相关论文: Assessing Mobile Learning System Performance in In…

In-context learning (ICL) has emerged as an effective approach to enhance the performance of large language models (LLMs). However, its effectiveness varies significantly across models and tasks, posing challenges for practitioners to…

A conceptual framework for measuring the usability characteristics of mobile learning (m-Learning) application has been developed. Furthermore, a software prototype for smartphones to assess usability issues of m-Learning applications has…

This study investigates the integration of Large Language Models (LLMs) into the feedback mechanisms of the architectural design studio, shifting the focus from generative production to reflective pedagogy. Employing a mixed-methods…

Large language models (LLMs) show remarkable human-like capability in various domains and languages. However, a notable quality gap arises in low-resource languages, e.g., Indonesian indigenous languages, rendering them ineffective and…

E-learning in higher education is exponentially increased during the past decade due to its inevitable benefits in critical situations like natural disasters, and pandemic. The reliable, fair, and seamless execution of online exams in…

Nowadays, systems containing components based on machine learning (ML) methods are becoming more widespread. In order to ensure the intended behavior of a software system, there are standards that define necessary quality aspects of the…

It is crucial that robots' performance can be improved after deployment, as they are inherently likely to encounter novel scenarios never seen before. This paper presents an innovative solution: an interactive learning-based robot system…

As Machine Learning (ML) becomes more prevalent in Industry 4.0, there is a growing need to understand how systematic approaches to bringing ML into production can be practically implemented in industrial environments. Here, MLOps comes…

Personalization and active learning are key aspects to successful learning. These aspects are important to address in intelligent educational applications, as they help systems to adapt and close the gap between students with varying…

Recent advancements in Artificial Intelligence have led to the development of Multimodal Large Language Models (MLLMs). However, adapting these pre-trained models to dynamic data distributions and various tasks efficiently remains a…

We consider the problem of assessing the changing performance levels of individual students as they go through online courses. This student performance (SP) modeling problem is a critical step for building adaptive online teaching systems.…

Quality competence of worker the present do not meet labor market criteria and the low level of labor productivity, the lack of communication between the labor market with education, changing of socio-economic structure and global political…

This paper gives an overview of electronic learning (E-Learning) and mobile learning (M-Learning) adoption and diffusion trends, as well as their particular traits, characteristics and issues, especially in terms of cross-cultural and…

Machine learning is traditionally studied at the model level: researchers measure and improve the accuracy, robustness, bias, efficiency, and other dimensions of specific models. In practice, the societal impact of machine learning is…

This study investigates the challenges in designing, data collection, and implementation of a web-based Tutoring System (TS) for teaching linear equations within a developing country context. Originally designed as an Android app, the…

The aim of this paper is to study m-learning literature in order to propose and develop a privacy-preserving framework which can be used to foster sustainable deployment of mobile learning within open and distance education in Kenya.…

The use of machine learning systems in clinical routine is still hampered by the necessity of a medical device certification and/or by difficulty to implement these systems in a clinic's quality management system. In this context, the key…

A central capability of intelligent systems is the ability to continuously build upon previous experiences to speed up and enhance learning of new tasks. Two distinct research paradigms have studied this question. Meta-learning views this…

Amid the growing need to keep learners abreast of rapid technological advancements brought about by the Fourth Industrial Revolution, this study explores perceived needs of students in virtual learning environments supported by emerging…

Mobile learning (M-learning) is receiving more attention as a method of delivering to learners study materials anytime and anywhere. It is a necessity for educators to come up with a layout for learning that can be accessed through mobile…