The higher education industry is in the midst of a data revolution. Over the years, institutions have been collecting data from myriad sources – including platforms like learning-management systems, as well as in-person student interactions.
However, despite having hordes of information at their disposal, many institutions have yet to leverage data to enable student success. Instead, data has lived in a variety of silos across campus. Fortunately, ongoing global digitalisation has introduced new data analysis technologies that higher education institutions can use to improve how they engage with students and nurture their success.
The definition of ‘data analytics’ is far-reaching. Regardless, leveraging data to better understand students sets the stage for new, innovative approaches for improved success strategies.
Deliver additional support
Communication with students is a vital way for institutions to enable success.
With advancements in machine learning and artificial intelligence (AI), institutions are being presented with the opportunity to implement enhanced chatbots and personal assistants that can deliver 24/7 support to students. In many cases, these channels provide students with safe, convenient ways to ask questions using familiar language, instead of resorting to formalised, jargon-ridden documentation. Likewise, these AI-driven channels can free up staff so they can engage with students on more personal levels.
Data analytics solutions can help institutions pull information from the chatbots to identify trends, such as what types of concerns proliferate during specific points of the term or if there are abundant interactions regarding specific domains (IT concerns, bill payment, etc). This insight may help institutions identify areas of investment that will support student success.
Easing the path to graduation
Students who have changed majors or transferred from another institution often find themselves facing complex academic paths to graduation, which can hinder success. If students take classes that don’t apply to their degree or certification, for example, they may not have enough credits to graduate on time. To prevent this, advisors can gather insight into students’ academic trajectories or use predictive models that allow them to provide clear guidelines to completion.
Advisors can also monitor student success by looking at attendance or how well students perform in certain classes to forecast academic trajectories. Sometimes, specific combinations of student entry characteristics, scholarship packages and courses of study can result in an increased chance of not persisting. By analysing these trends, as well as insight gathered from chatbot interactions, advisors can identify students who may be at risk and take additional intervention steps to nurture their success.
Evaluate and enhance curriculum
Some data analysis tools can provide faculty and administrators with insight into the performance of specific academic programmes. Using this information, institutions can assess their academic portfolios to identify if they need to sunset programmes, invest more in specific courses, market programmes better, and much more. The challenge is that assessing programmes requires analysing data contained in many different areas, such as tuition revenue, instructional cost, enrolment, etc.
All this information needs to be brought together to paint a holistic picture.
Academic programme economics analytics tools can help institutions regularly and easily quantify their programmes’ performance and identify which programmes are being sought-after and completed by students. This information can help advisors guide students to completion or implement additional measures to maximise success.
As institutions find themselves in charge of managing growing volumes of data, new light is being shed on analysis tools.
By monitoring student performance, identifying trends, studying trajectories, assessing programme performance, and more, institutions will be better equipped to make decisions that can positively impact student experiences, engagement and success.
You might also like: Data analytics make DfE data accessible to UK apprenticeship providers
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The role of analytics in enabling student success
Meghan Turjanica
The higher education industry is in the midst of a data revolution. Over the years, institutions have been collecting data from myriad sources – including platforms like learning-management systems, as well as in-person student interactions.
However, despite having hordes of information at their disposal, many institutions have yet to leverage data to enable student success. Instead, data has lived in a variety of silos across campus. Fortunately, ongoing global digitalisation has introduced new data analysis technologies that higher education institutions can use to improve how they engage with students and nurture their success.
The definition of ‘data analytics’ is far-reaching. Regardless, leveraging data to better understand students sets the stage for new, innovative approaches for improved success strategies.
Deliver additional support
Communication with students is a vital way for institutions to enable success.
With advancements in machine learning and artificial intelligence (AI), institutions are being presented with the opportunity to implement enhanced chatbots and personal assistants that can deliver 24/7 support to students. In many cases, these channels provide students with safe, convenient ways to ask questions using familiar language, instead of resorting to formalised, jargon-ridden documentation. Likewise, these AI-driven channels can free up staff so they can engage with students on more personal levels.
Data analytics solutions can help institutions pull information from the chatbots to identify trends, such as what types of concerns proliferate during specific points of the term or if there are abundant interactions regarding specific domains (IT concerns, bill payment, etc). This insight may help institutions identify areas of investment that will support student success.
Easing the path to graduation
Students who have changed majors or transferred from another institution often find themselves facing complex academic paths to graduation, which can hinder success. If students take classes that don’t apply to their degree or certification, for example, they may not have enough credits to graduate on time. To prevent this, advisors can gather insight into students’ academic trajectories or use predictive models that allow them to provide clear guidelines to completion.
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Advisors can also monitor student success by looking at attendance or how well students perform in certain classes to forecast academic trajectories. Sometimes, specific combinations of student entry characteristics, scholarship packages and courses of study can result in an increased chance of not persisting. By analysing these trends, as well as insight gathered from chatbot interactions, advisors can identify students who may be at risk and take additional intervention steps to nurture their success.
Evaluate and enhance curriculum
Some data analysis tools can provide faculty and administrators with insight into the performance of specific academic programmes. Using this information, institutions can assess their academic portfolios to identify if they need to sunset programmes, invest more in specific courses, market programmes better, and much more. The challenge is that assessing programmes requires analysing data contained in many different areas, such as tuition revenue, instructional cost, enrolment, etc.
All this information needs to be brought together to paint a holistic picture.
Academic programme economics analytics tools can help institutions regularly and easily quantify their programmes’ performance and identify which programmes are being sought-after and completed by students. This information can help advisors guide students to completion or implement additional measures to maximise success.
As institutions find themselves in charge of managing growing volumes of data, new light is being shed on analysis tools.
By monitoring student performance, identifying trends, studying trajectories, assessing programme performance, and more, institutions will be better equipped to make decisions that can positively impact student experiences, engagement and success.
You might also like: Data analytics make DfE data accessible to UK apprenticeship providers
Advertisement
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