Personalized and diversity-aware recommendation strategies for educational resources
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Transcript of Personalized and diversity-aware recommendation strategies for educational resources
A proactive strategy that fosters diversityThe reactive strategy: combining long-term and short-term learning goals
Personalized and diversity-aware recommendation strategies for educational resources
Almudena Ruiz Iniesta, Mercedes Gómez Albarrán and Guillermo Jiménez Díaz
Department of Software Engineering and Artificial Intelligence – Computer Science School – Complutense University of Madrid
Acknowledgments
Supported by: Spanish Ministry of Science and Education under grant TIN2009-13692-C03-03; and Complutense University of Madrid and BSCH under grant 921330-1079 for consolidated Research Groups.
Collaborative strategy
Case-based strategy
Reactive strategy Proactive strategy
Collaborative strategy
Cas
cade
Hyb
rid
Rec
omm
ende
r
Personalization
&
Diversity
Learning communityopinion
The development of electronic repositories with high number of educational resources has been intensified
Recommender systems support users in pre-selecting information they may be interested in
We propose a recommendation approach for repositories of LOs that adapts to the student learning profile
Motivation
Required knowledgeDomain ontology
Domain concepts
Precedence property among the concepts
Learning Objects
Cascade Hybrid Recommender
Retrieve LOs thatcover same orsimilar concepts
Filter LOs not readyto be explored
Rank according tothe LO Quality
Retrieval step
Ranking step
Student query{if, for,…}
Ranked list of recommended
LOs
Student profile
The goals achieved in the learning process
Mastery level achieved in each concept
Preference repository
Rating scores explicitly assigned by the students to each LO
The profile that the student had when she rated the LO
Diversity Select LOs from different partitions in thespace of LOs
First stage
Reinforce Discover
Recommender systems in the learning domain impose new challenges in theevaluation process
More important to measure the impact of the recommender in the final user
Goal-Questions Metrics method
Analyzing the usability of the repository
Analyzing students’ grades
Analyzing the impact of different recommendation strategies
Basili VR, Rombach HD. The TAME project: towards improvement-oriented software environments. Software Engineering, IEEE Transactions on. 1988;14(6):758-773
Open Issues
Collaborative strategy
Second stage
Repeat until leaves are reached
User-based nearest neighbour
Neighbourhood formation
candidates: students who rated the LOs proposed by the case-based recommender
similarity between the target student profile and the profile that the neighbour had when she rated the LO
Rating prediction and top-k selection
Repository of LOs for Computer Programming