Digital Commons Network^™

Taiwanese Elementary School Teachers’ Mindset Of Content And Language Integrated Learning (Clil) Instruction, Chin-Wen Chien

Journal of English Learner Education

Teachers need to undergo a mindset shift in language learning and teaching. This study used both qualitative and quantitative data to explore seven Taiwanese Content and language integrated learning (CLIL) teachers’ mindsets and led to the following major findings. First, the CLIL teachers had a stronger mindset regarding their instructional strategies, lesson preparations, and efficacy, compared to their more moderate mindset regarding their CLIL training and preparation. Secondly, the CLIL teachers had a different mindset regarding language use in their CLIL instruction due to their learners’ English proficiency levels. Finally, their lesson designs were lacking a 4Cs culture. Based on …

Digital Stories Of Resilience: A Case Study Of Urban Community College English Learners, Patricia George Hunter

Journal of English Learner Education

This study utilizes a qualitative case study approach to learn how project-based, digital storytelling (DST) voices the resilience of post-secondary English language learners (ELLs) attending a minority-serving, open-access, community college in New York City. Along with many studies that have examined the usefulness of DST in oral production, this research investigates whether project-based digital storytelling enhances students’ understanding of academic content related to resilience and conventional composition. Narrative inquiry (NI) research technique is the basis of the digital story project in an English as a Second Language (ESL) course. In this case, the study relies on observations and artifacts to …

Now I See: Video Supports For English Language Learners, Joanna C. Zimmerle, Hanrui He, Tara Hill

Journal of English Learner Education

General education teachers are often unprepared by teacher preparation programs and limited professional development opportunities to support English Language Learners (ELLs), despite the increasing number of ELLs entering US schools and the trend towards placing them in mainstream classrooms. Video resources offer one viable approach to supporting the language learning of ELLs, drawing upon theoretical frameworks such as dual-coding, constructivist theory, and sociocultural perspectives. This paper describes the use of video resources to assist general education teachers in effectively leveraging videos to support the language learning of ELLs in mainstream classrooms.

Survey: A Study On Image Encryption Using Dna In Bioinformatics, Rana M. Zaki, Zaed S. Mahdi, Matheel E. Abdulmunim

Journal of Soft Computing and Computer Applications

One area of study between computer science and biology is bioinformatics, which deals with methods for collecting, processing, storing, and evaluating biological data. Sequences of RiboNucleic Acid (RNA), DeoxyriboNucleic Acid (DNA), and proteins make up biological data, which has a wide range of uses in domains such as feature extraction, data segmentation, data security, and more. In cryptography, DNA sequences are used as data carriers, enhancing the unique properties of biomolecules. This approach involves using DNA sequences to enhance the security of confidential data that must be transmitted over networks or stored securely. Several DNA-based security techniques have been developed, …

New Feature Selection Using Principal Component Analysis, Zaid Mundher Radeef, Soukaena Hassan Hashem, Ekhlas Khalaf Gbashi

Journal of Soft Computing and Computer Applications

Dimensionality reduction techniques streamline machine learning by reducing data complexity, improving model accuracy, and cutting computational costs. They remove noise and irrelevant features, making models faster and more efficient. These techniques also enhance data visualization and interpretation by condensing data into manageable, insightful dimensions. Ultimately, dimensionality reduction leads to simpler, more interpretable models without sacrificing critical information, making it a cornerstone of efficient data analysis and machine learning applications. Theoretically, feature extraction tends to create new features that encapsulate more information by combining multiple existing features, resulting in more concentrated and informative features. In contrast, feature selection involves choosing a …

Development Of A Hybrid Methodology Of Deep Learning And Machine Learning For Lung Nodule Detection In Medical Computed Tomography Images, Zaed S. Mahdi, Rana M. Zaki, Alaa Kadhim Farhan, Negar Majma

