Abstract:

On the one hand, the Covid-19 pandemic period can be considered as one of the most successful in the history of school education, as we have become technologically more advanced and digitally smarter. The pandemic brought as many challenges as schools had not seen in the last century. And it is clear that in the near future the education system will change significantly and especially in the direction of acceleration.

On the other hand, distance learning has been a particular challenge for the subject of mathematics. And schools will need to come up with new 'recovery' strategies in the future to help teachers and students find and close knowledge gaps. Many students will need extra help in learning math content, communicating and accepting the new cultural environment of schools.

In this context, the research was motivated by the basic question "What conditions help to learn school mathematics faster and more efficiently?" As an answer to the question, a taxonomy of technology-based support for accelerated learning of mathematics (ALOM) in general education schools is proposed.

The results of a survey of 322 students from various Latvia general education schools and focus group interviews with 53 Latvia teachers of mathematics showed that the proposed model achieves its goal by allowing teachers of mathematics to understand the nature of ALOM, identify specific conditions and select the most appropriate strategies in order to tailor the learning process to each class and each student individually.

Acknowledgement:
The research is carried out within the framework of the postdoctoral project “Support of Artificial Intelligence for Accelerated Learning Approach of Mathematics (AI4Math) (1.1.1.2/VIAA/3/19/564)” at Vidzeme University with the support of ERDF.

Keywords:

Accelerated Math Learning, Support System, Technology-Based Learning, Taxonomy, AI4Math.