Learning Perspective Research Articles

Analysis of student interpreters’ self-assessment from the perspective of self-regulated learning

Abstract This study aims to examine the significance of self-assessment in interpreter training from the perspective of self-regulated learning. To this end, this study first explores the three cyclical stages of self-regulated learning as conceptualized by Zimmerman, while also examining the significance of self-evaluation in interpreter training. Based on this theoretical framework, the study analyzes self-evaluations from both high-performing and low-performing students at a graduate school of translation and interpretation. The findings of the study reveal three key points: first, the effective use of self-assessment enhances learning effectiveness in interpreter training; second, significant differences exist between high-performing and low-performing students in their self-assessment practices; third, these differences highlight the need for incorporating self-assessment into interpreter training programs. Finally, the study suggests that enhancing self-regulated learning capabilities by actively incorporating self-evaluation into interpreter training can improve educational outcomes in interpreter training, leveraging the cyclical and positive impact of self-regulated learning processes.

Time-frequency Spillover and Early Warning of Climate Risk in International Energy Markets and Carbon Markets: from the Perspective of Complex Network and Machine Learning

Time-frequency Spillover and Early Warning of Climate Risk in International Energy Markets and Carbon Markets: from the Perspective of Complex Network and Machine Learning

Modeling and Evaluating the Impact of Mobile Usage on Pedestrian Behavior at Signalized Intersections: A Machine Learning Perspective

Pedestrian safety is a growing global concern, particularly in urban areas, where rapid urbanization and increased mobile device usage have led to an increase in distracted walking. This study investigates the impact of technological distractions, specifically mobile usage (MU), on pedestrian behavior and safety at signalized urban intersections. Data were collected from 11 signalized intersections in New Delhi, India, using video recordings. Key inputs to the modeling process include pedestrian demographics (age, gender, group size) and behavioral variables (crossing speed, waiting time, compliance behaviors). The outputs of the models focus on predicting mobile usage behavior and its association with compliance behaviors such as crosswalk and signal adherence. The results show that 6.9% of the pedestrians used mobile phones while crossing the road. Advanced machine learning models, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Recurrent Neural Networks (RNN), have been applied to analyze and predict MU behavior. Key findings reveal that younger pedestrians and females are more likely to exhibit distracted behavior, with pedestrians crossing alone being the most prone to mobile usage. MU was significantly associated with increased levels of crosswalk violation. Among the machine learning models, the CNN demonstrated the highest prediction accuracy (94.93%). The findings of this study have a practical application in urban planning, traffic management, and policy formulation. Recommendations include infrastructure improvements, public awareness campaigns, and technology-based interventions to mitigate pedestrian distractions and to enhance road safety. These findings contribute to the development of data-driven strategies to improve pedestrian safety in rapidly urbanizing regions.

Integrating social media platforms into expatriate training and development programs: an experiential learning perspective

PurposeSocial media platforms, although often vilified in popular media, carry several potential benefits for expatriate training. This study set out to discuss in some depth whether social media platforms can be employed to enhance expatriate training outcomes, such as performance, adjustment and cultural integration.Design/methodology/approachDrawing on insights from experiential learning theory, this study develops propositions and a conceptual model relating to the incorporation of social media platforms into expatriate training.FindingsJoining insights from the expatriate training literature with experiential learning theory, this study proposes several potential avenues for social media platforms as training tools. While social media use is not likely to be efficacious in itself, this study argues that social media platforms can be leveraged to enhance expatriate language training, acculturation and professional networking. These mediating relationships enhance social media’s effects on expatriate performance and adjustment.Originality/valueThis study paves the way for future empirical works regarding the implementation of novel tools in the pursuit of expatriate excellence. This paper develops a comprehensive model illustrating how social media platforms can positively impact expatriate training outcomes.

Comparison of Remote Mixed Reality versus In-Person Training of ultrasound guided Percutaneous Nephrolithotomy (PCNL) with Urological Residents.

