Stephanie Ludi’s research while affiliated with University of North Texas and other places

Integrating artificial intelligence (AI) has become crucial in modern mobile application development. However, the current integration of AI in mobile learning applications presents several challenges regarding mobile app usability. This study aims to identify critical usability issues of AI-enabled mobile learning apps by analyzing user reviews. We conducted a qualitative and content analysis of user reviews for two groups of AI apps from the education category - language learning apps and educational support apps. Our findings reveal that while users generally report positive experiences, several AI-related usability issues impact user satisfaction, effectiveness, and efficiency. These challenges include AI-related functionality issues, performance, bias, explanation, and ineffective Features. To enhance user experience and learning outcomes, developers must improve AI technology and adapt learning methodologies to meet users’ diverse demands and preferences while addressing these issues. By overcoming these challenges, AI-powered mobile learning apps can continue to evolve and provide users with engaging and personalized learning experiences.

Accessibility is a required quality for websites today, and several tools exist to test for this quality. These tools are highly advantageous, but sadly they also have some limitations. A particular set of challenges they face is in the evaluation of Rich Internet Applications (RIAs). In this paper, we carry out an experiment to compare and analyze different accessibility testing tools as they evaluate 10 educational websites. We judged these tools based on their error detection, guideline coverage, speed, similarity to one another, and their relative performance when evaluating RIAs. The experiment findings revealed the strength and limitations of each tool. The results of this experiment also exposed that there are many guidelines and success criteria that accessibility testing tools are not able to cover, and that some evaluation tools are similar to each other in terms of the results they produce. Lastly, this experiment highlights a discrepancy in the behavior of the tools when evaluating RIAs compared to when evaluating static websites, although some more than others. This experiment has some limitations which we presented. As a future work, we intend to work with an expert to determine the accuracy of the results produced from the experiment. We also intend to delve deeper into the limitations of these tools and come up with possible solutions.KeywordsAccessibilityAccessibility evaluation toolsOnline educationWeb Content Accessibility Guideline

With the coronavirus (COVID-19) outbreak, educational systems worldwide were abruptly affected and hampered, causing nearly total suspension of all in-person activities in schools, colleges, and universities. Government officials prohibited the physical gatherings in educational institutions to reduce the spread of the virus. Therefore, educational institutions have aggressively shifted to alternative learning methods and strategies such as online-based platforms—to seemingly avoid the disruption of education. However, the switch from the face-to-face setting to an entirely online setting introduced a series of challenges, especially for the deaf or hard-of-hearing students. Various recent studies have revealed the underlying infrastructure used by academic institutions may not be suitable for students with hearing impairments. The goal of this study is to perform a literature review of these studies and extract the pressing challenges that deaf and hard-of-hearing students have been facing since their transition to the online setting. We conducted a systematic literature review of 34 articles that were carefully collected, retrieved, and rigorously categorized from various scholarly databases. The articles, included in this study, focused primarily on highlighting high-demanding issues that deaf students experienced in higher education during the pandemic. This study contributes to the research literature by providing a detailed analysis of technological challenges hindering the learning experience of deaf students. Furthermore, the study extracts takeaways and proposed solutions, from the literature, for researchers, education specialists, and higher education authorities to adopt. This work calls for investigating broader and yet more effective teaching and learning strategies for deaf and hard-of-hearing students so that they can benefit from a better online learning experience.

Some jargon can be offensive to newcomers and unintentionally shape our thoughts.

The modern era is about everything that can be handled virtually in human life, such as online banking, education, security, job, etc. This increase in technology use also makes it easy for a scammer to loot people and make money quickly. A popular scam nowadays is fake job advertisements. People apply for these fake job vacancies, pay application fees to scammers, send their data to the scammers, and end up with a scam and waste their money. For this purpose, we proposed a methodology that uses natural language processing and supervised machine learning techniques to detect fraudulent job ads from online recruitment portals. We used two feature extraction techniques to extract the features from data: Term Frequency-Inverse Document Frequency (TF-IDF) and Bag-of-Words (BoW). In the study, we used six machine learning models to analyze whether these job ads are fraudulent or legitimate. Then, we compared all models with both BoW and TF-IDF features to analyze the classifier’s overall performance. One of the challenges in this study is our used dataset. The ratio of real and fake job posts samples is unequal, which caused the model over-fitting on majority class data. To overcome this limitation, we used the adaptive synthetic sampling approach (ADASYN), which help to balance the ratio between target classes by generating the number of sample for minority class artificially. We performed two experiments, one with the balanced dataset and the other with the imbalanced data. Through experimental analysis, ETC achieved 99.9% accuracy by using ADASYN as over-sampling ad TF-IDF as feature extraction. Further, this study also performs an in-depth comparative analysis of our proposed approach with state-of-the-art deep learning models and other re-sampling techniques.

Existing programming environments pose a challenge for students with upper-body motor impairments. This is because these environments require a level of dexterity that these students do not possess. For example, text-based programming environments require a lot of typing using a keyboard, while block-based programming environments require the use of a pointing device to drag and drop blocks of code. In our research, we aim to make the block-based programming environment Blockly, accessible to students with upper-body motor impairment, by adding speech as an alternative form of input. This voice-enabled version of Blockly will reduce the need for the use of a mouse or keyboard, hence making it more accessible. Our system consists of the original Blockly application, a speech recognition API, predefined voice commands, and a custom function. A preliminary study has been conducted. The results are encouraging, but they also revealed the need to broaden the target population, which was originally people with cerebral palsy, to people with any type of upper-body motor disability. Additionally, the study showed the need to redesign some voice commands. A prototype of the system has been implemented. As a next step, two additional studies will be conducted using this prototype, a usability study, and an A/B test.

Accessibility issues with programming languages and programming environments pose a major barrier for students with visual impairments to participate in computing related courses as well as threatens the productivity of professional programmers with visual impairments. To remedy this, the past two decades have witnessed an increase in accessibility research designed to investigate and address the challenges faced by people with visual impairments while programming or learning how to program. We conducted a literature review of accessibility research in this domain. The aim was to identify, aggregate, and highlight known accessibility barriers to programming faced by professional programmers and students with visual impairments learning how to code as well as to identify all solutions that have been proposed to address these barriers. We selected and analyzed 70 papers reporting on accessibility of programming and programming environments for people with visual impairments. Numerous barriers to programming by people with visual impairments have been identified in the literature. Some of these barriers are understudied and present opportunities for future work. A lot of studies have also proposed tools and new accessible programming languages to address the accessibility issues of current programming languages and programming environments.