Reasoning Ability Research Articles

Investigating visual perspective taking and belief reasoning in autistic adults: A pre-registered online study.

Many autistic individuals report difficulties in social situations, where they are required to think about what goes on in others' minds. These states of the mind can include how others perceive the world around them, their beliefs, or their desires. While research has shown that autistic children could be delayed in developing their full capacity in this regard, less is known about how adults process others' experiences and beliefs. Here we used a novel task and asked adults to participate online. Participants self-reported whether they had been diagnosed with autism or not and we split them into two groups depending on their response. We also asked participants to fill in a self-report questionnaire about social preferences and habits and we also asked them to conduct a test of their nonverbal reasoning ability. Importantly, the autistic and the non-autistic groups did not differ in their nonverbal reasoning abilities, and on our task, we observed that the autistic group committed fewer mistakes than the non-autistic group. Autistic participants were particularly fast and made fewer mistakes on those responses that overlapped with their own view and belief of reality. In conclusion, our findings do not support a simple view of autism in terms of deficits in either social or more general thinking abilities. Instead, autistic adults might favour slightly different ways of thinking about other's experiences and beliefs that is more firmly linked to their own experience and knowledge.

Despite Contradictions, Artificial Intelligence and Block Chain Technology are Composing the Future Together

Now times, Block Chain technology and enhancements in Artificial Intelligence paradigm are facing more attention in the field of research with new advancements in many security techniques in all the sectors of IT and business. One cannot deny from the fact that the adoption of Artificial Intelligence paradigms and block-chain technology is shaping the new face of market. Beyond their contradictions both are proceeding at lightning speed. Both of them are aimed at providing something new to the world but the degree of complexity of each is quite different. With a secure, decentralized, and trustworthy system, block-chain technology did automate the bit-coin payments and provide the users an access to a shared ledger of records, transactions and data. With the useof smart contracts, a block-chain can also regulate the user interactions without the need for any central authority. In the contradiction, AI provides agents with the ability of reasoning, decision making and human-level intellect. At bottom one can say, block chain is concerned with keeping correct records, authentication and implementation while AI deals in conducting assessments, examining, and coming to a decision analyzing certain patterns and datasets, eventually giving rise to a self- directed interaction. Artificial Intelligence along with block chain allocates several descriptions which will make a seamless communication prospect certainly. AI and Block-Chain need sharing of data. The decentralized approach of database focuses on the significance of data sharing among various clients on a meticulous network. In the same way, AI depends very much on Big-Data, exclusively, for data distribution. The first section in the proposed article offers the Introduction part. The second section focuses on the majorly emphasized topics and domains about block chain technology indulging Artificial Intelligence into it, presents a detailed literature-review of many of the contributions by eminent research analysts. The third section describes the challenges in the collaborating AI with block-chain. The last section expresses a brief conclusion of the research work. Keywords- Block Chain Technology, Artificial Intelligence, AI, Machine Learning

Evaluation of the Predictive Capability of CMA Climate Prediction System Model for Summer Surface Heat Source on the Tibetan Plateau

Surface heat source (SHS) is a crucial factor affecting local weather systems. Particularly SHS on the Tibetan Plateau (TP) significantly influences East Asian atmospheric circulation and global climate. Accurate prediction of summer SHS on the TP is of urgent demand for economic development and local climate change. To evaluate the performance of SHS on the TP, the observed SHS data from the eleven sites on the TP verified against CRA40-land (CRA) is evidenced significantly better than ERA5-land (ERA5), another widely used reanalysis. The predictive capability of the CMA Climate Prediction System Model (CMA-CPS) for SHS on the TP was assessed using multiple scoring methods, including the anomaly correlation coefficient and temporal correlation coefficient, among others. Furthermore, relative variability and trend analysis were conducted. Finally, based on these assessments, the causes of the biases were preliminarily discussed. The CMA-CPS demonstrates a reasonable ability to predict the spatial distribution patterns of SHS, sensible heat (SH), and latent heat (LH) on the TP in summer. Specifically, the prediction results of SHS and LH exhibit an “east-high and west-low” distribution, while the distribution of the predicted SH is opposite. Nevertheless, the predicted values are generally lower than CRA, particularly in interannual variations and trends. Among the predictions, LH exhibits the highest temporal correlation coefficients, consistently above 0.6, followed by SHS, while SH predictions are less accurate. The spatial distribution and skill scores indicate that LH on the TP contributes more significantly to SHS than SH in summer. Furthermore, discrepancies in the predictions of surface temperature gradients, ground wind speed, and humidity on the TP may partly explain the biases in SHS and their components.

