Learning Strategies Research Articles

Analyzing the Role and Impact of International Service-Learning Programs in Engineering Education: A Case Study from École Polytechnique de Bruxelles

This study examines the role and impact of international service learning (ISL) programs in engineering education, focusing on the Codepo initiative at the École polytechnique de Bruxelles. Our primary objective is to analyze how ISL programs contribute to the development of students’ technical skills and civic engagement. We describe the structure and implementation of the Codepo teaching unit, present findings from a survey conducted with 150 former students (123 answers), and analyze the results in terms of skills acquisition, added value, and lessons learned. Our findings highlight the significant benefits of ISL programs for engineering students and provide actionable insights for other institutions aiming to implement similar initiatives. Notably, our results show that international service learning, generating large commitment from the students, can contribute to an engineering school in several ways. This type of initiative aims not only to train engineers in the field of cooperation (or in other technical professions) but also to educate them as world citizens. The results also highlight that it is not enough to propose projects linked to cooperation to make it a service learning scheme: working on reflexivity, encouraging autonomy, and finding the right partners remain challenges.

Analysis of influencing factors on the learning effectiveness of Chinese beginners of German

This article presents an in-depth analysis of the difficulties faced by German language learners based on a survey of their learning obstacles. The survey findings reveal the impact of individual factors such as gender, age, and interest on learning outcomes among German learners. Additionally, the study focuses on the potential influence of learners' prior language learning experiences, particularly Chinese and English, on the German learning process, as well as the impact of their foundation in Chinese and English on learning German. By comprehensively analyzing these factors, the article uncovers the multiple challenges faced by German learners during the learning process and provides valuable insights for the formulation of German language teaching and learning strategies.

Cross-Lagged Associations Between Caregiver-Child and Teacher-Child Relationships and Approaches to Learning in Rural Preschoolers: The Moderating Role of Boarding Status

ABSTRACT Research Findings: This study examined the associations between children’s Approaches to Learning (ATL) and their caregiver-child and teacher-child relationships in rural Chinese preschools, distinguishing between boarding and non-boarding children. Teachers reported on ATL and teacher-child relationships for 233 children from rural boarding preschools in December 2018 (Time 1 [T1]) and December 2019 (Time 2 [T2]), while the primary caregivers reported on caregiver-child relationships. Results showed that T1 caregiver-child conflict was linked to lower T2 competence motivation and attention and persistence in children, while T1 teacher-child closeness correlated with increased T2 attention and persistence. Moreover, boarding status moderated some associations between ATL and adult-child relationships. Specifically, T1 caregiver-child closeness had a negative impact on later learning strategy only for boarding preschoolers, whilst T1 competence motivation was positively associated with later teacher-child closeness among non-boarding preschoolers. Practice or Policy: These findings provide valuable insights for tailoring interventions to enhance the approaches to learning in rural boarding preschoolers. Additionally, practical recommendations are outlined for caregivers and teachers.

Machine learning assisted screening of nitrogen-doped graphene-based dual-atom hydrogen evolution electrocatalysts

Nitrogen doped graphene-based dual-atom catalysts (NG-DACs) usually exhibit high reactivity for hydrogen evolution reaction (HER). The experimental design of efficient DACs-based HER catalysts, however, is time-consuming and expensive. In this work, a density functional theory combined with machine learning (DFT-ML) strategy is adopted towards the rapid screening of NG-DACs HER catalyst. The adsorption energies of HER intermediates on NG-DACs obtained by DFT calculations and their elemental features were used for the ML database construction. The prediction performance of three ML algorithms was evaluated, and gradient boosted regression (GBR) is found exhibit the best prediction accuracy. Using the HER energetics on Pt (111) as reference, quick screening of the NG-DACs with high HER activity was achieved, resulting to twenty-four potential catalysts. Further selection and validation of the ML-screened catalysts were performed by the hydrogen adsorption Gibbs free energy analysis. Accordingly, four promising NG-DACs HER catalysts were predicted using the proposed DFT-ML based catalyst prediction framework. The DFT-ML strategy offers an promising approach for the screening of new HER electrocatalysts.

