Effective Learning Research Articles

Unleashing the secrets of academic writing: exploring Indonesian language courses in higher education

Academic writing has been a widely substantial course in higher education as it plays a vital role in cultivating students’ academic writing skills. Drawing on qualitative descriptive study, this study aims to explore the fundamental requirements for academic writing to base the creation of an effective learning framework. Data consisted of teaching materials, semester learning plan (SLP), assessment tools, and guidelines for academic writing from three universities in Surakarta, Central Java, Indonesia. Findings reveal that learning materials and the SLP have both strengths and weaknesses. Despite these, there is diversity in learning and assessment methods. Adopted teaching methods encompass project-based learning, problemsolving, and lectures, while learning assessment includes portfolio techniques, project assessment, and written exams. These findings provide insight into the imperative of academic writing in the academic setting. This serves as a basis for the development of more effective learning methods. Thus, the proposed model for academic writing adopts a project-based approach to improve students’ writing by integrating indigenous wisdom to promote students' character development.

Promoting effective teaching and learning through a professional development program: A randomized controlled trial.

Promoting effective teaching and learning through a professional development program: A randomized controlled trial.

Decisions to seek cognitive performance feedback: Potential determinants of feedback value and consequences for learning

Performance feedback is essential for effective learning. Feedback contains both informational and affective properties. Following negative feedback (indicating an incorrect response), the unpleasant experience of being wrong can diminish the value of constructive information that feedback also provides. This can hinder motivation to seek feedback, which can impede learning. Therefore, research into factors that directly shape the subjective value of feedback is critical. The current study investigated potential behavioral and physiological contributors to feedback valuation and to subsequent feedback-seeking behavior. Fifty-nine participants completed a willingness-to-pay associative memory task that measured feedback valuation via trial-wise decisions to either purchase or forgo feedback during a learning phase in service of maximizing a performance-contingent monetary reward during a future test phase. Skin conductance response (SCR) was also measured during feedback decisions. Lower confidence in response accuracy significantly predicted higher likelihood of purchasing feedback during learning. Neither self-reported emotional responses to feedback nor SCR during feedback decisions predicted feedback purchases. Purchase decisions yielding negative feedback significantly predicted better performance during test. These results suggest that confidence during learning significantly impacts performance feedback valuation and should be considered when devising methods to motivate feedback-seeking in settings where learning is critical to success.

Problem-based learning analysis in strengthening college students’ interpersonal communication skill

This study seeks to analyse the incorporation of problem-based learning (PBL) in strengthening students' interpersonal communication skills, as well as the level of proficiency of students' interpersonal communication skills and the obstacles they face in acquiring them. This study was a crosssectional survey conducted in May 2023 with 315 college students enrolled in six English Study Programs at six universities in three Indonesian provinces. The questionnaires and interviews were used for recording data, which was then analysed using Jeffreys's amazing statistics program (JASP). Based on the data collected, it can be shown that the interpersonal communication profiles of 315 participants can be classified as falling within the medium category (average score=3.26). The primary challenge encountered by participants in developing their interpersonal communication skills is attributed to difficulties arising from the accent of their conversation partner, as indicated by a mean score of 4.6. The result mandates that educators provide an appropriate and effective learning method. PBL is an effective alternative as its principles, syntax, implementation stages, orientation, and benefits are capable of helping students overcome interpersonal communication challenges. PBL model is intended to provide students with the skills needed for problem-solving, increasing logical thinking, collaboration, and communication.

Risk factors threatening children’s safety in the school environment

School safety encompasses risk reduction efforts to ensure that children act in an environment that is safe and promotes the well-being of the students to achieve their full potential. Therefore, a safe and secure environment is a prerequisite for effective teaching and learning. This scoping review aims to address a range of school safety risk factors and introduce the complexity of school safety. School is believed to be a safe learning and growing environment; however, various conditions can threaten children’s safety in a school setting and lead to injuries caused by accidents, risky or/ aggressive behavior, or environmental exposure. In addition to the physical, psychological, and behavioral characteristics of children that make them particularly vulnerable to accidents, there are several unpredictable risks associated with natural or man-made conditions or disasters that can also put children at extreme risk. A balance between safety, surveillance, and emergency preparedness measures may be beneficial to create a safety culture in schools that will enable student behavioral and intervention support.

