Learning Strategies Research Articles

The Role of Generative Learning Strategy in Bridging the Gap between High and Low Achievers in Genetics Studies in Sefwi-Wiawso Municipality, Ghana

The study sought to investigate the use of the Generative Learning Strategy (GLS) in bridging the performance gap between high- and low-achieving students in genetics in Ghana's Western North region's capital, Sefwi-Wiawso Municipality. The study adopted an embedded research design involving the quasi-experimental pretest/posttest groups with a sample size of 106 SHS 3 Biology students. Two intact classes were randomly selected, treated as one group and exposed to the same treatment conditions. The Genetic Concepts Test and a semi-structured interview guide collected the quantitative and qualitative data, respectively. The GCT's internal consistency was 0.784, indicating a preferable internal consistency. The quantitative data was analyzed using the SPSS version 26, by employing descriptive and inferential statistics. The study found no significant performance difference between high and low achievers after using the GLS. The approach improved the lower achievers’ performance. The interview results revealed that Senior High School Biology students perceived that the Generative Learning Strategy improved the understanding, motivation, retention and self-directed learning of genetic concepts, thereby enhancing their learning outcomes. The study recommended that SHS Biology teachers employ the Generative Learning Strategy in teaching genetic concepts to bridge the performance gap between high and low achievers.

Difficulties and Challenges Encountered by Saudi EFL learners: A Diagnostic Study

Saudi learners, like non-native learners in other non-English-speaking nations, face numerous difficulties and hurdles in their acquisition of the English language. This study aims to elucidate the difficulties and challenges Saudi EFL learners encounter in acquiring English. It also delineates the diverse learning strategies Saudi EFL learners utilize to address such issues and challenges. The study employs open-ended and unstructured interviews to gather qualitative data from selected Saudi EFL learners. The participants convey their experiences and perspectives regarding the obstacles and challenges encountered in language acquisition. The study's findings indicated that Saudi EFL learners face numerous challenges in terms of listening, reading, speaking, writing, grammar, pronunciation, vocabulary, cultural and environmental factors, and interference from their native language. This study aims to provide significant insights for course designers, teacher trainers, policymakers, and aspiring EFL teachers in Saudi Arabia, while also enhancing the awareness of the broader TEFL/TESOL community regarding the issues discussed within its contents.

Fun and Functional: Using Non-Digital Games to Promote Maths Engagement in Pre-service Teachers

Humans naturally engage in playful activities throughout their lives and play serves as a fundamental mechanism for cognitive development as well as the development of essential soft skills such as teamwork, decision-making, leadership, and communication. Recognizing this innate predisposition for play, educators are increasingly embracing gamification strategies into their classrooms. More attention has been driven to digital tools such as platforms Kahoot! and Duolingo which gained popularity and demonstrated the potential of game-based learning to increase engagement and knowledge retention. However, concerns about digital screen time fuelled a renewed interest in non-digital activities, including educational games. Non-digital games are often more accessible and affordable, making them suitable for schools with limited resources. Research suggests that non-digital games can effectively promote collaboration, communication, and critical thinking skills. In addition, they offer flexibility to teachers to adapt rules and scenarios to specific learning objectives and student needs. Recognizing the benefits of employing games as a pedagogical strategy, the first author of this study opted to incorporate game-based learning strategies into her classes, on a regular basis. Throughout the semester, various non-digital games were implemented, inspired on well-known classics such as memory card games, Monopoly and Pictionary. These games were carefully designed to address specific mathematics topics identified as either the most relevant or those where students revealed more difficulties. The participants in this study were pre-service pre- and primary school teachers enrolled in a maths undergraduate course. Since these students are future educators, the games served a dual purpose: improving current learning experiences and providing teaching resources for their future classrooms. At the end of the semester, a survey was conducted to evaluate students’ perceptions towards the use of games in the mathematics teaching-learning process. In this paper we describe some of the games used in this study and analyse the results of the survey. Previous studies reveal that students generally welcome the incorporation of games, perceiving them as effective tools for enhancing motivation and promoting positive learning outcomes. Our findings corroborate these studies.