Journal of Soft Computing and Computer Applications

Deep learning and machine learning play an important role in the medical field, helping doctors make accurate, fast and effective diagnosis. Despite the progress achieved in the use of modern technologies in detecting cancerous nodes, current studies still suffer from some challenges and limitations that must be addressed to obtain high efficiency in identifying cancerous nodes. These challenges include using image pre-processing, combining deep learning and machine learning techniques, and constantly adapting to clinical changes, in order to address this. A hybrid methodology has been proposed for detecting cancerous nodules in the lung in medical Computed Tomography (CT) images. It …

Enhancing Image Classification Using A Convolutional Neural Network Model, Zena M. Saadi, Ahmed T. Sadiq, Omar Z. Akif, Marwa M. Eid

Journal of Soft Computing and Computer Applications

In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as …

Improved Rapidly-Exploring Random Tree Using Firefly Algorithm For Robot Path Planning, Dena Kadhim Muhsen, Firas Abdulrazzaq Raheem, Yuhanis Yusof, Ahmed T. Sadiq, Faiz Al Alawy

Journal of Soft Computing and Computer Applications

In robotics, efficient path planning makes robots work independently and move through changing environments over time. This study combines the Rapidly-exploring Random Tree (RRT) architecture with the Firefly Algorithm (FA) to make robot’s path-planning better. The proposed ERRT-FA, which stands for "Enhanced RRT with Firefly Algorithm", generates better routes using Firefly social habits. Plan routes using Firefly social habits can effectively aid in exploring configuration space. The role of the FA is to enhance the RRT algorithm by providing an optimized exploration of the search space, ultimately leading to optimizing the path found by the RRT algorithm and better paths …

Foreword From Editor - 16th Edition: Toward An Inclusive Community Engagement, Yandi Andri Yatmo

ASEAN Journal of Community Engagement

This edition of AJCE defines and elaborates on the idea of inclusive community engagement as a means to involve the community in a meaningful process. ‘Inclusive’ refers to the principles of encompassing everyone, all individuals and groups alike, regardless of their identity, background, characteristics, needs, and perspectives, thereby ensuring that all voices are represented (Hodkinson, 2011). This practice extends beyond individuals with disabilities and embodies broader ideas of equality. Inclusive engagement plays a crucial part in fostering a constructive dialog that incorporates diverse perspectives within a community. Such engagements prioritize community participation in the decision-making process that affects their well-being …

Learning Paradigms For Rhythm Detection And Generation Using Mathematical Models, Biophysical And Artificial Neural Networks, Prianka Bose

Dissertations

Humans possess an inherent ability to recognize evenly-spaced rhythms, known as isochronous rhythms, owing to the brain's predisposition to entrain to external auditory stimuli with regular temporal intervals. The central focus of this research is to understand how the brain learns and retains rhythmic time intervals in the context of music. This dissertation studies rhythm detection and generation through mathematical models, biophysical networks, and artificial neural networks, addressing both isochronous and non-isochronous patterns.

A primary focus of the thesis is on isochronous rhythms. In particular, given a perturbation to an isochronous rhythm such as a tempo change or phase shift …

Pushing The Boundaries Of Large Language Models: Innovations And Limitations In Nlp, Finance, And Mathematics, A M Muntasir Rahman

Dissertations

Large Language Models (LLMs) have emerged as transformative tools across a spectrum of domains, yet their practical deployment reveals a blend of remarkable potential and notable limitations. This research explores innovative methodologies to extend the capabilities of LLMs while addressing critical challenges in their evaluation and application. By leveraging rule-based approaches, the in-context learning capabilities of LLMs, and human-in-the-loop validation across three focused studies, this research introduces robust strategies for dataset synthesis, model enhancement, and model assessment in three distinct domains: natural language processing, financial sentiment analysis, and mathematical reasoning

The first study proposes an efficient data augmentation framework, EASE, …

Game-Based Learning With Augmented Reality, Kantida Nanon

Dissertations

Augmented Reality (AR) technology provides an effective and controllable learning environment, especially for complicated, potentially dangerous, or critical content. Effective implementation of interaction and platforms for educational tools can maximize learner engagement and optimize the learning experience. This dissertation explores the potential of AR technology to enhance learning experiences across multiple platforms and subject areas, particularly within chemistry and mathematics. The dissertation is grounded in the field of Human-Computer Interaction (HCI) and explores the potential of AR to increase learner engagement, improve learning outcomes, and transform traditional pedagogical practices.

