Nowadays, the quality standards of higher education institutions pay special attention to the performance and evaluation of the students. Then, having a complete academic record of each student, such as number of attempts, average grade and so on, plays a key role. In this context, the existence of missing data, which can happen for different reasons, leads to affect adversely interesting future analysis. Therefore, the use of imputation techniques is presented as a helpful tool to estimate the value of missing data. This work deals with the academic records of engineering students, in which imputation techniques are applied. More specifically, it is assessed and compared to the performance of the multivariate imputation by chained equations methodology, the adaptive assignation algorithm (AAA) based on multivariate adaptive regression splines and a hybridization based on self-organisation maps with Mahalanobis distances and AAA algorithm. The results show that proposed methods obtain successfully results regardless the number of missing values, in general terms.

中文翻译：

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电气工程专业学生的学术记录中缺少数据归因

如今，高等学校的质量标准特别关注学生的表现和评价。然后，拥有每位学生的完整学业记录（例如尝试次数，平均成绩等）将发挥关键作用。在这种情况下，可能由于不同原因而发生的丢失数据的存在会导致不利的未来分析产生影响。因此，插补技术的使用被认为是估计丢失数据价值的有用工具。这项工作涉及工程学生的学术记录，其中应用了归因技术。更具体地说，它是通过链式方程方法进行评估并与多元插补的效果进行比较的，基于多元自适应回归样条的自适应分配算法（AAA），以及基于自组织映射与马氏距离和AAA算法的混合算法。结果表明，一般而言，无论缺失值的数量如何，所提出的方法都能成功获得结果。