Proceedings of: 3rd International Workshop on Motivational and Affective Aspects in Technology Enhanced Learning (MATEL 2012), Saarbrücken, Germany, 18-19 September , 2012.This paper describes the Learning Resources A ective Recommender, a widget that recommends resources to the learner based on her current a ective state and her learning objectives. The widget is meant to be used in a Personalized Learning Environment in combination with widgets to search for resources. The architecture that supports the widget follows a client-server pattern, with the widget as the client and a recommendation service on the server side. The paper includes the description of both client and server and a discussion about the possibilities of this approach.Work partially funded by the EEE project, "Plan Nacional de I+D+I TIN2011-28308-C03-01", the "eMadrid: Investigación y desarrollo de tecnolog  as para el e-learning en la Comunidad de Madrid" project (S2009/TIC-1650), and "Consejo Social - Universidad Carlos III de Madrid".Publicad

Delgado Kloos, Carlos

Leony, Derick

Parada Gélvez, Hugo A.

Pardo Sánchez, Abelardo

Information Technology and Nanotechnology

Leony Arreaga, Derick Antonio

Universidad Carlos III de Madrid e-Archivo

A widget to recommend learning resourcesbased on the learner affective stateDerick Leony, Abelardo Pardo, Hugo A. Parada G., and Carlos Delgado KloosTelematics Engineering Department,University Carlos III of Madrid,Av. Universidad 30, Legane´s, Spain{dleony,abel,hparada,cdk}@it.uc3m.eshttp://gradient.it.uc3m.esAbstract. This paper describes the Learning Resources Affective Rec-ommender, a widget that recommends resources to the learner basedon her current affective state and her learning objectives. The widget ismeant to be used in a Personalized Learning Environment in combina-tion with widgets to search for resources. The architecture that supportsthe widget follows a client-server pattern, with the widget as the clientand a recommendation service on the server side. The paper includes thedescription of both client and server and a discussion about the possibil-ities of this approach.Keywords: affective state recommender, recommendation widget, af-fective computing1 IntroductionThere is empirical evidence in the psychological literature that affective states af-fect cognitive processes like memorizing and decision making. This has provokedresearches in the field of Technology-Enhanced Learning (TEL) to investigatethe effects of including affective states into the design of technological tools forlearning. Among the tools that take into account affective states we can findintelligent tutoring systems (ITS) [1, 2], narrative-centered environments [3] andenvironments to support reflection in professional learning scenarios [4].However, there is a type of tools in the TEL field of TEL that still hasn’tshown evidence of including affective states into its design process: recommendersystems. As it has been reported by Manouselis et al. [5], there is a wide varietyof approaches for the design of a TEL recommender system, but none of thosepresents an explicit application of the learner’s affective state. Our hypothesis isthat the resources provided by this type of applications will be more beneficial forthe learner when her affective state takes part of the recommendation process.Thus, a frustrated learner might access a resource more suitable for her currentstateOur proposal to explore the inclusion of affective states in recommender sys-tems consists of a widget to be used in a Personalized Learning Environment1(PLE). The affective states are explicitly indicated by the learner, thus the de-tection of affective states is out of the objectives of the widget. The widgetarchitecture follows a client-server approach, where the widget plays the role ofthe client and a web-based recommender service plays the role of the server.Besides the affective state, the widget also considers the learning objectives ofthe learner in the recommendation process.The rest of the article is structured as follows. Section 2 describes the widget,its architecture and both of its components. Section 3 discusses the evaluationof the generated recommendations and future work.2 Learning Resources Affective RecommenderThe architecture supporting the Learning Resources Affective Recommender(LRAR) follows a client-server approach, where the widget and a recommenderweb service the role play the role of the client and server respectively.2.1 Recommendation serviceThe recommendation process follows the method known as collective intelligenceor collaborative filtering, and we have used the machine learning engine ApacheMahout for its implementation. Collaborative filtering can be item-based or user-based. When based on item similarity, a user is pointed to items that are relatedto the items already accessed by the user; a common example of this approachis the Amazon recommendation system. On the other hand, user-based collabo-rative filtering finds neighbors, users with similar patterns of resources accesses,and then points