User‐centred design and educational data mining support during the recommendations elicitation process in social online learning environments

Abstract

Social online learning environments provide new recommendation opportunities to meet users' needs. However, current educational recommender systems do not usually take advantage of these opportunities. To progress on this issue, we have proposed a knowledge engineering approach based on human–computer interaction (i.e. user‐centred design as defined by the standard ISO 9241‐210:2010) and artificial intelligence techniques (i.e. data mining) that involve educators in the process of eliciting educational oriented recommendations. To date, this approach differs from most recommenders in education in focusing on identifying relevant actions to be recommended on e‐learning services from a user‐centric perspective, thus widening the range of recommendation types. This approach has been used to identify 32 recommendations that consider several types of actions, which focus on promoting active participation of learners and on strengthening the sharing of experiences among peers through the usage of the social services provided by the learning environment. The paper describes where data mining techniques have been applied to complement the user‐centred design methods to produce social oriented recommendations in online learning environments.

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

“…Several user evaluation frameworks have been proposed to guide the design and execution of user experiments to evaluate recommender systems [31], [32], [17], however once again, due to the high costs of conducting user studies in TEL, these frameworks have not been extensively implemented nor comprehensively adapted to fit TEL requirements. Recently, this open challenge has been addressed and a user centred evaluation approach has been proposed and explored extensively [6], [33].…”

Panorama of Recommender Systems to Support Learning

“…Recommendation opportunities in educational scenarios that go beyond recommending learning resources need to be further explored. For this, user centered design approaches [87] can be of value, such as to consider recommending learning activities that, for instance, foster communication [1] and metacognition [124][77] [88]. At the same time, the potential of semantic technologies is being considered to describe the educational domain and therefore enrich the recommendation process In this sense, the application of affective computing in TEL recommender systems can provide added value to the recommendations when emotional and sentiment information is taken into account in the recommendation process [51] [92] and can provide interactive recommendations through sensorial actuators [91].…”

Towards Emotion Detection in Educational Scenarios from Facial Expressions and Body Movements through Multimodal Approaches

“…Support is being designed according with the learner's emotion management needs and accessibility preferences when she is performing cognitive tasks with the help of the TORMES recommendations elicitation approach based on the ISO standard 9241-210 [66], extended to consider affective information [67]. A preliminary experiment to evaluate the impact of the adaptive and personalized affective support delivered to the learner during the learning experience has been carried out in 2013 Madrid Science Week [63].…”