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User Interface (UI) acts as a mediator between human and computer or any other system or sub-systems which exhibit its function, structure, and behavior. The complexities of UI are open issues that make it formidable to explore it. Supervision makes any task easier by explaining the elements and intentions of UI. Self-Explanatory User Interfaces (SEUI) traps the complexities of UI and provides assistive tools with it in order to supervise users addressing usability using visual and aural syntax. UIs require adapting to the mental model of users and support them during their task. The study presented in this paper has been administered to adult users (age group: 50-64 years) with minimum exposure to technologies and often requires support to get accustomed to interfaces. To complete a task successfully, it is essential to justify the intent of the design, purpose of any textual entry, the usability of the buttons, importance of the labels, visibility, or blurriness of certain items to end-users. Literature associated with SEUI highlight the intervention of Model-Driven Engineering in UI. But very few studies in this area have investigated concatenation of visual and aural syntax in UI. The visual aspect aims at explaining a clear and concise diagrammatic representation of the hierarchy of UI and the significance of each user entry while the aural dimension intends to provide audio instructions to the users. The results include evidence of significant growth in task completion rate and improvement in the task completion time of the novice users assisted with combined visual and aural syntax tool embedded in an interface. The results of the current study are significant in light of the designing and development of UIs that address adults who are a novice and lack exposure to UIs. The solution presented in this paper would support them to migrate from being a novice to an expert.

An inherent criterion of evaluation in Design education is novelty. Novelty is a measure of newness in solutions which is evaluated based on relative comparison with its frame of reference. Evaluating novelty is subjective and generally depends on expert’s referential metrics based on their knowledge and persuasion. Pedagogues compare and contrast solution for cohort of students in mass examination aspiring admission to Design schools. Large number of students participate in mass examinations, and in situations like this, examiners are confronted with multiple challenges in subjective evaluation such as- 1) Errors encountered in evaluation due to stipulated timeline, 2) Errors encountered due to prolonged working hours, 3) Errors encountered due to stress in performing repeated task on a large-scale. Pedagogues remain ever-inquisitive and vigilant about the evaluation process being consistent and accurate due to monotony of repeated task. To mitigate these challenges, a computational model is proposed for automating evaluation of novelty in image-based solutions. This model is developed by mixed-method research, where features for evaluating novelty are investigated by conducting a survey study. Further, these features were utilized to evaluate novelty and generate score for image-based solutions using Computer Vision (CV) and Deep Learning (DL) techniques. The performance metric of the model when measured reveals a negligible difference between scores of experts and scores of proposed model. These comparative analysis of the proposed model with human experts’ confirm the competence of the devised model and would go a long way to establish trust of pedagogues by ensuring reduced error and stress during the evaluation process.

This cross-sectional study investigated the prevalence of work-related musculoskeletal disorders (WMSDs), ergonomic risk levels, and associated risk factors (personal and work-related) with WMSDs among 290 cashew workers in small-scale cashew processing units of Eastern India. The data were collected through a questionnaire [including Nordic musculoskeletal questionnaire (NMQ)] and direct observation of work posture using rapid entire body assessment (REBA) method. Results showed that 70.3% of cashew workers reported WMSDs during the past 12 months, with shoulders (52.4%), lower back (51.4%), and hands/wrists (43.4%) regions being the highest. Work-related factors, including work experience, daily working time, work fatigue, job stress, and personal factors (gender, age, material status, educational level, and type of work), were independently associated with WMSDs. Further, REBA final score (8-10) indicated that cashew workers' postures were at a high-risk level. Finally, it is concluded that WMSDs among cashew workers are a significant problem, which needs ergonomic work improvements.

The study reported in this paper investigates the effect of navigational mechanisms on students in a computer based assessment environment at a cross-cultural setting. Literatures in computer based assessment studies highlight the phenomenon of “test mode effect” that can explain the differences in test scores across two different formats of testing. In this study the authors posit that even different formats of presentation through the same media might affect students’ performance in a computer based assessment environment. The study reported here investigates the effect of two different navigational mechanisms namely, vertical menu and step based navigations on students’ performance. Students from Indian universities and American universities participated in the study. The results highlight that navigational mechanisms affect students’ performance and their subjective evaluation of the Computer based Assessment system. The results of the study would provide navigational heuristics for the Graphical User Interface designers to design a learner centered Computer based Assessment system.