Meningkatkan Motivasi Peserta Didik Melalui Model Problem Based Learning Berbantuan Media Audiovisual di Fase C Kelas 5 SDN 040519 Tanjung Barus

This study aims to enhance student learning motivation through the implementation of the Problem Based Learning (PBL) model supported by audiovisual media in the fifth grade of SDN 040519 Tanjung Barus. The method used is classroom action research, consisting of two cycles, with each cycle including planning, implementation, observation, and reflection. Data were collected through observations, questionnaires, and interviews to evaluate student motivation before and after the PBL model implementation. The results indicate that the application of the PBL model significantly improves student learning motivation, as reflected in increased active participation in discussions, a desire to complete assignments, and positive responses to feedback from teachers. The average motivation score increased from 65% in the first cycle to 85% in the second cycle, demonstrating that the applied learning strategy was successful. This study provides important contributions to the development of more engaging and relevant teaching methods and offers recommendations for teachers to integrate audiovisual media into the learning process to enhance student motivation and engagement.

Exploring the Use of Machine Translation in English-Medium Higher Education Contexts: Perspectives from Omani EFL Students

There has been a significant increase in the use of machine translation by university students as part of their coping strategies for their English-medium study. This practice has been a subject of debate among educators. Some argue that advancements in technology have made machine translation a valuable learning tool, while others remain skeptical, questioning its pedagogical benefits. This study is an attempt to explore Omani EFL students’ perceptions and attitudes regarding the use of machine translation as a coping strategy in their English-medium content learning activities. A qualitative research design, employing an open-ended survey and semi-structured interviews, was used in the study. Thirty-five students enrolled on different English-medium majors at a public Omani university voluntarily participated in the investigation. The data was analysed thematically and inductively. Results showed that all participants reported using machine translation for various learning activities, including looking up the meanings of discipline-specific terminology and translating individual sentences and longer texts from English to Arabic and vice versa. Some participants noted that machine translation tools tend to perform better when translating from Arabic to English compared to translating from English to Arabic. However, participants also highlighted several challenges related to the use of machine translation as a coping learning strategy. The implications for translation pedagogy and research are discussed.

A real-time network traffic classification system using cost-sensitive deep neural networks

Due to the continuous growth of Internet and online applications, network traffic classification is not only becoming one of the most crucial disciplines in network management but is also becoming quintessential for providing advanced tasks such as Quality of Service and network security. Moreover, even though many studies have been undertaken in recent years, real-time encrypted traffic classification continues to be an important challenge in the field of network traffic classification. Therefore, in this paper a real-time network traffic classification system is proposed together with five new models. The real-time classification system classified each incoming real-time packets into appropriate classes of interest and the five new models make use of a cost-sensitive learning strategy to deal with the unbalanced data problem during the training phase. The proposed models, which are called Cost-Sensitive Long-Short Term Memory (CSLSTM), Cost-Sensitive Gated Recurrent Unit (CSGRU), Cost-Sensitive Convolution Neural Network (CSCNN), CSNN with LSTM and CSCNN with GRU, can handle both traffic categorization and application identification. These proposed models were compared with prominent methods in this field and the proposed CSCNN was observed to outperform the researched deep learning models by at least 4% to 16% in correctly classifying packets from the ISCX VPN-nonVPN dataset.

Upaya Meningkatkan Hasil Belajar Pendidikan Agama Katolik dan Budi Pekerti dengan Metode Pembelajaran Problem Based Learning Berbantuan Media Video pada Kelas X Tari di SMK Negeri 1 Kasihan

This study aims to improve the learning of Catholic Religious Education in class X Tari at SMK Negeri 1 Kasihan, which was initially dominated by the less interactive lecture method, affecting the motivation and learning outcomes of students. To overcome this, the Problem Based Learning (PBL) method assisted by video media is applied, which involves active students in solving problems relevant to everyday life. The research was conducted in two cycles, with PBL steps such as problem orientation, individual and group investigation, and presentation and evaluation of results. The results showed significant improvements in the cognitive, skill and affective aspects of learners. The cognitive average score of the group task increased by 11.2%, from 79.7 in cycle I to 90.8 in cycle II, while the individual task increased by 12%, from 76.8 to 89. In terms of skills, the average score increased by 13%, from 76 to 88. The affective aspect also experienced a significant increase of 23.2%, from 63.4 to 88. The application of PBL assisted by video media proved effective in improving students' learning outcomes, both in terms of critical thinking, responsibility, and understanding and application of Catholic teachings. This study recommends the use of PBL method and video media as an effective learning strategy to improve the quality of learning Catholic Religious Education and Cultivation.

