The Curriculum Recommendations paradigm is dedicated to fostering learning
equality within the ever-evolving realms of educational technology and
curriculum development. In acknowledging the inherent obstacles posed by
existing methodologies, such as content conflicts and disruptions from language
translation, this paradigm aims to confront and overcome these challenges.
Notably, it addresses content conflicts and disruptions introduced by language
translation, hindrances that can impede the creation of an all-encompassing and
personalized learning experience. The paradigm's objective is to cultivate an
educational environment that not only embraces diversity but also customizes
learning experiences to suit the distinct needs of each learner. To overcome
these challenges, our approach builds upon notable contributions in curriculum
development and personalized learning, introducing three key innovations. These
include the integration of Transformer Base Model to enhance computational
efficiency, the implementation of InfoNCE Loss for accurate content-topic
matching, and the adoption of a language switching strategy to alleviate
translation-related ambiguities. Together, these innovations aim to
collectively tackle inherent challenges and contribute to forging a more
equitable and effective learning journey for a diverse range of learners.
Competitive cross-validation scores underscore the efficacy of
sentence-transformers/LaBSE, achieving 0.66314, showcasing our methodology's
effectiveness in diverse linguistic nuances for content alignment prediction.
Index Terms-Curriculum Recommendation, Transformer model with InfoNCE Loss,
Language Switching.

通过采用 Transformer Base Model 来增强计算效能、实现 InfoNCE Loss 以进行准确的内容 - 主题匹配，并采用语言切换策略以消除与翻译相关的歧义，这一研究提出的 Curriculum Recommendations 范式致力于克服课程开发领域不断演变中存在的教育技术和课程开发的不平等问题，以实现平等的学习体验。