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Student Engagement in Mobile Learning via Text Message

Published: 12 August 2020 Publication History

Abstract

Mobile learning is expanding rapidly due to its accessibility and affordability, especially in resource-poor parts of the world. Yet how students engage and learn with mobile learning has not been systematically analyzed at scale. This study examines how 93,819 Kenyan students in grades 6, 9, and 12 use a text message-based mobile learning platform that has millions of users across Sub-Saharan Africa. We investigate longitudinal variation in engagement over a one-year period for students in different age groups and check for evidence of learning gains using learning curve analysis. Student engagement is highest during school holidays and leading up to standardized exams, but persistence over time is low: under 25% of students return to the platform after joining. Clustering students into three groups based on their level of activity, we examine variation in their learning behaviors and quiz performance over their first ten days. Highly active students exhibit promising trends in terms of quiz completion, reattempts, and accuracy, but we do not see evidence of learning gains in this study. The findings suggest that students in Kenya use mobile learning either as an ad-hoc resource or a low-cost tutor to complement formal schooling and bridge gaps in instruction.

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cover image ACM Other conferences
L@S '20: Proceedings of the Seventh ACM Conference on Learning @ Scale
August 2020
442 pages
ISBN:9781450379519
DOI:10.1145/3386527
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 12 August 2020

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Author Tags

  1. clustering
  2. kenya
  3. learning curves
  4. mobile learning
  5. student engagement

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  • (2025)Does the Doer Effect Generalize To Non-WEIRD Populations? Toward Analytics in Radio and Phone-Based LearningProceedings of the 15th International Learning Analytics and Knowledge Conference10.1145/3706468.3706505(844-850)Online publication date: 3-Mar-2025
  • (2024)Comparative analysis of mobile learning in various countries: Literature study on five continentsAdvances in Mobile Learning Educational Research10.25082/AMLER.2024.02.0064:2(1114-1121)Online publication date: 25-Aug-2024
  • (2024) Supporting equitable access to learning via SMS in Kenya: Impact on engagement and learning outcomes British Journal of Educational Technology10.1111/bjet.13533Online publication date: 4-Nov-2024
  • (2024)Effective and Scalable Math Support: Experimental Evidence on the Impact of an AI-Math Tutor in GhanaArtificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky10.1007/978-3-031-64315-6_34(373-381)Online publication date: 2-Jul-2024
  • (2023)Exploring Student Involvement in E-Learning2023 14th International Conference on Intelligent Systems: Theories and Applications (SITA)10.1109/SITA60746.2023.10373724(1-7)Online publication date: 22-Nov-2023
  • (2023)The Effectiveness of a Mobile Educational Platform for Engaging Students in Out-of-class Activities2023 IEEE World Engineering Education Conference (EDUNINE)10.1109/EDUNINE57531.2023.10102869(1-6)Online publication date: 12-Mar-2023
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  • (2023)How can messaging apps, WhatsApp and SMS be used to support learning? A scoping reviewTechnology, Pedagogy and Education10.1080/1475939X.2023.220159032:3(275-288)Online publication date: 25-Apr-2023
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