Online teaching competencies and teacher effectiveness in higher education: the mediating effect of perceived career support

PurposeOnline teaching competencies are critical skills required for effective teaching and learning in higher education. However, evidence regarding the impact of online teaching competencies on teacher performance and career satisfaction is minimal in the literature. This study draws on the TPACK model to understand how online teaching competencies influence teacher performance and career satisfaction.Design/methodology/approachThe study adopts a quantitative approach and collected data from 243 faculty in both public and private universities in Ghana. Structural equation modelling was used to examine the relationships among the study constructs.FindingsThe results show that the various dimensions of TPACK have a significant positive effect on teacher performance and career satisfaction. Furthermore, the mediation results reveal that neither perceived career support nor teacher attributes mediate the relationship between online teaching competencies, teaching performance and career satisfaction.Practical implicationsThis paper provides a better understanding of how context knowledge, pedagogical knowledge, pedagogical context knowledge, technology pedagogical knowledge, career support and teacher attributes affect teacher performance.Originality/valueThis study draws on the TPACK (context knowledge, pedagogical knowledge, pedagogical context knowledge and technology pedagogical knowledge) model and incorporates perceived career support and teacher attributes as essential characteristics within an emerging economy country context.

Dirichlet latent modelling enables effective learning and sampling of the functional protein design space

Engineering proteins with desired functions and biochemical properties is pivotal for biotechnology and drug discovery. While computational methods based on evolutionary information are reducing the experimental burden by designing targeted libraries of functional variants, they still have a low success rate when the desired protein has few or very remote homologous sequences. Here we propose an autoregressive model, called Temporal Dirichlet Variational Autoencoder (TDVAE), which exploits the mathematical properties of the Dirichlet distribution and temporal convolution to efficiently learn high-order information from a functionally related, possibly remotely similar, set of sequences. TDVAE is highly accurate in predicting the effects of amino acid mutations, while being significantly 90% smaller than the other state-of-the-art models. We then use TDVAE to design variants of the human alpha galactosidase enzymes as potential treatment for Fabry disease. Our model builds a library of diverse variants which retain sequence, biochemical and structural properties of the wildtype protein, suggesting they could be suitable for enzyme replacement therapy. Taken together, our results show the importance of accurate sequence modelling and the potential of autoregressive models as protein engineering and analysis tools.

Using deep learning to detect atherosclerotic plaques on carotid ultrasound images in the UK Biobank

Abstract Background Atherosclerosis is the main underlying cause of cardiovascular disease (CVD). Existing CVD risk assessment tools do not consider the burden of subclinical atherosclerosis. The presence of carotid plaques on carotid ultrasound is a well-known marker of subclinical atherosclerosis. The accumulation of population-scale data on the presence of atherosclerotic plaques, along with deep phenotyping, can allow not only to address the effectiveness of carotid ultrasound in routine clinical practice, but to shed light on the biology of atherosclerosis development. Purpose To develop an effective deep learning model for plaque detection in carotid ultrasound images in the UK Biobank. Methods We used 680 carotid ultrasound images with manually annotated plaques to train a deep learning model employing the YOLOv8 architecture. Different augmentation techniques were used to increase the generalizability of the model. The developed model was applied to automatically detect plaques in raw ultrasound images from 19,507 UK Biobank participants. Logistic and Cox regression were used to explore the associations of plaque presence and number as predicted by the model with conventional CVD risk factors and the risk of future CVD events over follow-up. To explore the genetic architecture of subclinical atherosclerosis, we conducted a genome-wide association study (GWAS) on plaque presence, followed by meta-analysis with data from the CHARGE Consortium. Results Our plaque detection model achieved high classification metrics of accuracy, sensitivity, and specificity (89.3%, 89.5%, and 89.2%, respectively) and detected atherosclerotic plaques in 44% of UK Biobank participants. As expected, plaques were more common among men than women and their prevalence increased linearly with age. Both plaque presence and number of plaques were correlated with conventional CVD risk factors including diabetes, hypertension, and hyperlipidemia, and showed strong associations with future risk of incident CVD events (Hazard Ratio for plaque presence: 1.48 [95%CI: 1.21-1.82], for 2 plaques or more: 1.65, [95% CI: 1.28-2.13]). Incorporating plaque-derived phenotypes minimally altered the C-index of the time-to-event model. GWAS meta-analysis of carotid plaque presence revealed 5 previously known loci, as well as a significant locus including the LPA gene that had not previously been associated with carotid plaque. Conclusion We have developed and implemented an efficient plaque detection model to data from the UK Biobank, which holds significant promise for studying atherosclerosis at a population-wide scale through integration with multiomics data and electronic health records.

