Even though the field of Learning Analytics (LA) has experienced an expressive growth in the last few years. The vast majority of the works found in literature are usually focusing on experimentation of techniques and methods over datasets restricted to a given discipline, course, or institution and are still few works manipulating region and countrywide datasets. This may be since the implementation of LA in national or regional scope and using data from governments and institutions poses many challenges that may threaten the success of such initiatives, including the same availability of data. The present article describes the experience of LA in Latin America using governmental data from Elementary and Middle Schools of the State of Norte de Santander - Colombia. This study is focusing on students' performance. Data from 2013 to 2018 was collected, containing information related to 1) students’ enrollment in school disciplines provided by Regional Education Secretary, 2) students qualifications provided by educational institutions, and 3) students qualifications provided by the national agency for education evaluation. The methodology followed includes a process of cleaning and integration of the data, subsequently a descriptive and visualization analysis is made and some educational data mining techniques are used (decision trees and clustering) for the modeling and extraction of some educational patterns. A total of eight patterns of interest are extracted. In addition to the decision trees, a feature ranking analysis was performed using xgboost and to facilitate the visual representation of the clusters, t-SNE and self-organized maps (SOM) were applied as result projection techniques. Finally, this paper compares the main challenges mentioned by the literature according to the Colombian experience and proposes an up-to-date list of challenges and solutions that can be used as a baseline for future works in this area and aligned with the Latin American context and reality.

中文翻译：

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揭示哥伦比亚地区一级的教育模式：来自小学和公立高中机构的数据


尽管学习分析（LA）领域在过去几年中经历了显着的增长。文献中的绝大多数作品通常侧重于对仅限于特定学科、课程或机构的数据集进行技术和方法的实验，而操作区域和全国数据集的作品仍然很少。这可能是因为在国家或区域范围内实施 LA 并使用来自政府和机构的数据带来了许多挑战，可能会威胁到此类举措的成功，包括数据的相同可用性。本文使用哥伦比亚北桑坦德州中小学的政府数据描述了拉丁美洲洛杉矶的经验。这项研究的重点是学生的表现。收集了2013年至2018年的数据，包含以下信息：1）地区教育部长提供的学校学科学生入学情况；2）教育机构提供的学生资格；3）国家教育评估机构提供的学生资格。遵循的方法包括数据的清理和集成过程，随后进行描述性和可视化分析，并使用一些教育数据挖掘技术（决策树和聚类）来建模和提取一些教育模式。总共提取了八种感兴趣的模式。除了决策树之外，还使用 ​​xgboost 进行特征排名分析，并为了促进聚类的可视化表示，应用 t-SNE 和自组织图 (SOM) 作为结果投影技术。 最后，本文根据哥伦比亚的经验比较了文献中提到的主要挑战，并提出了最新的挑战和解决方案清单，可以作为该领域未来工作的基准，并与拉丁美洲的背景保持一致和现实。