Consistent Depth of Reasoning in Level-k Models

Level-k models often assume that individuals employ a fixed depth of reasoning across different games. We study this assumption by having subjects make choices in five classes of games chosen to identify inconsistent depth of reasoning. We demonstrate that depth of reasoning is pervasively inconsistent, changing both within and between classes of games. We show that this cannot easily be explained by factors such as subject confusion, changing beliefs about others’ depth of reasoning, stochastic choice, model misspecification, changing incentives, or low cognitive ability. We develop a simple model incorporating ambiguity aversion that predicts inconsistent depth of reasoning. (JEL C57, C90, D81, D83, D91)

Mobile-Based Realistic Mathematics Learning Design to Improve Students' Reasoning Ability and Problem-Solving Ability

<p> Mathematical reasoning and problem-solving skills have become a very necessary need at this time. Students who do not have mathematical reasoning and problem-solving skills will have difficulty adapting to current technological advances. The purpose of this study is to design mobile-based mathematics learning to improve students' mathematical reasoning and problem-solving skills. The subjects of this study were junior high school students. The research method developed was ADDIE (Analysis, Design, Development, Implementation, Evaluation). The results of the study showed that improving students' mathematical reasoning skills had a very positive impact on students' mathematical problem-solving skills. These results provide additional contributions and references for mathematics teachers who want to add knowledge related to improving the quality of mathematics learning in the classroom.</p>

Problem-Based Learning: An Effective Teaching Method for Science Competence Development

Modern society places exceptionally high premiums on science education. To make science classes more productive, researchers and educators have experimented with a plurality of non-traditional instructional approaches that are expected to be effective in fueling students’ interest in science subjects and fostering their scientific reasoning ability and creativity. Among them, problem-based learning has been well received by teachers. Its adoption in science classrooms was first recommended by certain researchers in the 20th century (Gallagher et al., 1995). Problem-based learning enables the student to develop knowledge through analyzing and solving problems, rather than through memorizing a wealth of existing information and theories. It can help the student transition from a passive receptacle of knowledge to an active learner and a problem solver (Akınoğlu & Tandoğan, 2007). Despite there being no conclusive definition of this teaching method, the three features summarized by Akcay (2009) are deemed basic elements of problem-based learning: (i) engaging students as stakeholders in a problem situation; (ii) organizing the curriculum around this holistic problem, enabling student learning in relevant and connected ways; (iii) creating a learning environment in which teachers coach student thinking and guide student inquiry, facilitating deeper levels of understanding.

Cognitive Maps for a Non-Euclidean Environment: Path Integration and Spatial Memory on a Sphere

Humans build mental models of the world and utilize them for various cognitive tasks. The exact form of cognitive maps is not fully understood, especially for novel and complex environments beyond the flat Euclidean environment. To address this gap, we investigated path integration—a critical process underlying cognitive mapping—and spatial-memory capacity on the spherical (non-Euclidean) and planar (Euclidean) environments in young healthy adults (N = 20) using immersive virtual reality. We observed a strong Euclidean bias during the path-integration task on the spherical surface, even among participants who possessed knowledge of non-Euclidean geometry. Notably, despite this bias, participants demonstrated reasonable navigation ability on the sphere. This observation and simulation suggest that humans navigate nonflat surfaces by constructing locally confined Euclidean maps and flexibly combining them. This insight sheds light on potential neural mechanisms and behavioral strategies for solving complex cognitive tasks.

Structural and dynamical insights revealed the anti-glioblastoma potential of withanolides from Withania coagulans against vascular endothelial growth factor receptor (VEGFR).

