Recommendation of Learning Objects Applying Collaborative Filtering and Competencies
1. Recommendation of Learning
Objects Applying Collaborative
Filtering and Competencies
Authors:
Sílvio César Cazella (UFCSPA,UNISINOS)
Eliseo Berni Reategui (UFRGS)
Patricia Alejandra Behar (UFRGS)
WCC/IFIP 2010
2. Sumary
Challenge
Goals and contribuition
Competencies
Recommender System
Collaborative Filtering
Model
Prototype and experiments
Results
Conclusion
Future Work
WCC/IFIP 2010
3. Challenge
The greatest challenge with which every
educator faces is the organization of
content and activities aimed at the
development of certain competencies in
students.
This challenge is intensified when we try
to identify and recommend different
materials, customized to each student
based on individual needs, interests and
skills to be developed.
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4. Goal and Contribuition
This paper describes a model for
recommender systems that is able to
suggest learning objects relevant to
undergraduate students, focusing on
competencies to be developed in the
disciplines.
The main contribution of this paper is to
present this model and its implementation
and evaluation with a group of students.
WCC/IFIP 2010
6. Competencies
In all definitions, we can easily see the
relationship between the concept of competence
and skills (know hw), knowledge and attitudes.
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Within this research, therefore, the question
arises as to how, when and how we can make a
recommendation of learning objects that enable
students to:
build knowledge related to specific issues,
develop particular skills related to given contents,
develop in students a critical awareness about the
importance of competence to understand how and when
to use it.
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8. Recommender Systems
Recommender systems have emerged, focusing on
the search for relevant information in accordance
with User's own characteristics.
Different techniques are applied in recommender
systems to find the most appropriate content for
users. In this research we applied Collaborative
Filtering (CF).
Bob
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10. Prototype and experiments
A prototype of the model was developed in order to evaluate its
efficiency in making appropriate predictions.
Initially, some students were invited to participate in a few
experiments for the evaluation of learning objects (in this case
scientific papers) that were recommended by the system.
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11. Research Method
The evaluation of the prototype was made
through two experiments with a sample by
convenience (not probabilistic) of 10 students
at the end of the undergraduate course of
Computer Engineering.
Learning objects used to recommend papers
were selected by a specialist teacher in the
area, and were directly related to the
competencies to be developed in the
discipline of database.
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12. Experiments and results
The experiments had the following
objectives:
To evaluate whether the prediction rate
calculated by the prototype was able to
match or approximate to real students’
rates, using the evaluation metric MAE
(Mean Absolute Error);
To evaluate the accuracy of the
recommendations made by the system
through the metrics Recall (coverage)
and Precision (precision).
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13. 1º Experiment
Goal:
Evaluation of Pre-Selected Items
Description:
Students were then requested to evaluate
papers that had been allocated randomly;
Use the tool prototype;
Calculation of Pearson's coefficient.
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14. 1º Experiment: Results
27.59% of the computed correlations
between the students using Pearson's
coefficient were considered strong (these
students had "tastes" that were similar to
the objects evaluated);
20.69% were considered weak (these
students had " tastes "different from the
objects evaluated);
51.72% of the correlations computed,
nothing could be said.
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15. 2º Experiment
Goal:
Generating Predictions;
Description:
Performing the computation of the correlation values
and prediction of similarity;
The rules of competencies;
Recommendations to users;
Evaluation Metrics.
Sample 10 students
Likert scale of 5 points
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16. Results of the experiment concerning
Precision
16
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18. Conclusion
Through experiments with a group of
undergraduate students in Computer Engineering,
it was found that the degree of precision achieved
by the recommendations generated by the
prototype was satisfactory.
The accuracy of 76% showed that the system was
able to recommend learning objects that satisfied
the students for their studies, without neglecting
the competencies required in the summary of the
course during the semester.
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19. Conclusion
As for the evaluation metrics Precision and
Recall, it can be said that:
the prototype succeeded to get the students to
have access to those materials that were relevant
to the competencies to be developed in that
moment, within the set of learning resources
available.
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20. Future work
we intend to test the system with other types of
learning objects to verify if its performance remains
satisfactory, using information from the metadata of
learning objects to select them according to specific
requirements also related to competencies
development (e.g. level of difficulty, level of
interaction, etc.);
include the relevance of the opinion of a User to
complement the process of recommendation;
we are also working on the formation of virtual
communities that have a similarity coefficient within
an acceptable range.
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21. That is all folks!
Questions?
Thanks!!!
Contact: Silvio Cesar Cazella
cazella@unisinos.br
silvioc@ufcspa.edu.br
WCC/IFIP 2010
