Aldeshov S.
PhD
Suleimenova L.
PhD
Kerimbekov E.
PhD
Abdimanapova G.
PhD student
Karabai A.
PhD student
South Kazakhstan Pedagogical University named after Uzbekali Zhanibekov

The work was carried out with the financial support of the grant of the Ministry of education and science of the Republic of Kazakhstan (grant AP19680169 «Integration of machine learning to predict learning strategies in LMS in the formation of professional competencies of students»).

 Abstract
Over the past decade, education has seen dramatic changes in the way data is collected and used to support high-stakes decision-making. Using big data as a meaningful objective to inform teaching and learning can enhance academic success. Data-driven research has been conducted to understand the effectiveness of student learning, such as student forecasting from the risk group at an early stage and recommendations of individual interventions to support services. However, few studies in the field of education have adopted Learning Analytics. Over the past decade, education has seen dramatic changes in the way data is collected and used to support high-stakes decision-making. Using big data as a meaningful lens to inform teaching and learning can enhance academic success. Data-based studies have been conducted to understand the effectiveness of student learning, such as predicting at-risk students at an early stage and recommending individual interventions to support services. However, few educational studies have adopted Learning Analytics.

 Keywords: learning analytics, predictive modeling, learning formats , higher education, academic performance forecasting