Optimizing elderly care: a data-driven AI model for predicting polypharmacy risk using SHARE data

Abstract Background Polypharmacy (PP), which rises with age, is a growing public health challenge, affecting health outcomes and escalating healthcare expenditure. Leveraging the comprehensive European coverage and longitudinality of the Survey of Health, Ageing, and Retirement in Europe (SHARE) study, we aim to assess PP prevalence patterns and identify the most effective machine learning (ML) model for predicting long-term PP risk, as a foundation for a scalable predictive tool. Methods We used data from participants aged above 50 who were present in wave 6 and at least one of the subsequent three waves of the SHARE study, aiming to predict PP risk at 2, 4, and 6-year intervals. PP was defined as the concurrent use of five or more medications. We analyzed PP prevalence trends and selected the predictor variables from wave 6 using LASSO regression analysis. We evaluated eight ML models, namely, ANN, SVM, DT, RF, GB, XGBoost, LightGBM, and CatBoost, using a rigorous cross-validation strategy to ensure robustness and reliability. Results Our analysis reveals an upward trend in PP prevalence across the surveyed countries, with aggregate figures rising from 34.03% (95% CI 33.1-34.9) in wave 7 to 36.75% (95% CI 35.6-37.9) in wave 8, reaching 39.91 (95% CI 38.9-40.9) in wave 9. Additionally, using the Categorical Boosting ML model to predict PP resulted in overall accuracies of 75.08%, 73.7%, and 71.65% and recall rates of 72.83%, 70.48%, and 67.96% for the 2, 4, and 6-year intervals, respectively. Conclusions This study reveals a rising trend of PP across European countries and demonstrates the potential of using longitudinal data and ML to enhance PP prediction. The tool developed represents a step forward in risk stratification, which would be particularly beneficial in practical settings where family physicians or pharmacists could employ the tool to monitor elderly patients, predict and thus prevent PP, and reduce the negative health and economic impact associated with it. Key messages • The integration of machine learning with longitudinal data presents a significant advance in polypharmacy risk prediction. • The tool we developed highlights the potential translational impact of our findings.

A novel training session: mixed reality evisceration surgery webinar.

Evisceration surgery is an important part of an ophthalmologist's skillset but is an uncommon procedure. Therefore, it is important for training ophthalmologists to have adequate knowledge and confidence in this procedure to maximise learning opportunities. Mixed reality involves the intertwining of real and virtual environments and has been used successfully in surgical training. To improve participants' knowledge of the relevant anatomy, steps and confidence in performing evisceration surgery using an extended reality online webinar coupled with conventional teaching. The teaching session comprised of a short lecture introducing evisceration and explaining mixed reality technology including the Microsoft HoloLens headset. This was followed by a 3D demonstration of the orbit and globe, then the live evisceration procedure on a porcine cadaver, with the surgeon's point of view via the headset. There was expert live running commentary during the procedure with the use of virtual signposts on relevant anatomy. Participants were encouraged to be interactive and given the ability to share their annotations on-screen. Ten doctors ranging from Foundation year 1 to Ophthalmology specialist training year 3 attended the 90-min session and completed pre- and post- session questionnaires (5-point Likert scales). Results showed statistically significant improvements in the knowledge, steps and confidence in performing the evisceration surgery (paired two-sample T-tests, SPSS version 29). This study has demonstrated that utilisation of mixed reality with conventional teaching methods can provide a useful initial learning experience which may facilitate effective further learning of the evisceration surgery in a wet-lab or patient setting.

