resources. Reportedly, The bigger half of this data belongs to the most active Internet users, among which are school and college students. Big data can help you address a range of business activities, from customer experience to analytics. He is currently CIO at the University of California San Diego (UCSD), a member of UCSD’s Chancellor's Cabinet, and Vice Chancellor and CFO of the UCSD senior management team. In the figure, you are seeing a one-week snapshot of a term schedule. The speed of big data is usually measured in real time. High compression rates means a smaller memory footprint, which means more data can fit in memory. This will let us see how diverse our university is in categorizing data elements. At first we were confused as SAP admittedly struggled early to properly describe its tool, HANA, to key decision makers, including why the product is different and how it relates to other SAP products. Because of the lack of proper software, it is harder to determine flaws, inconsistencies, and, most importantly, duplication, avoiding which is so essential for education. Figure 6 illustrates a more complicated future scenario. And we wanted to speed this cycle up dramatically. Normally we would assign the task of pulling the data together and visualizing it to an analyst who would probably write some code to perform the transformations on the small selection of data requested. Your email address will not be published. At the price of $50 million per building, this 10% increase in utilization can avoid a $250 million expense. Also include details on students who transfer in and out, including transfer institution, credit hours transferred in, etc. IP Addresses and Subnets All Rights Reserved. Each row shows a particular room in a building and then shows each day of the week and increments for each hour of the day. N + 1 Power Redundancies Research supports the simple notion that the more students are involved in their educational experience, the better they do. Out of the different perspectives may emerge a very different and a much better approach that a group of like-minded individuals would have failed to consider. In this case, the speedups where two to four orders of magnitude in performance (100x-10,000x). Help improve data quality. When envisioning the future, bring the student in on the process. However, after talking with our campus police and PR departments, we have a strong public safety need to monitor this kind of data. Predictive analytics and quick diagnosis. On the 22nd floor here, we live in a fact-free environment.". We are considering putting together a panel of students to weigh in on the issues and are considering an administrative governance group to ponder the issues. Not quite. As with most things with human beings, we vary in our ability to remember what to do next. But both groups were not looking forward enough and did not have the skill sets to deal with an increasingly analytically driven higher education world. For the next few years, we will need to rely on our "gut feeling" and more provisional measurement models to determine if we will be effective in this transformation. A common problem for many students lies in their inability to socially connect on campus. Students might “rewind” to watch a section a second time. Many universities today are recording instructors' lectures and letting students replay them back in their dorm rooms or in study spaces across campus. Supporting these processes may interfere with completing better self-service analytic models and tools. Organizations need to find ways to adequately engage their key people, whether they are managers, front-line employees, or in between, so that the visioning of the future is open enough. With six-year graduation rates (a common industry benchmark) hovering around 59%, each 1% increase in graduation rates not only helps those students who graduate, but the university gains about a $1 million improvement in revenue while perceptions of the quality of the university improve. Looking for, processing, and working with the information online, they leave digital breadcrumbs that become a part of big data, collected every day. Also, in a secure location, include additional personally identifiable demographic details such as name, address, email, etc. Don't drink the Kool-Aid. According to a study, published by the, Publications Office of the European Union. Thus, to keep up with current and future analytic demands, our "open data" approach brings the opportunity for internal crowdsourcing methods. As the student does this, we can begin to measure student involvement with critical class concepts. These work teams will be requesting both simple and more intricate analysis to guide their strategic planning processes. Monitor these small segments in a real-time fashion and use workflow technology to notify advisors and staff when segments appear to have difficulty. , the most significant change brought by the big data to education, is the ability to monitor educational systems. Don't be afraid of applying new, old, conventional, or unconventional approaches. [1] Top 3 big data use cases for mid-sized, large and very large organizations (fewer than 5,000 employees) are data warehouse optimization, predictive maintenance and customer analytics. Coursera tracks how its students watch those courses. What are some ways to bring groups across campus together on this difficult and important issue? The insights gleaned from IoT and other high-volume, high-velocity data sources holds vast promise for revolutionizing the manufacturing industry in a way that lives up to the transformative implications of the term "Industry 4.0." The volume of big data differs from the consumer: for someone, it may be tens of terabytes, and for someone else, it’s tens of petabytes. While it would be nice to establish alarms right on the student's phone, we could also use technology to make calls for us with wake-up calls for students. Data can be represented in multiple ways at the same time. The Internet population is growing very fast. First, it harkens us back to the process of making bourbon, a distinctly Kentucky product, and, second, it indicates that this process of developing highly usable models serves the analytic community. The possibilities are numerous and succinctly expressed in the following statement: In order to extract more value from data, you have to increase its surface area. And so we ended yet another coffee shop conversation with more questions than answers -- and more work to do. While we have enabled access to good analytical models, we have more work to do on dashboards and visualizations and little time. For example, we can publish all the analysis and research the institution would like to do and let colleges "bid" on the work, should they find the work of interest and valuable. We also have before us a new budgeting model in which colleges will have their own income statements and will have the ability to use additional "profit" generated within their college. What impediments are out there? Rather than use a project methodology with formal dates and formal requirements to be delivered, we immediately jumped into fluid, iterative sprints. Fun, PHONE: She wrote this blog on behalf of. The analysis of big data depends on many factors, like transparency, value to both the learner and the educator, expense, and openness. Present the enrollment data in such a way as to easily show the student's performance for each term, including credit hours earned, term GPA, cumulative GPA for that term, etc. The second bucket is in classification of data. We thought that students would not appreciate us having this data or offering these services. However, most if not all universities use a simpler, manual process of assigning classrooms to faculty and then copying assignments each year and modifying if needed. Chemistry labs need things that sociology classes don't. Universities also have to award