Learning analytics for the global south

Learning Analytics for the Global South is a compilation of papers commissioned for the Digital Learning for Development (DL4D) project. DL4D is part of the Information Networks in Asia and Sub-Saharan Africa (INASSA) program funded jointly by the International Development Research Centre (IDRC) of Canada and the Department for International Development (DFID) of the United Kingdom, and administered by the Foundation for Information Technology Education and Development (FIT-ED) of the Philippines. DL4D aims to examine how digital learning could be used to address issues of equity, quality, and efficiency at all educational levels in developing countries. Over the past two years, DL4D has brought together leading international and regional scholars and practitioners to critically assess the potentials, prospects, challenges, and future directions for the Global South in key areas of interest around digital learning. It commissioned discussion papers for each of these areas from leading experts in the field: Diana Laurillard of the University College London Knowledge Lab, for learning at scale; Chris Dede of Harvard University, for digital game-based learning; Charalambos Vrasidas of the Centre for the Advancement of Research and Development in Educational Technology, for cost-effective digital learning innovations; and for learning analytics, the subject of this compilation, Dragan Gašević of the University of Edinburgh Moray House School of Education and School of Informatics. Each discussion paper is complemented by responses from a developing country-perspective by regional experts in Asia, Latin America, Africa, and the Middle East. Learning Analytics for the Global South considers how the collection, analysis, and use of data about learners and their contexts have the potential to broaden access to quality education and improve the efficiency of educational processes and systems in developing countries around the world. In his discussion paper, Prof. Gašević articulates these potentials and suggests how learning analytics could support critical digital learning and education imperatives such as quality learning at scale and the acquisition of 21st century skills. Experts from Africa (Paul Prinsloo of the University of South Africa), Mainland China (Bodong Chen of the University of Minnesota, USA and Yizhou Fan of Peking University, People’s Republic of China), Southeast Asia (Ma. Mercedes T. Rodrigo of the Ateneo de Manila University, Philippines), and Latin America (Cristóbal Cobo and Cecilia Aguerrebere, both of the Ceibal Foundation, Uruguay) situate Prof. Gašević’s proposals in their respective regional contexts, framing their responses around six key questions: 1. What are the main trends and challenges in education in your region? 2. How can learning analytics address these challenges? 3. What models of learning analytics adoption would be most effective in your region? 4. What are the barriers in adoption of learning analytics in your region and how could these be mitigated? 5. How do you envision ethical use and privacy protection in connection with learning analytics being addressed in your region? 6. How can the operationalization of learning analytics be futureproofed in your region? We hope that this compilation will serve as a springboard for deeper conversations about the adoption and sustained use of learning analytics in developing countries – its potential benefits and risks for learners, educators, and educations systems, as well as the ways to move forward that are rigorous, context-appropriate, ethical, and accountable.This work was created with financial support from the UK Government’s Department for International Development and the International Development Research Centre, Canada. The views expressed in this work are those of the authors and do not necessarily represent those of the UK Government’s Department for International Development; the International Development Research Centre, Canada or its Board of Governors; the Foundation for Information Technology Education and Development; or the editors

Analíticas de aprendizaje para el sur global

El uso cada vez mayor de la tecnología en el ámbito de la educación ha dado lugar a una recopilación de datos sin precedentes sobre diversos aspectos del aprendizaje, la enseñanza y los sistemas educativos. Para hacer frente a los acuciantes desafíos, sectores de la educación de todo el mundo han reconocido el potencial que entraña analizar tales datos mediante métodos avanzados de análisis de datos. Este interés por los datos relativos a la educación, impulsó el desarrollo del campo de las analíticas de aprendizaje, cuyo objetivo es comprender y optimizar el aprendizaje y los entornos en los que ocurre. Si bien existen muchas historias de éxito sobre el uso de las analíticas de aprendizaje, estas historias provienen predominantemente del Norte Global. En el presente documento se examinan las oportunidades que genera la adopción de las analíticas de aprendizaje en el Sur Global en relación con los tres pilares fundamentales de la educación: calidad, equidad y eficiencia. En el documento se sugiere que la aplicación de las analíticas de aprendizaje en los países en desarrollo tiene un gran potencial para apoyar el aprendizaje a escala, proporcionar retroalimentaciones y experiencias de aprendizaje personalizadas, aumentar el porcentaje de egresados, identificar los sesgos que afectan al éxito de los estudiantes, promover el desarrollo de las competencias del siglo XXI y optimizar el uso de los recursos. El documento concluye haciendo hincapié en la importancia fundamental que tiene el desarrollo de políticas y códigos de prácticas referentes al uso ético de las analíticas de aprendizaje, la protección de la privacidad y la responsabilidad algorítmica para apoyar una adopción saludable de las analíticas de aprendizaje