Developing mobile augmented reality in picture book for teaching English for young learners

An effective language and learning process must contain interactive, fun, challenging, motivating aspects and provide more space for students to develop their creativity and independence, according to students' talents and interests; thus, appropriate teaching media can be as assisting tools. When selecting media, you need to consider several things, namely learning objectives, effectiveness, easy to obtain, students, use, not rigid, cost, and quality. One of the developments in learning media that is currently still new is learning media using Augmented Reality. This research aims to develop Augmented Reality-based text book learning media for English language learning. Developing text-book media using an application with a barcode scanner on text-books. This application displays 4D images or animations that have sound and movement so that it will help students to understand English texts. The research method used is the research and development method (Research & Development) while the product development model used is the ADDIE model (Analysis, Design, Development, Implementation, & evaluation. After developemnt anf evalution phase, the findings reveals that the outcome of teaching material product is deemed highly valid and appropriate for teaching English to yong learners, especially for primary school in this research context, as it was supported by the result of validation phase with media experts, subject matter experts, and student trials. Additionally, reflecting on the result of this study, the pedagogical implications of this result discussed further in this research.

Analysis of Students' Critical Thinking Skills Through the Pakem Learning Model in Science Subject Class VI SD Negeri 78 Palembang

Critical thinking ability is a high-level thinking ability that aims to make reasonable decisions to help the problem solving process actively and skillfully in analyzing, applying and evaluating information obtained or resulting from observations of elementary school students. The learning model is one of the important things in creating an active, creative, effective and enjoyable learning process. The PAKEM model is an appropriate model to use in the learning process. This research aims to determine the critical thinking abilities of class VI students using the PAKEM model in science learning at SD Negeri 78 Palembang. This research uses an experimental method with a quantitative approach. The subjects in this research are 25 VIC class elementary school students. Data collection techniques in this research use observation, tests, interviews and documentation. Data analysis techniques use the normality test, homogeneity test and T-Test. The results of this research found that there were 8 students with high critical thinking skills, with a score range of 85-100, getting a percentage of 32%. Medium critical thinking ability, totaling 12 students, with a score range of 75-80, got a percentage of 48% and low critical thinking ability, totaling 5 students, with a score range of 65-70, got a percentage of 20%. The results of the critical thinking skills of class VIC students using the PAKEM model in science learning at SD Negeri 78 Palembang overall students reached 82.4% in the good category.

Enhancing Digital Education: A Model for Effective Mobile Learning Implementation in Malaysian Higher Education

This study investigates the implementation of mobile learning (m-learning) services in the smart environment at Kolej Poly-Tech MARA (KPTM) in Malaysia, focusing on establishing effective practices for digital education. Despite the potential of m-learning as an educational tool, its adoption in Malaysia remains limited, with the COVID-19 pandemic highlighting the need for innovative approaches to learning. The research employs a mixed methods approach, gathering quantitative data from 365 respondents, including students, lecturers, and administrators, through Likert-scale questionnaires distributed via Google Forms. Qualitative data were obtained through interviews with 10 selected participants. The study identifies existing practices in m-learning, explores its benefits, and examines challenges faced during implementation. Utilizing descriptive analysis, one-way ANOVA, Independent T-Test, and Regression Correlation, the findings reveal a positive perception of m-learning among stakeholders, alongside challenges related to infrastructure and training. The primary contribution of this research is the development of a model of good practice that provides guidelines for KPTM and other higher education institutions aiming to enhance the quality of mobile learning services. This model emphasizes the importance of accessibility, professional development, and stakeholder collaboration, positioning KPTM toward a more effective digital education landscape. Overall, the study underscores the critical role of m-learning in fostering flexible and engaging learning environments while addressing the barriers to its implementation in Malaysia.

