Survey on Pedagogical Resources Recommendation using Cognitive Computing Systems
Gabriel Leitão, Eduardo Valentin, Elaine Harada Teixeira de Oliveira, Raimundo Barreto · 2018
Taking into account that education supported by technology is a basic need for the citizen of a world in which the demand for computation and the access to information grow exponentially, this paper highlights the creation of cognitive computing systems that help people to acquire knowledge and to learn effectively within digital education environments. With this goal in mind, this paper summarizes the outcomes of a Systematic Mapping of Literature where the main aim was to identify what pedagogical approaches, methods, techniques, tools, and education activities have been used to recommend learning objects in the context of cognitive computing systems. We analyzed 348 papers from Scopus and Engineering Village digital libraries from which we selected 19 papers for data extraction. In order to increase the confidence of the proposed systematic review we calculated the Kappa Coefficient obtaining that the agreement was substantial (79.47%). From the data extracted we did several analysis. Concerning to pedagogical theories, 47.37% of papers presents a humanist approach and 26.32% a cognitivist approach, which shows one coherence with proposal of cognitive computing based on emotional and language processing. Most papers use natural-language, image or audio processing to detect emotion, attention and interactions to generate a user profile and, thus, to perform educational resource recommendation. Despite almost half papers do not indicate exactly the educational resources recommended, around 36% are textual materials and 45% are related to personalized exercises or interactive/games activities. Furthermore, we identified the spread use of analytical learning and cognitive computing for pedagogical activities recommendation. This paper ends up proposing new approaches and methods to educational recommendation for improving the students performance from interaction data in educational environments.