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Jose, Deepa
- Big Data and its Application Areas in Higher Education
Abstract Views :297 |
PDF Views:175
Authors
Affiliations
1 MCA Department, Y.M.T College of Management, Kharghar, IN
1 MCA Department, Y.M.T College of Management, Kharghar, IN
Source
AADYA -National Journal of Management and Technology, Vol 6 (2016), Pagination: 90-93Abstract
Data mining and predictive analytics-collectively referred to as "big data"-are increasingly used in higher education to classify students and predict student behavior. But while the potential benefits of such techniques are significant, realizing them presents a range of ethical and social challenges. The immediate challenge considers the extent to which data mining's outcomes are themselves ethical with respect to both individuals and institutions. A deep challenge, not readily apparent to institutional researchers or administrators, considers the implications of uncritical understanding of the scientific basis of data mining. These challenges can be met by understanding data mining as part of a value-laden nexus of problems, models, and interventions; by protecting the contextual integrity of information flows; and by ensuring both the scientific and normative validity of data mining applications.References
- Ayesha, Shaeela, T Mustafa, AR Sattar, and MI Khan. “Data Mining Model for Higher Education System.” Eu-ropen Journal of Scientific Research 43, no. 1 (2010): 24–29.
- Baepler, Paul, and Cynthia James Murdoch. “Academic Analytics and Data Mining in Higher Education.” Inter-national Journal for the Scholarship of Teaching and Learning 4,no.2(2010).http://academics.georgisouthern.edu/ijsotl/v4n2/essays_about_sotl/PDFs/_BaeplerMurdoch.pdf .
- Baez, Benjamin, and Deron Boyles. The Politics of Inquiry: Education Research and the “Culture of Science.” Albany, NY: State University of New York Press, 2009.
- Baker, Ryan S.J.d. “Data Mining for Education.” In International Encyclopedia of Education, edited by B. McGaw, P. Peterson, and E. Baker. 3rd Edition. Oxford: Elsevier, 2010. http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Data+Mining+for+Education#3 http://users.wpi.edu/ rsbaker/Encyclopedia Chapter Draft v10 -fw.pdf.
- Baradwaj, Brijesh Kumar, and Saurabh Pal. “Mining Educational Data to Analyze Students’ Performance.” In-ternational Journal of Advanced Computer Science and Applications 2, no. 6 (2011): 63– 69.
- Danna, Anthony, and OH Gandy. “All That Glitters Is Not Gold: Digging Beneath the Surface of Data Mining.” Journal of Business Ethics 40, no. 4 (2002): 373–386.
- Deliso, Meredith. How Big Data Is Changing the College Experience, 2012. http://www.onlinedegrees.org/how-big-data-is-changing-the-college-experience/.
- Dworkin, Ronald. “Autonomy.” In Edited by Robert E. Goodin & Phillip Pettit, A Companion to Contemporary Political Philosophy.
- Big Data in Higher Education
Abstract Views :295 |
PDF Views:167
Authors
Affiliations
1 YMT College of Management, IN
1 YMT College of Management, IN
Source
AADYA -National Journal of Management and Technology, Vol 3 (2014), Pagination:Abstract
Data mining and predictive analytics-collectively referred to as "big data"-are increasingly used in higher education to classify students and predict student behavior. But while the potential benefits of such techniques are significant, realizing them presents a range of ethical and social challenges. The immediate challenge con-siders the extent to which data mining's outcomes are themselves ethical with respect to both individuals and institutions. A deep challenge, not readily apparent to institutional researchers or administrators, considers the implications of uncritical understanding of the scientific basis of data mining. These challenges can be met by understanding data mining as part of a value-laden nexus of problems, models, and interventions; by protecting the contextual integrity of information flows; and by ensuring both the scientific and normative validity of data mining applications.- Fuzzy Optimized Congestion Control in TCP/IP
Abstract Views :247 |
PDF Views:153
Authors
Affiliations
1 MCA Department, Y.M.T College of Management, Kharghar, Navi Mumbai, Maharashtra, IN
1 MCA Department, Y.M.T College of Management, Kharghar, Navi Mumbai, Maharashtra, IN
Source
AADYA -National Journal of Management and Technology, Vol 1 (2012), Pagination: 83-87Abstract
