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This chapter reviews successful pedagogical interventions in teaching literacy in languages of teaching and learning in the upper primary grades in low- and middle-income countries (LMICs) and explores how researchers and teachers decolonized classroom spaces to ensure student achievement of learning outcomes. Themes emerging from the analysis of data are biliteracy interventions, interventions in official and national languages, teacher professional development, and ecological interventions. Results of the review indicate that researchers decolonized classroom spaces by using student linguistic repertoires, evidence-based pedagogical strategies student cultural capital and engaging families and communities. The review also reveals that more research needs to be conducted on teaching and learning in upper primary grades. The linguistic diversity of LMICs provides rich contexts for more research in bilingual education and L2 acquisition which could be useful worldwide as stakeholders in the education process explore the best ways to improve learning outcomes in schools.

The behavioral decision-making of digital-self is one of the important research contents of the network of crowd intelligence. The factors and mechanisms that affect decision-making have attracted the attention of many researchers. Among the factors that influence decision-making, the mind of digital-self plays an important role. Exploring the influence mechanism of digital-selfs’ mind on decision-making is helpful to understand the behaviors of the crowd intelligence network and improve the transaction efficiency in the network of CrowdIntell.

In this paper, the authors use behavioral pattern perception layer, multi-aspect perception layer and memory network enhancement layer to adaptively explore the mind of a digital-self and generate the mental representation of a digital-self from three aspects including external behavior, multi-aspect factors of the mind and memory units. The authors use the mental representations to assist behavioral decision-making.

The evaluation in real-world open data sets shows that the proposed method can model the mind and verify the influence of the mind on the behavioral decisions, and its performance is better than the universal baseline methods for modeling user interest.

In general, the authors use the behaviors of the digital-self to mine and explore its mind, which is used to assist the digital-self to make decisions and promote the transaction in the network of CrowdIntell. This work is one of the early attempts, which uses neural networks to model the mental representation of digital-self.

This study analyzed 24 IELTS Task One (data explanation) prompts for task type, diagram type, subject matter, level of critical thought, and geographical references, in order to determine whether Emirati university students’ anecdotal claims of cultural bias on the IELTS academic writing exam (as experienced by the researcher in the past decade of teaching IELTS in the United Arab Emirates) are valid. The analysis found that the majority of the task types (88%) were non-process in nature (i.e. required the description of data in the form of a chart or graph, rather than the description of a process); 40% of the non-process prompts consisted of more than one diagram. The analysis revealed that 33% of the non-process prompts included bar graphs and 29% included line graphs. Pie charts appeared in 25% of the prompts and tables in only 17%. An Emirati student English preparatory program survey indicated the pie chart as the easiest to understand – a finding that may highlight a difference between the most commonly used IELTS prompt and the students’ prompt preference. A content analysis of topics found a high percentage (58%) of subject matter related to the social sciences, with 79% of the geographical references pertaining to Western contexts. An analysis of the amount of critical thought needed for graph interpretation revealed 52% of non-process prompts required some form of critical thought. The study therefore found that the cultural bias perceived by Emirati students has some validity, given the students’ socio-cultural and educational background.