The dissertation is divided into two parts. The first part examines …

First-Principles Study Of Ferroelectric Properties And Co2 Reduction Reaction Capabilities In Two-Dimensional Monolayers And Heterostructures, Mo Li

Dissertations

Two-dimensional (2D) materials hold significant potential for CO₂ reduction reactions (CO₂RR) due to their high surface-to-volume ratio. However, achieving high selectivity for desired products and overcoming limitations posed by scaling relationships remain challenging. Recent studies suggest that ferroelectric (FE) materials with switchable out-of-plane polarization (OOP) can effectively tune the adsorption behavior, thermodynamics, and kinetics of CO₂RR, offering promising solutions to these challenges. Using density functional theory (DFT) and the Berry phase approach, this work expands the family of 2D ferroelectrics by theoretically identifying Y₂CO₂, Y₂CS₂, and Sc …

Determination Of Electrochemical Parameters For Predicting Reaction Mechanism And Algorithmic Approaches To Pain Assessment, Huize Xue

Dissertations

This dissertation introduces novel advancements in electrochemical kinetics and pain assessment, structured into two main parts. The first part focuses on the comprehensive analysis of the kinetic and mechanistic aspects of electrochemical reactions, utilizing a combination of experimental techniques and simulation methods. A new software tool, Envismetrics, was developed using Python to facilitate the analysis of complex electrochemical data, including cyclic voltammetry (CV), chronoamperometry (CA), and hydrodynamic voltammetry (HDV). The software was rigorously tested and validated with well-characterized redox systems such as the ferricyanide/ferrocyanide couple, dimethylamine borane (DMAB), and Per- and Polyfluoroalkyl Substances (PFAS). It was successfully used to determine …

Differential Item Functioning Of The Region-Based National Examinationequipment, Adi Setiawan, Gulzhaina Kuralbaevna Kassymova, Vianney Mbazumutima, Anggit Reviana Dewi Agustyani

REID (Research and Evaluation in Education)

This research aims to detect Differential Item Functioning (DIF) in the 2014/2015 National Examination Questions in mathematics of junior high schools and equivalent- level schools in the Yogyakarta region as a reference group and the South Kalimantan region as a focus group using the Likelihood Ratio Test (LRT) method, Area Measure Raju, and Lord. A sensitivity analysis was conducted to determine the most sensitive method. The data consisted of 5,465 National Examination papers of the students from the two regions who worked on type A questions. A sample of 1,000 exam papers for each region was established using the simple …

2024 Scholarly Productivity Report, Missouri University Of Science And Technology

Civil, Architectural and Environmental Engineering Scholarly Productivity Reports

No abstract provided.

Advanced Worker's Compensation, Indiana Continuing Legal Education Forum (Iclef)

Indiana Continuing Legal Education Forum 2024

Meeting proceedings of a seminar by the same name, held July 25-26, 2024.

Understanding Barriers To Optimal Supervision And Delivery Of The Nationalcertificate (Vocational) Curriculum Through Tvet College Lecturers’Reflective Evaluations, Angelona Rewhydah Williams, Karel Prins, Bongani Innocent Nkambule, Sindile Amina Ngubane

REID (Research and Evaluation in Education)

Propounded by reflective theory, this qualitative case study drew on TVET lecturers' reflective evaluations of factors that they considered to have a bearing on optimal supervision and delivery of the National Certificate (Vocational) curriculum. Data were collected from participants across three campuses of a TVET college in the Eastern Cape Province, South Africa. Twelve lecturers of different seniority were purposively sampled and interviewed in two focus group sessions. The first focus session involved six participants: four post-level 2 and two post-level 3 personnel recognized by the South African Council for Educators (SACE) as "office-based lecturers" and classified within the middle …

Ensemble Learning Models For Large-Scale Time Series Forecasting In Supply Chain, Minjuan Zhang

Dissertations

Machine learning and AI techniques are transforming supply chain forecasting, driven by the expanding availability of data assets. These advanced methods offer powerful opportunities to optimize management processes, reduce operational costs, and enhance strategic decision-making, which is crucial for enterprise success. However, conventional statistical approaches, such as Autoregressive Integrated Moving Average Models (ARIMA), dynamic regression, and Unobserved Component Models (UCMs)—which have long dominated time series forecasting—often fall short in accuracy and scalability. These traditional models face limitations in batch processing, handling large-scale data, addressing uncertainty-induced disruptions, and synchronizing demand-supply scenarios.

