Personalized and diversity-aware recommendation strategies for educational resources

A proactive strategy that fosters diversity The 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 Cascade Hybrid Recommender Personalization & Diversity Learning community opinion 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 knowledge Domain ontology Domain concepts Precedence property among the concepts Learning Objects Cascade Hybrid Recommender Retrieve LOs that cover same or similar concepts Filter LOs not ready to be explored Rank according to the 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 the space of LOs First stage Reinforce Discover Recommender systems in the learning domain impose new challenges in the evaluation 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

Upload
almudena-ruiz
Category

Education
view
124
download
1

Embed Size (px):

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

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