Oxygen Content Control in the Electroslag Remelting Process: An Incremental Learning Strategy Based on Optimized Wasserstein Generative Adversarial Network with Gradient Penalty Data Augmentation

Electroslag remelting (ESR) is essential for producing high‐end special steel, but its complex process and numerous influencing factors make quality control challenging. This study addresses oxygen content control during ESR using a big data machine learning approach. An incremental learning strategy is proposed based on an optimized Wasserstein generative adversarial network with gradient penalty (WGAN‐GP) for data enhancement, focusing on G20Cr2Ni4A bearing steel. The WGAN‐GP model enhances time‐series data and metadata, utilizing long short‐term memory networks, fully connected networks, and attention mechanisms. The effectiveness of data enhancement is verified using a deep neural network classifier and statistical methods. Data is divided into historical and data streams, with an incremental learning strategy based on histogram gradient boosting regression trees to prevent catastrophic forgetting and improve efficiency through knowledge distillation and real‐time hyperparameter adjustment. Results show that the data augmentation method significantly improves model generalization and accuracy in small sample metallurgy. The incremental learning strategy enhances prediction accuracy for oxygen content, contributing to better cleanliness quality of electroslag steel. This study offers a novel approach for addressing small sample challenges in metallurgical processes.

Investigating deep learning strategies for fast denoising of 5D cardiac photon-counting micro-CT images


Photon-counting detectors (PCDs) for CT imaging use energy thresholds to simultaneously acquire projections at multiple energies, making them suitable for spectral imaging and material decomposition. Unfortunately, setting multiple energy thresholds results in noisy analytical reconstructions due to low photon counts in high-energy bins. Iterative reconstruction provides high quality photon-counting CT (PCCT) images but requires enormous computation time for 5D (3D + energy + time) in vivo cardiac imaging. 

Approach. 
We recently introduced UnetU, a deep learning (DL) approach that accurately denoises axial slices from 4D (3D + energy) PCCT reconstructions at various acquisition settings. In this study, we explore UnetU configurations for 5D cardiac PCCT denoising, focusing on singular value decomposition (SVD) modifications along the energy and time dimensions and alternate network architectures such as 3D U-net, FastDVDNet, and Swin Transformer UNet. We compare our networks to multi-energy non-local means (ME NLM), an established PCCT denoising algorithm. 

Main results. 
Our evaluation, using real mouse data and the digital MOBY phantom, revealed that all DL methods were more than 16 times faster than iterative reconstruction. DL denoising with SVD along the energy dimension was most effective, consistently providing low root mean square error and spatio-temporal reduced reference entropic difference, alongside strong qualitative agreement with iterative reconstruction. This superiority was attributed to lower effective rank along the energy dimension than the time dimension in 5D cardiac PCCT reconstructions. ME NLM sometimes outperformed DL with time SVD or time and energy SVD, but lagged behind iterative reconstruction and DL with energy SVD. Among alternate DL architectures with energy SVD, none consistently outperformed UnetU Energy (2D). 

Significance. 
Our study establishes UnetU Energy as an accurate and efficient method for 5D cardiac PCCT denoising, offering a 32-fold speed increase from iterative reconstruction. This advancement sets a new benchmark for DL applications in cardiovascular imaging.

