Intelligence Beliefs Predict Spatial Performance in Virtual Environments and Graphical Creativity Performance

Hong, Jon‐Chao; Ye, Jian-Hong; Chen, Mei Lien; Ye, Jhen Ni; Kung, Ling Wen

doi:10.3389/fpsyg.2021.671635

Cited by 2 publications

(1 citation statement)

References 71 publications

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“…This can be explained by individual differences in cognitive styles. The results of many studies on cognitive styles show that everyone has a unique cognitive pattern, and even the cognitive pattern of the designer itself may be different from that of consumers [65][66][67]. This means that it is not easy for designers to accurately grasp the consumer's point of view.…”

Section: The Meaning Of the High Gap Between The Prediction Results O...mentioning

confidence: 99%

A Feasibility Discussion: Is ML Suitable for Predicting Sustainable Patterns in Consumer Product Preferences?

Chen

2023

Sustainability

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In the era when product design must meet the needs of consumers, the products preferred by consumers are an important source of design creativity and design reference for product designers to design products. Therefore, how to effectively grasp the products that consumers prefer has become an important issue for product designers. In order to allow designers to have more convenient and accurate consumer preference product prediction tools, this study proposed machine learning (ML) to analyze and predict sustainable patterns in consumer product preferences and conducted a feasibility study on the use of ML for predicting sustainable patterns in consumer product preferences. A total of three experiments were carried out in this study: the KJ method to predict consumer product preference experiment, the AHP method to predict consumer product preference experiment, and ML to predict consumer product preference experiment. This study uses the three experiments to discuss and compare the prediction ability of ML and the current commonly used forecasting tools, namely the KJ method and AHP method. The research results show that no matter what kind of consumer product attribute preference is predicted, the accuracy rate of consumer product preference prediction by ML is much higher than that of the KJ method and AHP method. These research results show that no matter the product attribute dimension, ML has the ability to predict consumer preferences, and ML has a better ability to predict consumer preferences than traditional tools. Therefore, this study believes that ML can be used to analyze and predict sustainable patterns in consumer product preferences. Therefore, this study suggests that product designers can use ML technology to assist in the analysis and prediction of consumer product preferences, so as to improve the grasp of consumer preference products.

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Section: The Meaning Of the High Gap Between The Prediction Results O...mentioning

confidence: 99%

A Feasibility Discussion: Is ML Suitable for Predicting Sustainable Patterns in Consumer Product Preferences?

Chen

2023

Sustainability

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Structural model of intelligence beliefs, motivational beliefs, academic self-handicapping and academic adjustment in Chinese undergraduate students

Han

2024

BMC Psychol

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Background Academic adjustment is crucial for students' success and well-being, yet the factors influencing this process must be more adequately understood. Although prior research has explored the impact of Intelligence and motivational beliefs on academic outcomes, there is a need to clarify how these beliefs interact to influence academic adjustment. Specifically, the existing literature needs a comprehensive model that integrates intelligence beliefs, motivational beliefs, and academic self-efficacy and examines their combined effect on academic adjustment. Objectives This research aims to develop and analyze a model that explains the relationships among intelligence beliefs (fixed and growth mindsets), motivational beliefs (including self-efficacy and intrinsic value) and test anxiety in predicting academic adjustment. This study also seeks to investigate the mediating role of academic self-efficacy in these associations, providing a deeper understanding of the underlying mechanisms that contribute to students' academic adaptation. Method The research employed a correlational methodology, targeting male and female undergraduate students at Inner Mongolia Medical University during the 2021 academic year. The statistical population comprised students from various departments, and a convenience sampling approach was utilized. Data collection involved 384 participants who completed questionnaires, including the Academic Adjustment Questionnaire, Intelligence Beliefs Questionnaire, Motivational Beliefs Questionnaire, and Academic Self-Efficacy Questionnaire. Results The findings revealed a favourable fit between the proposed model and the collected data. Furthermore, through a comprehensive direct and indirect effects analysis, it was evident that academic self-efficacy mediated the relationships among intelligence beliefs (both inherent and incremental), self-efficacy, intrinsic value, test anxiety, and academic adjustment. These results contribute to a nuanced understanding of the factors influencing academic adjustment among undergraduate students. Conclusions and implications In conclusion, this research underscores the pivotal role of academic self-efficacy as a mediator in the nexus between intelligence beliefs (both inherent and incremental) and academic adjustment. The study advocates for heightened attention to these constructs in educational planning. It emphasizes the need for educational interventions that foster positive Intelligence and motivational beliefs and mitigate test anxiety to enhance academic self-efficacy and promote successful academic adjustment. The implications of these findings extend to educators, counsellors, and policymakers involved in shaping the educational experiences of undergraduate students.

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Intelligence Beliefs Predict Spatial Performance in Virtual Environments and Graphical Creativity Performance

Cited by 2 publications

References 71 publications

A Feasibility Discussion: Is ML Suitable for Predicting Sustainable Patterns in Consumer Product Preferences?

A Feasibility Discussion: Is ML Suitable for Predicting Sustainable Patterns in Consumer Product Preferences?

Structural model of intelligence beliefs, motivational beliefs, academic self-handicapping and academic adjustment in Chinese undergraduate students

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