To compare remote to in-person training during image-guided percutaneous nephrolithotomy (PCNL), we used mixed reality (MR), smart glasses, and a validated hydrogel simulation model to test surgical education uptake with urological residents. Twelve urology residents were randomized into two learning groups (MR-first or in-person-first) and completed a pretest, MR or IP guided training sessions, midtest, cross-over into MP or IP guided training, posttest, and a retention test 2 months post-training to evaluate upper and lower pole access and balloon dilatation. MR sessions utilized Vuzix smart glasses and HelpLightning MR software to allow the instructor to remotely instruct the student and share their first-person perspective. Performance was assessed against a checklist of metrics including attempts for access, procedure time, and a model specific checklist. Learner perspectives were assessed after each training session using 5-point Likert scales and open form comments. Overall attempt scores improved significantly for lower pole and upper pole procedures from pre to mid test and pre to post test (LP: 50% vs 74.5%, p = 0.0019, 50% vs 82.8%, p= 0.001; UP: 43.6% vs 63.75%, p = 0.0002; 43.6% vs 70.5%, p = 3.6e-05) in both MR and IP cohorts. Learner evaluations suggested that the majority still prefer IP instruction, citing technological difficulties. MR-based remote learning is equally effective when compared to in-person instruction of US-PCNL.

PDE-CNNs: Axiomatic Derivations and Applications

PDE-based group convolutional neural networks (PDE-G-CNNs) use solvers of evolution PDEs as substitutes for the conventional components in G-CNNs. PDE-G-CNNs can offer several benefits simultaneously: fewer parameters, inherent equivariance, better accuracy, and data efficiency. In this article, we focus on Euclidean equivariant PDE-G-CNNs where the feature maps are two-dimensional throughout. We call this variant of the framework a PDE-CNN. From a machine learning perspective, we list several practically desirable axioms and derive from these which PDEs should be used in a PDE-CNN, this being our main contribution. Our approach to geometric learning via PDEs is inspired by the axioms of scale-space theory, which we generalize by introducing semifield-valued signals. Our theory reveals new PDEs that can be used in PDE-CNNs and we experimentally examine what impact these have on the accuracy of PDE-CNNs. We also confirm for small networks that PDE-CNNs offer fewer parameters, increased accuracy, and better data efficiency when compared to CNNs.

Interactive impact of Human Resource Flexibility and Transformational Leadership on the Employee’s Assurance of Service Quality: Reinforcement Learning Perspective

Purpose: Nowadays, service area is indispensable in most of the businesses, therefore service quality assurance has become integral to generate profits. Inevitable organizational transitions and updates has made the assurance of service quality from the employee’s perspective quite challenging. The current study focuses on the influence of human resource (HR) flexibility on the employee’s service quality assurance under the buffering role of transformational leadership within the banking sector of Pakistan. Design/methodology/approach: This study was based on cross-sectional research design and data was collected in two-waves through structured questionnaires from 398 bank employees in Pakistan, following convenience sampling technique. Data was analyzed employing techniques such as bootstrapping, SEM, process macro in the SmartPLS4 software. Findings: The findings of the study showed evidence that the interaction of HR flexibility and transformational leadership have a significant positive influence on employee assurance of service quality within the banking sector. Further, the results indicated that autonomy and enriching employee creativity by transformational leader has actually the ability to assure the service quality in the employee’s mind by interact with HR practices flexibly. Practical implications: The study implies that HR managers/leaders should pursue actions that would enable a flexible workforce, continuity of learning and dispersion of skills to meet customers’ expectations and to invest in leadership development initiatives; such as inspiration, attention to employee growth, and support of innovation. Originality/Value: The characteristics of transformational leadership, i-e. Mentoring, autonomy and enriching employee creativity, has potential to moderate the relationship between HR flexibility and employee’s quality service assurance by adopting/adapting new skills to assure the service quality to customers.

