Abstract
This paper presents a tutoring system which uses three different granularities for helping students to classify animals from bone fragments in zooarchaeology. The 3406 bone remains, which have 64 attributes, were obtained from the excavation of the Middle Palaeolithic site of El Salt (Alicante, Spain). The coarse granularity performs a five-class prediction, the medium a twelve-class prediction, and the fine a fifteen-class prediction. In the coarse granularity, the results show that the first 10 most relevant attributes for classification are width, bone, thickness, length, bone fragment, anatomical group, long bone circumference, X, Y, and Z. Based on those results, a user-friendly interface of the tutor has been built in order to train archaeology students to classify new remains using the coarse granularity. A pilot has been performed in the 2019 excavation season in Abric del Pastor (Alicante, Spain), where the automatic tutoring system was used by students to classify 51 new remains. The pilot experience demonstrated the usefulness of the tutoring system both for students when facing their first classification activities and also for seniors since the tutoring system gives them valuable clues for helping in difficult classification problems.
Highlights
The use of technology opens new frontiers in learning and improves data mining from different sources in order to improve students’ learning processes [1]
We show the steps, for creating an intelligent tutoring systems (ITS), that have been developed from the application of artificial intelligence (AI) classification algorithms to a data set that includes bone fragments correctly identified in the archaeological site of El Salt
In this article we have presented a method to create intelligent tutoring systems in archaeology to help students in specialized tasks that require analysis of huge amounts of data
Summary
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 of. Sci. 2019, 9, 4960 interest in many areas [4,5] The interest in this concept is demonstrated by the many initiatives related to intelligent tutoring systems (ITS). Other more recent ITS guided students to choose their learning path according to their profile [10]
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