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In a rapidly evolving world, the education sector must adapt to the needs of its students just as corporations adapt to their markets. There is, therefore, a growing need for education providers to tailor the content of their curricula to fit the requirements of the workplace. In much the same way, there is also a need for applicants to be able to evidence their skills and attributes in an unbiased, objective form that is capable of reflecting all forms of learning and work experience. This is what TiiQu’s CEO and founder, Laura Degiovanni, spoke about at the recent ASIC International Education Conference: Education Beyond 2021.
It is no secret that the processes in place at large organizations responsible for hiring and promotion are subject to bias and outcome inequality. This is partly because employers make decisions about applicants based on purely subjective information, and
after all, a decision can only be as objective as the information on which it is made. Furthermore, the way in which sources of information are assembled is often inherently subjective.
This is sometimes due to HR departments lacking granular, tangible information about candidates, as well as unfair internal promotion standards.
To say nothing of the negative impact this has on individuals trying to advance their careers, hiring errors can cost companies as much as $240,000 each year, and can result in project failure. In extreme cases, an ill-informed hiring decision can even cause the company or institution itself to fail.
The problem is that one’s professional life is merely a series of small steps, each of which represents a learning experience, educating and improving us throughout our careers. Unsurprisingly, this is a difficult thing to put across on paper, and both applicants and companies struggle to generate an accurate account of what experience, qualifications and education an individual actually possesses. This is mainly because employers’ insight into an applicant’s skills is limited by the standard format in which most jobs are applied for. Usually, a job application is made up of a CV, which lists qualifications and relevant experience, some personal details and a few recommendations from previous employers. What this format does not accommodate for are the so-called ‘soft skills’ that an applicant might already possess. By soft skills, I refer to things such as critical thinking, time management, interpersonal/communicative abilities and creativity.
The impact of this is that both employers and employees lose out. Employers fail in as much as 50% of their hiring, and employees fail to find the right job for their skillset. In turn, the broader problem is that the schooling system fails to empower both the people it is charged with educating, as well as the broader society in which they live.
When it comes to development and learning, there is a misfit between what educational institutions teach and what businesses actually require of their employees. What we learn today is linked to what we deliver tomorrow, but all too often the skills we learn are outdated by the time we come to use them. This doesn’t necessarily need to be the case. Big data, machine learning and psychology are already used in the education sector to benchmark students’ performance, to gauge progress, and to predict career trajectories. However, a prediction can only be as reliable as the information on which the prediction is made.
One way of refining predictions is by drawing a wealth of information from multiple sources. We already know, for instance, that when multiple parties collaborate on a project, the more the parties communicate and learn from one another, resulting in a better outcome. In much the same way, generating skills for a rapidly changing labour market is an increasingly collaborative endeavour.
In order for educational institutions to meet the demands of the future’s labour market, there must be greater communication between the professional and educational sectors. This would involve providing feedback on what sorts of skills are most necessary for particular fields, to ensure that when an individual leaves education, they are adequately prepared for the world of work. This is an opportunity that is currently being missed.
Is the education sector aware of how their teaching impacts on the labour market? Is it aware of what soft or hard skills will be required from applicants for particular jobs?
Some will argue that the number of posted jobs can tell whether, for instance, machine learning specialists are more sought after than blockchain architects. However, the existence of demand does not show whether or not the skills learned on a specific Masters in Artificial Intelligence and ML effectively meet the requirements of a career in AI/ML. In other words, this kind of analysis is not precise enough to inform the teaching that institutions offer.
Blockchain technology has the potential to revolutionize the way employers and the education sector relate to one another. For instance, TiiQu uses blockchain technology to create a traceable, irrefutable data link between learners and their skills, meaning that when they leave education and come to apply for a job, they are able to prove their full potential.
With all this in mind, let’s look at some possible concepts of what higher education might look like in the future, as well as how data analytics might play a part in driving its evolution.
The four scenarios:
From the Ernst & Young report titled ‘Can the universities of today lead learning for tomorrow?’ with the aim to stress-test new policies, strategies and plans.
The 4 scenarios are
1 | Champion University
Universities controlled by the Government represent strategic national assets.
Most students enrol in traditional undergraduate and graduate degree programs.
Universities streamline operations by transforming service delivery and administration
2 | Commercial University
The government requires universities to be financially independent
Students favour degree programs that offer work-integrated learning.
Universities reposition by collaborating on teaching and research with industry
3 | Disruptor University
Government deregulates the sector to drive competition and efficiency.
Continuous learners prefer on-demand micro-certificates
Universities expand into new markets and services and compete against a range of new local and global educational services providers.
4 | Virtual University
Government restructures the tertiary sector to integrate universities and vocational institutes, prioritising training and employability outcomes as humans begin to be replaced by machines.
