Examining the 5th grade learning outcomes of the 2024 science curriculum and the questions in the textbook according to the SOLO Taxonomy

This study aims to determine the cognitive levels of the learning outcomes for the 5th- grade level in the 2024 science curriculum and the questions in the science textbook for the same grade level according to the SOLO taxonomy. The research method employed is document analysis, a type of qualitative research. The study data consist of a total of 28 learning outcomes at the 5th-grade level in the 2024 science curriculum and a total of 326 questions in the science textbook at the same grade level. The analysis of the learning outcomes and questions was conducted using a descriptive analysis, taking into account the SOLO Taxonomy's uni-structural, multi-structural, relational, and extended abstract levels. In the analysis of learning outcomes, it was determined that learning outcomes belonging to the uni-structural were fewer than those belonging to the other structures, while the number of learning outcomes belonging to the multi-structural, relational, and extended abstract structures were similar. In this case, it is understood that the curriculum expects students to acquire more detailed information, understand the meaning of the subject, and internalize it, rather than raising awareness about the subjects. It was observed that most of the questions were at the uni-structural level, and the number of questions decreased as the levels increased. The large number of questions at lower levels was not considered a qualitative deficiency but was thought to be useful in revealing deficiencies in learning. When the SOLO levels of learning outcomes were compared with the SOLO levels of questions related to the relevant learning outcomes, it was observed that all learning outcomes except for two corresponded to the questions in the textbook. The use of SOLO Taxonomy is recommended in studies that focus on central exam questions, learning outcomes, and textbook questions. The SOLO Taxonomy's ability to progress from superficial to deep learning makes it an effective tool for classifying learning processes; thus, its use is recommended in program development stages.