Retrieval supports word learning in children with Down syndrome

BackgroundRetrieving information during word learning significantly improves retention and recall in both typically developing children and those with language delays. However, the extent to which this strategy benefits all learners, particular those with cognitive impairment and significant learning difficulties is unclear. AimsThis study examined the effects of retrieval practice on novel word learning in children with Down syndrome. SampleThe sample consisted of 11 children with Down syndrome aged 8–13 years and 11 typically developing children with comparable receptive vocabulary skills aged 4–6 years. MethodsParticipants were taught a series of novel words and their meanings either using retrieval or restudy strategies. Learning was assessed after 5-min and one week later. Mixed effects models were used to compare the effect of learning condition on recall and recognition. ResultsRecall accuracy of novel words was significantly higher in the retrieval condition compared to restudy in both participant groups, and this effect remained one week later. Children with Down syndrome also recalled significantly more meanings of these words in the retrieval condition compared to restudy; this effect was not significant for typically developing children. ConclusionsRetrieval practice improves novel word learning in children with Down syndrome. These findings highlight retrieval practice as an effective learning strategy in diverse populations. Implications for theory and practice are discussed.

Transfer learning for accurate description of atomic transport in Al-Cu melts.

Machine learning interatomic potentials (MLIPs) provide an optimal balance between accuracy and computational efficiency and allow studying problems that are hardly solvable by traditional methods. For metallic alloys, MLIPs are typically developed based on density functional theory with generalized gradient approximation (GGA) for the exchange-correlation functional. However, recent studies have shown that this standard protocol can be inaccurate for calculating the transport properties or phase diagrams of some metallic alloys. Thus, optimization of the choice of exchange-correlation functional and specific calculation parameters is needed. In this study, we address this issue for Al-Cu alloys, in which standard Perdew-Burke-Ernzerhof (PBE)-based MLIPs cannot accurately calculate the viscosity and melting temperatures at Cu-rich compositions. We have built MLIPs based on different exchange-correlation functionals, including meta-GGA, using a transfer learning strategy, which allows us to reduce the amount of training data by an order of magnitude compared to a standard approach. We show that r2SCAN- and PBEsol-based MLIPs provide much better accuracy in describing thermodynamic and transport properties of Al-Cu alloys. In particular, r2SCAN-based deep machine learning potential allows us to quantitatively reproduce the concentration dependence of dynamic viscosity. Our findings contribute to the development of MLIPs that provide quantum chemical accuracy, which is one of the most challenging problems in modern computational materials science.

An ensemble deep learning model for classification of students as weak and strong learners via multiparametric analysis

Academic data predictions are significantly important for improving the overall education system's effectiveness by providing early identification of weak students and personalized learning strategies. This paper proposes a deep learning model for the identification of weak and strong students using ensemble learning and multiparametric analysis. It combines several machine learning algorithms, including Naive Bayes, Support Vector Machines, Multi-Layer Perceptron, and Logistic Regression using an ensemble learning approach to enhance the model’s performance. Additionally, a custom 1D Convolutional Neural Network (CNN) is designed for classification. It utilizes multiparametric analysis to identify weak and strong students considering various parameters such as age, academic performance, location, and online learning behavior. The evaluation results indicate the performance of the proposed model has been improved in comparison to MLA FIS, SHMM, and DRL by 16.5%, 5.5%, and 2.4%, in terms of precision, 16.4%, 6.5%, and 3.5 % in terms of accuracy and 10.4%, 2.5% and 6.5% in terms of recall. These improvisations described that the model is efficient for multidomain feature extraction, ensemble classification, and high-variance feature selection, which result in a deeper understanding of student performance.

A study of the role of data and model uncertainty in active learning

Uncertainty-based active learning strategies have demonstrated significant superiority in small data research of materials domain. This study explores the effects of model uncertainty and data uncertainty separately on the performance of active learning strategies, specifically focusing on the number of iterations required to identify the optimal samples. For model uncertainty, three kinds of acquisition functions are compared, including predicted value strategy (PV), ranking of predicted value strategy (PR) and expected improvement strategy (EI). Among these, the active learning model utilizing PR requires the fewest average iterations (1.75). For data uncertainty, we evaluate the iterations of active learning by Gaussian process models that incorporate the uncertainty of the observations and noise samples that takes account into the uncertainty of the input features respectively. The results indicate that the active learning iterations of the three strategies converge to similar at the optimal weighting when the uncertainty of the observations is considered in the model (EI for 1.75, PV for 1.21 and PR for 1.18). In contrast, incorporating noise samples into the augmented dataset after the original samples would severely deteriorate the efficiency of active learning recommendations. Our findings aim to offer guidance for exploring more favorable acquisition functions and methods for active learning strategies.