Continuous learners are the majority, preferring unbundled courses delivered flexibly and online. Universities restructure into networks that share digital platforms.
Let’s leave the four scenarios here and think about how Universities can benefit from analytics to achieve any of those scenarios:
Every time a student interacts with their university – be it going to the library, logging into their virtual learning environment or submitting assessments online – they leave behind a digital footprint. The measurement, collection, analysis and reporting of these data is known as Learning Analytics, which can provide insight into the attainment, progression and learning styles of students. The aim of capturing, archiving, and analysing student profiles and behaviours is to facilitate improved institutional decision-making, advancements in learning outcomes for at-risk students, greater trust in institutions due to transparency and significant developments in learning techniques. This technology is already in use in various forms, and we shall outline some examples of this as follows:
Student Information Systems:
SIS is an acronym for student information system. SISs hold a majority of the information students disclose on their applications for admission, their enrolment records, and their academic history. Over time, their digital records may be augmented with other information, including financial aid awards, involvement on campus, disciplinary and criminal records, and personal health information.
Learning Management System analytics:
The most common application of learning analytics technology is in the context of an institution’s learning management system (LMS). LMSs are traditionally used to support online or hybrid teaching environments, within which students interact with various learning resources and work collaboratively
Learning analytics systems record student behaviours as students navigate and interact with their peers and the digital space. LMS analytics can detail the date, time, and duration of students’ digital movements, including if, when, and for how long they read an electronic text or took an online quiz. Other statistics detail a student’s overall completion rate of a course, whether or not a student is predicted to succeed in the course, and map the strength of a student’s peer-to-peer/peer-to-instructor network. LMSs embedded within learning analytics tools create information dashboards from which instructors – and learners themselves – can monitor, benchmark and track their progress.
eAdvising analytics
eAdvising analytics rely heavily on data held within institutional SISs. The historical academic information, alongside current academic information, such as course grades and enrolment records, are crucial for predictive eAdvising analytics.
Some include a recommendation engine that suggests courses based on students’ academic profiles and considers their course path with the past success of their peers.
Other eAdvising systems warn students when they stray from their chosen path, blocking them completely from registering for courses if they fail to return to a predetermined set of courses; or if students are deemed to be ‘at risk,’ a professional advisor can target them and provide closer support if required.
eAdvising systems pull additional data from sources like personality profiles and geolocation information from student ID card swipes.
Institutional analytics:
Institutional analytics compare student activity and learning metrics within and between courses, departments, and colleges across a university. These measures are increasingly helpful when institutions and their departments need to respond to stakeholder pressures to demonstrate institutional effectiveness and to enable them to meet government reporting requirements more easily and accurately.
Moving on from analytics, when it comes to supporting internal staff and learners with data, universities dispose of all data they use and can benchmark their internal performance with big data (see Unizin Consortium, a data community that enables institutions to access analytical data that institutions then use to refine the teaching they provide). However, when it comes to adapting learning to the market demand, educational institutions have no way of measuring to what extent learners will be able to meet the demands of the jobs market.
The future of education as described by scenarios two, three and four are about learning hubs in which multiple providers collaborate across boundaries on multiple platforms so that education can become more like a collection of micro-modules which apply directly to the sector in which the student wishes to start a career.
In short, skills are expected to fit into the labour market.
All this requires moving from analytics simply referred to management, administration and report of learners’ experience to analytics that helps to improve market compatibility.
Privacy is a challenge that can be solved
The usage of personal student data presents unique privacy concerns.
The information students reveal about themselves on applications for admission, and materials in support of their applications, is not trivial; in fact, it is often highly sensitive. Admission applications include questions related to a student’s academic achievement, including transcripts and standardized test scores;
professional ambitions; demographic and socioeconomic information. In total, this information serves to build comprehensive individual profiles.
Within a multi-stakeholder education ecosystem, where students’ information needs to be shared, simply because policies or memoranda of understanding exist that detail how student data should be used, we cannot assume that such agreements work to the benefit of students.
But there is no necessary contradiction between maintaining data privacy and its accessibility. Education beyond 2021 can be informed about education without this compromising either security or privacy. The solution is blockchain-based credentialing. In a nutshell, this means converting any sort of information into anonymous immutable and verifiable proof that is always under the control of the student. This way it can be shared without infringing privacy and cannot be lost or compromised.
Credentials as granular irrefutable information
Let us leave aside for a moment the classical image of a credential or a certificate as something that allows the holder to prove an achievement. Of course, that’s what a credential is. But when we talk of anonymized proofs issued with certiif, such credentials are also a way for the institution to gather granular information about the impact of an institution’s graduates on the labour market. It is up to educational institutions themselves to ensure that the truth is known and integrated into education, in order to adequately adapt learning to the needs of the labour market.