Personalized assessment model for alphabets learning with learning objects in e-learning environment for dyslexia

Development of an intelligent system based on metaverse learning for students with disabilities

Due to the coronavirus-2019 pandemic, people have had to work and study using the Internet such that the strengthened metaverse has become a part of the lives of people worldwide. The advent of technology linking the real and virtual worlds has facilitated the transmission of spatial audio and haptics to allow the metaverse to offer multisensory experiences in diverse fields, especially in teaching. The main idea of the proposed project is the development of a simple intelligent system for meta-learning. The suggested system should be self-configurable according to the different users of the metaverse. We aimed to design and create a virtual learning environment using Open Simulator based on a 3D virtual environment and simulation of the real-world environment. We then connected this environment to a learning management system (Moodle) through technology for 3D virtual environments (Sloodle) to allow the management of students, especially those with different abilities, and followed up on their activities, tests, and exams. This environment also has the advantage of storing educational content. We evaluated the performance of the Open Simulator in both standalone and grid modes based on the login times. The result showed times the standalone and grid modes of 12 s and 16 s, which demonstrated the robustness of the proposed platform. We also tested the system on 50 disabled learners, according to the t-test of independent samples. A test was conducted in the mathematics course, in which the students were divided into two equal groups (n = 25 each) to take the test traditionally and using the chair test tool, which is one of the most important tools of the Sloodle technology. According to the results, the null hypothesis was rejected, and we accepted the alternative hypothesis that demonstrated a difference in achievement between the two groups.

Understanding student perceptions of social computing and online tools to enhance learning

Social computing software and online tools are gaining credence in teaching and learning spaces, including higher education contexts. However, the adoption of social computing software does not automatically translate into effective teaching and learning if students’ views and needs are not considered along with course learning outcomes. Thus, this study was conducted to explore students’ perspectives and preferences for social computing software and online tools in a university elective course. We employed quantitative and qualitative approaches to understand students’ shared and nuanced thoughts about social computing applications in the study. A questionnaire with quantitative and open-ended qualitative questions was used to collect data. Data were analyzed using descriptive statistics and an inductive thematic analysis approach. Our findings indicated mixed students’ viewpoints, with some tools reported as highly beneficial while others were less beneficial. For instance, students valued asynchronous sessions, assignment feedback, online discussion, videos, and gamification but reported less interest in phones, journals, icons, and blogs. Students’ specializations also appeared to influence their choice of tools. Those from Arts, Humanities, and Social Sciences expressed a slightly different preference than their comparts from Medicine and Health Sciences. Drawing on the findings, we discuss the implications for effective teaching and learning using social computing software, focusing on essential stakeholders. For instance, instructors must regularly conduct diagnostic feedback to determine appropriate tools that can effectively customize students’ learning.