Exploring Current Challenges and Opportunities in Media Literacy Skills for Youth: Stakeholders’ Perspectives

The widespread dissemination of contradictory information through various media platforms can increase anxiety and confusion among younger generations. This highlights the need to promote Media Literacy (ML) skills, enabling them to evaluate online information's validity critically. Video game-based learning can be valuable in this context, fostering active learning and critical thinking. Therefore, developing and sharing innovative resources like interactive content and video games is crucial to enhancing young people's ML skills and resilience to disinformation. The YO-MEDIA project, involving three universities in Italy, Portugal, and Spain and a journalist association, aims to develop ML in young people during times of crisis (e.g., pandemics and wars) through game-based learning strategies and professional training. The project carried out 29 semi-structured interviews in the partner countries with various stakeholders to understand perspectives on media design practices and ML formulation for young people in crises. These interviews aimed to collect education and media professionals' insights on promoting ML and critical thinking among youth during crises. It also seeks to gather best practices and understand these professionals' challenges when fostering ML among youth. This study's findings suggest that the interviewees deem ML vital for distinguishing accurate information from misinformation during crises, especially relevant for young individuals. Essential skills for these young people include critical thinking, filtering information, and recognizing online threats. Effective strategies involve operating within digital environments frequented by young people, encouraging critical thinking, and nurturing collaboration. Professionals face challenges such as a lack of training, engaging young people's attention, and keeping pace with digital environments. The interviewees also emphasized the importance of ML training for informal educators and media professionals. They recommended Massive Open Online Courses (MOOCs) for their flexibility and accessibility. According to the interviewees, an ML game should involve research, interpretation, and decision-making processes and be accessible to young people with disabilities. They identified several ML projects and resources, including multimedia labs, youth parliaments, and critical thinking tools. This study contributes to developing games that promote critical thinking and ML in young people. It also aids in establishing ongoing professional training in ML for informal educators, teachers, media professionals, and other stakeholders.

Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures

Recommender systems have become an integral part of online services due to their ability to help users locate specific information in a sea of data. However, existing studies show that some recommender systems are vulnerable to poisoning attacks, particularly those that involve learning schemes. A poisoning attack is where an adversary injects carefully crafted data into the process of training a model with the goal of manipulating the system’s final recommendations. Based on recent advancements in artificial intelligence (AI), such attacks have gained importance recently. At present, we do not have a full and clear picture of why adversaries mount such attacks, nor do we have comprehensive knowledge of the full capacity to which such attacks can undermine a model or the impacts that might have. While numerous countermeasures to poisoning attacks have been developed, they have not yet been systematically linked to the properties of the attacks. Consequently, assessing the respective risks and potential success of mitigation strategies is difficult, if not impossible. This survey aims to fill this gap by primarily focusing on poisoning attacks and their countermeasures. This is in contrast to prior surveys that mainly focus on attacks and their detection methods. Through an exhaustive literature review, we provide a novel taxonomy for poisoning attacks, formalise its dimensions, and accordingly organise 31 attacks described in the literature. Further, we review 43 countermeasures to detect and/or prevent poisoning attacks, evaluating their effectiveness against specific types of attacks. This comprehensive survey should serve as a point of reference for protecting recommender systems against poisoning attacks. The article concludes with a discussion on open issues in the field and impactful directions for future research. A rich repository of resources associated with poisoning attacks is available at https://github.com/tamlhp/awesome-recsys-poisoning .

Training needs for developing blended learning skills and strategies among Arabic language teachers in public schools

The study aimed to know the training needs for developing blended learning skills and strategies among Arabic language teachers in government schools. The researcher used the descriptive analytical approach. The study sample reached (265) male and female teachers of the Arabic language subject in government schools. To achieve the objectives of the study, the researcher prepared a questionnaire as a tool. The study consisted of (21) paragraphs. The study found that the training needs for developing blended learning skills and strategies among Arabic language teachers in government schools in Ma’an Governorate came in at a high level with an overall arithmetic average of (3.7775), as the results show that the paragraph: I provide students with some practical work through blended learning came in first place, where The arithmetic mean was (4.0852). The results also showed that there were no statistically significant differences between the responses of Arabic language teachers regarding the training needs for developing blended learning skills and strategies due to the gender variable, while there were statistically significant differences due to variables of academic qualification and years of experience. Based on these results, the study recommended the need for teachers to develop the skills of designing and implementing.

