Continuous improvement of deaf student learning outcomes based on an adaptive learning system and an Academic Advisor Agent. (March 2019)

• Record Type: Journal Article
• Authors: Hammami, Salah; Saeed, Fahman; Mathkour, Hassan; Arafah, Mohammed Amer
• Is Part Of: Computers in human behavior. Volume 92(2019)
• Page Start: 536
• Page End: 546
• Publication Date: 2019-03
• Subjects: e-assessment -- Students learning outcomes -- Adaptive learning technologies -- Multi-agents; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2017.07.006
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: This paper presents a development of an adaptive e-learning system to measure deaf student progress and interaction, and to enhance their. Enhancements are based on several recent technologies, including Bilingual/Bicultural methodologies, and adaptive learning, multi-agent, and mobile technologies. Bilingual/Bicultural methodology is the new trends in teaching for deaf students. The main language for the deaf would be the sign language while the second language is the common (official) one. Our system is based on a specific set of defined skills. These skills are usually referred to as Deaf Students Learning Outcomes (DSLO). To evaluate the Deaf Students Learning Outcomes results, we define an Academic Advisor Agent. The objective of the Academic Advisor is to monitor the students' achievements of the learning program and to provide them with advices to better achieve the program Student Outcomes. Considering the DSLO levels rather than the course grades level allows the Academic Advisor Agent to: (i) assess the progress achieved by the deaf student at the end of every period, (ii) identify any learning obstacles at an early stage, and (iii) continuously propose learning activities and learning targets that are more fitting to the learner weaknesses. The adaptive e-learning system is further improved with mobile technology to allow easy accessibility to the learning materials. Highlights: Adaptive e-learning system that uses several recent technologies. e-learning architecture using multi-agent technology. Evaluation of students' achievement learning outcomes. Continuous improvement of student learning outcomes. Academic advisor agent for evaluation of students' achievements.