Mobile Learning Technologies as drivers of School Enrolment Levels in South Africa: A Policy Simulation Experiment using Machine Learning-Driven Dynamic Autoregressive Distributed Lag Model

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Mobile Learning Technologies as drivers of School Enrolment Levels in South Africa: A Policy Simulation Experiment using Machine Learning-Driven Dynamic Autoregressive Distributed Lag Model

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Author(s): Fatima Ahmad Muazu , Festus Adedoyin , Huseyin Dogan , Paul Whittington , Nicholas Mavengere

Publication date (Print): July 2024

Conference name: 37th International BCS Human-Computer Interaction Conference (BCS HCI 24)

Conference theme: The International BCS Human-Computer Interaction Conference in 2024 was supported by the BCS Interactions Special Interest Group and hosted by the University of Central Lancashire in Preston. The BCS HCI Conference welcomed submissions on all aspects of human-computer interaction. Topics included: user experience (UX), usability testing, interaction design (IxD), human-centred AI (HCAI), education, health, sustainability, the Internet of Things (IoT), interaction technologies, and emerging interactive applications.

Conference date: 15–17 July 2024

Keywords: Mobile Learning, ARDL policy simulation, Secondary School Enrolment

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Author and article information

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Fatima Ahmad Muazu

Festus Adedoyin

Huseyin Dogan

Paul Whittington

Nicholas Mavengere

Conference

Publication date: July 2024

Publication date (Print): July 2024

Pages: 72-86

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[0001]Dept. of Computing & Informatics

Bournemouth University, UK

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DOI: 10.14236/ewic/BCSHCI2024.8

SO-VID: 4dd459c2-05f8-4e9e-9a8e-c9eadf2f2176

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This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: 37th International BCS Human-Computer Interaction Conference

Conference acronym: BCS HCI 24

Conference number: 37

Conference location: University of Central Lancashire (UCLan)

Conference date: 15–17 July 2024

Conference sponsor: Electronic Workshops in Computing (eWiC)

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1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/BCSHCI2024.8

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

AdamolekunG., AdedoyinF. F., & OzturkI. (2022). Supply-side sentiments and international travel: a novel dynamic simulation of policy options for business and investor sentiments. Journal of Policy Research in Tourism Leisure and Events, 1–23. doi:10.1080/19407963.2022.2142235.
AdedoyinF. F., MavengereN., & MutangaA. (2022). A simulation experiment on ICT and patent intensity in South Africa: An application of the novel dynamic ARDL machine learning model. Technological Forecasting and Social Change, 185(122044), 122044. doi: 10.1016/j.techfore.2022.122044.
BeckerS. A., CumminsM., DavisA., FreemanA., HallC. G., and AnanthanarayananV. (2017). NMC Horizon Report: 2017 higher education edition. Austin, TX: The New Media Consortium. Retrieved from https://www.learntechlib.org/p/174879/
BrownC., CzerniewiczL., & NoakesT. (2016). Online content creation: looking at students’ social media practices through a connected learning lens. Learning, Media & Technology, 41(1), 140–159. https://doi.org/10.1080/17439884.2015.110 7097.
Chaka, Chaka. (2021). Mobile learning studies conducted in certain secondary schools in South Africa from 2005 to 2015: A review study. Per Linguam, 37. 31-49. 10.5785/37-2-905.
Department of Basic Education. (2004). White Paper on e-Education. Government Gazette, 3-43
Global System for Mobile Communications Association. (2023). The mobile economy: Sub-Saharan Africa 2019. Retrieved from https://www.gsma.com/subsaharanafrica/re sources/the-mobile-economysub-saharanafrica-2023
HandalB., El-KhouryJ., CampbellC., & CavanaghM. (2013). A framework for categorising mobile applications in mathematics education. In Proceedings of the Australian Conference on Science and Mathematics Education (pp. 142–147). Retrieved from https://researchonline.nd.edu.au/edu_conference/70 .
Jantjies, M and JoyM. (2015). Mobile enhanced learning in a South African context. Educational Technology & Society 18 (1): 308–320.
KaliisaR., and PicardM. (2017). A systematic review on mobile learning in higher education: The African Perspective. Turkish Online Journal of Educational Technology-TOJET, 16(1), 1–18 Retrieved from ERIC database. (EJ1124918).
Sebastian Kripfganz & Daniel SchneiderC.. (2023). ardl: Estimating autoregressive distributed lag and equilibrium correction models. The Stata Journal 23, 983-1019. 10.1177/1536867X231212434.
Molebatsi, Mpaphi & Phorah, Kokisa. (2015). Incorporating Mobile Learning in Institutions of Higher Learning in South Africa. 21-25. 10.1109/GOCICT.2015.13.
MyBroadBand. (2019). MyBroadBand. Retrieved 03 November 2023, from https://mybroadband.co.za/news/government/290820-major-hi-tech-overhaulplannedfor-south-africas-education-system.html
Nicky Roberts & Riitta Vänskä (2011): Challenging assumptions: Mobile Learning for Mathematics Project in South Africa, Distance Education, 32:2, 243–259.
Ramaphosa C 2019. President Cyril Ramaphosa: State of the Nation Address 2019. 7 February, Parliament, Cape Town. Available at https://www.gov.za/speeches/2SONA2019. Accessed 08 November 2023.
SabaC. S. (2023). Nexus between CO2 emissions, renewable energy consumption, militarisation, and economic growth in South Africa: Evidence from using novel dynamic ARDL simulations. Renewable Energy, 205, 349–365. doi:10.1016/j.renene.2023.01.070.
SarkodieS.A., et al. (2019). Environmental sustainability assessment using dynamic autoregressive-distributed lag simulations— nexus between greenhouse gas emissions, biomass energy, food and economic growth. Sci. Total Environ. 668, 318–332.
TraxlerJ., & VoslooS. (2014). Introduction: The prospects for mobile learning. Prospects, 44(1), 13–28. https://doi.10.1007/s11125-014-9296-z.
The Global Economy, Business and Economic Data for 200 counrties. https://www.theglobaleconomy.com/rankings/Tertiary_school_enrollment/ accessed 28 March 2024, 11:40am
United Nations. (2015). Sustainable Development Goals. Retrieved from https://sustainabledevelopment.un.org/.
WuW.-H., Jim WuY.-C., ChenC.-Y., KaoH.-Y., LinC.-H. and HuangS.-H. 2012. Review of trends from mobile learning studies: A meta-analysis. Computers & Education, 59 (2): 817–827.

