Browsing by Author "Masese, Nelson Bogomba"

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Interface features, program complexity and memorability as indicators of learnability of mobile social software
(International Journal of Science and Research, 2017) Masese, Nelson Bogomba; Muketha, Geoffrey Muchiri; Mbuguah, Samuel Mungai
Social Computing aims to support the tendency of humans to interact with mobile devices. Technology reinforces this interaction by producing appropriate responses that then lead to improved communication between humans and computational devices. Although latest developments in mobile phone technologies have opened the way for a new generation of mobile social applications that allow users to interact and share information, there is still very limited user support information on how to use different applications. This problem either increases the learning curve of the users, thereby adversely affects their overall efficiency. The main purpose of this paper is to analyze factors that affect the learnability of mobile social software. A sample of 361 respondents was selected, with 345 respondents returning feedback. Primary data was collected through the use of questionnaires and interviews targeting mobile social users in Nakuru County Kenya. Three social networks were used, namely, WhatsApp, Facebook and Twitter. Data analysis was done using descriptive statistics. Findings indicate that interface features affect learnability across the three social networks, with learnability of WhatsApp turning out to be higher than that of compared to Facebook and Twitter. Findings also indicate that more than 60% program supports compatibility with other applications while 59.4% of the respondents agreed that maintaining language is cheap across the three social networks. Other findings indicate that WhatsApp’s memorability is easy to execute compared to that of Facebook and Twitter.
Interface Features, Program Complexity and Memorability as Indicators of Learnability of Mobile Social Software
(International Journal of Science and Research, 2017-10-26) Masese, Nelson Bogomba; Muketha, Geoffrey Muchiri; Mbuguah, Samuel Mungai
Social Computing aims to support the tendency of humans to interact with mobile devices. Technology reinforces this interaction by producing appropriate responses that then lead to improved communication between humans and computational devices. Although latest in mobile phone technologies have opened the way for a new generation of mobile social applications that allow users to interact and share information, there is still very limited user support information on how to use different applications. This problem either increases the learning curve of the users, thereby adversely affects their overall efficiency. The main purpose of this paper is to analyze factors that affect the learnability of mobile social software. A sample of 361 respondents was selected, with 345 respondents returning feedback. Primary data was collected through the use of questionnaires and interviews targeting mobile social users in Nakuru County Kenya. Three social networks were used, namely, WhatsApp, Facebook and Twitter. Data analysis was done using descriptive statistics. Findings indicate that interface features affect learnability across the three social networks, with learnability of WhatsApp turning out to be higher than that of compared to Facebook and Twitter. Findings also indicate that more than 60% program supports compatibility with other applications while 59.4% of the respondents agreed that maintaining language is cheap across the three social networks. Other findings indicate that WhatsApp’s memorability is easy to execute compared to that of Facebook and Twitter.
Predicting mobile social software learnability based on analysis of program complexity and memorability
(Kibabii University, 2018-11) Masese, Nelson Bogomba
With the evolution of the mobile platform and the rapid adoption of mobile devices such as cell phones and other handheld devices, social networks which began as web-based applications have migrated into the mobile platform. The technological innovation depends on user interface design to enhance the technical complexity of products that match the end user requirements. Existing metrics concentrate on user friendliness, reliability, efficiency and security of the mobile social software. Despite achieving some level of acceptability they have a deficit since the user is not subjected to contribute in the mobile social software analysis and development. There is therefore a need to include learnability attributes so as to have software’s that are as learnable as possible. Secondly there is need to study learnability indicators as well as metrics models that can be used to measure these indicators thereby enabling users to indicate predict learnability of software. The purpose of the study was to develop a model for predicting the learnability of mobile social software/. The specific objectives of the study included establishment of the level of awareness of mobile social systems, exploring the extent of utilization of mobile social systems, determining factors affecting learnability of mobile social software applications, designing and implementing a metrics model that can be used for evaluating the learnability of mobile social systems. The research adapted mixed method research design. Data was obtained using questionnaires, interviews and experiments. The target population was mobile social software users in Nakuru County. A sample size of 361 was used, and a response rate of 96% was achieved. Stratified sampling was used to aid in data collection. Data analysis was done using inferential statistics that include chi-square tests, correlation analysis, regression, Anova and descriptive statistics which encompassed standard deviation. Ethical issues rising from the research such as informed and voluntary consent, no harm to participants, beneficence and reciprocity, confidentiality of information and data integrity were taken into account;. A learnability model metrics was developed to guide the evaluation of the learnability of mobile social software. The model is assigned to provide software learnability insights that are vital in assisting software designers, software developers and open-source owners to improve their software products in a way that best supports learnability. The model was uploaded to the website address, www.mssl.co.ke. for testing purposes. From the results, there was consensus that the mode was valid. The significance of the study lied on its ability to provide insights that will identify aspects of software learnability especially to software developers and open-source owners. This study is of the view that there is need to conduct more case studies to further ascertain the validity of the model and to study other social software’s that are not included in this study.