Enhancing English as a Foreign Language (EFL) learning in Saudi Arabia: the academic contribution of YouTube in EFL learning and cultural awareness

The incorporation of technology in English as a Foreign Language (EFL) classroom has drastically transformed conventional learning models by offering innovative ways to boost students’ engagement and improve learning outcomes. This study investigates the impact of using YouTube as an educational tool to enhance EFL instruction in Saudi Arabia. A total of 200 EFL students were divided into two groups: An experimental group which used You Tube as a source of learning and another group which used normal curriculum. A mixed-methods approach was employed, combining quantitative data from a paired-sample t-test with qualitative feedback from students. The results for paired-sample t-test computations revealed a statistically significant difference (p = 0. 003) across the proficiency level indicating improvement in students’ speaking and listening skills at higher proficiency levels. Of the qualitative answers, motivational and participation enhancement were overemphasized. The research points that the integration of YouTube in EFL classrooms can lead to a remarkable increase in language learning especially in the learning area of listening and speaking abilities among the students. These results offer practical implications for educators seeking to integrate multimedia tools into their teaching strategies to promote more interactive and effective learning experiences.

The Complexity Of School-Based Management In Improving Teacher Quality

This research discusses the complexity of school-based management as a strategy to improve teacher quality. In the ever-evolving era of education, the role of school-based management becomes crucial in creating an effective learning environment. The concept of complexity is applied to describe the dynamics, interactions and challenges involved in managing teachers together in an educational environment. This study uses a qualitative approach with a focus on the implementation of school-based management and its impact on co-teachers at SD Negeri 3 Tirtanadi. Data were collected through interviews, observation and document analysis related to education policy. The research findings show that the complexity of school-based management includes factors such as leadership, stakeholder engagement, professional development and organisational culture. The results provide deep insights into how schools can effectively manage these complexities to improve teacher quality. The practical implications of this research involve crafting education policies that support the implementation of school-based management, empowering school leadership and strengthening collaboration among stakeholders. As such, this research makes a significant contribution to the understanding of the complexities of school-based management as a strategic endeavour in improving teacher quality in the contemporary education era.

Efficient and Compressed Deep Learning Model for Brain Tumour Classification With Explainable AI for Smart Healthcare and Information Communication Systems

ABSTRACTThe detection of brain tumours presents a significant challenge in the medical domain, where prompt and precise diagnosis is crucial as patient outcomes depend on it. Conventional deep neural networks perform well in carrying out various imaging tasks within the healthcare sector; however, their effectiveness often falls short of expectations in practical applications due to the substantial computational resources required and issues with reliability. In this research, an optimised and effective deep learning model founded on the DenseNet‐169 architecture is introduced for the classification of magnetic resonance imaging brain tumours, which is particularly advantageous for smart healthcare systems and information and communication technology (ICT) settings with limited computational capabilities. The model compression methodologies, including pruning and quantization, have been employed to significantly diminish the dimensions and intricacy of the model while achieving a classification accuracy of 97.07%. Furthermore, this endeavour necessitates the enhancement of the model's interpretability through the utilisation of explainable artificial intelligence methodologies such as Gradient‐weighted Class Activation Mapping (Grad‐CAM) and SHapley Additive exPlanations (SHAP), which will aid clinicians in highlighting crucial areas of the images and validating feature importance concerning the decisions rendered by the model. A comparative performance evaluation is conducted against DenseNet‐169, ResNet‐50 and various other models to delineate the superior efficacy of our model, rendering it exceptionally adept for knowledge‐driven, real‐time brain tumour diagnosis within smart healthcare and ICT systems where resources are constrained.

The effect of PBL integrated RMS on biological literacy and critical thinking ability of high school students

This study aimed to determine the effect of Problem-Based Learning (PBL) integrated Reading, Mind Mapping, and Sharing (RMS) on biological literacy and critical thinking skills in biotechnology material. This type of research is Quasi-Experimental with pre-test-post-test experiments and a control group design. The researcher conducted the research because most of the students who became respondents considered biotechnology material less attractive, so students' understanding and activeness could have been more optimal in the learning process. The research samples were 72 students of class X MIPA at SMA Negeri 2 Kota Serang, Banten. The research sample was divided into two groups: the experimental group that applied the PBL integrated RMS model and the control group that applied the PBL model. The research instruments used were biological literacy ability tests and critical thinking ability tests. Biological literacy skills were measured using biological literacy-based multiple-choice test instruments, while critical thinking skills were measured using essay test instruments. The hypothesis test used was MANOVA statistical analysis. The results of statistical tests showed a significant effect of PBL integrated with RMS on biological literacy and critical thinking skills on biotechnology material with a significance value of 0.000 < α. Students with an integrated RMS learning model are more effective than those with a PBL learning model, which is indicated by the post-test value being higher than the pre-test value. This research contributes to the field of biology education by showing that the PBL learning model integrated with RMS can be an effective alternative learning model to improve students' biological literacy and critical thinking skills. The findings of this study are expected to be useful for biology teachers

