# Recommending Learning Objects with Arguments and Explanations
## 3 Conversational Educational Recommender System (C-ERS)
### Recommendation Process  
C-ERS follows a hybrid recommendation technique that uses LOs _metadata_ and information extracted from the _student's profiles_ to compute the recommendations. Concretely, to represent LOs the proposed system follows the _IEEE-LOM_ standard (1484.12.1-2002--IEEE Standard for Learning Object Metadata: [[https://standards.ieee.org/findstds/standard/1484.12.1-2002.html](https://standards.ieee.org/findstds/standard/1484.12.1-2002.html)]([https://standards.ieee.org/findstds/standard/1484.12.1-2002.html](https://standards.ieee.org/findstds/standard/1484.12.1-2002.html))). These profiles provide personalized information about the students, their interactivity level, the language they prefer, some preferences about the format, their learning style ( which can be an auditory learning style, a kineesthetic learning style, a reader learning style, or a visual learning style), and their usage history (see Section 4). The learning styles have been obtained using the Spanish adaptation of the VARK questionnaire, available at [35]. This technique combines three types of recommender systems: one based on the content of the items being recommended, one based on comparing user's actions (called collaborative), and one based on the knowledge of the domain [36]. This technique models this expert knowledge in a logic program. The proposal of this work is to use a formalism based on defeasible argumentation (which is founded on logic programming, similar to _DeLP[37]_) to implement the logic of the recommendation system and build arguments to endorse the recommendation of selected Learning Objects. Thus, our C-ERS gives the users the Learning Objects that have better support by a greater number of arguments. A defeasible logic program \(P=(\Pi,\Delta)\) is modeled by strict knowledge (\(\Pi\)) and defeasible knowledge (\(\Delta\)) about the domain of the application. In C-ERS, \(\Pi\) represents a set of _facts_. These facts are strict inference rules with an empty body. In the example logic program of Table 1, lines 1 to 7 represent facts. The fact \(user\_type(alice,visual)\) represents a student named '_alice_' with a visual learning style. This learning style means that the student has a preference for learning objects with images or graphs such as png or jpeg, slides such as ppt, etc. Furthermore, the \(\Delta\) set depicts defeasible rules of the form \(P\gets Q_{1}\wedge\ldots\wedge Q_{k}\). The above rules encode the defeasible inference that literals \(Q_{1}\wedge\ldots\wedge Q_{k}\) can supply arguments to support the belief in \(P\). Several types of defeasible rules constitute the logic behind each of the recommendations of the system. A summary of these rules and their associated explanations (explanations are commented in Section 3.2) are shown in Table 2 (Due to readability reasons, the full set of rules are not provided). \begin{table}
\begin{tabular}{  
For instance, in Table 1, lines 8 to 13 represent _content-based rules_ that make recommendations for the student based on its profile. Then, an example of a rule that shows how the recommendation system chooses the learning object \(LO_{1}\) as the most suitable for _alice_ is _appropriate_interactivity_(_alice_, \(LO_{1}\)) \(\leftarrow\)_user_type_(_alice_, _visual_) \(\wedge\)_interactivity_type_(\(LO_{1}\), _low_). This is due to the fact that _alice_ has a visual learning style and \(LO_{1}\) has been labeled as a learning object with low interactivity, which fits with a learning style of type visual.