University students' current situation and potential options for improving their abilities to self-study

Due to the underlying differences between the high school and university learning methodologies. Students at universities typically do much of their own studying because there isn't a daily check-in with the professor. It is a large-scale, ongoing activity that involves the acquisition of a great deal of knowledge. As was already indicated, there won't be much teacher guidance at university, so students will have to learn on their own. This will help them develop self-discipline, optimism, and self-reliance so that they can successfully complete their assigned tasks once they graduate. The only way for each of us to acquire and amass more information for ourselves is through learning and selfstudy. Never forget to study, whether you are a student or have already graduated. To learn more for your own work and for life, always exercise learning techniques and self-study whenever and wherever you can. The findings of this study, four factors have a significant negative impact on one's ability to independently study. The findings demonstrate that the theoretical underpinnings are congruent with the students' current practices, providing the groundwork for ongoing student developmen

A conceptual model for e-learning supporting tools design based on cue model and Kansei engineering

The Covid-19 pandemic has triggered changes in learning due to the practice of social distancing to curb the spread of the virus. E-learning platforms have become the main platform for learning throughout the pandemic. However, e-learning does have challenges when it comes to ensuring student’s optimum participation throughout the learning experience that require extensive research about techniques and methods for an optimum e-learning experience. This includes various e-learning supporting tools that provides easy communication and immediate assistance to enhance user experience. The supporting tools or software usability and functionality design determined as imperative in enhancing the e-learning user experience. Thus, this research proposes a conceptual model for designing the e-learning supporting tools based on the CUE Model, integrated with Kansei Engineering for optimum user experience that can serve as a guideline for the e-learning supporting tools designer. The outcome of this research will create new research fields that incorporate multiple domains, including the e-learning domain, software and supporting tools design, emotions and user experience

A review of the Development Trend of Personalized learning Technologies and its Applications

Personalized learning tailors material and strategy to student requirements, interests, and goals in e-learning. These developments help educational institutions and other organizations to keep up with the fast pace of information technology, communications, and computing power. Studies show that self-adaptive learning and relevant learning information improve study efficiency. Compared to traditional teaching methods, the practice of online education is well in its infancy. On the other hand, the pedagogy and evaluation of students in online courses have a large gap that has to be filled, necessitating significant improvements in e-learning. We call this approach to education "personalized learning," which is a central focus of today's leading online education platforms. Several studies have been conducted on e-learning and personalized learning, but few investigated the development trend of personalized learning technologies and applications. Therefore this study examines the literature to close the gap and promote the development trend for personalized learning technologies and applications in higher education from 2010 to 2021 by analyzing related journal articles. The pivotal studies used inclusion criteria after a search generated 372 complete research articles and reduced them to 146 publications based on their proposed learning domains and research themes. Through carefully reviewing current trends and successes in numerous aspects of personalized learning, this discussion analyzes prospective future research directions in the field of personalized learning

Strengths and Limitations of SmallTalk2Me App in English Language Proficiency Evaluation

This paper explores the strengths and limitations of the SmallTalk2Me App, an AI-driven language assessment tool, in evaluating English language proficiency. The study adopts a mixed-method approach, combining interviews with three experienced English teachers and a comprehensive literature review to provide a comprehensive analysis of the app's performance. The research begins with an exploration of the app's strengths, which include its objective and consistent evaluation metrics. The app's automated nature ensures that all test takers are assessed based on the same predefined criteria, reducing human bias and enhancing the reliability of evaluations. Also, it offers immediate feedback, allowing learners to identify their areas of improvement promptly and adapt their learning strategies accordingly. Conversely, the limitations of the SmallTalk2Me App are also discussed. One notable limitation is the challenge of replicating the complexity of real-life communication contexts. App-based assessments may not fully capture the intricacies of natural conversations. Additionally, the app's pronunciation assessment may struggle with accurately recognizing variations in accents and speech patterns, leading to potential inaccuracies in pronunciation evaluation. The insights from the interviews and literature review contribute to a comprehensive understanding of the app's performance, offering valuable implications for its effective use in language teaching and learning settings

Runtime Requirements Monitoring Framework for Adaptive e-Learning Systems

International audienceAs academic learners and companies are turning to e-learning courses to achieve their personal and professional goals, it becomes more and more important to handle service quality in this sector. Despite scientific research conducted to personalize the learning process and meet learner's requirements under adaptive e-learning systems, however, the specification and management of quality attribute is particularly challenging due to problems arising from environmental variability. In our view, a detailed and high-level specification of requirements supported through the whole system lifecycle is needed for a comprehensive management of adaptive e-learning systems, especially in continuously changing environmental conditions. In this paper, we propose a runtime requirements monitoring to check the conformity of adaptive e-learning systems to their requirements and ensure that the activities offered by these learning environments can achieve the desired learning outcomes. As a result, when deviations (i.e., not satisfied requirements) occur, they are identified and then notified during system operation. With our approach, the requirements are supported during the whole system lifecycle. First, we specify system's requirements in the form of a dynamic software product line. This specification applies a novel requirements engineering language that combines goal-driven requirements with features and claims and avoid the enumeration of all desired adaptation strategies (i.e. when an adaptation should be applied) at the design time. Second, the specification is automatically transformed into a constraint satisfaction problem that reduces the requirements monitoring into a constraint program at runtime