P-986. Integration of the National HIV Curriculum (NHC) into health professions programs: faculty and student/trainee reflections and perspectives

Abstract Background Targeted Access Knowledge and Education on HIV for Health Professions Programs (TAKE on HIV for Health Professions Programs) is a Health Resources and Services Administration (HRSA) funded effort to support National HIV Curriculum (NHC) integration into health professions programs (HPPs) to cultivate HIV prevention and care knowledge into student/trainee (learners) practice and build the HIV workforce. We report on faculty and learners perspectives within integrating HPPs. Methods Using mixed methodology, faculty and learners were asked about their NHC integration experience which was facilitated by the TAKE on HIV training and technical assistance (TTA) Team. Following an integration cycle, faculty participated in 1:1 structured interviews and a focus group to identify facilitators/challenges of integration and sustainment plans. Learners completed a digital survey about time to complete NHC lessons and ratings of HIV knowledge, confidence, and usability of the NHC platform (Likert scale: 1- strongly disagree to 5=strongly agree). Data were analyzed using rapid qualitative analyses, descriptive statistics and mean Likert scores. Results Nine participating faculty were from the following HPP disciplines: 3 from Pharmacy, 1 each from Physician Assistant and Family Medicine Residency, and 4 from Nursing. Integration facilitators included TTA support and guidance and NHC student tracking capability. A barrier was learner difficulty with navigating the platform and suggestion for improvement was providing additional visual learner resources for platform navigation. All faculty reported desire to sustain NHC integration with subsequent cohorts and additional NHC lessons. 46 learners completed the survey (13% response rate). 67% reported taking 15-30 minutes to complete an NHC lesson. Overall learners reported having improved HIV knowledge (mean=4.42), confidence in providing HIV care (mean=4.07), and ease of NHC platform navigation (mean=4.29). Conclusion NHC integration into HPPs is sustainable if sufficient resources are provided to faculty. TAKE on HIV effectively provides HPP faculty with free TTA resources to facilitate NHC integration and a framework for replicability. HIV knowledge gain amongst learners within integrating HPPs was demonstrated. Disclosures All Authors: No reported disclosures

Exploring the safe haven properties of sukuk: a machine learning and K-cluster perspective

Purpose This study aims to explore the safe-haven properties of sukuk and analyze the co-movement and interdependence between sukuk and conventional bond indices to provide insights into the potential role of diversification. Design/methodology/approach The study uses the data set from 2012 to 2022, retrieved from the Eikon Reuter database. Different machine learning tools such as decision trees, random forests, gradient boosting and deep neural networks have been applied to capture the non-linear relationship and co-movement among the variables. Furthermore, K-clustering captures the hidden patterns and periods of high and low co-movements. Findings The results state that the sukuk and conventional bond indices exhibit various degrees of co-movement influenced by regional and global market sentiments. The clustering analysis shows strong positive and negative correlations. The sukuk shows some instances of zero co-movement, but the results are inconsistent across all scenarios. Moreover, investors need to do their research first before investing in sukuk. Originality/value This study uniquely applies K-clustering and advanced machine learning tools to understand the nonlinear relationship among variables better. In contrast, the previous studies mainly focused on linear relationships. It is critical to understand that financial variables tend to have nonlinear relationships, and these techniques best suit those needs.

Estimating Hydrogen Price Based on Combined Machine Learning Models by 2060: Especially Comparing Regional Variations in China

Hydrogen energy’s economic efficiency is the key for China to obtain the goal of “carbon neutrality” by 2060. Different from the bottom-up methods and learning rate methods, this study estimates the hydrogen prices in China and typical regions by 2060 from the perspectives of economics and machine learning. The main factors influencing hydrogen price are determined from the perspectives of economics: hydrogen production, demand, and cost. A novel model is established based on combined machine learning models to predict hydrogen price. The hydrogen production is predicted based on the trained BP neural network model optimized by particle swarm optimization considering the uses of hydrogen. The hydrogen prices prediction model is built by applying a least squares support vector machine optimized by Bayesian optimization considering the hydrogen production, hydrogen demand, natural gas price, coal price, electricity price, and green hydrogen share. Moreover, the hydrogen prices in typical regions in China are compared with the average prices. The results show that the hydrogen price is estimated to decrease below CNY 12/kg and the hydrogen price in Northwest China will be lower than CNY 7.5/kg due to low electricity cost by 2060.

