The Influence of Gender and Interest on the Use of Learning Strategies in Biology Lessons

<p style="text-align: justify;">For biology students, the diversity, complexity, and abundance of content in this field yield a heavy study load. Hence, appropriate learning strategies are key in supporting learners’ academic success. In biology, the factors gender and interest hold a unique position within the natural sciences, as there is an academic imbalance to the disadvantage of male students. In the present study, we examined the influence of gender and interest as well as its interdependences on the students’ use of learning strategies for biology learning. A total of 180 seventh through tenth grade students (Mage=14.47; SD=1.35; 60% female) from four general-track secondary schools located in Germany participated in this study. Data on the students’ level of interest and the use of learning strategies in biology lessons were collected. We used multivariate analysis of covariance with the students’ age as the covariate to analyse our data. Results revealed a significant effect of gender on the students’ use of the learning strategies rehearsal, organisation, effort, and time management. With regard to elaboration and effort, the effects of interest were found to be significant. The gender gap regarding learning strategy use was narrower for students with high levels of interest. These findings might have implications for beneficial teacher behaviour in biology.</p>

Microgrid Operations Planning Based on Improving the Flying Sparrow Search Algorithm

Microgrid operations planning is crucial for emerging energy microgrids to enhance the share of clean energy power generation and ensure a safe symmetry power grid among distributed natural power sources and stable functioning of the entire power system. This paper suggests a new improved version (namely, ESSA) of the sparrow search algorithm (SSA) based on an elite reverse learning strategy and firefly algorithm (FA) mutation strategy for the power microgrid optimal operations planning. Scheduling cycles of the microgrid with a distributed power source’s optimal output and total operation cost is modeled based on variables, e.g., environmental costs, electricity interaction, investment depreciation, and maintenance system, to establish grid multi-objective economic optimization. Compared with other literature methods, such as Genetic algorithm (GA), Particle swarm optimization (PSO), Firefly algorithm (FA), Bat algorithm (BA), Grey wolf optimization (GWO), and SSA show that the proposed plan offers higher performance and feasibility in solving microgrid operations planning issues.

Investigating Indonesian EFL Teachers’ Perceptions on Blended Learning Strategy Amid Post-Covid-19 Era

One of the good news of the new normal era is all humankind’s sectors are opened gradually by following the restricted health protocols. Without an exception, the educational sector is also allowed by the government to open schools in the green zone. As a result, a blended learning strategy is inevitably essential to support this post-covid-19 era. Blended learning strategy is a novel pedagogical approach where the online and offline systems are combined to promote more purposeful, organized, and meaningful learning dynamics for learners. This current small-scale qualitative study aimed to investigate the specific benefits of a blended learning strategy based on Indonesian EFL teachers’ perceptions. Thus, 5 open-ended written narrative inquiry questions were sent to two randomly invited Indonesian EFL teachers working in different elementary school institutions. The obtained research results unveiled that the effective utilization of blended learning strategy had successfully promoted more meaningful language learning enterprises, elevated EFL learners’ learning motivation, and increased their proactive learning behaviors. However, it should also be pondered carefully that the more contextualized language learning approaches, as well as stable internet connection, are urgently needed to strengthen the effectiveness of this learning approach. Due to the obtained research results, it is reasonable to be expected that Indonesian EFL experts, teachers, practitioners, and policy-makers can establish more collaborative networking to better design more qualified and meaningful blended learning activities in the future.

Modified teaching-learning-based optimization algorithm for multi-objective optimization problems

When solving multi-objective optimization problems, an important issue is how to promote convergence and distribution simultaneously. To address the above issue, a novel optimization algorithm, named as multi-objective modified teaching-learning-based optimization (MOMTLBO), is proposed. Firstly, a grouping teaching strategy based on pareto dominance relationship is proposed to strengthen the convergence efficiency. Afterward, a diversified learning strategy is presented to enhance the distribution. Meanwhile, differential operations are incorporated to the proposed algorithm. By the above process, the search ability of the algorithm can be encouraged. Additionally, a set of well-known benchmark test functions including ten complex problems proposed for CEC2009 is used to verify the performance of the proposed algorithm. The results show that MOMTLBO exhibits competitive performance against other comparison algorithms. Finally, the proposed algorithm is applied to the aerodynamic optimization of airfoils.

Q-Learning-based fuzzy energy management for fuel cell/supercapacitor HEV

A novel fuzzy energy management strategy (EMS) based on improved Q-learning controller and genetic algorithm (GA) is proposed for the real-time power split between fuel cell and supercapacitor of hybrid electric vehicle (HEV). Different from driving pattern recognition–based method, Q-Learning controller takes actions by observing the driving states and compensates to fuzzy controller, that is, no need to know the driving pattern in advance. Aimed to prolong the fuel cell lifetime and decrease its energy consumption, the initial values of Q-table are optimized by GA. Moreover, to enhance the environment adaptation capability, the learning strategy of Q-learning controller is improved. Two adaptive energy management strategies have been compared, and simulation results show that current fluctuation can be reduced by 6.9% and 41.5%, and H2 consumption can be saved by 0.35% and 6.08%, respectively. Meanwhile, state of charge (SOC) of supercapacitor is sustained within the desired safe range.

The Effects of Group differentiation by students’ learning strategies

This study analyses the effects of group differentiation by students’ learning strategies of around 1200 students in 46 classes from eight secondary schools in the Netherlands. In an experimental setup with randomization at the class level, division of students over three groups per class (an instruction-independent group, an average group, and an instruction-dependent group) is based on learning strategies, measures using the Motivated Strategies for Learning Questionnaire (MSLQ). Each group is offered instruction fitting their own learning strategy. The results show that student performance is higher in classes where the differentiation was applied, and that these students score higher at some scales of the posttest of the questionnaire on motivation, metacognition and self-regulation. However, there are differences between classrooms from different teachers. Additional teacher questionnaires confirm the discrepancy in teacher attitudes towards the intervention.