Data Driving the Modern Education

Education, despite being one of the most important pillars of our lives, hasn’t always been given the importance it deserves. We have taken this sector for granted time and again, missing out on an important opportunity to nurture the sphere’s growth on each of those occasions. However, all the mistakes were seemingly forgiven when we brought in technology to the education’s fold. The results were largely instantaneous, as suddenly we had a whole host of tools we could use to teach the students in the most comprehensible of ways. This not only fostered improvement on the academic front, but it also bolstered their intrinsic capabilities of handing different situations. However, technology’s association with education has been far from smooth. More often than not, we see educational institutions struggling to determine an optimal way of using these tools under a given set of circumstances. With institutions failing to extract all the scope put forth by the concept of EdTech, it pretty much brings us back to square one. However, this time we are not only failing to elevate the students, but we are also shrinking the resource tank at a much quicker rate. To tackle this situation, we have gone back to technology. The arrival of advanced tech promises a more guided journey towards EdTech excellence. One of the tools that are proving to be of utmost help is data analytics. These meticulously generated information is already making a noticeable difference across the board by assisting schools and colleges in understanding their students better. Nevertheless, to squeeze maximum benefit out of this technology, the institutions must have a clear idea of their own capabilities first. This introspection is now made possible by a creation from Educause.

Educause has created a self-assessment tool that does the work of gauging where a particular institution stands at present in their pursuit to use analytics for improving education quality. The self-assessment tool essentially presents you with a set of 26 questions that cover all the metrics required in the making of a robust data-centric system. In an attempt to make the whole procedure more productive, the capabilities adjudged in this questionnaire are divided into five separate categories. These categories are workforce, data governance, data management, leadership, and data-informed culture.

The category of workforce looks into the data literacy of the institution as whole. It also pays close attention to the state of communication channels, as well as the kind of personnel hired to fulfil analytics-related duties in particular.

Data governance and management cover areas like policies practiced by the institution in terms of data usage. Furthermore, the category takes into consideration the methodologies and infrastructure in place for facilitating data-driven decisions.

The leadership category examines the understanding of data amongst institution’s decision-makers, while data-informed culture is there to look for collaboration avenues, and the legitimacy of the data metrics being applied by the institution.




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