Learning analytics for the global south

Learning Analytics for the Global South is a compilation of papers commissioned for the Digital Learning for Development (DL4D) project. DL4D is part of the Information Networks in Asia and Sub-Saharan Africa (INASSA) program funded jointly by the International Development Research Centre (IDRC) of Canada and the Department for International Development (DFID) of the United Kingdom, and administered by the Foundation for Information Technology Education and Development (FIT-ED) of the Philippines. DL4D aims to examine how digital learning could be used to address issues of equity, quality, and efficiency at all educational levels in developing countries. Over the past two years, DL4D has brought together leading international and regional scholars and practitioners to critically assess the potentials, prospects, challenges, and future directions for the Global South in key areas of interest around digital learning. It commissioned discussion papers for each of these areas from leading experts in the field: Diana Laurillard of the University College London Knowledge Lab, for learning at scale; Chris Dede of Harvard University, for digital game-based learning; Charalambos Vrasidas of the Centre for the Advancement of Research and Development in Educational Technology, for cost-effective digital learning innovations; and for learning analytics, the subject of this compilation, Dragan Gašević of the University of Edinburgh Moray House School of Education and School of Informatics. Each discussion paper is complemented by responses from a developing country-perspective by regional experts in Asia, Latin America, Africa, and the Middle East. Learning Analytics for the Global South considers how the collection, analysis, and use of data about learners and their contexts have the potential to broaden access to quality education and improve the efficiency of educational processes and systems in developing countries around the world. In his discussion paper, Prof. Gašević articulates these potentials and suggests how learning analytics could support critical digital learning and education imperatives such as quality learning at scale and the acquisition of 21st century skills. Experts from Africa (Paul Prinsloo of the University of South Africa), Mainland China (Bodong Chen of the University of Minnesota, USA and Yizhou Fan of Peking University, People’s Republic of China), Southeast Asia (Ma. Mercedes T. Rodrigo of the Ateneo de Manila University, Philippines), and Latin America (Cristóbal Cobo and Cecilia Aguerrebere, both of the Ceibal Foundation, Uruguay) situate Prof. Gašević’s proposals in their respective regional contexts, framing their responses around six key questions: 1. What are the main trends and challenges in education in your region? 2. How can learning analytics address these challenges? 3. What models of learning analytics adoption would be most effective in your region? 4. What are the barriers in adoption of learning analytics in your region and how could these be mitigated? 5. How do you envision ethical use and privacy protection in connection with learning analytics being addressed in your region? 6. How can the operationalization of learning analytics be futureproofed in your region? We hope that this compilation will serve as a springboard for deeper conversations about the adoption and sustained use of learning analytics in developing countries – its potential benefits and risks for learners, educators, and educations systems, as well as the ways to move forward that are rigorous, context-appropriate, ethical, and accountable.This work was created with financial support from the UK Government’s Department for International Development and the International Development Research Centre, Canada. The views expressed in this work are those of the authors and do not necessarily represent those of the UK Government’s Department for International Development; the International Development Research Centre, Canada or its Board of Governors; the Foundation for Information Technology Education and Development; or the editors

Analíticas de aprendizaje para el sur global

El uso cada vez mayor de la tecnología en el ámbito de la educación ha dado lugar a una recopilación de datos sin precedentes sobre diversos aspectos del aprendizaje, la enseñanza y los sistemas educativos. Para hacer frente a los acuciantes desafíos, sectores de la educación de todo el mundo han reconocido el potencial que entraña analizar tales datos mediante métodos avanzados de análisis de datos. Este interés por los datos relativos a la educación, impulsó el desarrollo del campo de las analíticas de aprendizaje, cuyo objetivo es comprender y optimizar el aprendizaje y los entornos en los que ocurre. Si bien existen muchas historias de éxito sobre el uso de las analíticas de aprendizaje, estas historias provienen predominantemente del Norte Global. En el presente documento se examinan las oportunidades que genera la adopción de las analíticas de aprendizaje en el Sur Global en relación con los tres pilares fundamentales de la educación: calidad, equidad y eficiencia. En el documento se sugiere que la aplicación de las analíticas de aprendizaje en los países en desarrollo tiene un gran potencial para apoyar el aprendizaje a escala, proporcionar retroalimentaciones y experiencias de aprendizaje personalizadas, aumentar el porcentaje de egresados, identificar los sesgos que afectan al éxito de los estudiantes, promover el desarrollo de las competencias del siglo XXI y optimizar el uso de los recursos. El documento concluye haciendo hincapié en la importancia fundamental que tiene el desarrollo de políticas y códigos de prácticas referentes al uso ético de las analíticas de aprendizaje, la protección de la privacidad y la responsabilidad algorítmica para apoyar una adopción saludable de las analíticas de aprendizaje

Validity and Utility of the Patient Health Questionnaire (PHQ)‐2 and PHQ‐9 for Screening and Diagnosis of Depression in Rural Chiapas, Mexico: A Cross‐Sectional Study

Background: Depressive disorders are frequently under diagnosed in resource‐limited settings because of lack of access to mental health care or the inability of healthcare providers to recognize them. The Patient Health Questionnaire (PHQ)‐2 and the PHQ‐9 have been widely used for screening and diagnosis of depression in primary care settings; however, the validity of their use in rural, Spanish‐speaking populations is unknown. Method We used a cross‐sectional design to assess the psychometric properties of the PHQ‐9 for depression diagnosis and estimated the sensitivity and specificity of the PHQ‐2 for depression screening. Data were collected from 223 adults in a rural community of Chiapas, Mexico, using the PHQ‐2, the PHQ‐9, and the World Health Organization Quality of Life BREF Scale (WHOQOL‐ BREF). Results: Confirmatory factor analysis suggested that the 1‐factor structure fit reasonably well. The internal consistency of the PHQ‐9 was good (Cronbach's alpha > = 0.8) overall and for subgroups defined by gender, literacy, and age. The PHQ‐9 demonstrated good predictive validity: Participants with a PHQ‐9 diagnosis of depression had lower quality of life scores on the overall WHOQOL‐BREF Scale and each of its domains. Using the PHQ‐9 results as a gold standard, the optimal PHQ‐2 cutoff score for screening of depression was 3 (sensitivity 80.00%, specificity 86.88%, area under receiver operating characteristic curve = 0.89; 95% confidence interval [0.84, 0.94]). Conclusion: The PHQ‐2 and PHQ‐9 demonstrated good psychometric properties, suggesting their potential benefit as tools for depression screening and diagnosis in rural, Spanish‐speaking populations