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write a report on how Big Data can be used to overcome the challenges of plagiarism in higher education institutions. You are required to write a report on the problems and limitations of existing...

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write a report on how Big Data can be used to overcome the challenges of plagiarism in higher education institutions. You are required to write a report on the problems and limitations of existing enterprise technology used in Higher Education institutions in handling large datasets and how Big Data technologies can be used to overcome them.

required to discuss if “Can we detect contract cheating using existing assessment data”? Contract cheating is a form of academic misconduct that, in some forms, involves students paying a third-party to produce an unsupervised assessment item that they subsequently submit as if it was their own work. You are required to address an appreciation of the business requirements and applications of Big Data technologies in Higher Education system. What different kind of knowledge and skills are required to use contemporary Big Data technologies to store, manipulate and analyse large unstructured data sets by Higher education institutions.

required to do extensive reading of more than 15 appropriate and relevant readings in the chosen topics in Big Data use case. Please do in-text referencing of all chosen readings. Newspaper and magazine reports should be limited to a maximum of 2.
Answered Same Day Jul 20, 2020 COIT20253 Central Queensland University


Anju Lata answered on Jul 24 2020
128 Votes
Business Intelligence using Big Data 20
Student Name:………..
Submitted to:…………..
The report analyzes the drawbacks of traditional Enterprise system and demonstrates the role of Big Data Analytics in overcoming the challenges. The study explains the applications of Data Analytics in removing the plagiarism in higher education institutes and universities. Lastly the paper elaborates the use of Big Data Analytics in identifying the contract cheaters, and lists the qualities and skills required to use the Big Data.
Keywords: Big Data; Enterprise System; Analytics; Contract Cheaters.
The major trends affecting the higher education institutes fall into four categories: Educational (performance assessment and measurement, changing pedagogy and modifying learning requirements), Social (diverse backgrounds of students), Technological (new data sources and emerging platforms) and Economic (Globalization, funding, Stakeholders, Accountability, and transparency). To sustain the emerging trends, the institutes need to develop and utilize advanced technologies to facilitate commercialization, research, analysis and adequate transfer of knowledge. As global changes are getting more prevalent, the development of new technology is also influencing the research areas. The technological advancements like accessibility to online classroom sessions, and development of better computing devices, has made the learning more accessible. Now the classrooms have become e- classrooms where digital gadgets are used to demonstrate the chapters effectively for the students.
With rapidly developing digital learning technology, the sharing of knowledge and data has grown to a new level for the purpose of education and training. Big data is a fastest growing approach in the field of knowledge sharing facilitating the purpose of e-learning and research. This paper elaborates the Big Data and its role in preventing the plagiarism in e-learning of higher educational institutes. It also presents an overview of the limitations and problems associated with traditional enterprise technology and how the big data is used to overcome the challenges.
Big Data can be defined as a huge volume of data which may be of different data types and can be processed at a fast velocity. The data is so big that its processing is not possible with traditional data processing software.
According to Oxford Dictionaries online, Big Data is a large data set that is computationally analyzed to disclose trends, pattern, and relationship in terms of human interactions and behaviors. According to Daniel (2015), Big Data involves all the advanced technologies used to record, save, interpret and analyze large-sized data. The tools, algorithms and software used to collect, store, process, index and analyze the Big Data is known as Data Analytics.
Role of Big Data to overcome Plagiarism
Big data analyze large quantities of data through regression models to identify the similarity at word or sentence level (Banjade et al, 2015). Other methods like Resultant Vector based methods and Polish Internet Semantic Search engine like Natively Enhanced Knowledge Sharing Technology (NEKST) can also be used to detect plagiarism in large-sized analyzed content.
The process to retrieve the value of data in any Organisation involves three steps: Data Collection (to recognize and store the relevant data), Analysis of data (to identify relations, associations and similarity patterns), and Application (to make the analyzed data available for the users).
Big Data may positively influence the decision making and many other aspects of learning. Big Data may streamline the educational content, may provide the students with the customized learning environment, may make the academic resources more efficient, and may effectively improve the student access to these resources.
A lot of data is stored and collected in the databases of higher education institutes. Some of them also facilitate online teaching-learning. Moreover, there are many digital li
aries and online repositories, with their co
esponding tools. The student data is maintained in the Information Systems of the Universities, along with various other departments of the institutions. The data also comes in various formats of text, video, audio, and graphics. When the students log on to any electronic devices, their details of access can be retrieved from anywhere in the network. Various advanced data analytics techniques are used to explore this data.
Data can be retrieved by the scholars through many devices like mobiles, palmtops, tablets, television, game consoles and navigation displays. Now assimilating all this data collected through various different sources, to give it a shape and pattern is very important for the higher education institutes.
Various types of Data Analytics tools can be used to analyze and make automated decisions about whether the retrieved content is similar to others. In other words, it helps to detect plagiarism. These tools can check millions of documents which are stored online and can identify the redundant or duplicate content. The Universities and Higher education institutes are facing the challenges of storing, analyzing and managing a large amount of data. Big Data technology extracts useful information out of this large accumulation of data to provide
oader and better solutions. Multiple technologies like Artificial Intelligence, Data Mining tools and Hadoop can also be used together. Hadoop facilitates parallel computing to execute multiple algorithms simultaneously over a number of machines.
Big Data can make the learning more personalized to handle the requirements of diversity of students. The educational institutes can accurately identify the high risk students by tracking all their online activities, participation in discussion forums, time spent in doing assignments and reading patterns (Kellen, Bu
and Recktenwald, 2013). The education institutes can analyze the habits of students to assess their probability of academic success. Data is retrieved from all the sources available online.
The above graph shows a new mobile application to collect the student information. The data about each student remains unique and is shared across the different education institutes to verify the genuine candidate.
Limitations and Problems of Present Enterprise Technology
As the size of data is growing exponentially, it is not possible to manage and store such a large data using existing enterprise technology. The enterprise technology focuses more on the student data to build a data warehouse, which is a central repository so that the analysts across different universities could use it. However, the complex vendor data warehouse systems were expensive. The Enterprise Resource Planning (ERP) management information system had quite less speed and volume. Updating such ERP systems with dimension tables and facts require a lot of resources and efforts. Retrieving data sets from different sources is critical when performed with traditional enterprise technology.
Earlier the university staff and the analysts face a highly repetitive cycle which aims to make a model, reviewing the quality of data, sharing the status with the academic leaders and consistently repeating the process until the model is complete. However, many models were not perfect in answering the queries related to quality and interpretation of data. Business managers had to wait many days and weeks to seek answers to the database queries. There were inadequate technology and architecture to increase the speed of repetitive data modeling cycle.
All the institutes had their own data warehouses managed by different IT vendors. Mostly the data was incomplete and improperly classified. Multiple...

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