Academic journal article Research in Learning Technology

The SAPO Campus Recommender System: A Study about Students' and Teachers' Opinions

Academic journal article Research in Learning Technology

The SAPO Campus Recommender System: A Study about Students' and Teachers' Opinions

Article excerpt

The SAPO Campus recommender system: a study about students' and teachers' opinions

Luís Pedro*, Carlos Santos, Sara Filipa Almeida, Fernando Ramos, António Moreira, Margarida Almeida and Maria João Antunes

Department of Communication and Art, University of Aveiro, Portugal

(Received 26 September 2013; final version received 28 July 2014; Published 29 August 2014)

Abstract

This paper aims to assess the relevance and usefulness of the SAPO Campus recommender system, through the analysis of students' and teachers' opinions. Recommender systems, assuming a 'technology-driven' approach, have been designed with the primary goal of predicting user interests based on the implicit analysis of their actions and interactions. The results of this study reveal that although there is some confusion and unawareness about the recommender system, the participants consider that SAPO Campus recommendations are useful and they often find interesting people and content through the results provided by the system. The results also reveal that there is a negative correlation between finding and following people through the platform recommendations and the level of education, that is, the higher the level of education, the lower is the frequency regarding finding and following people suggested by the platform recommendation system.

Keywords: education; SAPO Campus; recommendations; interaction

*Corresponding author. Email: lpedro@ua.pt

Research in Learning Technology 2014. © 2014 L. Pedro et al. Research in Learning Technology is the journal of the Association for Learning Technology (ALT), a UK-based professional and scholarly society and membership organisation. ALT is registered charity number 1063519. http://www.alt.ac.uk/. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 Unported (CC BY 4.0) License (http://creativecommons.org/licenses/by/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.

Citation: Research in Learning Technology 2014, 22 : 22921 - http://dx.doi.org/10.3402/rlt.v22.22921

Introduction

In a context where it is recognised that learning occurs throughout individuals' life, not only in formal spaces but also in informal ones (Saz et al . 2011) and with the improvement and massification of technology - specially with all the tools and applications from the social Web - learners are becoming not only consumers but also producers of content and knowledge (Siemens 2008). The existence of a huge quantity of information makes the process of searching and selecting online content a hard task for the average user, who is generally overwhelmed by information overload (Gemmis et al . 2009). Therefore, recommender systems appear as useful tools in reducing the time and costs involved in the process of searching and selecting online information (Drachsler 2009). In this paper, we will present the main results of a study that aimed to assess the relevance and usefulness of the SAPO Campus recommender system, through the analysis of students' and teachers' opinions.

Developing and integrating a recommender system at the SAPO Campus platform

According to Adomavicius and Tuzhilin (2005) cited in Drachsler (2009), the general purpose of recommender systems is to pre-select information a user might be interested in. Thus, this kind of system tries to predict users' preferences based on an implicit analysis of their activity (Lee 2001), in order to support the process of searching and selecting online information (Gemmis et al . 2009).

The literature in this field tells us that there are two main types of recommender systems: content-based and collaborative (Gemmis et al . …

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