An educational recommender system based on argumentation theory

Rodríguez, Paula; Heras, Stella; Palanca, Javier; Poveda, Jhon M.; Duque, Néstor D.; Julián, Vicente

doi:10.3233/aic-170724

Cited by 15 publications

(20 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Studies comparing different recommendation techniques most often concluded by focusing on the proposed approach that obtained better results (Albatayneh, Ghauth, & Chua, 2018;Niemann & Wolpers, 2015). The studies comparing hybridization methods shone a light on the best hybridization techniques (Rodríguez et al, 2017;Zheng et al, 2015). Finally, some articles concluded by emphasizing the performance of the proposed approach, and solutions to problems encountered, like the problem of sparsity (Tadlaoui, Sehaba, George, Chikh, & Bouamrane, 2018), which is caused by a lack of sufficient information to identify similar users (Dascalu et al, 2015).…”

Section: Results Of the Experimentsmentioning

confidence: 99%

Recommender systems to support learners’ Agency in a Learning Context: a systematic review

Deschênes

2020

Int J Educ Technol High Educ

View full text Add to dashboard Cite

Recommender systems for technology-enhanced learning are examined in relation to learners’ agency, that is, their ability to define and pursue learning goals. These systems make it easier for learners to access resources, including peers with whom to learn and experts from whom to learn. In this systematic review of the literature, we apply an Evidence for Policy and Practice Information (EPPI) approach to examine the context in which recommenders are used, the manners in which they are evaluated and the results of those evaluations. We use three databases (two in education and one in applied computer science) and retained articles published therein between 2008 and 2018. Fifty-six articles meeting the requirements for inclusion are analyzed to identify their approach (content-based, collaborative filtering, hybrid, other) and the experiment settings (accuracy, user satisfaction or learning performance), as well as to examine the results and the manner in which they were presented. The results of the majority of the experiments were positive. Finally, given the results introduced in this systematic review, we identify future research questions.

show abstract

Section: Results Of the Experimentsmentioning

confidence: 99%

Recommender systems to support learners’ Agency in a Learning Context: a systematic review

Deschênes

2020

Int J Educ Technol High Educ

View full text Add to dashboard Cite

show abstract

“…The experiments evaluated the effectiveness of C-ERS to provide recommendations that suit the students' profile and learning objectives. The advantages of the argumentation-based recommendation over other approaches in this domain, such as content-based, collaborative filtering, and knowledge-based, was previously demonstrated in our previous work [4]. Therefore, we do not intend to evaluate the whole learning process of students, as proposed, for instance, in Kirkpatrick's model [40], which tries to evaluate the learning process from reaction, to learning, to behavior, and to organisational performance.…”

Section: Resultsmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

Recommending Learning Objects with Arguments and Explanations

et al. 2020

Self Cite

View full text Add to dashboard Cite

The massive presence of online learning resources leads many students to have more information than they can consume efficiently. Therefore, students do not always find adaptive learning material for their needs and preferences. In this paper, we present a Conversational Educational Recommender System (C-ERS), which helps students in the process of finding the more appropriated learning resources considering their learning objectives and profile. The recommendation process is based on an argumentation-based approach that selects the learning objects that allow a greater number of arguments to be generated to justify their suitability. Our system includes a simple and intuitive communication interface with the user that provides an explanation to any recommendation. This allows the user to interact with the system and accept or reject the recommendations, providing reasons for such behavior. In this way, the user is able to inspect the system’s operation and understand the recommendations, while the system is able to elicit the actual preferences of the user. The system has been tested online with a real group of undergraduate students in the Universidad Nacional de Colombia, showing promising results.

show abstract

“…In the hybrid RS of (Bedi and Vashisth 2015), recommendations are repaired using argumentation to align more closely with a user's preferences, giving adaptive recommendations. In (Rodríguez et al 2017), a hybrid, rule-based RS uses argumentation to differentiate between different techniques for generating recommendations, which is shown to outperform other hybridisation techniques. Meanwhile, a formalisation for explanations based on Toulmin's model of argumentation (Toulmin 1958) is given in (Naveed, Donkers, and Ziegler 2018), before being examined thoroughly with user studies showing that different levels of argumentation explanation are most acceptable for different users, an important motivation for our work.…”

Section: Related Workmentioning

confidence: 99%

Argumentation as a Framework for Interactive Explanations for Recommendations

Rago

Cocarascu

Bechlivanidis

et al. 2020

Proceedings of the Seventeenth International Conference on Principles of Knowledge Representation and Reasoning

View full text Add to dashboard Cite

As AI systems become ever more intertwined in our personal lives, the way in which they explain themselves to and interact with humans is an increasingly critical research area. The explanation of recommendations is thus a pivotal functionality in a user’s experience of a recommender system (RS), providing the possibility of enhancing many of its desirable features in addition to its effectiveness (accuracy wrt users’ preferences). For an RS that we prove empirically is effective, we show how argumentative abstractions underpinning recommendations can provide the structural scaffolding for (different types of) interactive explanations (IEs), i.e. explanations supporting interactions with users. We prove formally that these IEs empower feedback mechanisms that guarantee that recommendations will improve with time, hence rendering the RS scrutable. Finally, we prove experimentally that the various forms of IE (tabular, textual and conversational) induce trust in the recommendations and provide a high degree of transparency in the RS’s functionality.

show abstract

An educational recommender system based on argumentation theory

Cited by 15 publications

References 24 publications

Recommender systems to support learners’ Agency in a Learning Context: a systematic review

Recommender systems to support learners’ Agency in a Learning Context: a systematic review

Recommending Learning Objects with Arguments and Explanations

Argumentation as a Framework for Interactive Explanations for Recommendations

Contact Info

Product

Resources

About