Congestion occurs in the network when arrival rate to a router is greater than its departure rate. In this paper, using fuzzy logic approach, we have proposed a modified TCP delay-based congestion avoidance mechanism which is based on traditional TCP-Africa algorithm. Here we present our fuzzy controller which is expected to act as a congestion controller in the routers. The proposed fuzzy controller, uses queue delay and link capacity as input linguistic variables. The output of fuzzy controller is the window size to which the sending window must be adjusted. Both the current standard and most of the experimental congestion control methods are fundamentally loss-based, that is they rely on packet loss to detect that the network is above full capacity.Keywords
Fuzzy, TCP/IP, Bandwidth, Congestion, Queue Delay, Window Size.- Big Data in Education Sector
Abstract Views :493 |
PDF Views:230
Authors
Affiliations
1 MCA Department, Y.M.T College of Management, Kharghar, Navi Mumbai, Maharashtra, IN
1 MCA Department, Y.M.T College of Management, Kharghar, Navi Mumbai, Maharashtra, IN
Source
AADYA -National Journal of Management and Technology, Vol 7, No 2 (2017), Pagination: 33-37Abstract
Universities either public or private and its colleges enroll thousands of students into various courses or programs every year. They collect information from students at the time of admissions and store the same in computers. Understanding the benefits of data is essential from business point of view. Data can be used for classifying and predicting the students behaviour, performance, dropouts as well as teachers' performance. Therefore, this paper examines the role of data mining in an education sector. In addition, lays emphasis on application of data mining that contribute to offer competitive courses and improve their business. Data mining and predictive analytics-collectively referred to as "big data"-are increasingly used in higher education to classify students and predict student behavior. But while the potential benefits of such techniques are significant, realizing them presents a range of ethical and social challenges. The immediate challenge considers the extent to which data mining's outcomes are themselves ethical with respect to both individuals and institutions. A deep challenge, not readily apparent to institutional researchers or administrators, considers the implications of uncritical understanding of the scientific basis of data mining. These challenges can be met by understanding data mining as part of a value-laden nexus of problems, models, and interventions; by protecting the contextual integrity of information flows; and by ensuring both the scientific and normative validity of data mining applications.References
- Ayesha, Shaeela, T Mustafa, AR Sattar, and MI Khan. “Data Mining Model for Higher Education System.” Eu-ropen Journal of Scientific Research 43, no. 1 (2010): 24–29.
- Baepler, Paul, and Cynthia James Murdoch. “Academic Analytics and Data Mining in Higher Education.” Inter-national Journal for the Scholarship of Teaching and Learning 4, no. 2 (2010). http://academics.georgi-asouthern.edu/ijsotl/v4n2/essays_about_sotl/PDFs/_BaeplerMurdoch.pdf.
- Baez, Benjamin, and Deron Boyles. The Politics of Inquiry: Education Research and the “Culture of Science.” Albany, NY: State University of New York Press, 2009.
- Baker, Ryan S.J.d. “Data Mining for Education.” In International Encyclopedia of Education, edited by B. McGaw, P. Peterson, and E. Baker. 3rd Edition. Oxford: Elsevier, 2010. http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Data+Mining+for+Education# 3 http://users.wpi.edu/ rsbaker/Encyclopedia Chapter Draft v10 -fw.pdf.
- Baradwaj, Brijesh Kumar, and Saurabh Pal. “Mining Educational Data to Analyze Students’ Performance.” In-ternational Journal of Advanced Computer Science and Applications 2, no. 6 (2011): 63–69.
- Danna, Anthony, and OH Gandy. “All That Glitters Is Not Gold: Digging Beneath the Surface of Data Mining.” Journal of Business Ethics 40, no. 4 (2002): 373–386.
- Deliso, Meredith. How Big Data Is Changing the College Experience, 2012. http://www.onlinedegrees.org/how-big-data-is-changing-the-college-experience/.
- Dworkin, Ronald. “Autonomy.” In Edited by Robert E. Goodin & Phillip Pettit, A Companion to Contemporary Political Philosophy.