ASSA: Adaptive E-Learning Smart Students Assessment Model

Abstract

Adaptive e-learning can be improved through measured e-assessments that can provide accurate feedback to instructors. E-assessments can not only provide the basis for evaluation of the different pedagogical methods used in teaching and learning but they also can be used to determine the most suitable delivered materials to students according to their skills, abilities, and prior knowledge's. This paper presents the Adaptive Smart Student Assessment (ASSA) model. With ASSA instructors worldwide can define their tests, and their students can take these tests on-line. ASSA determines the students' abilities, skills and preferable Learning Style (LS) with more accuracy and then generates the appropriate questions in an adaptive way then presents them in a preferable learning style of student. It facilitates the evaluation process and measures the students' knowledge level with more accuracy and then store it in the student's profile for later use in the learning process to adapt course material content appropriately according to individual student abilities.

Learner modeling in cloud computing

“…Previous work in the literature has presented different parameters. For example, ASSA (Aljohany et al 2018) allowed to generate the appropriate questions in an adaptive way according to the learner's level of knowledge, the learning goals and the learning style. Another example is AES (Siddique et al 2018), who used the Felder silverman learning style to offer learning courses adapted to the learners' needs.…”

“…Previous work has utilized different techniques (Aljohany et al 2018;Ahmed et al 2017) to create and generate an interoperable and reliable learner model. In addition, classic personalized systems (Yang et al 2013;Paavolainen 2016) may represent the learner model in a reusable way.…”

“…The learner model is an essential component of personalized learning systems. It contains personalization parameters that define the characteristics and needs of learners, such as interests, preferences, and levels of knowledge, goals, tasks, backgrounds, learning performances, learning styles, aptitudes and environments, as well as other useful features (Aljohany et al 2018). In order to achieve the personalization of elearning courses, learning systems use different personalization parameters.…”