Inventory Optimization Model Design with Machine Learning Approach in Feed Mill Company

This article aims to address the impacts that companies can have with the application of machine learning to carry out their demand forecasts, knowing that a more accurate demand forecast improves the performance of companies, making them more competitive. The methodology used was a literature review through descriptive, qualitative and with bibliographical surveys in International Journal from 2010 – 2022 by different authors. Findings show that the references prove that demand forecasting with the use of machine learning brings many benefits to organizations, for example, since the results are more accurate, there is better inventory management, consequently customer satisfaction for having the product at the right time and place. Further, this article concludes and suggests that the use of machine learning is able to identify variables that affect the demands, with this it makes a forecast closer to reality and helps managers to make more accurate decisions, improving strategic planning and supply chain management. of company supplies

Automated Reading Passage Generation with OpenAI's Large Language Model

The widespread usage of computer-based assessments and individualized learning platforms has resulted in an increased demand for the rapid production of high-quality items. Automated item generation (AIG), the process of using item models to generate new items with the help of computer technology, was proposed to reduce reliance on human subject experts at each step of the process. AIG has been used in test development for some time. Still, the use of machine learning algorithms has introduced the potential to improve the efficiency and effectiveness of the process greatly. The approach presented in this paper utilizes OpenAI's latest transformer-based language model, GPT-3, to generate reading passages. Existing reading passages were used in carefully engineered prompts to ensure the AI-generated text has similar content and structure to a fourth-grade reading passage. For each prompt, we generated multiple passages, the final passage was selected according to the Lexile score agreement with the original passage. In the final round, the selected passage went through a simple revision by a human editor to ensure the text was free of any grammatical and factual errors. All AI-generated passages, along with original passages were evaluated by human judges according to their coherence, appropriateness to fourth graders, and readability

Automated Scoring of Speaking and Writing: Starting to Hit its Stride

This article reviews recent literature (2011–present) on the automated scoring (AS) of writing and speaking. Its purpose is to first survey the current research on automated scoring of language, then highlight how automated scoring impacts the present and future of assessment, teaching, and learning. The article begins by outlining the general background of AS issues in language assessment and testing. It then positions AS research with respect to technological advancements. Section two details the literature review search process and criteria for article inclusion. In section three, the three main themes emerging from the review are presented: automated scoring design considerations, the role of humans and artificial intelligence, and the accuracy of automated scoring with different groups. Two tables show how specific articles contributed to each of the themes. Following this, each of the three themes is presented in further detail, with a sequential focus on writing, speaking, and a short summary. Section four addresses AS implementation with respect to current assessment, teaching, and learning issues. Section five considers future research possibilities related to both the research and current uses of AS, with implications for the Canadian context in terms of the next steps for automated scoring

VAST: a practical validation framework for e-assessment solutions

The influx of technology in education has made it increasingly difficult to assess the validity of educational assessments. The field of information systems often ignores the social dimension during validation, whereas educational research neglects the technical dimensions of designed instruments. The inseparability of social and technical elements forms the bedrock of socio-technical systems. Therefore, the current lack of validation approaches that address both dimensions is a significant gap. We address this gap by introducing VAST: a validation framework for e-assessment solutions. Examples of such solutions are technology-enhanced learning systems and e-health applications. Using multi-grounded action research as our methodology, we investigate how we can synthesise existing knowledge from information systems and educational measurement to construct our validation framework. We develop an extensive user guideline complementing our framework and find through expert interviews that VAST facilitates a comprehensive, practical approach to validating e-assessment solutions.Horizon 2020 (H2020)883588Prevention, Population and Disease management (PrePoD)Public Health and primary car

The impact of digital technology, social media, and artificial intelligence on cognitive functions: a review

In our modern society, digital devices, social media platforms, and artificial intelligence (AI) tools have become integral components of our daily lives, profoundly intertwined with our daily activities. These technologies have undoubtedly brought convenience, connectivity, and speed, making our lives easier and more efficient. However, their influence on our brain function and cognitive abilities cannot be ignored. This review aims to explore both the positive and negative impacts of these technologies on crucial cognitive functions, including attention, memory, addiction, novelty-seeking and perception, decision-making, and critical thinking, as well as learning abilities. The review also discusses the differential influence of digital technology across different age groups and the unique challenges and benefits experienced by children, adolescents, adults, and the elderly. Strategies to maximize the benefits of the digital world while mitigating its potential drawbacks are also discussed. This review aims to provide a comprehensive overview of the intricate relationship between humans and technology. It underscores the need for further research in this rapidly evolving field and the importance of informed decision-making regarding our digital engagement to support optimal cognitive function and wellbeing in the digital era

