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Professor Dr. Syed Akhter Hossain

Introduction
Big data analytics is a systematic process of examining large data sets, originated from social media and other related sources, containing a variety of data types: unstructured data including text, image, video, link etc to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful domain specific information from the data. The analytical findings can lead to more productive processes, effective marketing, new revenue opportunities, better customer services, and improved operational processes.

The main goal of big data analytics is to help organizations take effective and pragmatic business decisions through enabling data scientists, predictive modelers and other analytics professionals to analyze large volumes of transaction data, as well as other forms of data that may be untapped by conventional business intelligence programs. This data may originate from web server logs and Internet clickstream data, social media content and social network activity reports, text from customer emails and survey responses, mobile-phone call detail records and machine data captured by sensors connected to the Internet of Things (IoT).

Big data analytics in education thus refers to the data analytics in the education domain where data is primarily originates from the teacher, student, parents, and related networks. The students in the education domain start their journey in the kindergarten and over the time from school to high school through college with further higher studies in the university. At every phase of these vibrant colorful education journey, student keeps performing and creating data traces that leads to either success or failure of any kind to each of them. These huge data bodily engaged with the student draws special attention today and demand meaningful data analytics to serve and cater quality and personalized education services to the student that meets the specific needs and make a student better equipped for the nation building activity. This education data analytics will play a vital role in cognitive domain and behavior analysis for the student. In this article, a perspective is presented on the education data analytics with specific cases and examples along with the future of data analytics in education.

Big Data in Education and Analytics
Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools. It is a fine grain of information for example customer experience, organizational processes, emergent trends etc. Based on the present advancement of technology, more than 2.5 quintillion (A quintillion is 10^18) of data are generated every day. All these data originates from social media sites, sensors, digital photos, business transactions, location based data etc.

As seen from the Figure 2, huge challenge is involved in the data analytics due to volume, velocity, variety and veracity. Real-time data analytics is still in research. The big data in education is associated with a student in the digital domain.

A student is associated with Parents, Teachers, Peers and Mentors and Coaches in them domain of human network. While in the digital domain, a student is interacting with information management and communication tools, learning communities, using knowledge building tools, personal learning network, online tutoring and guided courses, joining with expertise and authoritative sources, and always connected to social media and peers with common interests. This huge network with people and information resources emblaze the source of big data in education for a student.

In big data analytics, there are two forms: Predictive analytics and data mining and Business Intelligence.

Big Data and Data Analytics in Education: Some Scenarios

Big data in education and data analytics in already in practice for quite some time. Top universities in North America are taking initiatives to foster the data analytics in education. Among them, one very prominent initiative is “Open Learning Initiative” (http://oli.cmu.edu) for student learning at Carnegie Melon University, USA. In this platform, more than 500,000 students are interacting and it is a web based learning environments. Students are using self directed learning. The instructional software are adaptive to student learning style. This platform help students improving individual performances, enhance course redesign to meet the market as well as student demand, and help predicting the future performances.

Another good example is the course enrollment system of Saddleback College in US which is used by more than 40,000 students (http://saddleback.edu). The information system provides a course recommendation engine which is based on web services architecture and act like personalization system. The system maintains student profiles, past courses, and schedules. It provides different tools including tutors, time management tool, life planning resources etc.

Among other ongoing initiatives, “Course Success” of Purdue University, USA also an example to follow. The information system is based on education data analytics and big data and it is called “Course Signals”. The system acts like a early warning system (http://itap. purdue.edu/learning/tools/signals). The system maintains student study patterns and performances.

Another interesting example is “Student Life Style Management” at Persistence Plus which is a platform for learning
communities.

Let us now see the usage of data analytics at some of the top universities. In University of Michigan, USA: learning analytics task force is formed in the year 2012. The university has planned to use learning analytics to improve learning and university productivity. As a result the university developed community support structure to assist researchers, faculty members and others in utilizing analytics in teaching and learning.

USA serves 180,000 students per year. In the information system of the university, learning analytics is used to help improve overall quality and effectiveness of the university. As an outcome, the university developed a platform called “Desire2Learn”. Queensland University of Technology, Australia is serving 45,000 students. In the information system of the university, data analytics is used to identify students who are predicted to be at risk of disengaging from their studies. The information system uses behavioral and cognitive indicators. As an outcome, the university retains students who are potentially to be the dropped out. It is clear that education data analytics plays a vital role and will lead in the future education success. There are challenge lies with development and deployment of learning analytics policies and strategies. It is the game of knowledgeable people linked to pedagogy and data analytics.

Big Data in Education: Prospects and Challenges


Big data in education is just emerging and will continue to evolve in coming 20 years. Despite the huge growth of big data, the development of data analytics algorithms is still in research. The prospects of big data in education are as follows:
  1.Enable Institutional educational data inventory
  2. Creating centralized databases that are accessible to universities, decision makers and researchers
  3.Digital systems enable real-time assessment and more effective systems for mining information
  4. Quality business process development and alignments for attaining specific goals
  5.Educational TQM and Traceability
  5. Projection on trends of skills development Besides, the challenges are as follows:
  6. Implementation of more sophisticated forms of analytics such as machine learning and natural language     
  7.Addressing stakeholder concerns around educational data of learners including privacy and ethics
  8.The limited capacity of universities in implementing analytics strategies

Big Data in Education: Prospects and Challenges
Big data in education is just emerging and will continue to evolve in coming 20 years. Despite the
huge growth of big data, the development of data analytics algorithms is still in research. The
prospects of big data in education are as follows:
   1.Enable Institutional educational data inventory
   2. Creating centralized databases that are accessible to universities, decision makers and
   researchers
   3.Digital systems enable real-time assessment and more effective systems for mining
   information
   4.Quality business process development and alignments for attaining specific goals
   5.Educational TQM and Traceability
   6.Projection on trends of skills development
   Besides, the challenges are as follows:
   7.Implementation of more sophisticated forms of analytics such as machine learning and
   natural language processing requires high-level knowledge
   8.Addressing stakeholder concerns around educational data of learners including privacy
   and ethics
   9.The limited capacity of universities in implementing analytics strategies

Conclusion
It is undoubtedly challenging and phenomenal that data analytics in education can drive substantial gains in productivity. Within the educational context this relates to both student learning gains, and also improvements in program delivery through curriculum efficiencies and enhanced quality. The prospects and challenges of big data in education indicate potential development agenda. From the presented cases, it is observed that national data and analytics strategies and frameworks plays key role in the successful utilization of data analytics in education.

 




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