A new AI-powered tool developed by Coursera aims to be that metric. The recent feature announced by the Bay Area company allows the companies that have subscribed to its training programs monitor the performances of their employees earning top scores in Coursera programs; what courses would help fill any knowledge gaps; and how their employees’ skills measure up to their competitors’. Companies will be able to access the tool in the online dashboard of their Coursera profiles later this year.
The new feature is just one way of showing how online-learning providers are incorporating AI to accord with learners with courses, assess their ability, and modify program content in response to the received feedback. Coursera’s data-science team “collects and stores data in a warehouse to interpret information for making internal decisions. The team also builds an algorithm that feedbacks into the site,” says Emily Glassberg Sands, the team lead.
Established in 2017, Udacity’s AI research team, studies student reaction to tweak the lessons as per the needs of learners and processes whether the apprentices like the changes. The app also has brought AI chatbots into play to aid students in finding relevant courses and answer some common questions during the registration process. Whereas, edX is currently experimenting with Ai to escalate how well people teach and learn.
These AI features can potentially more companies and people to enroll for such training by tackling some of the most pernicious obstacles in online learning. According to a 2017 survey of US-based businesses by Training magazine, the two top priorities for the companies that pay to train their staff includes measuring the impact and effectiveness of educational programs.
Coursera’s business program, targeting companies, at present has 14,00 customers, indicating towards the fact that the market has plenty of room to grow. Leah Belsky, who heads the Coursera for Business program notes, “ The Achilles’ heel of the corporate learning industry is that no one knows how to demonstrate the return on investment. Companies know their people need to learn new skills to stay competitive, but they have a hard time communicating what the value of that learning is.”
Like the other apps, Coursera too wanted to quantify the pros of its classes. Its data science team began developing machine-learning algorithms a year and a half ago to map the 40,000 skills taught by Coursera. Firstly, the team incorporated natural language processing (NLP) to ascertain how often trainers mentioned concepts during the lectures. This vital piece of information identified which classes imparted which skills.
Glassberg Sands further says, “Coursera can get some of that data by simply asking its instructors. But it also uses NLP because educators often think of themselves as teaching theoretical concepts while learners want to know what specific tools and technologies they will master.”
Based on how they performed on Coursera quizzes and assignments, the team used a psychometrics methodology known as item response theory (IRT) into certain machine-learning modules to assess learner’s knacks.
Glassberg Sands also noted, “The new methodology enables the team to evaluate expertise in a given skill area across learners who were answering different questions of varying difficulty.” As per her statement, it is important because on average an advanced learner will take harder courses on Coursera and attempt harder questions than a beginner. She further added, “What you get is a percentile for your employees in each skill area, relative to whatever comparison group you choose, whether that’s all the companies on Coursera or just companies in your industry or your country or companies of a similar size.”