Investigating Influence of Demographic Factors on Study Recommenders

Huptych, Michal; Hlosta, Martin; Zdrahal, Zdenek and Kocvara, Jakub (2018). Investigating Influence of Demographic Factors on Study Recommenders. In: Artificial Intelligence in Education (Rosé, Carolyn Penstein; Martínez-Maldonado, Roberto; Hoppe, H. Ulrich; Luckin, Rose; Mavrikis, Manolis; Porayska-Pomsta, Kaska; McLaren, Bruce and du Boulay, Benedict eds.), Lecture Notes in Artificial Intelligence, Springer, Cham, pp. 150–154.

DOI: https://doi.org/10.1007/978-3-319-93846-2_27

Abstract

Recommender systems in e-learning platforms, can utilise various data about learners in order to provide them with the next best material to study. We build on our previous work, which defines the recommendations in terms of two measures (i.e. relevance and effort) calculated from data of successful students in the previous runs of the courses. In this paper we investigate the impact of students’ socio-demographic factors and analyse how these factors improved the recommendation. It has been shown that education and age were found to have a significant impact on engagement with materials.

Viewing alternatives

Metrics

Item Actions

Export

You can export this page using these formats Digital Object Identifier - DOI

About

• Item ORO ID
• 56293
• Item Type
• Conference or Workshop Item
• ISBN
• 3-319-93845-2, 978-3-319-93845-5
• ISSN
• 0302-9743
• Keywords
• personalised learning; educational recommender systems
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Copyright Holders
• © 2018 Springer International Publishing AG
• Depositing User
• Kay Dave