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.

翻译：课程推荐范式致力于在不断发展的教育技术和课程开发领域中促进学习公平。通过承认现有方法固有的障碍，如内容冲突和语言翻译带来的干扰，该范式旨在面对并克服这些挑战。值得注意的是，它解决了语言翻译引入的内容冲突和干扰，这些障碍可能阻碍创造全面且个性化的学习体验。该范式的目标是培养一个不仅拥抱多样性，而且根据每个学习者的独特需求定制学习体验的教育环境。为克服这些挑战，我们的方法基于课程开发和个性化学习领域的显著贡献，引入了三个关键创新。这包括整合Transformer基础模型以提高计算效率，采用InfoNCE损失函数实现精确的内容-主题匹配，以及引入语言切换策略以缓解翻译相关的歧义。这些创新共同旨在应对固有挑战，并为多样化学习者群体打造更公平、更有效的学习之旅。竞争性交叉验证得分凸显了句子变换器/LaBSE的有效性，达到0.66314，展示了我们的方法在内容对齐预测中应对多样语言细微差别的能力。索引词——课程推荐、基于InfoNCE损失的Transformer模型、语言切换。