Balanced Scorecard Based Performance Management: Case Study From PT. Semen Indonesia Distribution

The imbalance between cement supply and consumption in Indonesia has caused consumers' bargaining position to increase, demands to increase, and create fierce competition. One of the marketing functions, namely Place, has the meaning of distributing goods, so that if a company wants to win the business competition, it must be good at planning, implementing and evaluating the distribution system. This study aims to determine how the use of balanced corecard in managing performance management at PT. Semen Indonesia Distribusi using financial, customer, internal process, and learning and growth perspectives. The results of the study show that the process of managing performance management at PT. Semen Indonesia Distribusi begins with determining the strategic targets of each perspective in the balanced scorecard which is outlined in the strategy map. The company needs to compile Key Performance Indicators (KPI) in order to find out the results of these strategic achievements.

Assessment and Examination of Emergency Management Capabilities in Chinese Rural Areas from a Machine Learning Perspective

The Chinese government’s rural rejuvenation program depends on improving the national Rural Emergency Management Capability (REMC). To increase the resilience of Chinese rural areas against external dangers, REMC and its driving elements must be effectively categorized and evaluated. This study examines the variations in REMC levels and driving factors across different cities and regions, revealing the spatial distribution patterns and underlying mechanisms. To improve REMC in Chinese rural areas, this research employs the Projection Pursuit Method to assess REMC in 280 cities from 2006 to 2020. Additionally, we identify 22 driving factors and use the Random Forest algorithm from machine learning to analyze their impact on REMC. The analysis is conducted at both national and city levels to compare the influence of various driving factors in different regions. The findings show that China’s REMC levels have improved over time, driven by economic growth and the formation of urban clusters. Notably, some underdeveloped regions demonstrate higher REMC levels than more developed areas. The four most significant driving factors identified are rural road density, rural Internet penetration, per capita investment in fixed assets, and the density of township health centers. At the city level, rural Internet penetration and the e-commerce turnover of agricultural products have particularly strong driving effects. Moreover, the importance of driving factors varies across regions due to local conditions. This study offers valuable insights for the Chinese government to enhance REMC through region-specific strategies tailored to local circumstances.

AUTOMATING METER CLASSIFICATION OF ARABIC POEMS: A HARRIS HAWKS OPTIMIZATION WITH DEEP LEARNING PERSPECTIVE

Meter classification in Arabic poetry is a crucial factor that describes the rhythmic structure of poems. Classical Arabic poetry relies on explicit meters, referred to as “Arud”, to create a structured and harmonious flow. Arabic meter is based on the pattern of short and long syllables, and each meter has a particular combination of feet (taf’ilah) that defines its unique rhythmic structure. Poets use diverse Arabic meters to evoke aesthetic or emotional qualities in their poetry. The mastery of meter is considered a sophisticated and skillful aspect of traditional Arabic poetry, which reflects the rich heritage of Arabic literature. The meter provides poets with unique opportunities and constraints, influencing the style and tone of their verses. Using deep learning (DL) for the meter classification of Arabic poems includes leveraging a neural network to automatically learn the features and patterns that discriminate between various meters. This paper presents a Fractal Harris Hawks Optimization with DL-based Meter Classification of Arabic Poems (HHODL-MCAP) technique. The HHODL-MCAP technique exploits the optimal DL model for the identification of distinct classes of meters of Arabic poems. The HHODL-MCAP technique involves a three-layered process. Primarily, the HHODL-MCAP technique performs data preprocessing to transform the data into a beneficial format. Second, the HHODL-MCAP technique applies long short-term memory (LSTM) with a Bidirectional Temporal Convolutional Networks (BiTCNs) model for the automated identification of various Arabic meter classes. At last, the HHO algorithm can be exploited to choose the hyperparameter values of the LSTM-BiTCN model optimally. A series of experiments were conducted to ensure the improved detection outcomes of the HHODL-MCAP technique. The extensive simulation results underline the supremacy of the HHODL-MCAP technique in the meter classification process.