The Effect of Historical Places Through Virtual Museums on Students' Academic Achievement And Attitudes Towards The Course In Social Sciences Course Concept Teaching

This research, 7. the classroom social studies course was conducted in order to determine the effect of historical sites on students' academic achievements and attitudes towards the course through virtual museums in concept teaching. In line with the aim, 20 concepts included in the social studies curriculum were selected. A 20-question academic achievement test was prepared and applied to be applied to students in relation to these concepts. In addition, a 35-item attitude scale was applied to determine the students' attitudes towards the Social studies course. The study group of the research consists of 40 7th grade students (20 experimental, 20 control). The study was carried out over a total period of 3 weeks. The experimental group was taught concepts through virtual museums in historical places, and the control group was taught according to the Social Studies curriculum. SPSS, which is a statistical analysis program, was used for the analysis of quantitative data. In the measurements related to the research, dependent and independent variable t test, mean, standard deviation values were calculated. According to the results of the analysis, it has been determined that the use of virtual museums with historical sites in social studies course concept teaching enables more effective learning compared to traditional methods and creates significant differences in students' attitudes towards social studies course.

A new approach to business ethics education: Virtual reality-based flipped learning

Paying attention to business ethics and educating ethical leaders and employees can impact organizational performance. Effective professional ethics education and learning can prevent administrative corruption, embezzlement and fraud in organizations. On the other hand, the development of digital technologies has created transformations in educational processes and methods. Evaluating the effectiveness of innovative teaching methods will greatly help the learning process of individuals and educational planning of business schools. Flipped learning is an educational approach in which students learn about course content before class. Accordingly, the aim of this research is to evaluate the impact of virtual reality (VR)-based flipped learning compared to the traditional method of teaching business ethics. This study was applied and quasi-experimental. Among business students of Shiraz University, 135 people were randomly divided into three groups. The first experimental group underwent training with the flipped learning method based on VR and the second experimental group underwent training with the traditional method and the third group was the control group. The control group didn't receive training and was selected only for the purpose of comparison with experimental groups. Pretest and posttest designs were used and the results of the three groups were compared using the Tukey test. The results showed that the two groups that received business ethics education had a significant difference compared to the control group. An important finding of this study revealed that among the two teaching methods, VR-based flipped learning had a greater impact on students' business ethics compared to the traditional teacher-centered approach. Accordingly, university managers and business ethics course instructors can facilitate the learning process using VR-based flipped learning. Designing simulation software for ethical scenarios is also a suitable solution for teaching business ethics.

Establishing effective learning bridge cross multi-scale feature maps for object detection and semantic segmentation

ABSTRACT In the field of image object detection and semantic segmentation, improving the accuracy of object identification and segmentation is a primary goal. To achieve this, leveraging the potential of multi-scale information through feature map refinement and fusion has been widely recognized. However, existing feature fusion methods either design more complex feature pyramid networks, replace existing detectors, or incrementally introduce feature fusion modules, overlooking the effective approach of enhancing spatial information in deep feature maps. We propose a novel pluggable feature fusion paradigm termed ‘Effective Learning Bridge’. Our research introduces an efficient and adaptive learning mechanism that builds learning bridges between feature maps at different scales within the feature pyramid, thereby enhancing the spatial information of objects in deep feature maps. This mechanism is specifically designed for multi-scale feature maps and can be seamlessly integrated into any network incorporating feature maps. By altering the model’s backpropagation path, we successfully improve learning efficiency, which in turn enhances the accuracy of object detection and segmentation. Our proposed paradigm and method were extensively evaluated through experiments on SIMD, HRSID, and WHDLD datasets and benchmark models. The results unequivocally demonstrate the effectiveness of our approach in significantly improving the accuracy of object detection and semantic segmentation, as well as the overall learning efficiency of the model.