Data Analytics is the process of drawing and interpreting conclusions and decisions from huge data sets through varied techniques of data mining, predictive analytics, clustering, classification and visualization. In Education sector, a lot of data flows in and out every day in form of students, faculty, staff data, attendance records, test, exam and mark sheets data, textbooks reference, cafeteria menu etc. but little has been done with this structured, semi-structured and unstructured data on how to store it in a meaningful manner and draw effective conclusions and reports from it. Now days, lot of technological softwares have crept into Education in form of advanced softwares, flipped classrooms, smart boards etc. but yet a lot of opportunities are awaited for better data gathering and analysis.
Education analytics and data mining captures learning patterns of students, evaluation of effectiveness of study material, time and effort spent on each activity, attendance, frequency of meetings with faculty members, test and assignment submission on time etc. For example, Moodle, or Desire2Learn, Learning Management Systems (LMS) help to track time invested, frequency of postings, login attempts and development track of students much similar to what Google Analytics does on web and social network traffic. Such LMS techniques though are good to track completion of an assignment but fail before analytics where distributed social and information networks are more authentic and trusted through visual dashboards and scoreboards interpretation.
Data and Learning Analytic tools, if applied to Education, will help to monitor how inputs produce and influence output and various factors that contribute to learner success. It helps each student to study and progress in a course as per their level, understanding, convenience and learning capacity as data analytics ensures flexibility, adaptability, regular updates and retests in courses taught making them more concurrent with industry and academic standards of other National and International institutes. It helps in analyzing a student not only within campus but in external world too.
Artificial Intelligence, Machine learning, neural networks, fuzzy logic, language recognition are thus now combined with routine teaching pedagogy and collaborative learning. Massive Open Online Courses (MOOC), Open Study Tools, Learning Management System (LMS), SAP etc. are highly promoting analytics in Education sector. However, it is still facing some issues related to capturing soft elements of learning such as motivation, group discussions and face to face interactions. Data privacy, security and ownership are yet to be resolved as to determine who will be accountable for maintaining data warehouse, data access for different sets of users, access privileges, monitoring authentic usage of resources, correct dashboard interpretations etc are currently a big question to be answered. But if analytics is seriously taken in Education sector, emphasis should be more on techniques of evaluating the analytics models and understanding in which contexts those analytics are not valid. The complex challenges that institutes face can, at least partially, be resolved through analytics applications.
By Ms Palak Gupta