As technological advancements in the creation and storage of information have progressed, the utilization of data to inform decision-making processes has grown manifold. This has led to the emergence of disciplines like data science and analytics and, subsequently, big data. The rapid increase in processing power has led to the development of smaller and faster processors that can be linked to perform far more complex tasks and decipher massive data sets. While big data has seen a fairly large degree of discussion in the numerous sectors of human activity, utilization of this discipline in education has been fairly limited. It is understood that machine learning and artificial intelligence are technological instruments that must be utilized with a degree of caution; however, it is these algorithms that will allow humans to decode larger and more complex blocks of data. With that in mind, it is clear that big data requires a component of machine learning and other neural network architecture to produce useful inferences from raw information.
While conversations are on about the impact of artificial intelligence on education and also the utilization of analytics in institutions, justifying the place of big data in education is equally important, as the analysis of information often comes as a precursor to the deployment of AI-aided solutions. Vast amounts of information are generated by each institution every year, and keeping track of these details becomes increasingly cumbersome with rudimentary technical tools. Structuring all of this information and drawing crucial insights from them will require specialized tools in the form of big data to aid problem-specific solutions for universities and schools. Big data in education might also have the potential to influence policy-making decisions, which will impact regulations and the implementation of AI in education at large.
What is Big Data?
Big data has been instrumental in helping decision-making processes across industries.
Big data refers to the discipline of making sense of information that has a vast amount of variety and is generated in incremental volumes while also arriving at greater velocities. Essentially, this refers to the synthesis of widely diverse sets of information in short durations of time and vast quantities. The basis of big data lies in the “3 V’s” which refer to variety, volume, and velocity. Data that is generated in this manner cannot be efficiently handled by traditional and more rudimentary software algorithms. Big data often deals with information that is from a large array of sources and belongs to several different categories. Deciphering the data after assigning value to it forms one of the main aspects of this discipline.
Big data has been the backbone of consistent development and steady progress in language models such as the popular GPT series, which has come out with its most advanced GPT-4 iteration in recent times. The volumes entail vast, unrelated, undeciphered, and low-density information blocks, which often necessitate artificial intelligence and data science to address these challenges. This makes both subsets of information technology closely related. More recently, big data has also transitioned to include two other crucial parameters—veracity and value. Along with the management of data volumes, its rate of storage, and timescales, deciphering the validity of data and its innate value is just as important. Modern inventions like generative AI constantly look to upgrade the validity and value of their data sets to prevent shortcomings like bias and AI hallucinations.
Big Data in Education: Decoding Potential Applications
Utilizing big data in education can result in tangible improvements for students and teachers.
Apart from adaptive learning and personalization of education, big data and machine learning find other important uses that can help academicians and regulatory bodies make key decisions regarding the future of education. This will also lead to students making the best of their courses. The first of these uses involves precision-based educational solutions as the necessity for specificity increases. Educators are increasingly becoming aware of the need to define educational concerns and problems faced by students succinctly. Precise definitions of problems allow for the pointed collection of data and relevant information. AI can then infer patterns and crucial parameters from the collected information. Big data in education enables precise problem diagnostics, predictions of outcomes, and promotes a goal-based approach to attaining better student outcomes. This also becomes more relevant as the impact of generative AI on education becomes widespread and the entire education system shifts from a rigid one-size-fits-all approach to a more nuanced and hybrid philosophy that is willing to accept degrees of variability.
Artificial intelligence for big data in education can also change the way we approach scoring and grading. Statistical analysis of performance data can put together student metrics that are more accurate and specific, allowing teachers to understand areas that need improvement for each student. Moreover, these machine learning models can also be used to predict student outcomes and chart a career path for the students based on their innate capabilities and performance data. That’s not all, efficient big data solutions can detect issues that might be hampering student performance, allowing institutions to take necessary steps to bridge potential gaps. Big data, machine learning, artificial intelligence and data science also have the potential to permanently alter the way research is conducted in an academic environment, further helping institutions boost their credentials in a highly competitive scholastic domain.
The Implications of Big Data in Academia
The future of big data and artificial intelligence in education are interlinked.
As big data research and capabilities grow further, the utilization of this novel technological discipline by educational institutions seems imminent. With a considerable push already being made to help institutions deploy technology more effectively, the process of making sense of data and utilizing crucial insights can be exploited to help students make the best of their time at university. Big data applications can range from optimizing each course to overcome student challenges to designing course material to ensure students remain attentive in their classes. While regulations and ethical principles are still being drafted for the deployment of artificial intelligence and machine learning in education, big data and analytics have the potential to branch into just about any stream of human activity that relies on information to define its approach. As big data is perfectly suited to address these needs, its foray into education and academics might just be the biggest turning point in the history of pedagogy.