aid based on need so that they don't turn away capable but financially strapped students. The data designer does not have to build a model that has to address the usual size and speed constraints found in conventional data warehouse systems. Then focus on the front-line employees that interact with the customers and how analytics can automate tasks and help them prioritize their efforts. Such social media mining can correlate disparate pieces of social data in ways not previously possible. During 2011, however, we began to put the pieces together after direct discussions with SAP senior product team members and after reviewing Hasso Plattner's book on the topic (In-Memory Data Management, Springer, 2012). In this case, we started with a rather small file that contains our class schedules for the term. Partly as the result of low digital literacy and partly due to its immense volume, big data is tough to process. Why not make it easier? There is a growing interest by institutions of higher education to take advantage of Big Data to improve student performance and raise teacher/professor effectiveness, while reducing administrative workload. In addition, we needed to build a data warehouse so that a broader group of analysts across the university data could access data for their own uses in a format that would be easier and simpler to use. Not everyone will make the leap. Connect with Cutter for the best minds in business technology to help you leverage disruptive new models and create value. In short, we found the approach technically viable, but didn't see it as enough of an architectural leap forward. Expanding our scope was going to require additional effort linearly to the scope. The use of Big Data in the education sector is significant. The teams have documented many forms of data classification and codified the rules within some of our HANA models. To get started on your big data journey, check out our top twenty-two big data use cases. Your thoughts are welcome: Be safe and secure. Except for those truly visionary people, it takes time for us normal folks to realize that utilizing a new tool requires changing one's mind about the way the world works. Data now connects to decision makers and students in many ways. When one adds into the mix advanced data visualization capabilities, one gets something different for university administrators and faculty: better and approachable insight into university operations and even the minds of the students. For each row, we include just a few numbers, including room capacity, number enrolled, and a few statistics about the student, such as whether he or she is a freshman, sophomore, junior, senior, or graduate student. This prevents one student from notifying a bunch of other students back in the dorms of the code for check-in, since the code has a limited lifespan (15 minutes) and is restricted to a building and floor. Provide the basis for calculating student's K-Score. Thus, both educators and students have a hard time getting the benefits they could get from big data. Since parts of our data analysis environment is designed to allow for greater transparency, analysts will potentially be able to see other unit data. Different degrees are taught in sometimes profoundly different ways, requiring deep expertise in a body of knowledge that nearly requires ignorance of other bodies of knowledge in order to become the master of one. While the teams take great care to enable multiple views of the data to support the community, you might have a valid and unique perspective. Data's thickness and the difficulty in bending it into shape has long been an impediment for organizations, requiring a class of people who have the knowledge and skill to bring data together from different sources, combine them, analyze them, and find patterns previously hidden. But these challenges are also opportunities. When the system detects a student taking one or more classes that don't seem to advance toward his or her chosen degree and, more importantly, when the class doesn't seem to contribute toward progress for any degree (for undeclared students), workflow tools can alert the student and advisors quickly. Use and share the data with the best interests of the university community in mind. While having obvious benefits for the education system, big data still has many drawbacks, linked to the lack of technology to process it and put it to use. Transforming the incremental updates into a traditional data warehouse with fact and dimension tables was possible but was going to require more effort and resources than we were willing to allocate. Now the teams are looking into ways of improving user interfaces so that missing or incomplete data counts decrease. We are a loose collection of airplane parts flying in close formation. These three goals -- merit, need, and success -- often cause tension. I can help you with that.". The IR office had the responsibility for determining official enrollment and retention numbers and many other various university metrics reported externally to state and federal agencies. While each of us believes these services are important for improving student success (and our student panel that advises us agrees), not everyone across campus does. We are currently adding personalization technology in front of this feature so that micro-surveys can be sent out to small segments of students or even on a 1:1 basis. The student reaction was interesting. It was reported that several countries around the world, including a few European countries, have adopted, digital literacy as a part of their educational strategy, . Some of our architects have been prototyping solutions for this. While we will make private to a unit what absolutely needs to be private, the way the university runs its business often involves multiple colleges and units at the same time. While initially sounding eerie, we point out that it might not be if the university offers this as an opt-in service to students. About one month ago, Stephen, Adam, and I met in the local eclectic coffee shop to discuss where to go next. 1. It might be possible for us to infer a student's prospective memory ability by analyzing his or assignment submissions or by asking the student some survey questions up front either as part of the mobile application micro-survey feature or through additional tests as part of the admission process. The K-Score also includes items like academic alerts that faculty and staff can enter when they identify a student struggling (see Figure 2). We are also at the beginning of a large and encompassing strategic planning process that will involve six work teams and as many as 100 faculty and administrators in a year-long process. But when big data analytics and artificial intelligence are used correctly and ethically, personalized learning experiences can be created, which may in … Recommendations can include enhancing their social network on campus by recommending students to connect with/trigger interactions with advisors to help with any social integration issues and access to self-service information. HANA is now a transaction database engine and is rapidly becoming an application development environment capable of processing relational and non-relational data in a distributed and potentially federated heterogeneous environment in which a single query could be processed via HANA and also via tools like Hadoop. 9360 W. Flamingo Rd. Commonly, this data is too large and too complex to be processed by traditional software. Like many universities, we had two separate and somewhat competing central units for supporting or conducting analysis. Pulling together our enterprise architects and best business intelligence (BI) folks, we stepped into a series of ideation meetings where we would collect our thoughts beforehand and then share and discuss. The results of that super-query are then materialized for speed.

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