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Data science has played an increasingly important role in the education sector over the past several years. Between 2020 and 2027, Artificial Intelligence based investment in education alone is expected to grow from $1 billion to $20 billion. These investments are likely to transform not only the pedagogical approach, but also in the way educational organizations attract, train, and retain students and teachers.
One of the biggest impediments to quality education is the inability to scale. Classroom-based education relies on a ‘one size fits all’ approach that has time and again proven to be ineffective in training all students equally.
In recent times, the growth of virtual classrooms, and video-based lessons have helped level the field to a certain degree. But with machine learning and artificial intelligence, educators can truly impart personalized education at scale to their students.
Machine learning tools can help grade the proficiency of each student personally, while with AI, educators can build assignments and assessments tailored to each individual student. This way, students who need more help with their lessons can get it done while allowing prodigious learners with more advanced assessments and training.
Data science can also help educators build customized lesson plans based on requirements. For example, a vast majority of students in middle school experience summer slide during their break, and such AI tools can help build customized lessons to retrain these young minds based on the exact subjects they have regressed on.
While the impact of data science on learning has the greatest degree of visibility, there are other areas of application that can have a much more profound impact on the economy and growth of a country.
Data science can help governments study the impact of their policies on various parameters like student attendance, dropout rates, graduation rates, and so on. Further, the performance of graduating students at the workplace can be correlated further to identify their employability, and this can be used as a feedback loop to design better curriculum.
The largest investments in the use of data science in the educational sector has come from the institutions themselves. This is natural given the direct impact of its use on the bottom line of these organizations.
There have been several case studies on how the use of data science has helped institutions be more student-ready.
One example comes from Georgia State University that observed low graduation rates of just 32% in their university. Using data science, educators were able to identify at-risk students based on their test scores, attendance, and economic status. This helped the organization nearly double graduation rates to 54%.
Carnegie Mellon University used data analytics collected from student interactions to help design courses that had a near 18% improvement on learning gains for their Open Learning Initiative.
As an evolving field, there are new data science tools solving the various challenges facing the education sector every day. Here is a short list of tools and how they are impacting the education industry.
Carnegie Learning: Founded in 1998 by scientists from Carnegie Mellon University, this organization today offers AI-powered math education tools that help learners with personalized learning experiences. The machine learning algorithm helps build interactive learning lessons and offers real-time feedback to students.
Smart Sparrow: Similar to Carnegie Learning, Smart Sparrow uses machine learning and AI to build personalized interactive lessons that lets each student learn concepts at their own pace. The real-time feedback system lets learners identify areas of improvement without the need for a personal tutor.
Eklavvya: Eklavvya is an AI based tool with a number of different applications to help students prepare for their examinations and assessments. Their tools help students prepare for audio and video interviews, take part in psychometric assessments, analyze exam performance data, get assessed on screen, and perform a host of other things.
Turnitin: This is an AI-based tool targeting educators to help them ensure academic integrity. The tool uses machine learning to analyze academic submissions and compare them with a pre-existing database of academic content to identify plagiarism.
Descript: As courses and data science degrees go online, and students go global, there is an increasing need to offer transcription and subtitling of videos. Descript is an AI-based tool that educators use to automatically transcript their audio and video lessons. It also has an array of video editing tools that can assist educators in building professional video lessons with little to no editing skills.
While the use of data science has brought about a perceptible difference to the way the education system works, there are still some challenges surrounding its adoption.
The biggest issue is with the lack of regulations. In most countries, AI and machine learning related regulations are still in their infancy. As such, current laws do not really address the ethical and moral issues surrounding the use of these technologies.
The success of Artificial Intelligence and Machine learning systems depends to a major extent on the data samples used to train these systems. As such, a lot of such databases may be copyrighted, or personal. This would mean that the introduction of useful tools in this industry cannot come about without potentially violating copyright laws.
Take the example of the use-case involving the identification of plagiarism in academic work. In order for such a tool to work, it needs to index millions of copies of previous coursework that may be copyrighted or commercial. As such, building a commercially viable tool that does not potentially infringe on copyrights may be nigh impossible.
As things stand today, data science holds terrific promise in the education sector. However, tools that can cause a paradigm shift in the way current systems work won’t be possible unless governments allocate dedicated budgets to the growth and proliferation of such systems. That, along with friendly regulations that can make the growth of data science tools possible is the sole way to build a future where education can be transformed by data science.
