"Why SuaCode?": Understanding African Students' Motivations for Taking a Smartphone-Based Online Coding Course
- URL: http://arxiv.org/abs/2607.22940v1
- Date: Fri, 24 Jul 2026 22:49:51 GMT
- Title: "Why SuaCode?": Understanding African Students' Motivations for Taking a Smartphone-Based Online Coding Course
- Abstract summary: MOOCs are beneficial to post-secondary African students as less than 1% of them leave secondary school with fundamental coding skills.<n>One strategy for increasing their efficacy for African students is to understand students' motivation for enrolling.<n>We analyzed a random sample of 450 (of 3000) responses using a grounded theory approach.<n>We found that most African students participated in SuaCode for intrinsic reasons such as improving themselves, learning with like-minded individuals, and gaining skills to help address societal issues.
- Score: 0.1957338076370071
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Computer programming MOOCs are instrumental in providing students with high-quality instruction in areas where there is limited access. They are especially beneficial to post-secondary African students as less than 1% of them leave secondary school with fundamental coding skills. One strategy for increasing their efficacy for African students is to understand students' motivation for enrolling. These insights can inform the design of MOOC content and assessments to align with students' interests. We administered an open-ended response survey to (self-identified) Africans enrolled in a smartphone-based online coding course (SuaCode). We analyzed a random sample of 450 (of 3000) responses using a grounded theory approach. We found that most African students (68.7%) participated in SuaCode for intrinsic reasons such as improving themselves, learning with like-minded individuals, and gaining skills to help address societal issues. We discuss the implications of these findings in the design of programming MOOCs targeted at African students.
Related papers
- Adesua: Development and Feasibility Study of an AI WhatsApp Bot for Science Learning in West Africa [0.17590081165362778]
Adesua is a WhatsApp-based AI Teaching Assistant for science education.<n>System integrates curated textbooks and 33 years of national examination questions with generative AI.<n>Students can ask science questions, take timed or untimed multiple-choice tests by topic or exam year, and receive instant grading.
arXiv Detail & Related papers (2026-05-14T20:04:39Z) - MakOne: Behavioural Data of University Students' Smart Devices in Uganda [1.0260880679794957]
This paper introduces MakOne, a novel multimodal dataset collected over six weeks from 72 students at Makerere University, Kampala.<n>The dataset integrates passive smartphone sensor data-including location, physical activity, and screen usage-with ecological momentary assessments (EMAs) that capture students' moods and daily routines.
arXiv Detail & Related papers (2025-09-26T13:28:52Z) - Embracing AI in Education: Understanding the Surge in Large Language Model Use by Secondary Students [53.20318273452059]
Large language models (LLMs) like OpenAI's ChatGPT have opened up new avenues in education.<n>Despite school restrictions, our survey of over 300 middle and high school students revealed that a remarkable 70% of students have utilized LLMs.<n>We propose a few ideas to address such issues, including subject-specific models, personalized learning, and AI classrooms.
arXiv Detail & Related papers (2024-11-27T19:19:34Z) - Leveraging AI to Advance Science and Computing Education across Africa: Challenges, Progress and Opportunities [1.2691047660244332]
We describe our work developing and deploying AI in Education tools in Africa for science and computing education.
SuaCode is an AI-powered app that enables Africans to learn to code using their smartphones.
AutoGrad is an automated grading, and feedback tool for graphical and interactive coding assignments.
Kwame for Science is a web-based AI teaching assistant that provides instant answers to students' science questions.
arXiv Detail & Related papers (2024-02-12T04:10:09Z) - AfroBench: How Good are Large Language Models on African Languages? [55.35674466745322]
AfroBench is a benchmark for evaluating the performance of LLMs across 64 African languages.<n>AfroBench consists of nine natural language understanding datasets, six text generation datasets, six knowledge and question answering tasks, and one mathematical reasoning task.
arXiv Detail & Related papers (2023-11-14T08:10:14Z) - Giving Feedback on Interactive Student Programs with Meta-Exploration [74.5597783609281]
Developing interactive software, such as websites or games, is a particularly engaging way to learn computer science.
Standard approaches require instructors to manually grade student-implemented interactive programs.
Online platforms that serve millions, like Code.org, are unable to provide any feedback on assignments for implementing interactive programs.
arXiv Detail & Related papers (2022-11-16T10:00:23Z) - MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity
Recognition [55.95128479289923]
African languages are spoken by over a billion people, but are underrepresented in NLP research and development.
We create the largest human-annotated NER dataset for 20 African languages.
We show that choosing the best transfer language improves zero-shot F1 scores by an average of 14 points.
arXiv Detail & Related papers (2022-10-22T08:53:14Z) - ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback [54.142719510638614]
In this paper, we frame the problem of providing feedback as few-shot classification.
A meta-learner adapts to give feedback to student code on a new programming question from just a few examples by instructors.
Our approach was successfully deployed to deliver feedback to 16,000 student exam-solutions in a programming course offered by a tier 1 university.
arXiv Detail & Related papers (2021-07-23T22:41:28Z) - Kwame: A Bilingual AI Teaching Assistant for Online SuaCode Courses [2.4366811507669124]
We developed a bilingual Artificial Intelligence (AI) Teaching Assistant (TA) that provides answers to students' coding questions from SuaCode courses in English and French.
Kwame is a Sentence-BERT (SBERT)-based question-answering (QA) system that we trained and evaluated offline using question-answer pairs created from the course's quizzes, lesson notes and students' questions in past cohorts.
Our results showed that fine-tuning on the course data and returning the top 3 and 5 answers improved the accuracy results.
arXiv Detail & Related papers (2020-10-22T02:26:12Z) - Attentional Graph Convolutional Networks for Knowledge Concept
Recommendation in MOOCs in a Heterogeneous View [72.98388321383989]
Massive open online courses ( MOOCs) provide a large-scale and open-access learning opportunity for students to grasp the knowledge.
To attract students' interest, the recommendation system is applied by MOOCs providers to recommend courses to students.
We propose an end-to-end graph neural network-based approach calledAttentionalHeterogeneous Graph Convolutional Deep Knowledge Recommender(ACKRec) for knowledge concept recommendation in MOOCs.
arXiv Detail & Related papers (2020-06-23T18:28:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.