The document proposes a hybrid recommendation approach for educational resource repositories that combines personalized and diversity-aware strategies. It includes a reactive strategy that combines long-term and short-term learning goals, a proactive strategy that fosters diversity, and a collaborative recommendation strategy. It also discusses the required knowledge including a domain ontology, learning objects, student profiles, and preference data. Finally, it outlines open issues regarding evaluating recommender systems in educational domains.

1. Personalized and diversity-aware recommendation
strategies for educational resources
Almudena Ruiz Iniesta
Mercedes Gómez Albarrán
Guillermo Jiménez Díaz
Dpt. of Software Engineering and Artificial Intelligence
Complutense University of Madrid

2. Outline
 Motivation
 Required knowledge
 Cascade hybrid approach
 The reactive strategy: combining long-term and short-term learning goals
 A proactive strategy that fosters diversity
 A collaborative recommendation strategy
 Open Issues
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources

3. Outline
 Motivation
 Required knowledge
 Cascade hybrid approach
 The reactive strategy: combining long-term and short-term learning goals
 A proactive strategy that fosters diversity
 A collaborative recommendation strategy
 Open Issues
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources

4. Motivation
 The development of electronic repositories for educational resources
has been intensified
 High number of Learning Objects (LOs) exists in these repositories
 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
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources

5. Motivation
Personalization
Cascade Hybrid Recommender Reactive Proactive
strategy strategy
Diversity
Case-based strategy
Collaborative
strategy
Collaborative strategy Learning community
opinion
 Obtain recommendations even when
 a LO has only few ratings
 a student is new in the repository
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources

6. Outline
 Motivation
 Required knowledge
 Cascade hybrid approach
 The reactive strategy: combining long-term and short-term learning goals
 A proactive strategy that fosters diversity
 A collaborative recommendation strategy
 Open Issues
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources

7. Required knowledge: domain ontology
 The ontology is populated with concepts in the field of study
 Ontologies provide a general indexing scheme that lets include
similarity knowledge between the concepts representing the
domain topics
 The ontology should also establish a precedence property among
the concepts
 The precedence helps to establish the learning paths
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources

8. Required knowledge: Learning Objects
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources

9. Required knowledge: student profile
 The goals achieved in the learning process
 Concepts that the student should know and the mastery level
achieved in each of them
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources

10. Required knowledge: preference repository
 The preference repository
 Rating scores explicitly assigned by the students to each LO
 The profile that the student had when she rated the LO
 Evolving nature of the profile
 The collaborative recommendation strategy looks for students with similar
mastery level
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources

11. Outline
 Motivation
 Required knowledge
 Cascade hybrid approach
 The reactive strategy: combining long-term and short-term learning goals
 A proactive strategy that fosters diversity
 A collaborative recommendation strategy
 Open Issues
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources

12. Cascade First-stage: Case-based strategies
 Two alternative, each of them Reactive Proactive
satisfies an essential strategy strategy
requirement of recommenders
of educational resources
Collaborative
 Personalization strategy
 Reactive strategy: combining long-
term and short-term learning goals
 Overspecialization
 Proactive strategy that fosters
diversity
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources

13. The reactive strategy: combining long-term and short-term
learning goals
Student query
{C1,1; C2,1… }
Retrieve LOs that
cover same or
similar concepts
Retrieval step Filter LOs
not ready to be
explored
Rank according to
Ranking step the LO Quality
Ranked list of
recommended
LOs
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources

14. The reactive strategy: combining long-term and short-term
learning goals
 The retrieval step
 Concepts “ready to be explored”
 Concept already explored by the student  competence level
 It is a concept that the student has not explored yet but she can discover it
 if a concept c1 precedes a concept c2 in the ontology, a student can discover c2
if the student competence level for c1 exceeds a given “progress threshold”
Concept unreachable
Concepts already explored Concept ready to be
discovered
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources

15. The reactive strategy: combining long-term and short-term
learning goals
 The ranking step
 Quality: weighted sum up of two relevancies
 Similarity between the query concepts and the concepts that the LO covers
 Pedagogical utility
 Strategy that promotes filling the student’ knowledge gaps
 High values of PU indicates that the student has a poor knowledge of most of
the concepts covered by the LO
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources

16. A proactive strategy that fosters diversity
Propose a set of LOs according
to the student profile
Do it
Student END proactive
selects a LO recommendation
More like this
Refine the recommendation
according to the student feedback
Yes Can refine the No
recommendation?
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources

17. A proactive strategy that fosters diversity
 Navigation-by-proposing
 Diversity  Select LOs from different partitions
in the space of LOs
 First stage
 Reinforce vs. Discovering
 Ranking
 Priority to LOs that maximize the number of
covered concepts
Reinforce Discover
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources

18. A proactive strategy that fosters diversity
 Second stage
 Breadth-first traversal of the taxonomy
 One partition for each concept in the current level
 Ranking
 Priority to LOs that maximize the number of covered concepts
 In case of reinforcement, clusters in which the student has shown a low
mastery level
 Repeat until leaves are reached
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources

19. Cascade Second-stage: A collaborative recommendation
strategy
 Select the LOs provided by the case-based
recommender with higher interest according to
the opinions of other members of the community
 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
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources

20. Outline
 Motivation
 Required knowledge
 Cascade hybrid approach
 The reactive strategy: combining long-term and short-term learning goals
 A proactive strategy that fosters diversity
 A collaborative recommendation strategy
 Open Issues
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources

21. Open Issues
 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
 Our evaluation
 Goal-Questions Metrics method 
 Analyzing the usability of the repository
 Analyzing students’ grades
 Analyzing the impact of different recommendation strategies
 Repository of LOs for Computer Programming
 Basili VR, Rombach HD. The TAME project: towards improvement-oriented software environments. Software Engineering,
IEEE Transactions on. 1988;14(6):758-773
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources

22. Thank you!
Personalized and diversity-aware recommendation
strategies for educational resources
Almudena Ruiz-Iniesta
almudenari@fdi.ucm.es
http://gaia.fdi.ucm.es/people/almudena
RecSys 2010 - Doctoral Symposium: Personalized and diversity-aware recommendation strategies for educational resources