Glioblastoma (GBM), well known as grade 4 tumors due to its progressive malignant features such as vascular proliferation and necrosis, is the most aggressive form of primary brain tumor found in adults. Mutations and amplifications in the vascular endothelial growth factor receptor (VEGFR) contribute to almost 25% of GBM tumors. And thus, VEGFR has been declared the primary target in glioblastoma therapeutic strategies. However, many studies have been previously reported that include GBM as global therapeutics challenge, but they lack the molecular level insights that could help in understanding the biological function of a therapeutically important protein playing a major role in the disease and design the best strategies to develop the potential drugs. Therefore, to the best of our knowledge, the present study is the first time of kind, which involves multi-in silico approaches to predict the inhibition potential of withanolides from Withania coagulan against VEGFR. The study is actually based on determining the mode of action of five isolates: withanolide J, withaperuvin, 27-hydroxywithanolide I, coagule E, and coagule E, along with their respective binding energies. Molecular docking simulations revealed primarily four ligands, withanolide J (- 7.33kJ/mol), 27-withanolide (- 7.01kJ/mol), ajugine, withaperuvin (- 6.89kJ/mol), and ajugine E (- 6.39kJ/mol), to have significant binding potencies against the protein. Ligand binding was found to enhance the confirmational stability of the protein revealed through RMSD analysis, and RMSF assessment revealed the protein residues especially from 900-1000 surrounding the binding of the protein. Structural and dynamics of the protein via dynamics cross-correlation movement (DCCM) and principal component analysis (PCA) in both the unbound form and complexed with most potent ligand, withanolide J, reveal the ligand binding affecting the entire conformational integrity of the protein stabilized by hydrogen bonds and electrostatic attractions. Free energy of binding estimations by means of molecular mechanics Poisson-Boltzmann surface area (MMPBSA) method further revealed the withanolide J to have maximum binding potency of the all ligands. Withanolide J in final was also found to have suitable molecular characterizations to cross the blood-brain barrier (BBB +) and reasonable human intestinal absorption ability determined by ADMET profiling via admetSAR tools.

A Theoretical Model for Measuring Upper Primary School Learners’ Scientific Reasoning Abilities in Science

One agenda of science instruction in primary schools is to develop learners’ scientific reasoning abilities and to equip them with the necessary twenty-first-century skills. Few studies have been conducted to develop measurement tools that aspire to assess primary school learners’ scientific reasoning. Most of these studies lack a theoretical model backing the development of these assessment tools. This paper introduces the model for measuring scientific reasoning (MMSR) as a theoretical model for guiding the development of assessment tools in the context of measuring upper primary school learners’ scientific reasoning. We first conceptualise scientific reasoning using a review of the literature and then present evidence to justify and validate this model for use in measuring primary school learners’ scientific reasoning abilities. Furthermore, using research in history and philosophy of science and science education, the MMSR model supports the development of assessment tools measuring learners’ patterns of scientific reasoning and also considering the nature of science across different contextual settings.

문제중심학습 교육이 간호대학생의 문제해결능력, 자기주도 학습능력, 임상추론역량에 미치는 효과

Objectives The purpose of this study was to determine the effect of problem-based learning education on problem-solving ability, self-directed learning ability, and clinical reasoning competency of nursing students. Methods The research design is a quasi-experimental study with a single-group before/after design. A preliminary survey was conducted on April 3 and 4, 2024, targeting 55 fourth-year nursing students, and a follow-up survey was conducted on April 24 and 25. Data analysis included descriptive statistics, paired t-test, and Pearson correlation analysis using the SPSS Statistics 22.0 program. Results After problem-based learning education, nursing students' problem-solving ability(t=-5.91, p<.001), self-directed learning ability(t=-6.02, p<.001), and clinical reasoning ability(t=-7.39, p<.001) was statistically significantly improved. Conclusions Through this study, it was confirmed that problem-based learning is an effective teaching and learning method in improving nursing students' problem-solving ability, self-directed learning ability, and clinical reasoning competency. Therefore, efforts should be made to strengthen the capabilities of nursing students by expanding subjects that apply problem-based learning in nursing education.

간호대학생의 간호인계역량에 미치는 영향요인

Objectives This study attempted to investigate the factors affecting nursing students' nursing handover competence and to identify the relationship between clinical decision-making ability, clinical reasoning ability, and handover self-efficacy to serve as a basis for future handover education courses for nursing students. Methods This study is a descriptive survey study. 145 nursing students The data were collected from September 26, 2023 to October 13, 2023 from 145 nursing students at a university. The data were analyzed by frequency and percentage, independent t-test, one-way ANOVA, and Pearson Correlation Coefficient using SPSS/WIN 28.0 program. Results Nursing handoff competency was positively correlated with clinical decision-making ability (r=.384, p=.000), clinical reasoning ability (r=.541, p=.000), and handoff self-efficacy (r=.656, p=.000). The most significant predictors of nursing handoff competence were handoff self-efficacy (β=0.422, p<.001), clinical reasoning ability (β=0.270, p<.001), Satisfaction with Academic Major (β=0.192, p=.019), grade ((β=-.13, p=.036), and Handover Fear (β=-0.224, p=.007), with an explanatory power of 49.0%. Conclusions The factors that affect nursing handover competency are cognitive processes, such as problem solving, communication, judgment, and perception, and efforts are needed to maintain the quality and continuity of care. Therefore, it is necessary to develop teaching methods and build systems to improve clinical decision-making, clinical reasoning, handover self-efficacy, and enhance nursing handover competency.

