Universidad de Córdoba.
Psicothema. 2018 Aug;30(3):322-329. doi: 10.7334/psicothema2018.116.
Process mining with educational data has made use of various algorithms for model discovery, principally Alpha Miner, Heuristic Miner, and Evolutionary Tree Miner. In this study we propose the implementation of a new algorithm for educational data called Inductive Miner.
We used data from the interactions of 101 university students in a course given over one semester on the Moodle 2.0 platform. Data was extracted from the platform's event logs; following preprocessing, the mining was carried out on 21,629 events to discover what models the various algorithms produced and to compare their fitness, precision, simplicity and generalization.
The Inductive Miner algorithm produced the best results in the tests on this dataset, especially for fitness, which is the most important criterion in terms of model discovery. In addition, when we weighted the various metrics according to their importance, Inductive Miner continued to produce the best results.
Inductive Miner is a new algorithm which, in addition to producing better results than other algorithms using our dataset, also provides valid models which can be interpreted in educational terms.
教育数据的流程挖掘已经利用了各种算法来进行模型发现，主要有 Alpha Miner、启发式 Miner 和进化树 Miner。在本研究中，我们提出了一种新的教育数据算法，称为归纳 Miner。
我们使用了在 Moodle 2.0 平台上进行了一个学期的一门课程中 101 名大学生交互的数据。从平台的事件日志中提取数据；在进行挖掘之前，对 21629 个事件进行了预处理，以发现各种算法产生的模型，并比较它们的适应性、精度、简单性和泛化性。
在对该数据集的测试中，归纳 Miner 算法产生了最好的结果，特别是在适应性方面，这是模型发现的最重要标准。此外，当我们根据重要性对各种指标进行加权时，归纳 Miner 仍然产生了最好的结果。
归纳 Miner 是一种新的算法，除了在使用我们的数据集时产生比其他算法更好的结果外，还提供了可以在教育方面进行解释的有效模型。
