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Adaptive learning allows students to learn effectively based on their abilities and characteristics at their own pace. Currently, numerous SLRs exist regarding students' adaptive learning characteristics, analyzed from various perspectives by previous researchers. However, it is essential to note that the findings of these studies are based on analyses conducted between 2010 and 2020. This SLR aims to extend and bridge the gaps in identifying essential student characteristics for implementing an adaptive learning system. It specifically focuses on the most recent five-year period, from 2018 to 2022. This study chose 39 articles according to the specified inclusion and exclusion criteria. The findings from the SLR indicate that learning style is the most commonly used element of adaptation in the reviewed articles, followed by knowledge characteristics, cognitive traits, student preference, and motivation. This SLR also revealed that most of the reviewed articles used more than one student's characteristics in modeling the student model, and results show that educators integrated online learning for implementing adaptive learning in the teaching and learning process. The ILS instrument, which is traditional detection, is widely used in collecting learning style data, and besides FSLSM, other learning style models, including VARK, Kolb, and Honey & Mumford, are used to assign students' learning style preferences.

In this paper we define and study three extensions of the notion of´Ciri´cof´ of´Ciri´of´Ciri´c quasicontraction to the context of partial metric spaces. For such mappings, we prove fixed point theorems. Among other things, we generalize a recent result of Altun, Sola and Simsek, of Ili´cIli´c et al., of Matthews, and the main result of´Ciri´cof´ of´Ciri´of´Ciri´c is also recovered. The theory is illustrated by some examples. MSC: 15A09; 15A24

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