Cooperative Path Planning for Multi-UAVs with Time-Varying Communication and Energy Consumption Constraints

In the field of Unmanned Aerial Vehicle (UAV) path planning, designing efficient, safe, and feasible trajectories in complex, dynamic environments poses substantial challenges. Traditional optimization methods often struggle to address the multidimensional nature of these problems, particularly when considering constraints like obstacle avoidance, energy efficiency, and real-time responsiveness. In this paper, we propose a novel algorithm, Dimensional Learning Strategy and Spherical Motion-based Particle Swarm Optimization (DLS-SMPSO), specifically designed to handle the unique constraints and requirements of cooperative path planning for Multiple UAVs (Multi-UAVs). By encoding particle positions as motion paths in spherical coordinates, the algorithm offers a natural and effective approach to navigating multidimensional search spaces. The incorporation of a Dimensional Learning Strategy (DLS) enhances performance by minimizing particle oscillations and allowing each particle to learn valuable information from the global best solution on a dimension-by-dimension basis. Extensive simulations validate the effectiveness of the DLS-SMPSO algorithm, demonstrating its capability to consistently generate optimal paths. The proposed algorithm outperforms other metaheuristic optimization algorithms, achieving a feasibility ratio as high as 97%. The proposed solution is scalable, adaptable, and suitable for real-time implementation, making it an excellent choice for a broad range of cooperative multi-UAV applications.

Students’ Challenges with Online Remote Teaching and Learning in South Africa

ABSTRACT Online remote teaching and learning strategies have become part of tertiary institutions worldwide. This knowledge delivery method became more profound during the COVID-19 era, forcing institutions to keep this method even after the COVID-19 pandemic. Most tertiary institutions in African countries, including South Africa, have also embraced online remote teaching and learning strategies. This study investigated the challenges of built environment tertiary students with online remote teaching and learning methods. A qualitative approach was adopted using open-ended interview questions to solicit the views of the built environment tertiary students regarding the challenges they experience with the online remote teaching and learning method. A thematic content analysis method was used to categorize the data into themes and sub-themes. The results show that students face several online remote teaching and learning challenges. These challenges were categorized into power, schedule, technology issues, lack of focus, communication, and cost-related issues. The tertiary institution authorities must address these challenges to ensure the sustainability of online remote learning and teaching as many students work and attend schools, thus relying on this method to acquire knowledge. The study used data from tertiary institutions in South Africa; hence, the findings may not be generalizable to other countries.

A Multi-Strategy Siberian Tiger Optimization Algorithm for Task Scheduling in Remote Sensing Data Batch Processing

With advancements in integrated space–air–ground global observation capabilities, the volume of remote sensing data is experiencing exponential growth. Traditional computing models can no longer meet the task processing demands brought about by the vast amounts of remote sensing data. As an important means of processing remote sensing data, distributed cluster computing’s task scheduling directly impacts the completion time and the efficiency of computing resource utilization. To enhance task processing efficiency and optimize the allocation of computing resources, this study proposes a Multi-Strategy Improved Siberian Tiger Optimization (MSSTO) algorithm based on the original Siberian Tiger Optimization (STO) algorithm. The MSSTO algorithm integrates the Tent chaotic map, the Lévy flight strategy, Cauchy mutation, and a learning strategy, showing significant advantages in convergence speed and global optimal solution search compared to the STO algorithm. By combining stochastic key encoding schemes and uniform allocation encoding schemes, taking the task scheduling of aerosol optical depth retrieval as a case study, the research results show that the MSSTO algorithm significantly shortens the completion time (21% shorter compared to the original STO algorithm and an average of 15% shorter compared to nine advanced algorithms, such as a particle swarm algorithm and a gray wolf algorithm). It demonstrates superior solution accuracy and convergence speed over various competing algorithms, achieving the optimal execution sequence and machine allocation scheme for task scheduling.

