Higher education institutions are catching up on their high competition and challenges are in their analysis productivity. The major challenge is to monitor and analyze student progress through learning outcomes in the curriculum. One of the approaches is the outcome‐based education (OBE) model to deal with learning outcomes. OBE is an integral part of higher education institutions. The OBE system is a key step for accreditation in engineering education. OBE focuses on a student‐centered approach. The OBE is not restricted to well‐defined teaching strategies or direct evaluations but also encompasses indirect evaluations to help students achieve the intended outcomes. In this investigation, engineering students’ data have been analyzed forming three distinct clusters to group students according to best, average, and worst achievement of learning outcomes in two different computer engineering courses generally taught in the early semesters in higher education institutions. A data mining clustering approach is used to segment students using k‐means and k‐medoids techniques. Clustering can be regarded as a data modeling technique that provides summary data that interact with multiple disciplines and plays an important role in a wide range of computer applications. The investigation comprises of two parts for analysis: one part of the analysis is the mid‐term and final exam scores, the quiz and assignment results, the laboratory results, and the evaluation, together with the learning outcomes achieved, and the other part is the comparative analysis of learning outcomes achieved in both engineering courses clustering with the best, average, and worst attainments, respectively. In this investigation, the results obtained from clustering data points show that the same group of clusters with the best, average, and worst learning outcomes achievements formed using both k‐means and k‐medoid clustering for one course. On the other hand, a diverse group of clusters with the best, average, and worst learning outcomes achievements formed using both k‐means and k‐medoids clustering for another course.

中文翻译：

---

用数据挖掘调查工程教育的学习成果

高等教育机构正在迎头赶上他们的激烈竞争，而挑战在于他们的分析生产力。主要的挑战是通过课程中的学习成果来监控和分析学生的进步。其中一种方法是基于结果的教育（OBE）模型来处理学习结果。OBE 是高等教育机构的一个组成部分。OBE 系统是工程教育认证的关键步骤。OBE 侧重于以学生为中心的方法。OBE 不仅限于明确定义的教学策略或直接评估，还包括间接评估以帮助学生实现预期结果。在本次调查中，工程学生的数据被分析形成三个不同的集群，根据最佳、平均、在高等教育机构的早期学期通常教授的两门不同的计算机工程课程中，学习成果最差。数据挖掘聚类方法用于使用 k-means 和 k-medoids 技术对学生进行细分。聚类可以被视为一种数据建模技术，它提供与多个学科交互的汇总数据，并在广泛的计算机应用中发挥重要作用。调查包括两部分进行分析：一部分分析是期中和期末考试成绩、测验和作业结果、实验室结果和评估以及取得的学习成果，另一部分是分析对两门工程课程中取得的学习成果进行比较分析，其中最好的、平均的、和最差的成就，分别。在本次调查中，从聚类数据点获得的结果表明，对于一门课程，使用 k-means 和 k-medoid 聚类形成了具有最佳、平均和最差学习成果的同一组集群。另一方面，对于另一门课程，使用 k-means 和 k-medoids 聚类形成了具有最佳、平均和最差学习成果的多样化集群。