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In our experiment, we identified a significant mediation effect of the content coverage, reading patterns and the two levels of with-me-ness on the relation between students\u2019 motivation and their learning performance. Such variables enable common measurements for the different kind of stimuli present in distinct MOOCs. Our long-term goal is to create student profiles based on their performance and learning strategy using stimuli-based gaze variables and to provide students gaze-aware feedback to improve overall learning process. One key ingredient in the process of achieving a high level of adaptation in providing gaze-aware feedback to the students is to use Artificial Intelligence (AI) algorithms for prediction of student performance from their behaviour. In this contribution, we also present a method combining state-of-the-art AI technique with the eye-tracking data to predict student performance. The results show that the student performance can be predicted with an error of less than 5%.<\/jats:p>","DOI":"10.1186\/s40561-020-00122-x","type":"journal-article","created":{"date-parts":[[2020,4,26]],"date-time":"2020-04-26T09:02:36Z","timestamp":1587891756000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":103,"title":["Eye-tracking and artificial intelligence to enhance motivation and learning"],"prefix":"10.1186","volume":"7","author":[{"given":"Kshitij","family":"Sharma","sequence":"first","affiliation":[]},{"given":"Michail","family":"Giannakos","sequence":"additional","affiliation":[]},{"given":"Pierre","family":"Dillenbourg","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,26]]},"reference":[{"key":"122_CR1","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1016\/j.compedu.2018.06.023","volume":"125","author":"E Alemdag","year":"2018","unstructured":"Alemdag, E., & Cagiltay, K. 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