Transforming HE through machine learning

Undoubtedly, the digital revolution has transformed nearly every industry. At the forefront of this transformation are Artificial Intelligence (AI) and Machine Learning (ML). While many industries, like transportation and retail, have become leaders in adopting emerging technology to improve their business models, the higher education industry has fallen behind. This gap is present in some lower levels of schooling as well, but, historically, we’ve seen that larger university settings have encountered more challenges in the path to adoption.

Prior to entering higher education, students are exposed to, and leverage, various forms of technology. As a result, they have very high expectations for continued technology usage, both inside and outside of the campus gates. Unfortunately, when students arrive on campus, many find that their universities do not provide the same digital options they have grown accustomed to.

Though more people attend college now than ever before, post-secondary institutions have remained static in structure for generations, and are notoriously slow to adopt change – despite the shifting needs and demands of students. Continuing to account for these evolving trends is already negatively impacting the ability of institutions to attract, engage, retain, and advance students. As universities move toward a digital future, it will be critical that they embrace technologies like AI and ML to remain relevant in a highly competitive higher education market.

The Evolving Classroom

The traditional classroom environment is outdated, inefficient, and does not meet the expectations of the new-gen student. Professors still prepare paperwork and lesson plan materials, manually grade student assignments, and provide written and verbal feedback, often without the aid of any digital tools. With university classroom sizes growing, instructors are not always able to give students their full attention. As a result, many university students rarely enjoy a personalized approach to their learning, which can affect their academic standing and overall performance within their class and negatively impact the personal development.

One of the primary ways ML can aid the classroom environment is speeding up the review and grading process. University exams can encompass complicated, multi-part assessments, and students often have to wait more than 40 hours for calculation of exam results. Similarly, the review and grading of typical homework and essays can take even longer, due to the cadence of assignments and class sizes sometimes exceeding 100 students in large universities. Integrating ML into the process could assess and score student work on a massive scale, automatically – thereby helping assuage the challenge teachers face in grading dozens, and sometimes, hundreds of exams individually.

Personalizing the learning experience is another area where ML can make an immediate impact. Now more than ever before, teachers are being challenged to move away from a “one-size-fits-all” approach to teaching and learning, to meet the needs of every student. As classes expand there is still only one professor for, sometimes, hundreds of students. Theoretically, ML could recognize the unique learning patterns and styles of individual students – determining what course material proves more difficult for each student, and specific learning areas where they excel – and can help teachers tailor their lessons to account for everyone in the classroom. This is especially valuable in university classes exceeding 50 students. Armed with personalized bot-generated performance assessments, educators can finally have the ability to scale their lessons and effectively teach students of all ability levels, regardless of classroom size.

Using similar data generated by ML-enabled curation and moderation bots, educators can also improve the organization and planning of teaching materials. Learners benefit most when they are engaged in lessons that stimulate their curiosity. ML has the ability to analyze real-time engagement patterns in classroom content to help teachers streamline assignments – determining where past students needed further insight, or where a difficult lecture may need improvement. This significantly enhances a student’s learning experience, while making a professor’s lesson planning more efficient, and is especially applicable to online courses, where university students engage exclusively through digital platforms.

The Evolving Campus

While ML allows the classroom to enjoy increased efficiency and student engagement, the business and administrative side of higher education is also ripe for disruption. Colleges and universities can augment staffing decisions, admissions, and support models with advanced analytics and AI to implement digital solutions that fundamentally change how they manage their students, employees, finances, and campuses.

One area where AI and ML are making an impact is in connected infrastructure and facility management. With a large faculty and staff needing access to a variety of facilities, often spanning an entire campus, IT teams have begun to implement digital systems which control these permissions digitally, using AI. Digitally coded access passes allow for customized building access control on an individual basis, cloud-based smart classroom and conference room booking lets faculty and staff view and reserve meeting space ad hoc, and facilities teams have even turned to AI to help track and manage equipment like microscopes, laptops, and heavy machinery.

More than ever before, teachers are being challenged to move away from a “one-size-fits-all” approach to teaching and learning

Colleges and universities also face a growing number of applicants every year, but are still forced to review every submission individually to determine a student’s qualifications. Digitizing this process is essential to improving the admissions process as class sizes continue to grow. By adopting AI and ML, higher education institutes could not only speed up the review process by sorting through thousands of applicants simultaneously And automating admission decisions, but also make it more selective (and competitive) by looking for pre-determined qualifications.

Additionally, as more universities move to standardized online and mobile application submissions, bots with predetermined student requirements can also sort and even select qualified candidates online before an admissions team begins its review process. This would narrow down the pool of eligible students proactively, and allow the human element of the admissions process to remain intact.

Embrace the Rise of Machine Learning

Higher education needs to improve to enhance the experience for students, faculty, and staff, and it can do so by leveraging emerging technology – particularly AI and ML. When these tools are properly implemented, and used in tandem as part of an innovative data platform designed with universities in mind, the results are powerful, and effective. In years past, the application of such technology wasn’t practical in the education space, but today, we see an entirely different environment. Both technologies can help teachers better engage with students, create personalized learning plans, optimize the submission and grading process, and make the admissions process more competitive. 

As advances are made in machine learning, the impacts in higher education will be even greater. These are just a few examples of the many opportunities available to colleges and universities with new tools and technology.