Implementasi Pembelajaran Matematika Realistik dalam Menganalisis Kemampuan Penalaran Matematis Siswa pada Materi Satuan Debit

This study aims to (1) describe the learning of unit debit with PMR approach and (2) describe the reasoning ability of grade V students on unit debit material. This research was conducted at SDK Lamawalang on April 20, 2024. The subjects in this study were fifth grade students of SDK Lamawalang. The type of research used is descriptive qualitative research, where researchers describe learning practices using PMR models and students' mathematical reasoning abilities after the learning process. Data analysis techniques used include data collection, data reduction, data presentation, and verification/conclusion. The results showed that, (1) the steps of learning unit debit material with PMR model are: (a) understanding contextual problems, (b) solving problems, (c) comparing and discussing answers, (e) drawing conclusions, (2) based on indicators of mathematical reasoning ability, (a) making conjectures, (b) performing mathematical manipulations, (c) compiling evidence, providing reasons/evidence for the correctness of the solution, and (d) drawing conclusions from a statement, obtained for problem I, 20 students can achieve 1-4 indicators of mathematical reasoning, 1 student can achieve 1-3 indicators of mathematical reasoning. For problem 2, 18 students can achieve 1-4 indicators of mathematical reasoning, while 3 students can achieve 1-2 indicators of mathematical reasoning.

The Effect of Google Classroom-Assisted Realistic Mathematics Education Approach on Students’ Mathematical Reasoning Ability

Mathematical reasoning is an important aspect, but there are still many students who are weak when it comes to mathematical reasoning. Factors that affect the level of mathematical reasoning of low students, including: a) students easily forget the material taught; b) the student has no idea in completing it; c) students are not careful in understanding the problems in the problem; d) students do not understand which formula will be used in solving the problem; e) Students do not understand the concept of the material. This research aims to determine whether or not there is an influence of the approach realistic mathematics education helpgoogle classroomon mathematical reasoning abilities. Quantitative Approach method used in this researchpre-experimental design with one group pretest posttest design. The research subject is the experimental class. Data collection was carried out with pretest and posttest data on the mathematical reasoning abilities of grade 7 students at one of the junior high schools in Purwasari district, kabupaten Karawarang. Data analysis techniques in this study that is analyze the scores of pretest and posttest results students by t-test for 2 dependent samples (Paired Sample T-Test) done with the help of IBM SPSS 21 software. The research results show that there is value of sig 0,000 < 0,05 which means a positive influence approach realistic rathematic education help google classroomon ability students' mathematical reasoning. Based on the result, we can be concluded that there is a positive influence of the approach realistic mathematic education help google classroomon students mathematical reasoning abilities. The learning process uses an approach realistic mathematic education help google classroom can be used as an alternative learning to improve students mathematical reasoning abilities.

Metaphorical Thinking Intervention in Learning and Its Impact on Mathematical Reasoning Ability

Dalam beberapa tahun terakhir, penelitian tentang intervensi Metaphorical Thinking (MT) dalam pendidikan matematika semakin mendapat perhatian. Namun, penelitian terbatas telah meneliti bagaimana intervensi MT, ketika diintegrasikan dengan model pembelajaran yang menantang, dapat meningkatkan Kemampuan Penalaran Matematis (MRA) siswa. Untuk mengatasi kesenjangan ini, penelitian kuasi-eksperimental dengan menggunakan desain kelompok kontrol posttest-only dilakukan terhadap 50 siswa di SMP Widya Bhakti Ruteng, NTT, Indonesia. Penelitian ini bertujuan untuk menganalisis pengaruh intervensi MT dalam lingkungan Pembelajaran Berbasis Masalah (PBL) terhadap MRA. Data dikumpulkan melalui tes PMA yang terdiri dari lima soal, dan hasilnya dianalisis menggunakan uji t setelah melakukan analisis prasyarat. Temuannya menunjukkan bahwa siswa yang menerima intervensi MT dikombinasikan dengan PBL menunjukkan MRA yang jauh lebih tinggi dibandingkan dengan mereka yang tidak menerima intervensi MT. Hasil ini menggarisbawahi potensi manfaat dari mengintegrasikan intervensi MT dengan pendekatan pedagogi menantang lainnya. In recent years, research on Metaphorical Thinking (MT) interventions in mathematics education has gained increasing attention. However, limited studies have examined how MT interventions, when integrated with challenging instructional models, can enhance students' Mathematical Reasoning Ability (MRA). To address this gap, a quasi-experimental study utilizing a posttest-only control group design was conducted with 50 students at SMP Widya Bhakti Ruteng, NTT, Indonesia. The study aimed to analyze the effects of MT interventions in a Problem-Based Learning (PBL) environment on MRA. Data were collected through a PMA test consisting of five questions, and the results were analyzed using a t-test after performing a prerequisite analysis. The findings revealed that students who received MT interventions combined with PBL demonstrated significantly higher MRA compared to those who did not receive MT interventions. These results underscore the potential benefits of integrating MT interventions with other challenging pedagogical approaches.

