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With the surge in energy demand worldwide, renewable energy is becoming increasingly important. Solar power, in particular, is positioning itself as a sustainable and environmentally friendly alternative, and is increasingly playing a role not only in large-scale power plants but also in small-scale home power generation systems. However, small-sca...
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Introduction: The growing demand for cleaner energy alternatives has led to a significant increase in solar photovoltaic (PV) installations. However, the integration of solar energy into the grid remains challenging due to the inherent variability of solar power, influenced by factors such as irradiance, temperature, and wind speed. Accurate foreca...
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... In conclusion, this research addresses the pressing need to explore how constructivist teaching approaches can foster learning independence among elementary school students. By focusing on this specific aspect of education, the study will provide much-needed empirical evidence on the relationship between constructivism and student autonomy (Gao dkk., 2025;Lee & Jeong, 2025). The findings from this research have the potential to contribute significantly to educational practices, offering new insights into how teachers can apply constructivist principles to help young learners become more self-reliant and engaged in their educational journey (Andreou dkk., 2025; Galema dkk., 2025). ...
Background. This study aims to examine the influence of constructivism philosophy application on elementary school students’ learning independence. The rapid changes in educational paradigms emphasize the need for students to actively engage in their learning processes. Constructivism, with its focus on learners building knowledge through experience and reflection, has been identified as a potential catalyst in fostering students’ independence. Purpose. The objective of this research is to determine how the implementation of constructivist-based learning strategies can impact students’ ability to work autonomously in the learning environment. This research adopts a quasi-experimental method with a pre-test and post-test design. Method. The sample consists of two groups of elementary school students, with one group receiving constructivist-based instruction and the other group following traditional teaching methods. Data collection involved observation, questionnaires, and tests to measure the level of students’ learning independence before and after the intervention. Results. The results revealed that the application of constructivist principles significantly improved students’ learning independence, as seen from the increased scores in the post-test and positive feedback from observational data. Conclusion. This study concludes that constructivist-based learning can effectively enhance students’ independence, suggesting that educators should consider incorporating these strategies into their teaching practices.
As a core strategy for carbon emission reduction, carbon trading plays a critical role in policy guidance and market stability. Accurate forecasting of carbon prices is essential, yet remains challenging due to the nonlinear, non-stationary, noisy, and uncertain nature of carbon price time series. To address this, this paper proposes a novel hybrid deep learning framework that integrates dual-mode decomposition and a TKMixer-BiGRU-SA model for carbon price prediction. First, external variables with high correlation to carbon prices are identified through correlation analysis and incorporated as inputs. Then, the carbon price series is decomposed using Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) to extract multi-scale features embedded in the original data. The core prediction model, TKMixer-BiGRU-SA Net, comprises three integrated branches: the first processes the raw carbon price and highly relevant external time series, and the second and third process multi-scale components obtained from VMD and EWT, respectively. The proposed model embeds Kolmogorov–Arnold Networks (KANs) into the Time-Series Mixer (TSMixer) module, replacing the conventional time-mapping layer to form the TKMixer module. Each branch alternately applies the TKMixer along the temporal and feature-channel dimensions to capture dependencies across time steps and variables. Hierarchical nonlinear transformations enhance higher-order feature interactions and improve nonlinear modeling capability. Additionally, the BiGRU component captures bidirectional long-term dependencies, while the Self-Attention (SA) mechanism adaptively weights critical features for integrated prediction. This architecture is designed to uncover global fluctuation patterns in carbon prices, multi-scale component behaviors, and external factor correlations, thereby enabling autonomous learning and the prediction of complex non-stationary and nonlinear price dynamics. Empirical evaluations using data from the EU Emission Allowance (EUA) and Hubei Emission Allowance (HBEA) demonstrate the model’s high accuracy in both single-step and multi-step forecasting tasks. For example, the eMAPE of EUA predictions for 1–4 step forecasts are 0.2081%, 0.5660%, 0.8293%, and 1.1063%, respectively—outperforming benchmark models and confirming the proposed method’s effectiveness and robustness. This study provides a novel approach to carbon price forecasting with practical implications for market regulation and decision-making.