Improving mathematical proof based on computational thinking components for prospective teachers in abstract algebra courses

Understanding and constructing mathematical proofs is fundamental for students in abstract algebra courses. The computational thinking approach can aid the process of compiling mathematical proofs. This study examined the impact of integrating computational thinking components in constructing mathematical proofs. The researcher employed a sequential explanatory approach to ascertain the enhancement of algebraic proof capability based on computational thinking through the Wilcoxon test. A total of 32 prospective teachers in mathematics education programs were provided with worksheets for seven meetings, which were combined with computational thinking stages. Quantitative data were collected from initial and subsequent test instruments. Moreover, three prospective teachers were examined through case studies to investigate their mathematical proof capability using computational thinking components, including decomposition, abstraction, pattern recognition, and algorithmic thinking. The study's findings indicated that CT intervention enhanced students' logical reasoning, proof-writing abilities, and overall engagement with abstract algebra concepts. The findings illustrate that integrating computational thinking into learning strategies can provide a framework for developing higher-order thinking skills, especially in proving, which are essential for studies in mathematics education programs.

Integrasi Dan Penerapan Teori Signiant-Signified Saussure Dalam Pembelajaran Mufradāt

Learning Arabic mufradat, strategies, methods, and approaches are often less meaningful and only a formality for students. This research describes integrating Saussure's signifier-signified theory into Arabic vocabulary learning. The process in this research uses library research, which is a method of collecting data then understanding the data and studying theories from various sources of articles, and books by the research. The results of this study show that Saussure's signifiant-signified theory in the application of the principles of the relationship between the signifier and signified includes four aspects, including a) written sign (sign in the form of writing, b) oral-audio sign (sign in the form of sound), c) visual sign (sign in the form of pictures), d) object sign (a sign of a concrete object). Implementation in learning should be done sequentially, starting from the written sign as an introduction to the vocabulary and its meaning, oral-audio sign as an improvement of pronunciation, visual sign as strengthening mastery of mufradat, and object sign as contextual use of mufradat.

Active learning strategies in entrepreneurship. Perceptions and experiences of students at a Mexican University

The intention of the educational research was to explore the expressions of students after experiencing various active learning strategies to enhance entrepreneurship. In 2023, an educational research project was conducted at a public Mexican university with three groups and two teachers. At the beginning of the course, the students' initial perceptions of the strategies were obtained. Subsequently, during the teaching-learning period, these strategies were guided by the teaching team. At the end of the course, the same students were asked to narrate their experiences with these strategies, and a thematic content analysis of the written narratives was conducted. The results indicate that the students' expressions were mostly positive and encouraging, especially in the early stages, though they declined slightly by the end, with nearly nine out of ten students expressing the intention to start a business someday. Learning through projects, conducting laboratory practices, receiving feedback-mentorship and counseling, attending conferences and visiting companies, and presenting business plans were the strategies perceived as most successful. Meanwhile, collaborating among peers and with teachers from various disciplines was less well received, yet still above 80% in the final evaluation. The students expressed the need for teacher support and solid institutional organization. The findings suggest improvements for teaching entrepreneurship at the university level through active learning, highlighting the challenges of entrepreneurial education.

The effects of peer-assisted learning on rhythmic and melodic sight-reading in a middle school chorus

The purpose of this study was to investigate the ways that children engage in collaborative processes with adults and their peers in music sight-reading skill acquisition in a middle school chorus in the Southeastern United States. Intact groups of sixth-, seventh-, and eighth-grade choruses were assessed on melodic and rhythmic sight-reading using a quasi-experimental pretest/posttest non-equivalent control group design. The instruments to evaluate melodic and rhythmic sight-reading included an adapted version of the Vocal Sight-Reading Inventory and a researcher-developed Rhythmic Skills Hierarchy. A comparison of the effectiveness of three learning models on rhythmic and melodic sight-reading achievement occurred: teacher-only and two types of peer-assisted learning. Three Analyses of Covariance (ANCOVAs) compared differences between groups on adjusted posttest scores. There were significant differences between the teacher-only (T-O) and the symmetrical peer-assisted learning (SPAL) groups when compared to the asymmetrical peer-assisted learning group (APAL). The T-O and SPAL treatment types were effective learning models for melodic sight-reading achievement but with nonsignificant results on rhythmic sight-reading. Symmetrical peer-assisted learning strategies are a suggested practice as a complement to teacher-directed instruction.

