Collected Data

Survey

I Cannot See You—The Perspectives of Deaf Students to Online Learning during COVID-19 Pandemic: Saudi Arabia Case Study

The COVID-19 pandemic brought about many challenges to course delivery methods, which have forced institutions to rapidly change and adopt innovative approaches to provide remote instruction as effectively as possible. Creating and preparing content that ensures the success of all students, including those who are deaf and hard-of-hearing has certainly been an all-around challenge. This study aims to investigate the e-learning experiences of deaf students, focusing on the college of the Technical and Vocational Training Corporation (TVTC) in the Kingdom of Saudi Arabia (KSA). Particularly, we study the challenges and concerns faced by deaf students during the sudden shift to online learning. We used a mixed-methods approach by conducting a survey as well as interviews to obtain the information we needed. Our study delivers several important findings. Our results report problems with internet access, inadequate support, inaccessibility of content from learning systems, among other issues. Considering our findings, we argue that institutions should consider a procedure to create more accessible technology that is adaptable during the pandemic to serve individuals with diverse needs.


More specifically, the research questions that we investigated are:

RQ. What are the challenges and concerns that deaf and hard-of-hearing students are having with an online education during COVID-19 pandemic?

This RQ will guide this research by investigating the difficulties, challenges, and concerns of deaf students in the pandemic period. We will answer this question by exploring the students’ perspectives of TVTC college by interviews and survey investigating of the learning processes during COVID-19.


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If you are interested to learn more about the process we followed, please refer to our paper.

Related Paper

Wajdi Aljedaani, Mohamed Wiem Mkaouer, Stephanie Ludi, Ali Ouni, Ilyes Jenhani, "On the Identification of Accessibility Bug Reports in Open Source Systems", Published at the 19th International Web for All Conference (W4A’22). [preprint]

Eman Abdullah AlOmar, Wajdi Aljedaani, Murtaza Tamjeed, Mohamed Wiem Mkaouer, Yasmine N. El-Glaly, "Finding the Needle in a Haystack: On the Automatic Identification of Accessibility User Reviews", the international conference on Human-Computer Interaction (CHI'2021). [preprint]

Wajdi Aljedaani, Mohamed Wiem Mkaouer, Stephanie Ludi, Yasir Javed, "Automatic Classification of Accessibility User Reviews in Android Apps", Published at the 7th International Conference on Data Science and Machine Learning Applications (CDMA'22). [preprint]

Wajdi Aljedaani, Furqan Rustam, Stephanie Ludi, Ali Ouni, and Mohamed Wiem Mkaouer, "Learning Sentiment Analysis for Accessibility User Reviews", Published at the 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW'21) [preprint]