Students from different cultures possess varying levels of skills in learning, remembering, and understanding concepts. Some terms and their explanations may seem easy for one group of students but difficult for another. Therefore, delivering educational content that aligns with student’s learning capabilities is a challenging task based on cultural orientations. This study addresses the learning challenges by developing a Thesaurus Glossary E-learning (TGE) framework method. This study introduces the TGE method which is a multi-language tool with visual associations that adapts to students’ capabilities. It also examines cultural differences and native languages, particularly aiding Arab Native to visualize appropriate terms (thesaurus) and their explanations (glossary) based on students’ learning capabilities. TGE learns from students’ term selection behavior and displays terms at a simple or advanced level that matches their learning ability. Additionally, TGE demonstrated its effectiveness as an e-learning tool, accessible to all students anytime and anywhere. The study analyzed 314 records related to student performance, out of which 114 students were surveyed to evaluate the effectiveness of the TGE method. This work presents TGE as a novel e-learning tool designed to enhance conceptual thinking within the context of modern educational practices during the digital transformation. TGE is based on artificial intelligence algorithms and associative rules that simulate the human brain, establishing logical connections between related key terms and sketching associations among diverse facets of a situation. An experiment was conducted at a private university in the Sultanate of Oman to assess the effectiveness of the proposed TGE tool. TGE was integrated with selected subjects in information systems and used by the students as a resource for e-learning methods and materials. The results show that 85% of students who used TGE improved their performance by 19%. We believe this work could establish a new smart e-learning teaching method and attract modern and digital universities to enhance student learning outcomes linked with conceptual thinking.

Accurate prediction of US Treasury bond yields is crucial for investment strategies and economic policymaking. This paper explores the application of advanced machine learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models, in forecasting these yields. By integrating key economic indicators and policy changes, our approach seeks to enhance the precision of yield predictions. Our study demonstrates the superiority of LSTM models over traditional RNNs in capturing the temporal dependencies and complexities inherent in financial data. The inclusion of macroeconomic and policy variables significantly improves the models’ predictive accuracy. This research underscores a pioneering movement for the legacy banking industry to adopt artificial intelligence (AI) in financial market prediction. In addition to considering the conventional economic indicator that drives the fluctuation of the bond market, this paper also optimizes the LSTM to handle situations when rate hike expectations have already been priced-in by market sentiment.

This study examined the dissatisfaction among Chinese medical students with online medical English courses, which overemphasize grammar yet fail to provide practical opportunities related to medical situations. This study compared co-teaching’s effects, involving native and non-native instructors, with a single-instructor (traditional) model on student satisfaction in online medical English courses. Using a qualitative design, pre- and post-course interviews were conducted with 49 second-year medical students across seven classes, exploring their perceptions of instruction, curriculum, and course satisfaction. The findings indicated that the co-teaching model improved student engagement and satisfaction, not specifically due to the native English-speaking instructor but likely because of the focus on more interactive and discussion-oriented strategies. In contrast, the single-instructor model maintained the traditional grammar-focused instruction, leading to lower satisfaction levels. Both instructional models faced limitations related to their reliance on textbooks for delivering core material needed for the course’s comprehensive exam. These results suggest that the instruction design and approach, rather than the native instructor alone, was the main driver of positive outcomes in co-teaching. The study’s findings suggest a need for curriculum reforms that reduce textbook dependence and incorporate more practical, interactive learning strategies. Future research should consider applying various research techniques, such as mixed-method approaches, longitudinal studies, and experimental designs, to comprehensively assess the long-term effects of instructional strategies and curriculum innovations on student outcomes.

The progress of a country can be directly related to the education level of its countrymen. Over a time period, the internet has become a game changer for the world of disseminating education. From 2000 onwards, the scale of online courses has increased manyfold. The main reason for this growth in online learning can be attributed to the flexibility in course delivery and scheduling. Through this study, the authors analyzed the challenges in adopting Online degree programs in higher education in management in India. The authors used Focus Group discussions, semi-structured interviews, and in-depth interviews to collect the data from the various stakeholders. Thematic analysis was used to analyze the responses. Considering the challenges and constraints in India, the authors proposed a sustainable model for implementation. Based on the viewpoints of the different stakeholders, the authors find that online degrees can be instrumental in bringing inclusivity in higher education. There are obvious constraints like a lack of IT infrastructure, the inexperience of faculty in online pedagogy, and the need for more expertise in the administration of online programs by existing universities. However, using SWAYAM as a platform can overcome most of these constraints, as it reduces the burden on individual universities. Hence, the authors proposed models where SWAYAM (technology platform) and Universities (academic partners) can come together to provide a sustainable education model.

Data literacy is an important skill for students in studying physics. With data literacy, students have the ability to collect, analyze and interpret data as well as construct data-based scientific explanations and reasoning. However, students’ ability to data literacy is still not satisfactory. On the other hand, various learning strategies still provide opportunities to design learning models that are more directed at data literacy skills. For this reason, in this research a physics learning model was developed that is oriented towards physics objects represented in various modes and is called the Object-Oriented Physics Learning (OOPL) Model. The learning model was developed through several stages and based on the results of the validity analysis; it shows that the OOPL model is included in the valid category. The OOPL model fulfils the elements of content validity and construct validity. The validity of the OOPL model and its implications are discussed in detail in the discussion.