Teaching Computing to Middle and High School Students from a Low Socio-Economic Status Background: A Systematic Literature Review

Abstract

Information technology (IT) is transforming the world. Therefore, exposing students to computing at an early age is important. And, although computing is being introduced into schools, students from a low socio-economic status background still do not have such an opportunity. Furthermore, existing computing programs may need to be adjusted in accordance to the specific characteristics of these students in order to help them to achieve the learning goals. Aiming at bringing computing education to all middle and high-school students, we performed a systematic literature review, in order to analyze the content, pedagogy, technology, as well as the main findings of instructional units that teach computing in this context. First results show that these students are able to learn computing, including concepts ranging from algorithms and programming languages to artificial intelligence. Difficulties are mainly linked to the lack of infrastructure and the lack of pre-existing knowledge in using IT as well as creating computing artifacts. Solutions include centralized teaching in assistive centers as well as a stronger emphasis on unplugged strategies. However, there seems to be a lack of more research on teaching computing to students from a low socio-economic status background, unlocking their potential as well to foster their participation in an increasing IT market.

Teaching Machine Learning to Middle and High School Students from a Low Socio-Economic Status Background

“…As a result of a systematic literature review analyzing the teaching of AI/ML to middle and high school students from a low socio-economic status background, only very few courses were encountered (Table 1) (Martins and Gresse von Wangenheim, 2023). In general, the concept of low socio-economic status varies from including students from low-income families, underprivileged conditions, living in marginalized communities, or studying at schools with a high level of vulnerability.…”

“…Learning is assessed based on the students' performance (Everson et al, 2022;Zhang et al, 2022) or through tests (Araya et al, 2021). The findings suggest that students in underprivileged contexts can comprehend computational models, reflect on their limitations, and understand AI/ML concepts, such as artificial neural networks and supervised learning, as well as even complex concepts, including Generative Adversarial Networks (GANs) (Martins and Gresse von Wangenheim, 2023). Limitations reported in the context of low SES students include the lack of infrastructure in their schools and homes, creating a social and regional digital divide, affecting student activities and engagement.…”

“…Focusing on this issue, some initiatives have begun to address AI/ML education in a way that increases student inclusion and diversity, including as AI4ALL (AI4ALL, 2023), The Coding School (TCS, 2023), and IBM SkillsBuild (IBM, 2023), by offering free educational programs to unleash students' potential. Yet, few courses specifically aim to make AI/ML education accessible to students with no prior basic computing competencies or experience with digital devices in middle and high school (Martins and Gresse von Wangenheim, 2023). These courses generally use unplugged activities, reinforce STEM content and computational thinking (CT), and adopt a slower pace of learning.…”

“…Overview of courses that teach AI/ML competencies to low SES youth at the middle and high school level(Martins and Gresse von Wangenheim, 2023).…”

A model to reconstruct lecturer dilemma in balancing research and teaching responsibilities: A case of one rural university in South Africa

“…On the other hand, lecturers are also tasked with providing high-quality teaching to students who often come from disadvantaged backgrounds (Robinson, 2022). Many students in rural universities have limited educational opportunities or face socioeconomic barriers that affect their learning experiences, as supported by literature and observations (Belando-Montoro, 2022;Martins & Gresse von Wangenheim, 2023). Therefore, there is an urgent need for dedicated educators who can offer effective pedagogical support and help bridge the educational gap, but this leaves a gap in research outputs and the development of rural lecturers.…”