Automatic Web Navigation Problem Detection Based on Client-Side Interaction Data

The current importance of digital competence makes it essential to enable people with disabilities to use digital devices and applications and to automatically adapt site interactions to their needs. Although most of the current adaptable solutions make use of predefined user profiles, automatic detection of user abilities and disabilities is the foundation for building adaptive systems. This work contributes to diminishing the digital divide for people with disabilities by detecting the web navigation problems of users with physical disabilities based on a two-step strategy. The system is based on web user interaction data collected by the RemoTest platform and a complete data mining process applied to the data. First, the device used for interaction is recognized, and then, the problems the user may be having while interacting with the computer are detected. Identification of the device being used and the problems being encountered will allow the most adequate adaptation to be deployed and thus make the navigation more accessible

Software Design of Image-based Autocorrection Essays using Deep Learning on the Website and Mobile Application

Traditional essay assessment methods are often time-consuming and prone to subjectivity. This study proposes a novel Automated Essay Scoring (AES) system, "Essay Mathematic Auto Correction (Emath Toco)," featuring web and mobile app interfaces. Emath Toco leverages visual stimuli and deep learning algorithms like 1D CNN, NasNet Mobile, and GoogleNet to offer objective and efficient essay evaluation. Extensive testing on a 40/60 training/testing data split yielded accurate data classification, validating successful implementation on Flutter-built Android applications and a Firebase-powered web interface. User experience surveys revealed positive feedback on Emath Toco's ease of use, visually appealing interfaces, and effective data collection, confirming its user-friendliness. Emath Toco's innovative use of visual stimuli and deep learning algorithms significantly reduces subjectivity and improves the accuracy of essay evaluation. Emath toco is promising technology with the potential to revolutionize essay assessment and educational methodologies. The research contributes to the field of automated essay scoring in two key ways. First, by integrating visual stimuli as a novel approach, Emath Toco expands the range of factors considered in scoring, potentially leading to more comprehensive and efficient. Second, the successful implementation of the system on both web and mobile platforms demonstrates its flexibility and accessibility, offering educators a versatile tool regardless of technological limitations