The effectiveness of technology-supported personalised learning in low- and middle-income countries: A meta-analysis - Major, Louis; Francis, Gill A.; Tsapali, Maria (British Journal of Educational Technology, 2021)
Digital technology offers the potential to address educational challenges in resource-poor settings. This meta-analysis examines the impact of students' use of technology that personalises and adapts to learning level in low- and middle-income countries. Following a systematic search for research between 2007 and 2020, 16 randomised controlled trials were identified in five countries. Studies involved 53,029 learners aged 6–15 years. Coding examined learning domain (mathematics and literacy); personalisation level and delivery; technology use; and intervention duration and intensity. Overall, technology-supported personalised learning was found to have a statistically significant—if moderate—positive effect size of 0.18 on learning (p = 0.001). Meta-regression reveals how more personalised approaches which adapt or adjust to learners' level led to significantly greater impact (an effect size of 0.35) than those only linking to learners' interests or providing personalised feedback, support, and/or assessment. Avenues for future research include investigating cost implications, optimum programme length, and teachers' role in making personalised learning with technology effective. Practitioner notes What is already known about this topic? Promoting personalised learning is an established aim of educators. Using technology to support personalised learning in low- and middle-income countries (LMICs) could play an important role in ensuring more inclusive and equitable access to education, particularly in the aftermath of COVID-19. There is currently no rigorous overview of evidence on the effectiveness of using technology to enable personalised learning in LMICs. What this paper adds? The meta-analysis is the first to evaluate the effectiveness of technology-supported personalised learning in improving learning outcomes for school-aged children in LMICs. Technology-supported personalised learning has a statistically significant, positive effect on learning outcomes. Interventions are similarly effective for mathematics and literacy and whether or not teachers also have an active role in the personalisation. Personalised approaches that adapt or adjust to the learner led to significantly greater impact, although whether these warrant the additional investment likely necessary for implementation at scale needs to be investigated. Personalised technology implementation of moderate duration and intensity had similar positive effects to that of stronger duration and intensity, although further research is needed to confirm this. Implications for practice and/or policy: The inclusion of more adaptive personalisation features in technology-assisted learning environments can lead to greater learning gains. Personalised technology approaches featuring moderate personalisation may also yield learning rewards. While it is not known whether personalised technology can be scaled in a cost-effective and contextually appropriate way, there are indications that this is possible. The appropriateness of teachers integrating personalised approaches in their practice should be explored given ‘supplementary’ uses of personalised technology (ie, additional sessions involving technology outside of regular instruction) are common.
Archive: https://zenodo.org/record/4817787 KerkoCite.ItemAlsoKnownAs: 10.1111/bjet.13116 10/gj6k5n 2339240:382K6GCA 2339240:53R3ZYT5 2339240:CYQPAERV 2339240:EXFAZMDQ 2339240:P2I2EU98 2339240:RM59PWFE 2405685:5U948655 2405685:FIXCCB5I 2405685:HBGSTQ7X 2405685:QFTDGH3C 2405685:RZNJVMWH 2405685:V8UFGT52 2405685:WTT4UQ43 2405685:ZD4YGMMJ 4042040:G8KVDVSC
Keywords: _DOILIVE; _EdTechHub_Output; _MELA_seen; _cover:analysis:nopdf; _zenodo:submitted; computer-assisted learning; learning outcomes; low- and middle-income; meta-analysis; personalisation; personalised adaptive learning; ⛔ No DOI found

A Country-Level Research Review: EdTech in Tanzania - Jordan, Katy; Proctor, Jamie; Koomar, Saalim; Bapna, Akanksha (Zenodo, 2021)
This document presents a review of the research landscape in Tanzania in relation to EdTech research focused on learners, teachers, or systems at the level of school-based education, excluding higher education (HE). The review draws upon the research literature, policy documents, grey literature, and interviews with key stakeholders in order to present an overview of the research landscape in Tanzania. There is a substantial body of relevant EdTech research which has been undertaken in Tanzania in the past decade. Eighty research articles were identified for inclusion in the review. This document provides an overview of trends in this literature and key actors and projects, and provides a specific focus upon existing research which relates to EdTech Hub’s ‘high-potential evidence gaps’ (HPEG) framework. In combination with political economy analysis, three areas for future research which would be practical and likely to have high impact are identified and described.
KerkoCite.ItemAlsoKnownAs: 10.5281/zenodo.4618376 10.5281/zenodo.5652060 2339240:F8ER2X27 2339240:KZ7L8K2X 2339240:YKGMJASP 2405685:3F4ASVFA 2405685:IA77HHMV 2405685:RQGJRKVS 2405685:TYZ4Y65R 2405685:VRE5DB5K
Keywords: _DOILIVE; _EdTechHub_Output; _MELA_seen; _cover:v3; _zenodoETH

Problem Analysis and Focus of EdTech Hub’s Work: Technology in education in low- and middle-income countries - Hennessy, Sara; Jordan, Katy; Wagner, Daniel A.; EdTech Hub Team (EdTech Hub, 2021)
ZenodoArchiveID: 4332693 KerkoCite.ItemAlsoKnownAs: 10.5281/zenodo.4332693 2339240:2FHWWFA3 2339240:322MPSF7 2339240:8BXMXD7P 2339240:BTJ8QHAD 2339240:BTPX7CIX 2339240:DZSHD5WZ 2339240:EEUENHU3 2339240:HSFA8Z7Q 2339240:M3YUXFYS 2339240:Q2I8K6L8 2339240:SILWX2ZS 2339240:WEEFFITS 2339240:XHVAUXYZ 2405685:347U73VH 2405685:4LA8N3BT 2405685:8F46F44X 2405685:94F2MYRK 2405685:BHLDEAHG 2405685:CQXKYRLY 2405685:CSJHU24F 2405685:DYB6V7DK 2405685:EIQD4I2E 2405685:GUHMK274 2405685:HXF4GM6M 2405685:PBXBB7LF 2405685:QVG25MBV 2405685:R2YGTN82 2405685:R7Q7CT4P 2405685:SNZ55MKE 2534378:42JICVEU
Keywords: _C:Afghanistan AFG; _C:Bangladesh BGD; _C:Botswana BWA; _C:Congo; Democratic Republic COD; _C:El Salvador SLV; _C:Gambia GMB; _C:Germany DEU; _C:Ghana GHA; _C:India IND; _C:Indonesia IDN; _C:Ireland IRL; _C:Jordan JOR; _C:Kenya KEN; _C:Lebanon LBN; _C:Liberia LBR; _C:Malawi MWI; _C:Pakistan PAK; _C:Peru PER; _C:Philippines PHL; _C:Rwanda RWA; _C:Senegal SEN; _C:Sierra Leone SLE; _C:South Africa ZAF; _C:Tanzania TZA; _C:Uganda UGA; _C:United Kingdom GBR; _C:United States USA; _C:Uruguay URY; _C:Zambia ZMB; _C:eSwatini SWZ; _DOILIVE; _EdTechHub_Output; _MELA_seen; __C:scheme:1; ___working_potential_duplicate; _cover:v3; _zenodo:submitted; _zenodoETH; ⛔ No INSPIRE recid found