Recommendation Systems and AI Solutions Guiding Open (Lige-Long) Learning - a Development Project in Five Universities of Applied Sciences in Finland
Tani, Petri; Huttunen, Salla; Ahokallio-Leppälä, Heidi; Moisio, Anu; Ruotsalainen, Taru; Toukkari, Pia-Mariana (2021)
IATED, International Association of Technology, Education and Development
2021
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2021113057852
https://urn.fi/URN:NBN:fi-fe2021113057852
Tiivistelmä
As a result of the corona pandemic, lives both at workplaces and universities have changed. Working “from anywhere” and studying online have come to stay. The pandemic has also caused significant changes in the demand and supply of labor, there are industries where job losses are a problem, and there are industries where there is a labor shortage or there is a need to acquire new skills quickly, some people are even looking for a new profession.
Before the pandemic, in Finland, about 36% of the workforce was considering a change in career and a new profession. The corona pandemic added a special boost to these needs. One reason could be the lack of digital skills. According to recently published study, half of the working population is lacking in digital skills and has difficulties with digital tools. This causes stress and burnout. The so-called “digital leap” and remote work have increased the need for learning new digital skills. As a result, there is an exceptionally high need for boosting skills, digital and others, in Finland. This is the need universities of applied sciences have recognized and responded to by collaborating with businesses and labor authorities to increase the amount and variety of continuous learning offering.
This paper aims to describe a development project, where recommender systems and AI together are used to guide in the selection of open studies. We have co-created a joint artificial intelligence powered digital environment which is conceived as an ecosystem - a matching tool consisting of our study offering and the need for skills and labor in the world of work. In the system, people who would like to upgrade their own skills and knowledge have an option of taking studies matching their needs from our universities of applied sciences.
During the last few decades, with the rise of Amazon, Netflix and many other web services, recommender systems have become omni-present. From e-commerce (suggesting to buyers products that could interest them) to online advertisement (suggesting to users the right contents, matching their preferences), systems are unavoidable in our daily online journeys. For a higher education institution (HEI) to use of these systems in marketing their learning offering, they should pause to think whether they serve customers, students, or both.
This paper discusses ways of using recommender systems in guiding people looking for upskilling and reskilling offering, and of using artificial intelligence to identify the skills needed in the world of work and meeting those needs in higher education institutions. This all, in effect, constitute the first building blocks of a digital learning ecosystem. In this paper, we will present some ideas and practical experience. By using a combination of these systems and AI, our “webstore” is fast, economical, reachable, and student or customer centered. The initial findings of the project are promising but tuning the systems and study offerings to work optimally together still requires work and tackling technical challenges. Even if we try to optimize the possibilities of recommender systems and artificial intelligence, it does not eliminate the need for personal support and guidance of students.
We conclude that in the future, higher education will also be seen as a market-based activity and that future learners will behave as normal consumers when looking for educational opportunities, and thus, expect similar functionalities.
Before the pandemic, in Finland, about 36% of the workforce was considering a change in career and a new profession. The corona pandemic added a special boost to these needs. One reason could be the lack of digital skills. According to recently published study, half of the working population is lacking in digital skills and has difficulties with digital tools. This causes stress and burnout. The so-called “digital leap” and remote work have increased the need for learning new digital skills. As a result, there is an exceptionally high need for boosting skills, digital and others, in Finland. This is the need universities of applied sciences have recognized and responded to by collaborating with businesses and labor authorities to increase the amount and variety of continuous learning offering.
This paper aims to describe a development project, where recommender systems and AI together are used to guide in the selection of open studies. We have co-created a joint artificial intelligence powered digital environment which is conceived as an ecosystem - a matching tool consisting of our study offering and the need for skills and labor in the world of work. In the system, people who would like to upgrade their own skills and knowledge have an option of taking studies matching their needs from our universities of applied sciences.
During the last few decades, with the rise of Amazon, Netflix and many other web services, recommender systems have become omni-present. From e-commerce (suggesting to buyers products that could interest them) to online advertisement (suggesting to users the right contents, matching their preferences), systems are unavoidable in our daily online journeys. For a higher education institution (HEI) to use of these systems in marketing their learning offering, they should pause to think whether they serve customers, students, or both.
This paper discusses ways of using recommender systems in guiding people looking for upskilling and reskilling offering, and of using artificial intelligence to identify the skills needed in the world of work and meeting those needs in higher education institutions. This all, in effect, constitute the first building blocks of a digital learning ecosystem. In this paper, we will present some ideas and practical experience. By using a combination of these systems and AI, our “webstore” is fast, economical, reachable, and student or customer centered. The initial findings of the project are promising but tuning the systems and study offerings to work optimally together still requires work and tackling technical challenges. Even if we try to optimize the possibilities of recommender systems and artificial intelligence, it does not eliminate the need for personal support and guidance of students.
We conclude that in the future, higher education will also be seen as a market-based activity and that future learners will behave as normal consumers when looking for educational opportunities, and thus, expect similar functionalities.