Pedagogical Conditions for Developing the Ability of Logical Thinking in Primary School Children

The integration of our country into the world community, the development of science and technology require the young generation to be competitive in the changing world. It is ensured by implementing international experience and models in teaching mother tongue and reading literacy as well as natural sciences. This article discusses the concept of pedagogical conditions for developing the ability of logical thinking in primary school children.

Integrating Clinical Reasoning Into Medical Students' First Weeks of Education Improves Understanding of Cranial Nerve Anatomy.

Clinical reasoning is essential to the practice of medicine.Such reasoning involves analytical (deductive) and non-analytical (recall) processes. Non-analytical reasoning is taught extensively in medical schools, and it dominates medical students' time as they review question banks and lecture notes, watch videos online, and memorize flashcards, algorithms, and illness scripts. However, few opportunities are provided in the curriculum to develop students' clinical reasoning skills, and when they are, the diverse levels of innate reasoning ability among students often lead to significant learning disparity. To address this deficiency, a pilot module on cranial nerve anatomy was developed to foster analytical clinical reasoning in an individualized manner. It was hypothesized that this module would not only introduce the foundations of an essential medical skill but also improve overall student understanding of the subject and reduce learning disparities among students. A comparative study was conducted using thismodule in one group and adidactic module in the other, employing pre- and post-testing measures. Results indicated a 26% improvement in average scores following the analytical module, whereas the control module showed no significant improvement. In addition, the disparity between students improving or not improving following the intervention was reduced, with 74% ofstudentsimproving after the reasoning module andonly33% of students improving after the didactic module. A novel cranial nerve educational module introduced analytical reasoning in medical students' first few weeks of education, facilitating the learning of complex anatomy and reducing learning disparity between students.

Predictive models for cholesterol crystals and plaque vulnerability in acute myocardial infarction: Insights from an optical coherence tomography study

BackgroundCholesterol crystals (CCs) are recognized as a risk factor for vulnerable atherosclerotic plaque rupture (PR) and major adverse cardiovascular events. However, their predictive factors and association with plaque vulnerability in patients with acute myocardial infarction (AMI) remain insufficiently explored. Therefore, This study aims to investigate the association between CCs and plaque vulnerability in culprit lesions of AMI patients, identify the factors influencing CCs formation, and develop a predictive model for CCs. MethodsA total of 431 culprit lesions from AMI patients who underwent pre-intervention optical coherence tomography (OCT) imaging were analyzed. Patients were divided into groups based on the presence or absence of CCs and PR. The relationship between CCs and plaque vulnerability was evaluated. A risk nomogram for predicting CCs was developed using the least absolute shrinkage and selection operator and logistic regression analysis. ResultsCCs were identified in 64.5 % of patients with AMI. The presence of CCs was associated with a higher prevalence of vulnerable plaque features, such as thin-cap fibroatheroma (TCFA), PR, macrophage infiltration, neovascularization, calcification, and thrombus, compared to patients without CCs. The CCs model demonstrated an area under the curve (AUC) of 0.676 for predicting PR. Incorporating CCs into the TCFA model (AUC = 0.656) significantly enhanced predictive accuracy, with a net reclassification improvement index of 0.462 (95 % confidence interval [CI]: 0.263–0.661, p < 0.001) and an integrated discrimination improvement index of 0.031 (95 % CI: 0.013–0.048, p = 0.001). Multivariate regression analysis identified the atherogenic index of plasma (odds ratio [OR] = 2.417), TCFA (OR = 1.759), macrophage infiltration (OR = 3.863), neovascularization (OR = 2.697), calcification (OR = 1.860), and thrombus (OR = 2.430) as independent risk factors for CCs formation. The comprehensive model incorporating these factors exhibited reasonable discriminatory ability, with an AUC of 0.766 (95 % CI: 0.717–0.815) in the training set and 0.753 (95 % CI: 0.704–0.802) in the internal validation set, reflecting good calibration. Decision curve analysis suggested that the model has potential clinical utility within a threshold probability range of approximately 18 % to 85 %. ConclusionsCCs were associated with plaque vulnerability in the culprit lesions of AMI patients. Additionally, this study identified key factors influencing CCs formation and developed a predictive model with potential clinical applicability.

