CASA: An Architecture to Support Complex Assessment Scenarios

Abstract

The evaluation or assessment of student performance and knowledge is a central element of most instructional design models, as it provides the information required to take remediation actions and improve the learning process. However, the assessment contexts present such a diverse range of cases due to course, teacher and student idiosyncrasies that it is difficult to support all the possibilities via software. To deal with this problem, this paper presents an architecture that satisfies the requirements to support complex assessment scenarios, those that potentially involve different evaluators using diverse assessment methods, tools and techniques at the same time. An analysis of the literature and the assessment scenarios of courses from Computer Engineering grades was carried out to identify the requirements for systems that support such complex assessment scenarios and to infer the information needed to support them. This information was modeled and formalized in the Complex Assessment Scenarios Architecture (CASA) that comprises functional modules, knowledge bases and the relationships among them. Following this architecture, an assessment system (AdESMuS) was developed using agile methodologies. AdESMuS has been satisfactorily tested under different complex assessment scenarios proving to be able to support those scenarios' requirements. This paper presents CASA, the architecture for developing a system to support complex assessment scenarios along with the satisfactory case study of AdESMuS.INDEX TERMS Software architecture, data models, knowledge management, learning management systems, requirements management, software tools.

Achievements and challenges in learning analytics in Spain: The view of SNOLA

“…In this order, one category of the research works reported in this study is devoted to the definition of new data sources for LA (see Table 7). These works explore the use of human-generated data like grades (Villamañe, Alvarez, & Larrañaga, 2020) or information provided by teachers in the learning design (Rodríguez-Triana, Martínez-Monés, Asensio-Pérez, & Dimitriadis, 2015); biometric signals captured with sensors (de Arriba-Pérez, Caeiro-Rodríguez, & Santos-Gago, 2018); or multimodal data including both self-reported and sensor-based data (Vujovic & Hernández-Leo, 2019). Some of these works are exploratory, and are aimed at identifying indicators for learning based on new data sources or at defining better learner models.…”