To address these challenges, a novel class of AI-powered ensemble …

Machine Learning Methods For Pattern Recognition Analysis Of Genomic And Molecular Data, Kuang Du

Dissertations

While immune therapies achieve remarkable success in treating various cancers, only a subset of patients achieves a durable clinical response, and many exhibit innate or acquired resistance. Precision medicine aims to tailor treatments to individual patients based on specific biological markers, ensuring that each patient receives the therapy most likely to be effective. Predictive biomarkers and gene signatures offer potential for more personalized treatment strategies by identifying patients likely to benefit. Recent studies suggest that gene signatures, comprising sets of genes, hold predictive value for certain clinical variables. Typically derived from biological expert knowledge, these signatures demonstrate substantial predictive potential, …

Knowledge Diffusion In Networks Of Artificial Learners, Ehsan Beikihassan

Dissertations

The dissertation draws inspiration from the topic of peer learning in the social sciences and the study of information dissemination and knowledge diffusion in network science. In particular, it introduces and studies a setting involving a population or network of artificial learners, with the objective of optimizing aggregate performance measures under constraints on training resources. In this context, natural knowledge diffusion processes in networks of interacting artificial learners are studied. The term "natural" refers to processes that emulate human peer learning, where the internal state and learning processes of students remain largely opaque, and the main degree of freedom lies …

Interactive Visualization Workflows For Mitigating Analytical Uncertainty, Kaustav Bhattacharjee

Dissertations

This dissertation takes a process-centric and stakeholder-first perspective for handling analytical uncertainty: the form of uncertainty that confronts data analysts' insight-generation processes in high-consequence decision-making scenarios. The cost of an incorrect decision when data is used for movie recommendations as opposed to when personal data is used to drive insights or when data-driven modeling is used to drive real-time decisions for maintaining the health of a grid are vastly different in terms of consequences. This dissertation looks at analytical uncertainty in two real-world scenarios: i) how sensitive information leakage can be prevented during the open data release process with data …

Visual Analytic Techniques For Interpretable Algorithmic Ranking Systems, Jun Yuan

Dissertations

Rankings have a profound impact on the increasingly data-driven society. From leisurely activities like the movies to watch, the restaurants to patronize; to highly consequential decisions, like making educational and occupational choices or getting hired by companies— these are all driven by sophisticated yet mostly opaque algorithmic rankers. A small change in how these rankers order the data items can have profound consequences, like deterioration of the prestige of a university or a job applicant missing out on being on the list of the top candidates for an organization. These scenarios necessitate data-driven and human-centered innovation to make rankers accessible, …

Crowd-Sourced Learning For Computer Graphics Applications, Yunhao Zhang

Dissertations

Computer Graphics (CG) revolves around virtual content creation using computational methods, spanning applications from games to visual effects. Typically, the creation of CG content is led by expert practitioners who guide computational algorithms towards satisfactory results. Thus, creating CG content often requires manual iterations encompassing algorithm design, parameter tuning, and aesthetic feedback. This work investigates how to leverage crowd-sourcing to streamline such creation processes, focusing on animation and simulation. In animation, a novel crowd-sourcing framework is proposed for combat animation, enabling users to analyze motion similarities, and retrieve matching motions using novel crowd-sourced motion features. Such features enable quantifying previously …

Number Sense Profile Of Prospective Elementary School Teachers In Blendedmathematics Learning, Welly Novitasari, Herwin Herwin, Supartinah Supartinah, Putri Wulandari, Budiharti Budiharti

REID (Research and Evaluation in Education)