the user to resources relevant to her neighborhood. Henceforth,given our educational context we will refer to the users as learners and to theitems as resources.Our recommendation service is follows the user-based approach. The key el-ement in the process is the similarity function that identifies the neighbors ofa given learner. In our implementation, the similarity of two learners is propor-tional to the amount of resources accessed by the learners when indicating thesame affective state. The proportion of common learning objectives also affectsproportionally the similarity metric.The recommendation service also handles secondary but needed functionali-ties, such as the creation of a learner profile, the change of mood informed by thelearner and the addition of a new learning resource. For this, it was needed tomodel four entities and their respective relationships: affective states, learners,learning objectives and resources. The creation, edition and elimination of theseentities is available through a RESTful web API, using the JSON format.2.2 Description of the widgetThe widget has been developed on top of the ROLE Project [6], which aimsto provide the learner with a framework to build her Personalized Learning2Environment. The widget has been developed with JavaScript and HTML, andit follows the OpenSocial Gadget specification.Three different sections have been included in the widget: Resources, Profile,and Settings. Resources is the main tab and has to functions; first, it allows thelearner to state her affective state from a static list provided by the recommenda-tion service. Second, it provides a list of learning resources ordered by relevancefor the learner in her current state.The Profile tab presents a time-line of the affective states reported by thelearner. Its objective is to provide the learner with a visualization of her emo-tional changes during the learning activity being performed. The log of affectivestates is also provided by the learning resource service.Finally, the Settings tab allows the learner to set her learning objectives.These might be changed during the learning activity, which also triggers a changeof the learning resources that are recommended. Fig. 1 presents a screen cap-ture of the widget in action, with emphasis on the resources recommended to afrustrated learner.Fig. 1. Recommendations provided to a learner feeling frustrated.33 DiscussionThe Learning Resources Affective Recommender is our proposal to analyze theeffect of including affective information into the logic of a TEL recommendersystem. This approach allows learners to obtain recommendations based not onlyon their learning interests but also on their changes of affective states during alearning activity.Further work for LRAR is the evaluation of the recommendations generated,specially when compared with a system unaware of affective information. Ourplan to perform this evaluation is through the comparison of metrics used ininformation retrieval, like precision and recall.Another line of future work consists of the use of sensors as a complementto the affective state self-reported by the learner. We have special interest insensors for galvanic skin response and for the recognition of face gestures. Theinclusion of these sensors might provide affective states with higher accuracyand thus help to improve the recommendation process.AcknowledgmentWork partially funded by the EEE project, “Plan Nacional de I+D+I TIN2011-28308-C03-01”, the “eMadrid: Investigacio´n y desarrollo de tecnolog´ıas para ele-learning en la Comunidad de Madrid” project (S2009/TIC-1650), and “ConsejoSocial - Universidad Carlos III de Madrid”.References1. Arroyo, I., Cooper, D., Burleson, W., Woolf, B., Muldner, K., Christopherson, R.:Emotion sensors go to school. In: Proceeding of the 2009 conference on ArtificialIntelligence in Education: Building Learning Systems that Care: From KnowledgeRepresentation to Affective Modelling, IOS Press (2009) 17–242. D’Mello, S., Graesser, A., Picard, R.: Toward an affect-sensitive autotutor. Intelli-gent Systems, IEEE 22(4) (2007) 53–613. Lester, J., McQuiggan, S., Sabourin, J.: Affect recognition and expression innarrative-centered learning environments. New Perspectives on Affect and LearningTechnologies (2011) 85–964. Fessl, A., Rivera-Pelayo, V., Mu¨ller, L., Pammer, V., Lindstaedt, S.: Motivationand user acceptance of using physiological data to support individual reflection. In:2nd MATEL Workshop at European Conference for Technology Enhanced Learning(ECTEL 2011). (2011)5. Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., Koper, R.: Recommendersystems in technology enhanced learning. Recommender Systems Handbook (2011)387–4156. Consortium, R.: ROLE Project. http://www.role-project.eu/ (2009-2012) Lastvisited July 2012.4

A Widget to Recommend Learning Resources Based on the Learner Affective State

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A Widget to Recommend Learning Resources Based on the Learner Affective State

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