A Self-Regulated Learning Perspective on Smartphone Presence, Usage, and Multitasking While Studying

The implications of the presence and usage of smartphone technology in learning contexts are not fully understood. Self-regulated learning (SRL) theory offers a framework in which smartphone use can be explored. The current study seeks to extend our understanding of the role of the smartphone while studying through a qualitative thematic analysis of responses to open-ended questions regarding smartphone use. A sample of 210 college students responded to questions about their smartphone use and multitasking behavior while studying. The findings indicate that college students use smartphones while studying to communicate, find information, and entertain themselves by media multitasking with their smartphones.

Male-female young learners' perspective on the use of crossword puzzle aids to enrich their vocabulary

Vocabulary is very essential in communication, but the fact indicates that lack of vocabulary still hampers the effective communication. This paper investigates the perspective of young female and male learners on the application of Crossword puzzle aids to enrich their vocabulary. The goal of this study is to find out male and female young learners’ perspectives on the use of crossword puzzle aids to enrich their vocabulary. The research design of this current study uses the type of case study so that twenty-one students from grade seven in a Junior High School in Ciamis were involved. The data come from a questionnaire which deals with the perspective of young female and male learners on the application of Crossword puzzle aids to increase their vocabulary. The close-ended questionnaire data are then analyzed and proceeded. The result shows that the young female and male learners perceive the application of Crossword puzzle as the aids to enrich their vocabulary. In learning, vocabulary can enrich their vocabulary with different skills based on gender because gender differences can influence young and female learners to increase their vocabulary skills. Besides that, Crossword puzzle aids are interesting, gives many opportunities, and helps male and female learners to receive and understand the vocabulary more easily. Further, using crossword puzzles aids learning vocabulary and can increase the achievement of male and female young learners. Also, it makes change towards the situations such as playing games when learning vocabulary process.

Balanced Scorecard-Based Performance Measurement of the Catholic Education Institution in Indonesia (A Case Study in SMPS Maniamas Ngabang, Pontianak)

This research applied Balance Scorecard to analyze the performance measurement of a Catholic Education Institution in Indonesia and studied SMPS Maniamas Ngabang, Pontianak as the case. It emphasized two important values of spirituality and nationality that are very common become the focus of the Catholic education institutions in Indonesia. Based on descriptive-qualitative analysis, the performance of each perspective of the Balance Scorecard results in the financial perspective is passably, the customer perspective is passably, the internal business process is passably, and the learning and growth perspective is fairly good. In general, SMPS Maniamas Ngabang has a good performance. Furthermore, the depth interview indicated that the spirituality and nationality aspects became a central focus in the entire school management system. The school managed by the brotherhood of Province Capuchin of Pontianak has made efforts to grow and develop the values of Capuchin spirituality and nationality in the students. The efforts of cultivation are well integrated in the fourth perspective of BSC.

Clinical and demographic predictors of heart failure outcomes: A machine learning perspective

Heart failure (HF) is a multifaceted clinical condition associated with high morbidity and mortality rates. It is an increasing public health concern, impacting millions globally and placing considerable strain on healthcare systems. In recent decades, there has been a growing interest in using machine learning techniques to predict HF outcomes. Hence, this study aims to explore the clinical and demographic characteristics associated with HF outcomes using a comprehensive dataset obtained from Kaggle. The dataset, “Heart Failure Clinical Records.csv,” was preprocessed to address missing values and prepared for analysis. Feature importance analysis and correlation matrix computations were conducted to identify significant predictors of death events among HF patients, including age, serum creatinine, and ejection fraction. Various machine learning models, such as logistic regression, random forest, support vector machine, and gradient boosting machine, were employed to predict death events. The results revealed varying levels of performance among the models, with some demonstrating promising accuracy and predictive power. However, further refinement of these predictive models is warranted to enhance clinical decision-making and patient care in HF management. Overall, this study underscores the value of data-driven approaches in understanding HF outcomes and highlights the necessity for ongoing research in this field.

