The Effect Of A Mobile Device User Interface Design Based On Personality Nuances On Learner's Perceptual Experience And Satisfaction

User experience (UX) encompasses the concepts of usability and affective engineering. It is a crucial factor in the design of mobile devices. However, it has been difficult to gain a common understanding of the design characteristics that suit the different personalities of individuals when using mobile device technology. Therefore, in this thesis, a novel design of a mobile UI-based on the personality nuances was proposed. It was argued that such design would stimulate information processing in according to learners‘ mental model in order to obtain an effective and satisfying learning experience with a mobile phone. A total of 87 undergraduate students (15 male, and 35 female) were participated in this study

Examining the effect of learners' gender on their learning achievement: Proposing a multimedia courseware

Although the use of multimedia web design in education has drawn the interest of instructors in the modern learning process, the role of gender in such a process has not yet explored properly. The purpose of this research is therefore to evaluate the effects of gender on learners’ achievements when studying from the proposed platform. Therefore, multimedia courseware was built at the very early stage. Then, the students were divided based on their gender into two groups (male and female). The learning performance of each group gets compared using t-test. Result showed that no significant difference occurs between the two groups. This means that the proposed design is equally fitting the learners of both genders. In addition, the result obtained has shown that gender has no effect on learners' output. In conclusion, this study provides a guideline for the design of multimedia courseware. It contributes to the theories of gender and learning to come up with a clear understanding of the type of design elements that can be used for positive learning experience

Early-stage pregnancy recognition on microblogs: Machine learning and lexicon-based approaches

Pregnancy carries high medical and psychosocial risks that could lead pregnant women to experience serious health consequences. Providing protective measures for pregnant women is one of the critical tasks during the pregnancy period. This study proposes an emotion-based mechanism to detect the early stage of pregnancy using real-time data from Twitter. Pregnancy-related emotions (e.g., anger, fear, sadness, joy, and surprise) and polarity (positive and negative) were extracted from users' tweets using NRC Affect Intensity Lexicon and SentiStrength techniques. Then, pregnancy-related terms were extracted and mapped with pregnancy-related sentiments using part-of-speech tagging and association rules mining techniques. The results showed that pregnancy tweets contained high positivity, as well as significant amounts of joy, sadness, and fear. The classification results demonstrated the possibility of using users’ sentiments for early-stage pregnancy recognition on microblogs. The proposed mechanism offers valuable insights to healthcare decision-makers, allowing them to develop a comprehensive understanding of users' health status based on social media posts

Influence of personality traits on users’ viewing behaviour

Different views on the role of personal factors in moderating individual viewing behaviour exist. This study examined the impact of personality traits on individual viewing behaviour of facial stimulus. A total of 96 students (46 males and 50 females, age 23–28 years) were participated in this study. The Big-Five personality traits of all the participants together with data related to their eye-movements were collected and analysed. The results showed three groups of users who scored high on the personality traits of neuroticism, agreeableness and conscientiousness. Individuals who scored high in a specific personality trait were more probably to interpret the visual image differently from individuals with other personality traits. To determine the extent to which a specific personality trait is associated with users’ viewing behaviour of visual stimulus, a predictive model was developed and validated. The prediction results showed that 96.73% of the identified personality traits can potentially be predicted by the viewing behaviour of users. The findings of this study can expand the current understanding of human personality and choice behaviour. The study also contributes to the perceptual encoding process of faces and the perceptual mechanism in the holistic face processing theory

Influence of Aging Time on Asphalt Pavement Performance

Aging of asphalt pavements typically occurs through oxidation of the asphalt and evaporation of the lighter maltenes from the binder. The main objective of this study is to evaluate influence of aging on performance of asphalt paving materials.nAsphalt concrete mixtures, were prepared, and subjected to short term aging (STA) procedure which involved heating the loose mixtures in an oven for two aging period of (4 and 8) hours at a temperature of 135 o C. Then it was subject to Long term aging (LTA) procedure using (2 and 5) days aging periods at 85 o C for Marshall compacted specimens. The effect of aging periods on properties of asphalt concrete at optimum asphalt content such as Marshall Properties, indirect tensile strength at 25 o C, Resilient Modulus and resistance to permanent deformation were evaluated. The impact of Short-term and long-term aging on asphalt concrete properties was evaluated. The stiffness of the mixture increases by increasing aging period that lead to increase of Marshall Stability, indirect tensile strength, and the resilient modulus, which leads to increases the resistance of mixtures against permanent deformation. The 8 hr. short term aging causes the Marshall stability, indirect tensile strength at 25 o C and resilient modulus to be increased by 52%, 34 % , 20% respectively as compared with control mixture while, the permanent deformation decreased by (33 %) as compared with control mixture

A non-invasive machine learning mechanism for early disease recognition on Twitter: The case of anemia

Social media sites, such as Twitter, provide the means for users to share their stories, feelings, and health conditions during the disease course. Anemia, the most common type of blood disorder, is recognized as a major public health problem all over the world. Yet very few studies have explored the potential of recognizing anemia from online posts. This study proposed a novel mechanism for recognizing anemia based on the associations between disease symptoms and patients' emotions posted on the Twitter platform. We used k-means and Latent Dirichlet Allocation (LDA) algorithms to group similar tweets and to identify hidden disease topics. Both disease emotions and symptoms were mapped using the Apriori algorithm. The proposed approach was evaluated using a number of classifiers. A higher prediction accuracy of 98.96 % was achieved using Sequential Minimal Optimization (SMO). The results revealed that fear and sadness emotions are dominant among anemic patients. The proposed mechanism is the first of its kind to diagnose anemia using textual information posted on social media sites. It can advance the development of intelligent health monitoring systems and clinical decision-support systems

Bridging Web 4.0 and Education 4.0 For Next Generation User Training in ERP Adoption

This study addresses the critical issue of user comprehension and application within the sphere of cloudbased Enterprise Resource Planning (ERP) systems, a recurrent challenge exacerbated by the intricate nature of these systems. To bridge the existing gaps in training methodologies, a novel paradigm that synergizes Web 4.0 and Education 4.0 modules with traditional ERP systems is proposed. This innovative framework ushers in a paradigm shift in ERP adoption strategies, promising a marked enhancement in user interaction and efficiency. Rigorous qualitative evaluations, conducted with expert panels and potential end-users, provided robust validation of the framework's transformative potential in the realm of user training for ERP systems. This pioneering approach not only makes a substantial academic contribution by reframing the perception of ERP systems but also holds a significant practical value in ameliorating the user experience with cloud-based ERP systems. In essence, the adoption of a Web 4.0-oriented approach in user training heralds a revolutionary shift in ERP adoption strategies, setting a solid foundation for future explorations in this domain