Towards a context-aware personalized formative assessment in a collaborative online environment

Assessment is a critical component of any teaching-learning process. It is the only way to gain a better understanding of the knowledge and abilities of the learners, especially in a collaborative learning environment. The learner must pay special attention to interactions and real-world context when selecting collaborative situations and activities. As a result, it is critical to ensure that assessment methods and techniques are adapted so that the learning process is better suited to each group of learners, with their unique learning style, background, needs, previous experiences, and most importantly, context. This paper addressed two major issues in the development of a personalized formative assessment system that will be used to assess the knowledge, skills, and competencies of individual and group learners in an online collaborative learning environment. One approach is to design an adaptive assessment path that helps learners achieve specific learning goals based on previous learner performance, level, and context. The other is to make a personalized recommendation using a Context-Aware Recommender System (CARS), which monitors group formation while performing collaborative assessment activities. To formalize and well define our proposed system, we suggest a semantic web approach using ontologies and eLearning standards, CMI5 to describe the proposed assessment path, and IEEE PAPI for the learner model, all of which allow data reusability and interoperability.