Mobile Application User Interfaces (UIs) are crucial for user experience, with well-designed UIs enhancing usability and poorly designed ones causing frustration. The increasing needs of humans, demands UIs that support diverse tasks and contexts. Designing user interfaces requires prior experience and knowledge in designing interfaces that meet user needs. However, the shortage of specialized designers frequently results in designs that are not as user-centered. This study proposes a design guideline framework to annotate Android mobile UI images collected from RICO dataset, by combining Material Design Guidelines with most common HCI principles, to identify and report mobile UI design issues through machine learning.

The COVID-19 pandemic has forced educationist to come up with innovative solutions in delivering, engaging synchronous online academic modules. An innovative collaborative teaching approach was utilized in delivering programming concepts for freshers. A team of six academics functioned as a resource panel in delivering synchronous online lecture content. These interactive sessions were led by a moderator inquiring the resource panel on topics related to the content of the lecture. This was done in the same spirit on how a panel discussion would be conducted led by a moderator in a conference. Microsoft Teams Live was used in the delivery of the content to an audience of up to 800 students. Delivering a freshers programming course is known to be challenging in face- to-face delivery. A collaborative programming environment was used to engage students in live coding activities during the lectures. Students had opportunities to interact with the resource panel through quizzes, QA and through coding related activities. These lectures also introduced the innovative use of QR codes to get students engagement through a mobile device for the interactive sessions. Results based on a survey shared among the participated students, confirmed the collaborative teaching approach in conducting webinar was more effective over a traditional webinar that is conducted by one person. Interactive programming environment (Repl.it) allowed the resource personal to give feedback on the programs submitted by the students during synchronous sessions conducted. The best practices used in delivering this course can be easily adopted in delivering highly engaging online lectures for other courses.

In Sri Lanka, most of the population uses the Sinhala Language as their first language to communicate and for documentation in most government departments. It is evident that the digitalization of the Sinhala Language is essential in a country like Sri Lanka. The specialty of Sinhalese characters is that they have very tiny differences in feature, and the number of different characters formed from the letters of the Sinhala alphabet and its elements is relatively high, leading to the classification among the Sinhala letters becoming quite a complex task. Previous proposed research case studies involved machine learning based feature detections related to rule-based theories and geometry features that had average accuracy rates, which indicate that further improvement is required with new features. Consequently, in this research paper, a Deep Learning Character Classification method for Sinhala OCR is proposed, which is for both Printed and Handwritten Sinhala texts as well as an Intelligent Sinhala Form Automation technique to read both answers and questions in an application to convert them into e-texts. The converted e-texts will be sharpened and fixed through a Sinhala Spelling & Grammar checking feature that is developed in the system more intelligently. In this research work, it was a success to obtain an overall accuracy level of more than 90% considering all components.

Despite the widespread success of online shopping, it is not available to all consumer types. In this sense, visually impaired and elderly users, in particular, frequently face daunting barriers. Due to the inaccessibility and difficulty of current online shopping mobile applications, millions of visually impaired and elderly people are unable to benefit from the convenience provided by online shopping. Developing ideas that inspire people is really essential for visually impaired and elderly people to engage in social life. Generic product explanations, unhelpful images, and visually appealing user experiences are provided to average eyes in online shopping, and they are incompatible with visually impaired people, even with visually impaired assistive devices. Due to visual barriers and inaccessible user experiences, the visually impaired are struggling to do online shopping independently. During this COVID-19 pandemic situation, online shopping is one of the better ways to meet everyone's needs and wants. Ordinary individuals can meet their needs and desires, but the visually impaired and elderly people who live alone find it difficult to manage their daily life. Therefore, we have come up with a solution for this by having an online shopping mobile application. Our objective with this application is to assist visually impaired and elderly individuals in meeting their underlying needs and to help them in this pandemic situation. This paper presents an online shopping mobile application for visually impaired and elderly people that allows them to shop online in a variety of convenient ways.

Massive Open Online Courses (MOOCs) are the new revolution in the field of e-learning, providing a large number of courses in different domains to a wide range of learners. Due to the availability of several MOOC providers (including edX, Coursera, Udacity, FutureLearn), a specific domain has multiple courses spread across these platforms that confuses a learner on selecting the most suitable course for him. It is a tedious manual task for the learner to browse through various courses before he finds the best course that meets his learning requirements and objectives. MoocRec is a unique learning styles-oriented system that recommends the most suitable courses to a learner from different MOOC platforms based on their learning styles and individual needs. The courses are recommended based on the mapping of Felder and Silverman learning styles with the standard video styles used in MOOC videos (including talking head, slide, tutorial/demonstration). MoocRec also allows the learners to search for courses using specific topics to provide an enhanced personalized learning environment. Results show that MoocRec is strongly reliable and can be used for personalized learning.