1 Peoples’ Friendship University of Russia (RUDN University) (RUSSIAN FEDERATION)
2 Perm National Research Polytechnic University (RUSSIAN FEDERATION)
About this paper:
Appears in: INTED2023 Proceedings
Publication year: 2023
Pages: 3725-3735
ISBN: 978-84-09-49026-4
ISSN: 2340-1079
doi: 10.21125/inted.2023.0996
Conference name: 17th International Technology, Education and Development Conference
Dates: 6-8 March, 2023
Location: Valencia, Spain
Post-COVID-19 tech-based world and disruptive technologies have dramatically changed the way people learn. The introduction of artificial intelligence (AI), machine learning (ML) generate significant opportunities for higher education (HE), namely, for the creation of personalized pathways. Worldwide education systems increase efforts to personalized learning (PL). For instance, the University of Murcia in Spain began testing an AI chatbot to answer students’ questions about the internal rules and regulations of the university. Knewton is one of the first companies that applied data analytics technology to create an adaptive educational platform to identify learners’ strengths and weaknesses.

In recent years, the concept of PL has received increasing attention in academia. Even though considerable research efforts have been undertaken, the effect of PL on student educational outcomes has yet to be enhanced. Therefore, the aim of this study is to examine the grounded understanding of the current literature on the PL implementation in higher education.

Our study raises two research questions:
1) What are the effects of digital tools in PL implementation on learning outcomes?
2) What are the factors that affect the adoption of PL (teachers’ and students’ perception)?

Based on these two overarching research questions we have created a coding sheet:
1) search terms: learning, e-learning, distance learning, online learning, education, online education, distance education, corporate training, corporate learning, adult learning with the words personalize, personalise (British spelling) and personalized, personalised as the prefix;
2) limiters: academic research databases (Scopus and Google Scholar), publication data range (2012-2022), postsecondary education, English written articles, peer-rewired journals, empirical investigation;
3) research purpose: evaluating the technology effectiveness that supports PL (web-based adaptive learning systems, intelligent learning systems, VR learning systems, computer games etc.), exploring characteristics of PL environments, investigating student, teacher perception of PL;
4) field of study: based on the authors’ affiliation identified in the publication;
5) research approach: quantitative, qualitative, mixed method;
6) content area: a discipline or subject;
7) educational outcomes: motivation, metacognitive skills, etc.

In conclusion we discuss current gaps in the field of PL and solutions for its future development.
Personalized learning, personalized pathways, personalization.