Flipped Classrooms: Addressing Students Participation and Preparedness

The flipped classroom concept has gained attention in recent years as a modern pedagogical approach that offers an alternative to traditional teaching methods. While the flipped classroom model represents a significant shift in teaching strategies, its effectiveness in promoting active student participation and engagement cannot be guaranteed. The widespread use of mobile phones and computers has had a negative impact on digital learners, leading to a decline in the review of pre-class materials among higher education students. This paper critically examines the potential of the flipped classroom as an active learning strategy and raises questions about its effectiveness in meeting students' diverse needs. Results indicate that the flipped classroom teaching approach enhances student engagement and academic achievement. However, the effectiveness of this method hinges on digital learners adapting their study habits. A significant number of students fail to review course materials outside of class due to their reliance on digital tools, resulting in reduced pre-class preparation and fewer active participants in classroom discussions.

Do Emergency Certified Teachers Require Differentiated Administrative Support? A Comparison of Teacher Evaluation Scores by Certification Status

ABSTRACT To address growing K-12 teacher shortages, many districts are hiring emergency certified teachers at higher rates than in the past. Yet, little empirical research evaluates the performance of emergency certified teachers. In this study, we estimate the relationship between certification status and teacher evaluation scores (n = 1,312) in a large urban district with a high percentage of emergency certified teachers. Results indicate lower overall evaluation scores (β = -0.41, p < .001) for emergency certified teachers. The largest gaps were found for measures relating to learning strategies and classroom management. Findings suggest that targeted support in these two areas could be beneficial when leaders integrate emergency certified teachers.

Becoming community researchers: The development and implementation of a competency-based program enabling older adult learning of qualitative research in a public health setting

ABSTRACT Partnering a local nonprofit social agency, the authors developed and implemented a learning program for a group of older persons preparing them to conduct qualitative research within a healthcare setting as community researchers in Singapore. This paper illustrates how learning for new occupations, job identities, and work can be supported via a competency-based approach that enables and clarifies links between research work, older adult learning, and assessment. It demonstrates how learning processes and contexts matter as much, if not more than outcomes, skills, and tasks typical of conventional competency-based training. Thus, the paper builds upon but also challenges the paradigm of competency-based training. It offers a more developmental approach or strategy for older adult learning that promotes dialogic, reflexive, and hands-on/applied learning, corresponding with educational gerontology principles.

A Pragmatic Approach to Fetal Monitoring via Cardiotocography Using Feature Elimination and Hyperparameter Optimization.

Cardiotocography (CTG) is used to assess the health of the fetus during birth or antenatally in the third trimester. It concurrently detects the maternal uterine contractions (UC) and fetal heart rate (FHR). Fetal distress, which may require therapeutic intervention, can be diagnosed using baseline FHR and its reaction to uterine contractions. Using CTG, a pragmatic machine learning strategy based on feature reduction and hyperparameter optimization was suggested in this study to classify the various fetal states (Normal, Suspect, Pathological). An application of this strategy can be a decision support tool to manage pregnancies. On a public dataset of 2126 CTG recordings, the model was assessed using various standard CTG dataset specific and relevant classifiers. The classifiers' accuracy was improved by the proposed method. The model accuracy was increased to 97.20% while using Random Forest (best classifier). Practically speaking, the model was able to correctly predict 100% of all pathological cases and 98.8% of all normal cases in the dataset. The proposed model was also implemented on another public CTG dataset having 552 CTG signals, resulting in a 97.34% accuracy. If integrated with telemedicine, this proposed model could also be used for long-distance "stay at home" fetal monitoring in high-risk pregnancies.