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[1] AdamolekunG., AdedoyinF. F., & OzturkI. (2022). Supply-side sentiments and international travel: a novel dynamic simulation of policy options for business and investor sentiments. Journal of Policy Research in Tourism Leisure and Events, 1–23. doi:10.1080/19407963.2022.2142235.

[2] AdedoyinF. F., MavengereN., & MutangaA. (2022). A simulation experiment on ICT and patent intensity in South Africa: An application of the novel dynamic ARDL machine learning model. Technological Forecasting and Social Change, 185(122044), 122044. doi: 10.1016/j.techfore.2022.122044.

[3] BeckerS. A., CumminsM., DavisA., FreemanA., HallC. G., and AnanthanarayananV. (2017). NMC Horizon Report: 2017 higher education edition. Austin, TX: The New Media Consortium. Retrieved from https://www.learntechlib.org/p/174879/

[4] BrownC., CzerniewiczL., & NoakesT. (2016). Online content creation: looking at students’ social media practices through a connected learning lens. Learning, Media & Technology, 41(1), 140–159. https://doi.org/10.1080/17439884.2015.110 7097.

[5] Chaka, Chaka. (2021). Mobile learning studies conducted in certain secondary schools in South Africa from 2005 to 2015: A review study. Per Linguam, 37. 31-49. 10.5785/37-2-905.

[6] Department of Basic Education. (2004). White Paper on e-Education. Government Gazette, 3-43

[7] Global System for Mobile Communications Association. (2023). The mobile economy: Sub-Saharan Africa 2019. Retrieved from https://www.gsma.com/subsaharanafrica/re sources/the-mobile-economysub-saharanafrica-2023

[8] HandalB., El-KhouryJ., CampbellC., & CavanaghM. (2013). A framework for categorising mobile applications in mathematics education. In Proceedings of the Australian Conference on Science and Mathematics Education (pp. 142–147). Retrieved from https://researchonline.nd.edu.au/edu_conference/70 .

[9] Jantjies, M and JoyM. (2015). Mobile enhanced learning in a South African context. Educational Technology & Society 18 (1): 308–320.

[10] KaliisaR., and PicardM. (2017). A systematic review on mobile learning in higher education: The African Perspective. Turkish Online Journal of Educational Technology-TOJET, 16(1), 1–18 Retrieved from ERIC database. (EJ1124918).

[11] Sebastian Kripfganz & Daniel SchneiderC.. (2023). ardl: Estimating autoregressive distributed lag and equilibrium correction models. The Stata Journal 23, 983-1019. 10.1177/1536867X231212434.

[12] Molebatsi, Mpaphi & Phorah, Kokisa. (2015). Incorporating Mobile Learning in Institutions of Higher Learning in South Africa. 21-25. 10.1109/GOCICT.2015.13.

[13] MyBroadBand. (2019). MyBroadBand. Retrieved 03 November 2023, from https://mybroadband.co.za/news/government/290820-major-hi-tech-overhaulplannedfor-south-africas-education-system.html

[14] Nicky Roberts & Riitta Vänskä (2011): Challenging assumptions: Mobile Learning for Mathematics Project in South Africa, Distance Education, 32:2, 243–259.

[15] Ramaphosa C 2019. President Cyril Ramaphosa: State of the Nation Address 2019. 7 February, Parliament, Cape Town. Available at https://www.gov.za/speeches/2SONA2019. Accessed 08 November 2023.

[16] SabaC. S. (2023). Nexus between CO2 emissions, renewable energy consumption, militarisation, and economic growth in South Africa: Evidence from using novel dynamic ARDL simulations. Renewable Energy, 205, 349–365. doi:10.1016/j.renene.2023.01.070.

[17] SarkodieS.A., et al. (2019). Environmental sustainability assessment using dynamic autoregressive-distributed lag simulations— nexus between greenhouse gas emissions, biomass energy, food and economic growth. Sci. Total Environ. 668, 318–332.

[18] TraxlerJ., & VoslooS. (2014). Introduction: The prospects for mobile learning. Prospects, 44(1), 13–28. https://doi.10.1007/s11125-014-9296-z.

[19] The Global Economy, Business and Economic Data for 200 counrties. https://www.theglobaleconomy.com/rankings/Tertiary_school_enrollment/ accessed 28 March 2024, 11:40am

[20] United Nations. (2015). Sustainable Development Goals. Retrieved from https://sustainabledevelopment.un.org/.

[21] WuW.-H., Jim WuY.-C., ChenC.-Y., KaoH.-Y., LinC.-H. and HuangS.-H. 2012. Review of trends from mobile learning studies: A meta-analysis. Computers & Education, 59 (2): 817–827.

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Mobile Learning Technologies as drivers of School Enrolment Levels in South Africa: A Policy Simulation Experiment using Machine Learning-Driven Dynamic Autoregressive Distributed Lag Model

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