Integrative Learning Strategies for Enhancing Critical Thinking in Islamic Religious Education

This research employs a qualitative approach to explore integrative learning strategies aimed at enhancing critical thinking skills in Islamic Religious Education at the secondary school level in Padang, Indonesia. The study involved four teachers and 120 students from various classes to provide a comprehensive perspective. Given the increasing importance of critical thinking in education, especially in subjects that shape students' moral and ethical foundations, this research emphasizes the crucial role of developing these skills within Islamic Religious Education. Data were collected through direct classroom observations, in-depth interviews with teachers and students to capture their experiences and insights on the strategies used, and a detailed analysis of relevant teaching materials. The findings reveal that group discussions create a dynamic environment where students can exchange ideas, broaden perspectives, and develop critical arguments. Case study analyses enable students to connect religious concepts to real-life situations, reinforcing the practical significance of religious teachings. Problem-solving activities promote logical, systematic, and reflective thinking skills, especially when addressing complex moral and ethical dilemmas. The study also highlights that these strategies not only enhance students' critical thinking but also increase their active engagement in learning, deepen their understanding of religious teachings, and foster a reflective approach toward moral values. This research offers an in-depth understanding of the effectiveness of integrative learning strategies in Islamic Religious Education and underscores their relevance in preparing students to meet the demands of modern education, which prioritizes critical thinking skills.

FINDING SILVER LININGS: UNDERSTANDING THE CHALLENGES FACED BY NON-SPECIAL NEEDS EDUCATION TEACHERS IN THE ERA OF INCLUSIVE EDUCATION

This qualitative phenomenological study aimed to uncover the lived experiences of twelve (12) non-special needs education (non-SNed) teachers at New Corella National High School, focusing on the challenges they faced in the era of inclusive education. Additionally, the study described how these teachers coped with these challenges and the insights they gained from their experiences. In-depth interviews (IDI) were conducted with twelve (12) participants selected through purposive sampling. Results indicated that non-SNed teachers encountered various challenges, including managing diverse learning needs, lacking proper training and resources, and difficulties with integration and inclusion. Participants coped with these struggles by receiving comprehensive training, creating an inclusive atmosphere, enhancing student engagement and participation, achieving academic progress, and employing flexible learning strategies. The insights drawn from their experiences emphasized the importance of cultivating patience and adaptability, enriching understanding and empathy, navigating professional growth and challenges in inclusive education, and embracing and loving their students. Future researchers should adopt a mixed-methods approach with more participants to comprehensively grasp inclusive education processes. KEYWORDS- Non-Special Needs Education Teachers, Lived Experience, Challenges, Inclusive Education, Qualitative, Phenomenology, Philippines

Using case-based learning to teach evidence-based practice: A pilot study.

The American Speech-Language-Hearing Association (ASHA) promotes the use of evidence-based practice (EBP) in the provision of clinical services by speech-language pathologists (SLPs). Therefore, training in the EBP process is vital in the education of future SLPs. Case-based learning (CBL) has been found effective in facilitating transfer of knowledge and skills in various fields; however, its utilisation to teach EBP to SLP students is understudied. This phenomenological investigation explores the use of CBL, an active learning strategy that gives students the opportunity to practice application of knowledge to realistic scenarios, to teach EBP to graduate SLP students. Graduate SLP students worked in small groups to complete an article review activity centred around an assigned hypothetical clinical case. Students submitted article review papers and written reflections on their learning process. The written reflections and review papers were analysed using qualitative content analysis to understand students' approach to clinical cases and their perspectives on the CBL approach. Analysis of the papers and reflections indicated that students tended to rely heavily on scientific evidence for clinical decision making, often to the exclusion of clinical expertise or client perspectives. Students reported that the project was applicable to their future clinical practice. Further, they reported learning from multiple sources, including the instructor, class discussion and their peers. Results indicate that graduate SLP students found CBL activities beneficial for learning the principles of EBP. Students specifically indicated an appreciation for the realistic nature of the activities. Future applications are examined.

Psychometric Properties of Social Climate in the Classroom Scale for University Students in Chile

Background: Classroom social climate is a significant phenomenon within educational contexts; however, it has predominantly been studied among high school students, with limited evidence available at the university level and, consequently, in the psychometric properties of measurement instruments. This study aimed to analyze the psychometric properties of the University Classroom Social Climate Scale (ECSA-U) among Chilean students from the La Araucanía region. Method: 422 students participated, responding to the adapted version of the ECSA-U and the Motivation subscale of the Motivation and Learning Strategies Questionnaire. Exploratory structural equation modeling (ESEM), reliability analyses, and correlation analyses were conducted to provide valid evidence for the Chilean University Classroom Social Climate Scale (ECSA-UCL). Results: The scale demonstrated a three-factor structure with good fit indicators, excellent reliability indices, and significant positive associations between the ECSA-UCL and the Motivation Subscale. Conclusions: The ECSA-UCL has proven valid and reliable for measuring the perception of Classroom Social Climate among Chilean university students, making it suitable for use as a measurement tool in studies or interventions that include this variable.

