The adoption of Software Engineering practices in a Scrum environment

The competition in the software market demands that the time required for any software product to reach the market be reduced if the product is to survive competition from other developers. The pursuit of this goal has led to the adoption of agile software development methodologies. While other agile methodologies provide guidelines as to the software engineering (SE) practices to be used during the development lifecycle, Scrum does not. The purpose of this study is twofold: first, to identify the usage and level of importance of software engineering practices in the Scrum development environment; and second, to investigate how Scrum teams adopt an appropriate set of SE techniques and whether a hybrid Scrum/Extreme Programming (XP) methodology is an appropriate approach to take. This research was conducted by examining sample data from five organizations using the Scrum methodology. The sample included a range of industries including communications and embedded systems, financial asset management, software development houses and consulting firms in South Africa. The study employed a mixed method approach. A key finding was that, regardless of the fact that Scrum does not explicitly recommend engineering practices, there was extensive use of these practices by all of the participating organizations. The study also found that the lack of software engineering practices in Scrum does not constitute a barrier to a successful adoption of Scrum, provided the 'inspect and adapt' principle inherent in Scrum is properly followed. The study discusses the findings, explains the implications and suggests future research.Peer reviewe

The appraisal of Facebook online community: An exposition of mobile commerce in social media reviews

Peer reviewe

Assessment of Mobile Money Enablers in Nigeria

This chapter describes how mobile money is an emerging and innovative financial service delivery mechanism. With huge success, recorded mostly in the developing economies, it is scholarly unclear the antecedents of its adoption. Using a survey of 151 respondents comprising both the banked and underbanked in the South-Western part of Nigeria, the authors used the PLS-SEM to test the research hypothesis. The results reveal the enablers of mobile money, which are social influence, performance expectancy, security and effort expectancy, and inhibitors such as system anxiety and cost. Privacy, trust, image and convenience were not found significant in this study. Social influence, performance expectancy and effort expectancy variables adapted from the UTAUT model have considerable influence on mobile money in Nigeria. Study implications and future directions are offered.peerReviewe

Improving performance, security and mobile money users' experience: a study of service design

Mobile technologies have changed the way people interact with their surroundings. Despite the growth of mobile money in Africa, especially in Kenya, Nigeria as a frontier market is lacking behind. Most Nigerians are far from experiencing a cashless economy, and about two-fifths of Nigerians have bank accounts, while four-fifths of Nigerians are ignorant of mobile money services. Quantitative methodology was employed in the study with a focus on mobile money users. The study administered a survey as a hard copy to the community that comprises students and workers in Nigeria with (n = 151) participants. The study combined performance expectancy, effort expectancy users experience, and security to predict mobile money users' satisfaction, while performance expectancy is the highest predictor of user's satisfaction. The insight from this study suggests to mobile money managers strategies to optimise the mobile money platform to enhance the mobile money users' experience and satisfaction

Exploring teachers' preconceptions of teaching machine learning in high school: A preliminary insight from Africa

The teaching of machine learning is now considered essential and relevant in schools globally. Despite the ongoing discourse and increased research in the emerging field, teachers' conceptions of machine learning remain under-researched. This study aims at filling the gap by describing the initial conceptions of teaching machine learning by 12 African in-service teachers. We detailed the result of a phenomenographic analysis of teachers' pre-conceptions on teaching machine learning in K-12 settings. Twelve high school (Grades 10–12) computer science teachers in some selected African countries were recruited for a semi-structured interview. Five categories emerged from the analysis of the semi-structured interviews as follows: supporting student technical knowledge, having knowledge of the concept, focusing on professional development practices, contextualizing teaching resources and tools, and sustainability for development goals. These involve the relevance of teaching machine learning, the pedagogical approaches, strategies, and sustainability relating to practical implementation in schools. The results suggest the need to train in-service teachers to use existing tools designed for introducing machine learning. The teachers should also be involved in the co-designing process of resources considering contextual factors and, significantly, the curriculum to integrate machine learning into mainstream education. Involving teachers in the development process would help contextualize machine learning, contributing to real impact and societal changes.Godkänd;2022;Nivå 0;2022-01-17 (johcin)</p

Developing a pedagogical evaluation framework for computational thinking supporting technologies and tools

Frameworks for the evaluation of technological instructional tools provide educators with criteria to assess the pedagogical suitability and effectiveness of those tools to address learners’ needs, support teachers’ understanding of learning progress, and recognize the levels of achievement and the learning outcomes of the students. This study applied secondary document analysis and case study to identify five pedagogical indicators for teaching and learning computational thinking, including technology, pedagogical approaches, assessment techniques, data aspect, and teacher professional development. Based on the pedagogical indicators, this study proposed a computational thinking pedagogical assessment framework (CT-PAF) aimed at supporting educators with a strategy to assess the different technological learning tools in terms of pedagogical impact and outcome. Furthermore, three case-study instructional tools for teaching CT in K-12 were analyzed for the initial assessment of CT-PAF. Scratch, Google Teachable Machine, and the iThinkSmart minigames were marched to the underpinning characteristics and attributes of CT-PAF to evaluate the framework across the instructional tools. The initial assessment of CT-PAF indicates that the framework is suitable for the intended purpose of evaluating technological instructional tools for pedagogical impact and outcome. A need for expanded assessment is, therefore, necessary to further ascertain the relevance of the framework in other cases.Validerad;2022;Nivå 2;2022-08-17 (hanlid)</p

The need for green companies in Nigeria:a study of electronic invoicing

Abstract Manual invoicing is a major business document that the supplier uses to request payments from a purchaser for services rendered. It contains the contact information of the seller, the list of goods or the services provided and the payment instructions. Attention is being shifted from manual invoicing these days because of some factors like increased man-hours, risks of human error and risks of high carbon footprint. The study applied Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate the need for green Companies in Nigeria. Empirical data were collected from Nigerian companies to measure intention-behavior for electronic invoicing and structural equation modelling was used for data analyses. This study provides some useful guidelines for industry players such as the einvoicing service providers (EISPs), policy makers and the marketers. With the newly integrated framework, a greater level of comprehension can be achieved about e-invoicing acceptance among Nigerian companies

A systematic review of teaching and learning machine learning in K-12 education

The increasing attention to Machine Learning (ML) in K-12 levels and studies exploring a different aspect of research on K-12 ML has necessitated the need to synthesize this existing research. This study systematically reviewed how research on ML teaching and learning in K-12 has fared, including the current area of focus, and the gaps that need to be addressed in the literature in future studies. We reviewed 43 conference and journal articles to analyze specific focus areas of ML learning and teaching in K-12 from four perspectives as derived from the data: curriculum development, technology development, pedagogical development, and teacher training/professional development. The findings of our study reveal that (a) additional ML resources are needed for kindergarten to middle school and informal settings, (b) further studies need to be conducted on how ML can be integrated into subject domains other than computing, (c) most of the studies focus on pedagogical development with a dearth of teacher professional development programs, and (d) more evidence of societal and ethical implications of ML should be considered in future research. While this study recognizes the present gaps and direction for future research, these findings provide insight for educators, practitioners, instructional designers, and researchers into K-12 ML research trends to advance the quality of the emerging field.