Search results

TED Talks are short videos of experts talking about a variety of topics. This paper outlines six TED Talks that connect with the leadership literature and topics commonly taught with an explanation of how they enhance teaching about a corresponding leadership topic. The researcher shares how introducing TED talks related to leadership can stimulate critical thinking about leadership while keeping the class interesting.

The paper aims to look into the implications of urban informality in Chris Abani's Graceland as represented in slum life and urban poverty as products of over urbanization and globalization, seeking to unravel multi-layers of the human side of the slum.

The paper examines slum life from a descriptive approach to highlight how people survive under poverty. The study of the culture of slums entails an analysis of the survival techniques and everyday practices of slum dwellers, the relations and patterns of behavior and the outcomes of the interplay between place, culture and power relations in such communities.

The urban slum dwellers utilize everyday forms of resistance which comprise a number of “low-profile techniques” to subvert state-imposed power structures and break the cycle of poverty.

Despite the relevance of a post-colonial approach to the texts, this paper is limited to the study of the impact of urban poverty on individuals.

The margin, represented in the urban poor, is brought into focus and perceived in a new light of empowerment which challenges alienating discourses.

The multidimensional vision of Nigeria in Abani's text highlights the cultural and economic impacts of multiculturalism, neocolonialism and globalization on the urban poor.

The paper formulates a framework for understanding the culture of the slum as a space of a peculiar nature, seeking to deconstruct a fixed view of slum life and poverty culture.

This paper aims to shed light on how children's literature in Africa deserves to be studied because African writers “decolonize” the minds of African children and children and adults around the world.

This paper defines children's literature from an African perspective and the “decolonization of the mind.” This is done to examine how two African writers provide narratives for children inspired by their cultures. They deal with themes, characters and symbols that interest children and adults.

Achebe and Youssef crossed many borders: the world of children and adults, animals and humans, vice and virtue, supernatural and real. Their stories take the reader on journeys that involve enriching, engaging and inspiring adventures.

Youssef and Achebe are prolific writers. Providing a survey of what is available in Arabic and Nigerian literature for children, is beyond the scope of this paper.

This paper sends a message to those in charge of the curriculum in schools in Egypt, the Arab countries, Africa and the world at large: decolonize the syllabi in schools because the world is not black and white. Literature for children that encourages critical thinking is available by African writers in Egypt, Nigeria and elsewhere.

The works discussed show that African writers are creative, and their works inspire the African child with pride in his/her identity, culture and heritage.

To the best of the author’s knowledge, no one has compared Egyptian and Nigerian literature for children before. Youssef and Achebe provide evidence that “Good literature gives the child a place in the world … and the world a place in the child.” – Astrid Lindgren.

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.