# A Granularity-Based Intelligent Tutoring System for Zooarchaeology
## 1 Introduction  
The use of technology opens new frontiers in learning and improves data mining from different sources in order to improve students' learning processes [1]. One of the biggest challenges of including communication technologies in learning is the way in which interaction between teachers and students is simulated by automatic methods [2; 3]. In this context, tutoring and the possibility of turning an automatic system into an effective instrument for counseling students are stirring a great amount ofinterest in many areas [4; 5]. The interest in this concept is demonstrated by the many initiatives related to intelligent tutoring systems (ITS). For example, [PERSON] et al. [6] incorporated natural language processing (NLP) to the tutor in order to enhance the student's learning process. In other studies, authors used gamification techniques to enhance the student's learning process [7; 8] or integrate affect sensors [9]. Other more recent ITS guided students to choose their learning path according to their profile [10].  
The use of ITS in archaeology is a very interesting topic because of the great amount of data that is available for analysis. In this discipline, data analysis has been performed by many statistical methods [11], including artificial intelligence (AI) in both supervised and unsupervised forms [12; 13; 14]. In [15], authors focused on computer vision and gave a visual analysis in archaeology. Random forest [16] has also been used as a supervised learning method to classify archaeological remains. There have also been initiatives [17] which use artificial neural networks in the archaeological domain claiming that they are suitable for the sparse data commonly found in this discipline. Augmented reality and serious games have also been used as tools for improving learning in related subjects such as history or archaeology [18; 19]. Virtual reconstructions of ancient cities have also been developed in many different formats [20; 21].  
Unfortunately, there is a lack of references of ITS in more technical tasks that could help students in more specialized techniques, such as zooarchaeology, which is the topic included in this article. In archaeological sites, there are many different specialized tasks that must be performed by well-trained students [22]. In the case of zooarchaeology, the identification and characterization of animals or species from bone fragments is a crucial task that requires the analysis of many parameters obtained from the characteristics of the remains [16]. First, students require good training in the taxonomical and anatomical characteristics. Later, they must identify the animals and all the information related with their use by humans (trapping, butchering process, discard, technological use, etc.). This data analysis is a great effort and an ITS would be of great help. Moreover, implementing and defining an effective protocol for implementing an intelligent tutor in bone fragment characterization will be also useful in other similar disciplines that also need the analysis of huge amounts of data in archaeology such as the identification of constructive styles [23] or reconstruction of ceramics from small fragments [24].  
In this article, we show the steps, for creating an ITS, that have been developed from the application of AI classification algorithms to a data set that includes bone fragments correctly identified in the archaeological site of El Salt. A comparison between different granularities and algorithms is also shown. By choosing the most effective combination, we have developed a user-friendly interface of the ITS with the objective of testing it in the 2019 season in the archaeological site. Finally, we show the results and conclusions of the pilot experience in the archaeological site during August 2019.  
This article is structured as follows: Section 2 explains the materials and methods used in the study, Section 3 presents the results, Section 4 discusses the results, and finally Section 5 details the conclusions and future work.