Learning to explain is a good biomedical few-shot learner.

Significant progress has been achieved in biomedical text mining using deep learning methods, which rely heavily on large amounts of high-quality data annotated by human experts. However, the reality is that obtaining high-quality annotated data is extremely challenging due to data scarcity (e.g. rare or new diseases), data privacy and security concerns, and the high cost of data annotation. Additionally, nearly all researches focus on predicting labels without providing corresponding explanations. Therefore, in this paper, we investigate a more realistic scenario, biomedical few-shot learning, and explore the impact of interpretability on biomedical few-shot learning. We present LetEx-Learning to explain-a novel multi-task generative approach that leverages reasoning explanations from large language models (LLMs) to enhance the inductive reasoning ability of few-shot learning. Our approach includes (1) collecting high-quality explanations by devising a suite of complete workflow based on LLMs through CoT prompting and self-training strategies, (2) converting various biomedical NLP tasks into a text-to-text generation task in a unified manner, where collected explanations serve as additional supervision between text-label pairs by multi-task training. Experiments are conducted on three few-shot settings across six biomedical benchmark datasets. The results show that learning to explain improves the performances of diverse biomedical NLP tasks in low-resource scenario, outperforming strong baseline models significantly by up to 6.41%. Notably, the proposed method makes the 220M LetEx perform superior reasoning explanation ability against LLMs. Our source code and data are available at https://github.com/cpmss521/LetEx.

When politics trumps truth: Political concordance versus veracity as a determinant of believing, sharing, and recalling the news.

Resistance to truth and susceptibility to falsehood threaten democracies around the globe. The present research assesses the magnitude, manifestations, and predictors of these phenomena, while addressing methodological concerns in past research. We conducted a preregistered study with a split-sample design (discovery sample N = 630, validation sample N = 1,100) of U.S. Census-matched online adults. Proponents and opponents of 2020 U.S. presidential candidate Donald Trump were presented with fake and real political headlines ahead of the election. The political concordance of the headlines determined participants' belief in and intention to share news more than the truth of the headlines. This "concordance-over-truth" bias persisted across education levels, analytic reasoning ability, and partisan groups, with some evidence of a stronger effect among Trump supporters. Resistance to true news was stronger than susceptibility to fake news. The most robust predictors of the bias were participants' belief in the relative objectivity of their political side, extreme views about Trump, and the extent of their one-sided media consumption. Interestingly, participants stronger in analytic reasoning, measured with the Cognitive Reflection Task, were more accurate in discerning real from fake headlines when accurate conclusions aligned with their ideology. Finally, participants remembered fake headlines more than real ones regardless of the political concordance of the news story. Discussion explores why the concordance-over-truth bias observed in our study is more pronounced than previous research suggests, and examines its causes, consequences, and potential remedies. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Optimalisasi Kemampuan Penalaran Matematis melalui Metode Mind Mapping

Increasingly rapid global developments raise complex problems that require effective and innovative solutions. Mathematical reasoning is the key to solving the problem. But on the other hand, learning in schools is still a lot of conventional learning and the development of the students' mathematical reasoning skills is not optimal. The aim of this study was to explore the effectiveness of using mind map-based learning methods that were judged to be effective in developing mathematical reasoning skills. With a qualitative approach to literature studies, research results were obtained which showed that the method of mind mapping had a significant influence on the mathematical reasoning ability of the students. Library study sources are derived from previous research that had the same focus of study. Library reviews are used to analyze several studies related to the use of mind mapping-based learning methods and their influence on mathematical reasoning capabilities. This research is expected to provide an understanding of the importance of developing mathematical reasoning skills to form intelligent and critical generations