VAST: a practical validation framework for e-assessment solutions

The influx of technology in education has made it increasingly difficult to assess the validity of educational assessments. The field of information systems often ignores the social dimension during validation, whereas educational research neglects the technical dimensions of designed instruments. The inseparability of social and technical elements forms the bedrock of socio-technical systems. Therefore, the current lack of validation approaches that address both dimensions is a significant gap. We address this gap by introducing VAST: a validation framework for e-assessment solutions. Examples of such solutions are technology-enhanced learning systems and e-health applications. Using multi-grounded action research as our methodology, we investigate how we can synthesise existing knowledge from information systems and educational measurement to construct our validation framework. We develop an extensive user guideline complementing our framework and find through expert interviews that VAST facilitates a comprehensive, practical approach to validating e-assessment solutions.Horizon 2020(H2020)883588Algorithms and the Foundations of Software technolog

VAST: a practical validation framework for e-assessment solutions

The influx of technology in education has made it increasingly difficult to assess the validity of educational assessments. The field of information systems often ignores the social dimension during validation, whereas educational research neglects the technical dimensions of designed instruments. The inseparability of social and technical elements forms the bedrock of socio-technical systems. Therefore, the current lack of validation approaches that address both dimensions is a significant gap. We address this gap by introducing VAST: a validation framework for e-assessment solutions. Examples of such solutions are technology-enhanced learning systems and e-health applications. Using multi-grounded action research as our methodology, we investigate how we can synthesise existing knowledge from information systems and educational measurement to construct our validation framework. We develop an extensive user guideline complementing our framework and find through expert interviews that VAST facilitates a comprehensive, practical approach to validating e-assessment solutions

Learning from Sequential User Data: Models and Sample-efficient Algorithms

Recent advances in deep learning have made learning representation from ever-growing datasets possible in the domain of vision, natural language processing (NLP), and robotics, among others. However, deep networks are notoriously data-hungry; for example, training language models with attention mechanisms sometimes requires trillions of parameters and tokens. In contrast, we can often access a limited number of samples in many tasks. It is crucial to learn models from these `limited\u27 datasets. Learning with limited datasets can take several forms. In this thesis, we study how to select data samples sequentially such that downstream task performance is maximized. Moreover, we study how to introduce prior knowledge in the deep networks to maximize prediction performance. We focus on four sequential tasks: computerized adaptive testing in psychometrics, sketching in recommender systems, knowledge tracing in computer-assisted education, and career path modeling in the labor market. In the first two tasks, we devise novel sample-efficient algorithms to query a minimal number of sequential samples to improve future predictions. We propose a Bilevel Optimization-Based framework for computerized adaptive testing to learn a data-driven question selection algorithm that improves existing data selection policies. We also tackle the sketching problem in the recommender system, with the task of recommending the next item using a stored subset of prior data samples. In this setting, we develop a data-driven sequential selection algorithm that tackles evolving downstream task distribution. In the last two tasks, we devise novel neural models to introduce prior knowledge exploiting limited data samples. For knowledge tracing, we propose a novel neural architecture, inspired by cognitive and psychometric models, to improve the prediction of students\u27 future performance and utilize the labeled data samples efficiently. For career path modeling, we propose a novel and interpretable monotonic nonlinear state-space model to analyze online user professional profiles and provide actionable feedback and recommendations to users on how they can reach their career goals. The data-driven differentiable data selection algorithms for the first two tasks open up future directions to query (a non-differentiable operation) a minimal number of samples optimally to maximize prediction performance. The structures, introduced in the neural architecture for the models in the last two tasks using prior knowledge, open up future directions to learn deep models augmented with prior knowledge using limited data samples

Система для дистанційного навчання англійської мови

Метою магістерської дисертації є збільшення ефективності вивчення англійської мови з використанням сучасних технологій. Предметом дослідження є використання сучасних технологій для навчання. Для досягнення поставлених в магістерській роботі задач, використано проектування програмного забезпечення, дослідження, обчислення, обробка природної мови. Магістерське дослідження є самостійно виконаною роботою, в якій відображено особистий авторський підхід та особисто отримані теоретичні та прикладні результати, що відносяться до проектування та розробки системи для дистанційного навчання англійської мови. Формулювання мети та завдань дослідження проводилось спільно з науковим керівником. Розроблено метод для автоматичного створення завдань для вивчення англійської мови. Розроблено спосіб для персоналізації навчання англійської мови.The purpose of the master’s thesis is to increase the efficiency of learning English with usage of modern technologies. Distance learning system for English language learning is object of research. To achieve the objectives of the master's thesis, used software design, research, calculation, natural language processing. Master’s thesis is a self-performed work, which reflects the personal author’s approach and personally obtained theoretical and applied final results related to designing and developing of distance learning system for English language learning. Developed the method for automated task creation. Developed approach for personalization of English language learning