Best IDEAS: Special Issue of the International Database Engineered Applications Symposium

Database engineered applications cover a broad range of topics including various design and maintenance methods, as well as data analytics and data mining algorithms and learning strategies for enterprise, distributed, or federated data stores [...]

Day2Dark: Pseudo-Supervised Activity Recognition Beyond Silent Daylight

AbstractThis paper strives to recognize activities in the dark, as well as in the day. We first establish that state-of-the-art activity recognizers are effective during the day, but not trustworthy in the dark. The main causes are the limited availability of labeled dark videos to learn from, as well as the distribution shift towards the lower color contrast at test-time. To compensate for the lack of labeled dark videos, we introduce a pseudo-supervised learning scheme, which utilizes easy to obtain unlabeled and task-irrelevant dark videos to improve an activity recognizer in low light. As the lower color contrast results in visual information loss, we further propose to incorporate the complementary activity information within audio, which is invariant to illumination. Since the usefulness of audio and visual features differs depending on the amount of illumination, we introduce our ‘darkness-adaptive’ audio-visual recognizer. Experiments on EPIC-Kitchens, Kinetics-Sound, and Charades demonstrate our proposals are superior to image enhancement, domain adaptation and alternative audio-visual fusion methods, and can even improve robustness to local darkness caused by occlusions. Project page: https://xiaobai1217.github.io/Day2Dark/.

Arabic Language Learning Strategy for Elementary School Graduates in Pondok Pesantren Yusuf Abdussatar Kediri Lobar West Nusa Tenggara, Indonesia

This article discusses the Arabic language learning strategy for elementary school graduates at Pondok Pesantren Yusuf Abdussatar, Kediri Lobar, West Nusa Tenggara. The background of this research is based on the importance of mastering Arabic as a means of communication and understanding religious texts in Islamic boarding schools. However, elementary school graduates who enter pesantren often have limitations in Arabic language skills. Therefore, effective learning strategies are needed to facilitate their understanding. This article examines the various methods used, ranging from the communicative approach, context-based learning, to the use of interactive media and a supportive learning environment. This research uses qualitative methods with observation, interview and document analysis techniques. The results show that the applied learning strategies succeeded in improving students' learning motivation and their Arabic language skills, especially through the combination of theory and hands-on practice. In addition, the active involvement of teachers and the support of an intensive boarding school environment play an important role in the success of the learning process. This article recommends further development on the integration of technology in Arabic language learning to improve the efficiency and effectiveness of learning in Islamic boarding schools.

Towards transformative learning: students’ disorienting dilemmas and coping strategies in interdisciplinary problem-based learning

Towards transformative learning: students’ disorienting dilemmas and coping strategies in interdisciplinary problem-based learning

The ‘Universe in a Box’: a hands-on activity to introduce primary school students to cosmology

Abstract Physics Education Research shows that active learning and interdisciplinary strategies can enhance students’ engagement in physics. In primary school, the implementation of active learning pedagogies such as Inquiry-Based Science Education aims to encourage students’ autonomy and participation in their learning process. Indeed, active learning promotes pupils’ creativity, helping them develop the skills that increasingly determine their future employability and personal development, introducing them to STEM. In this regard, stories and storytelling can help improve teaching and students’ physics learning. Telling the Universe and its history can afford this job. The Big Bang, the Cosmic Microwave Background, and the formation of stars and planets are valuable tools for introducing primary school students to physics and the scientific method while fostering their curiosity about science. Moreover, it helps the instructors monitor the development of peculiar misconceptions on these topics, preventing them from fully understanding physics phenomena. In this paper, we present an innovative short-term program (one session of three hours) called ‘The Universe in a Box’ to introduce primary school students (grades 4–5, ages 9–10) to cosmology. We illustrate our design and the educational purposes of the program and present the outcomes from its implementation in five different laboratories in Sardinia, Italy, from 2022 to 2024 (60 students involved). This work can furnish a theoretical and methodological guide for primary school teachers and instructors on integrating formal curricula with contemporary physics topics using interdisciplinary approaches, engaging their students in STEAM (STEM plus Arts).