# EduTVA: Metadata Schema for Educational Audiovisual Contents in Digital Television Environments
## 1 Introduction  
During the COVID-19 pandemic, the educational model faced unprecedented changes due to the lockdown restrictions established by different countries and territories around the world. Several educational institutions moved to online learning solutions for enabling remote communication between teachers and learners and continuing the education programs during the pandemic. Moreover, online education platforms (e.g., Coursera, EdX) offered access to most of their courses as Massive Open Online Courses (MOOC) [1]. However, people with fewer financial resources and from rural areas (particularly, in third-world countries) had difficulty accessing these online resources due to either the lack of or a poor Internet connection at home [2]. Television, on the other hand, facilitates a more widespread communication technology with easier access than the Internet. For decades, television has played an educational role for the mass audience through the broadcast of films, documentaries, news, and educationalprograms [3; 4]. Its nature as an entertainment medium has been particularly important for permanent and informal learning [5; 6]. In fact, interactive digital television has incorporated educational video content (such as from MOOCs) to promote the communication and interaction for knowledge, entertainment, and recreation in distance education. Digital television then requires description structures, called metadata schemas, to organize, represent, and recover the audiovisual content [7]. If providers cannot find their own content, it is as if they lack it; if end-users cannot find the provider's content, it is nonexistent for them [8]. Metadata schemas implement metadata and a neutral language (e.g., E-learning standards). Metadata provides information that describes, identifies, explains, and defines a resource for facilitating its recovery, usage, and management [7]. Neutral language establishes the value of such metadata and describes all the components of a particular resource using a representation that any actor in the television value-chain (i.e., content creator, content aggregator, broadcaster, end-user) understands. Existing metadata schemas for audiovisual content rely on either educational metadata standards [9; 10; 11], multimedia metadata standards [12], or both educational and multimedia metadata standards [13; 14]. However, these metadata schemas lack different elements for fully describing audiovisual educational content [15], particularly from a pedagogical perspective. Moreover, metadata schemas based on educational standards disregard the segmentation property of the television content, restricting the description of audiovisual content that includes one or more educational segments. Note that audiovisual television content can include different educational topics during the transmission, therefore, segmentation enables learning services to reuse a larger quantity of educational resources. This paper proposes a metadata schema, called Educational TV-Anytime (EduTVA), which allows describing educational audiovisual content and educational segments of the content for the digital television environment. EduTVA extends TV-Anytime [16], a metadata schema for digital television, by adding educational metadata derived from a revision and mapping of various E-learning metadata schemas. We evaluated both the acceptance and the functionality of the EduTVA metadata schema. The acceptance evaluation involved implementing a metadata authoring tool and surveying a group of users that described educational content with the EduTVA metadata provided by the tool. The functionality assessment validated the educational content obtained from a digital television search service that we also implemented. Results show that EduTVA allows detailed descriptions of educational content through specific metadata and the creation of segments, enabling a more accurate search of educational content than with TV-Anytime. The remainder of this paper is structured as follows.