# Recommending Learning Objects with Arguments and Explanations
## 1 Introduction  
Nowadays, the increasing number of learning resources available online has encouraged the massive development of Technology Enhanced Learning systems (TEL) [1]. TEL systems make education accessible to anyone, regardless of where they live. Using this technology, many universities now offer Massive Online Open Courses (MOOCs) and courses in a flipped classroom format [2]. This new teaching methodology is based on the use of TEL systems, which offer different types of learning objects (LO, e.g., videos, tutorials) that students can consume online in a non-classroom setting, and thus classroom sessions can be used to resolve exercises and doubts, group work, and other activities that require the direct presence of the teacher. However, this massive availability of LOs brings an overload problem, since students must be able to select the most suitable LOs for their learning objectives from a large number of available LOs. This opens new challenges for the research community on recommender systems, where Educational Recommender Systems (ERS) is now a trending topic. By using ERS, studentscan efficiently get those LOs that are most appropriate to their educational level, learning objectives, and learning style. The student's learning style determines which LOs are more adequate for particular types of students. For instance, the widely applied VARK model ([[http://vark-learn.com/](http://vark-learn.com/)]([http://vark-learn.com/](http://vark-learn.com/))) identifies four distinct learning styles (i.e., Visual, Auditory, Reading, and Kinesthetic) and uses these dimensions to determine the appropriateness of specific types of online learning resources. For instance, visual learners rely more on diagrams, charts or graphs, while kinesthetic students learn best from simulations, videos and movies of'real' things. Therefore, it is important to consider students' learning styles while developing online learning systems [3] and, consequently, educational recommender systems. In [4], an ERS was presented that helps undergraduate students to find the LOs that are more suitable for them, taking into account their profile (level of education, preferred language, topic and format, learning style, and other personal information), learning practices (LOs that they already used), and similarity with other students. The system is a hybrid recommendation engine that combines computational argumentation with other several recommendation techniques (collaborative, content-based, and knowledge-based). This technique, which selects those LOs whose recommendation can be better supported by arguments, demonstrated being the most effective for this application. A detailed description of the technique and the different recommender algorithms can be found in [5]. The system offers a straightforward way of generating textual explanations from arguments. These arguments capture common inferences of human reasoning. Thus, while arguments are formal representations for the justification of each recommendation (as elements of an argumentation theory), explanations are textual representations of these arguments (as elements of the user interface). These explanations are used to convince students to accept the recommendations of the system and make use of the LO recommended. However, online education is a tricky domain where learners' preferences can be highly time-variant. In addition, some students may have preferences that contradict the recommendations of learning theories (for example, because of their usual study practices). These acquired study habits are difficult to modify and students can quickly leave the ERS if they do not find it useful and effective for their learning objectives. Learners can even dynamically change their learning preferences or exhibit ad-hoc ones for specific topics or LOs. Conversational recommender systems are particularly effective for preference elicitation through question-asking and recommendation critiquing [6; 7].