BALANCED SCORECARD IN STRATEGY FORMATION AMID TRANSFORMATIONAL PROCESSES

The article investigates the application of the balanced scorecard in shaping management strategies amid transformational processes, emphasizing its importance in aligning strategic goals with dynamic external conditions. Transformational processes significantly impact public and private sector organizations, necessitating adaptive management approaches. This study emphasizes the balanced scorecard as a vital instrument for achieving strategic alignment and enhancing operational efficiency, especially within the framework of state programs. The research methodology combines theoretical analysis, comparative studies to evaluate the balanced scorecard's applicability in strategic management. A comprehensive review of key literature and case studies was undertaken to uncover patterns and best practices for adapting performance indicators to transformational contexts. Additionally, the study employs SWOT analysis to assess the strategic alignment of the balanced scorecard with internal and external factors influencing organizational performance. The findings indicate that the balanced scorecard empowers organizations to integrate varied performance metrics, aligning operational objectives with long-term strategic goals. By prioritizing targets across financial, customer, internal processes, and learning perspectives, it enhances decision-making effectiveness. In the context of state programs, this framework strengthens accountability and transparency while fostering optimal resource utilization. The study underscores the adaptability of the balanced scorecard, positioning it as an essential tool for managing the challenges of transformational processes. The practical value of the article lies in providing actionable recommendations for implementing the balanced scorecard within the framework of state programs and organizational strategies. These recommendations encompass techniques for selecting appropriate indicators, tailoring them to organizational requirements, and leveraging digital tools for effective performance monitoring. By addressing these aspects, the study enhances strategic management practices, particularly in the face of rapidly changing conditions.

Urgent findings in CT chest examinations: Radiography and medical students' capabilities and learning perspectives - a preliminary study.

Radiography and medical students (RMS), upon graduation, require capabilities to provide life-saving care through identification and communication of urgent findings on radiological imaging. This preliminary study investigated RMS' ability to identify and categorise urgent findings on CT examinations. It also explored their experiences of image interpretation education. A sequential explanatory mixed-methods study was employed. Participants were students who had recently completed Year-4 (radiography/medicine) or Year-2 (medicine-only) from three Australian universities. Urgent finding identification capabilities were assessed via a test-set of 10 CT examinations with a range of findings (normal, abnormal but non-urgent, abnormal but urgent) that was developed on a validated learning and assessment platform. Each case required selection of: normal versus abnormal; where abnormal, finding/s from a pre-defined list; urgency level; self-rated confidence. Learning experiences were then explored via a survey (Likert statements, free-text responses). Mean sensitivity, specificity and accuracy (with min‒max ranges), respectively, were: All participants [n=30] 0.87 (0.60-1.0), 0.63 (0.40-1.0), 0.75 (0.50-1.0); 4th-year radiography [n=17] 0.89 (0.6-1.0), 0.66 (0.4-1.0), 0.78 (0.60-1.0); 4th-year medicine [n=8] 0.85 (0.6-1.0), 0.50 (0.4-0.6), 0.68 (0.50-0.8); 2nd-year medicine [n=5] 0.80 (0.60-1.0), 0.75 (0.75-1.0), 0.78 (0.78-0.78). False positives were highest for cases with non-urgent abnormalities in all groups. Free-text responses revealed students' desire for dedicated urgent finding educational resources with high 'repetitive learning' effects. RMS demonstrated considerable performance in identifying urgent abnormalities as a group, but individual capabilities varied from pass-level to perfect. Together, participants demonstrated limited ability to correctly classify non-urgent CT chest abnormalities. This study highlights opportunities for targeted urgent-finding resource development, focused on improving consistency within cohorts and reducing false positive rates.

Energy independence, rural sustainability and potential of bioenergy villages in Germany: machine learning perspectives

Energy independence, rural sustainability and potential of bioenergy villages in Germany: machine learning perspectives