Number sense is a skill that contributes significantly to learning mathematics. However, number sense is often positioned as a fundamental skill whose development is more focused on children. The contribution of number sense in mathematics is even more apparent at higher levels of education. Ironically, number sense seems ignored and has become a rarely studied topic in higher education. Thus, the student’s number sense ability profile seems buried with various problems. This study aims to reveal the profile of prospective elementary school teachers’ number sense abilities and the factors causing their failure in solving math problems during the implementation of …

Simulation Of Low-High Method In Adaptive Testing, Rukli Rukli, Noor Azeam Atan

REID (Research and Evaluation in Education)

The era of disruption significantly engineered a classic testing system into an adaptive testing system where each test taker takes a unique test. However, the carrying capacity of the adaptive testing system engineering is experiencing obstacles in terms of the method of presenting the test questions. The study aims to introduce the low-high adaptive tracking method with the item response theory approach, where the difficulty level of the questions is adapted to the test takers' abilities. The number of test questions in the question bank is 400 questions. Data analysis used the Bilog-MG program. The range of the difficulty level …

Construction Of An Instrument For Evaluating The Teaching Process In Highereducation: Content And Construct Validity, Risky Setiawan, Wagiran Wagiran, Yasir Alsamiri

REID (Research and Evaluation in Education)

This study aims to reveal the content validity, construct validity, and reliability of the instrument for evaluating the teaching process in higher education. This research is development research applying the ADDIE model from Molenda. The indicators evaluated consist of context, inputs, processes, and products. The sample consisted of 1200 students from eight faculties, each represented by three study programs. Data analysis uses three stages: content validity test analysis using the V-Aiken method involving six panellists or experts; construct validity test using Confirmatory Factor Analysis (CFA). Quantitative descriptive analysis and interpretive qualitative analysis used the Miles and Huberman method. The results …

Constructing And Providing Content Validity Evidence Through The Aiken's Vindex Based On The Experts' Judgments Of The Instrument To Measuremathematical Problem-Solving Skills, Nia Kania, Yaya S. Kusumah, Jarnawi Afgani Dahlan, Elah Nurlaelah, Ferit Gürbüz, Ebenezer Bonyah

REID (Research and Evaluation in Education)

Test content-based proof of validity is a type of evidence that supports the validity of a measuring instrument. This research aims to develop a mathematical problem-solving assessment instrument utilizing five experts. This study is classified as developmental research and follows a research design that includes two separate stages: the preliminary design stage and the prototype stage. However, its application is restricted to Prototype 1 and Prototype 2, specifically for expert evaluation. This instrument was designed explicitly for grade VIII students studying mathematics, covering all the topics from the odd semesters. The analysis progressed through three distinct stages— curriculum analysis, content …

Stability Of Estimation Item Parameter In Irt Dichotomy Considering The Number Of Participants, Zulfa Safina Ibrahim, Heri Retnawati, Alfred Irambona, Beatriz Eugenia Orantes Pérez

REID (Research and Evaluation in Education)

This research is related to item response theory (IRT) which is needed to measure the goodness of a test set, while item parameter estimation is needed to determine the technical properties of a test item. Stability of item parameter estimation is conducted to determine the minimum sample that can be used to obtain good item parameter estimation results. The purpose of this study is to describe the effect of the number of test takers on the stability of item parameter estimation with the Bayes method (expected a posteriori, EAP) on dichotomous data. The minimum sample for stability is 1,000 participants …

Automatic Generation Of Physics Items With Large Language Models (Llms), Moses Oluoke Omopekunola, Elena Yu Kardanova

REID (Research and Evaluation in Education)

High-quality items are essential for producing reliable and valid assessments, offering valuable insights for decision-making processes. As the demand for items with strong psychometric properties increases for both summative and formative assessments, automatic item generation (AIG) has gained prominence. Research highlights the potential of large language models (LLMs) in the AIG process, noting the positive impact of generative AI tools like ChatGPT on educational assessments, recognized for their ability to generate various item types across different languages and subjects. This study fills a research gap by exploring how AI-generated items in secondary/high school physics aligned with educational taxonomy. It utilizes …