Educational Data Mining (EDM) is a research field that focuses on the application of data mining, machine learning, and statistical methods to detect patterns in large collections of educational data. Different machine learning techniques have been applied in this field over the years, but it has been recently that Deep Learning has gained increasing attention in the educational domain. Deep Learning is a machine learning method based on neural network architectures with multiple layers of processing units, which has been successfully applied to a broad set of problems in the areas of image recognition and natural language processing. This paper surveys the research carried out in Deep Learning techniques applied to EDM, from its origins to the present day. The main goals of this study are to identify the EDM tasks that have benefited from Deep Learning and those that are pending to be explored, to describe the main datasets used, to provide an overview of the key concepts, main architectures, and configurations of Deep Learning and its applications to EDM, and to discuss current state‐of‐the‐art and future directions on this area of research.

This paper analyzes the application of LDA topic modeling to a corpus of poetry. First, it explains how the most coherent LDA-topics have been established by running several tests and automatically evaluating the coherence of the resulting LDA-topics. Results show, on one hand, that when dealing with a corpus of poetry, lemmatization is not advisable because several poetic features are lost in the process; and, on the other hand, that a standard LDA algorithm is better than a specific version of LDA for short texts (LF-LDA). The resulting LDA-topics have then been manually analyzed in order to define the relation between word topics and poems. The analysis shows that there are mainly two kinds of semantic relations: an LDA-topic could represent the subject or theme of the poem, but it could also represent a poetic motif. All these analyses have been undertaken on a large corpus of Golden Age Spanish sonnets. Finally, the paper shows the most relevant themes and motifs in this corpus such as “love,” “religion,” “heroics,” “moral,” or “mockery” on one hand, and “rhyme,” “marine,” “music,” or “painting” on the other hand.

In this article an automatic scansion model for fixed-metre Spanish poetry is presented. It is a hybrid model that combines hand-made rules with probabilistic information. Through the set of rules, the model is able to extract the syllabic structure of each word, to classify them as stressed or unstressed and to resolve metrical phenomena such as synaloephas or diaereses. The article is mainly focused on the metrical ambiguities produced by synaloephas: verse lines from which it is possible to derive two or more metrical patterns. This metrical ambiguity is resolved through probabilities, assuming a relation between high probabilities and metricality. The system has been evaluated through more than 1,000 lines extracted from a corpus of Golden-Age Spanish sonnets. An accuracy of 95% has been achieved, resulting in not only considerable progress if we compare it to previous proposals, but also in an adequate way of performing the task when compared to human performance.