In recent years, SLA and L2 learning motivation have received extensive attention of researchers and teachers across the globe but the issue remains underdeveloped and there are only some small-scale studies on this s...In recent years, SLA and L2 learning motivation have received extensive attention of researchers and teachers across the globe but the issue remains underdeveloped and there are only some small-scale studies on this subject in Pakistan. Among different factors that affect L2 learning motivation, the current study focuses on exploring differences in L2 learning motivation by college type （private vs. public） and major subject of study （Arts vs. Sciences）. Analyzing the questionnaire data from 547 first year college students, the study singles out different situation-specific factors that account for variation in ESL learning motivation. Results indicate that private college students have a higher motivation level and better achievements in ESL learning as compared to public college students. Public college students have strong instrumental motivation while private college students show preferences for an ideal L2 self. L2 motivation does not differ a great deal between students with different subjects of study but there is a big gap in the achievement of both groups. Arts majors＇ motivation depends heavily on their attitude towards English while science majors are instrumentally motivated to learn English. We also discuss some possible reasons for the differences in motivation and implications of the study for ESL teachers and learners.展开更多
In the digital music landscape, the accuracy and response speed of music recommendation systems (MRS) are crucial for user experience optimization. Traditional MRS often relies on the use of high-performance servers f...In the digital music landscape, the accuracy and response speed of music recommendation systems (MRS) are crucial for user experience optimization. Traditional MRS often relies on the use of high-performance servers for large-scale training to produce recommendation results, which may result in the inability to achieve music recommendation in some areas due to substandard hardware conditions. This study evaluates the adaptability of four popular machine learning algorithms (K-means clustering, fuzzy C-means (FCM) clustering, hierarchical clustering, and self-organizing map (SOM)) on low-computing servers. Our comparative analysis highlights that while K-means and FCM are robust in high-performance settings, they underperform in low-power scenarios where SOM excels, delivering fast and reliable recommendations with minimal computational overhead. This research addresses a gap in the literature by providing a detailed comparative analysis of MRS algorithms, offering practical insights for implementing adaptive MRS in technologically diverse environments. We conclude with strategic recommendations for emerging streaming services in resource-constrained settings, emphasizing the need for scalable solutions that balance cost and performance. This study advocates an adaptive selection of recommendation algorithms to manage operational costs effectively and accommodate growth.展开更多
文摘In recent years, SLA and L2 learning motivation have received extensive attention of researchers and teachers across the globe but the issue remains underdeveloped and there are only some small-scale studies on this subject in Pakistan. Among different factors that affect L2 learning motivation, the current study focuses on exploring differences in L2 learning motivation by college type （private vs. public） and major subject of study （Arts vs. Sciences）. Analyzing the questionnaire data from 547 first year college students, the study singles out different situation-specific factors that account for variation in ESL learning motivation. Results indicate that private college students have a higher motivation level and better achievements in ESL learning as compared to public college students. Public college students have strong instrumental motivation while private college students show preferences for an ideal L2 self. L2 motivation does not differ a great deal between students with different subjects of study but there is a big gap in the achievement of both groups. Arts majors＇ motivation depends heavily on their attitude towards English while science majors are instrumentally motivated to learn English. We also discuss some possible reasons for the differences in motivation and implications of the study for ESL teachers and learners.
文摘In the digital music landscape, the accuracy and response speed of music recommendation systems (MRS) are crucial for user experience optimization. Traditional MRS often relies on the use of high-performance servers for large-scale training to produce recommendation results, which may result in the inability to achieve music recommendation in some areas due to substandard hardware conditions. This study evaluates the adaptability of four popular machine learning algorithms (K-means clustering, fuzzy C-means (FCM) clustering, hierarchical clustering, and self-organizing map (SOM)) on low-computing servers. Our comparative analysis highlights that while K-means and FCM are robust in high-performance settings, they underperform in low-power scenarios where SOM excels, delivering fast and reliable recommendations with minimal computational overhead. This research addresses a gap in the literature by providing a detailed comparative analysis of MRS algorithms, offering practical insights for implementing adaptive MRS in technologically diverse environments. We conclude with strategic recommendations for emerging streaming services in resource-constrained settings, emphasizing the need for scalable solutions that balance cost and performance. This study advocates an adaptive selection of recommendation algorithms to manage operational costs effectively and accommodate growth.
