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Relevant bibliographies by topics / Flexible Learning

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Dissertations / Theses
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Academic literature on the topic 'Flexible Learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Flexible Learning"

1

Evans, David W., and Penelope J. Hood. "Flexible Learning." Management in Education 11, no. 4 (1997): 24–25. http://dx.doi.org/10.1177/089202069701100412.

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Irvine, Jan, and Amanda Cossham. "Flexible learning." Library Review 60, no. 8 (2011): 712–22. http://dx.doi.org/10.1108/00242531111166728.

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Hill, Janette R. "Flexible Learning Environments: Leveraging the Affordances of Flexible Delivery and Flexible Learning." Innovative Higher Education 31, no. 3 (2006): 187–97. http://dx.doi.org/10.1007/s10755-006-9016-6.

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Bhoite, Dr Sudhakar D. "E-Learning: the Technology Enhanced Learning (TEL) Makes Learning Easier And Flexible." International Journal of Scientific Research 2, no. 9 (2012): 84–85. http://dx.doi.org/10.15373/22778179/sep2013/29.

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Talosa, Arlene D., Billy S. Javier, and Estela L. Dirain. "flexible-learning journey." Linguistics and Culture Review 5, S3 (2021): 422–34. http://dx.doi.org/10.21744/lingcure.v5ns3.1590.

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Philippine higher education has been driven by the urgency of alternative learning modalities from traditional to flexible teaching and learning. Much research has already been embarked on flexible learning. New to the platform, this study purports to document and describe the learning opportunities and influencing factors on the self-efficacy of students in a flexible learning set-up. Using In-depth individual interviews as its method, the qualitative study was administered to higher education students who consented to take part in the study. Findings revealed that self-regulated learning and

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Richmond, Terry. "Making Learning Flexible." British Journal of Special Education 20, no. 1 (2007): 20–23. http://dx.doi.org/10.1111/j.1467-8578.1993.tb00023.x.

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Cendon, Eva, Ülle Kesli, and Kevin Orr. "University Lifelong Learning as flexible learning." European Journal of University Lifelong Learning 7, no. 1 (2023): 1–6. http://dx.doi.org/10.53807/0701f9s8.

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Nicoll, Katherine. "‘Flexible learning'—unsettling practices." Studies in Continuing Education 19, no. 2 (1997): 100–111. http://dx.doi.org/10.1080/0158037970190202.

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Lewis, R. "Flexible and Distance Learning." Journal of Computer Assisted Learning 9, no. 2 (1993): 127–28. http://dx.doi.org/10.1111/j.1365-2729.1993.tb00166.x.

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Espineda, Jovita E. "Exploring Flexible Learning Experiences." International Journal of Multidisciplinary: Applied Business and Education Research 5, no. 12 (2024): 4932–44. https://doi.org/10.11594/ijmaber.05.12.04.

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Flexible learning is a popular mode of learning in this 21st century since it allows flexibility in almost all aspects of the teaching and learning process. The study explored the flexible learning experi-ences of college students enrolled in the school year 2022-2023. The focus were in terms of internet connection, communication, conferencing platform, online teaching strategies, assessment of learning, student participation and submission of requirements. Results revealed that internet connection is a problem as they en-gaged in flexible learning modality since most of the activities re-late

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Dissertations / Theses on the topic "Flexible Learning"

1

Littler, Craig George. "Perspectives on learning and information in flexible learning environments /." Electronic version, 2004. http://adt.lib.uts.edu.au/public/adt-NTSM20040730.113839/index.html.

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Plagemann, Christian. "Gaussian processes for flexible robot learning." [S.l. : s.n.], 2008. http://nbn-resolving.de/urn:nbn:de:bsz:25-opus-61088.

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Adams, Evelyn May. "Lifelong learning and the learning culture of a college flexible learning centre." Thesis, University of Stirling, 2009. http://hdl.handle.net/1893/2751.

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This study aims to address a gap in knowledge about Further Education college flexible learning centres and their contribution to lifelong learning. Flexible learning centres were established as a response to the lifelong learning agenda of the 1990s and are now in the front line in responding to government initiatives to improve employability and foster social inclusion. Their tutors work in a contested area where the boundaries between teaching and supporting learning are blurred and the learning achieved may be undervalued by the Inspectorate and college authorities. This study adopts a qua

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Grüne, Markus. "Flexible Abrechnungssysteme für digitale Produkte im E-Learning." Giessen : VVB Laufersweiler, 2007. http://d-nb.info/985504501/04.

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Kawaguchi, Nobuo, and Yuya Negishi. "Instant Learning Sound Sensor: Flexible Environmental Sound Recognition System." IEEE, 2007. http://hdl.handle.net/2237/15456.

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Ediriweera, Damjee Dharshana. "A learning-based architecture for flexible sensor network management." Thesis, Lancaster University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.658320.

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The thesis investigates the use of machine learning as an effective means of supporting autonomous flexibility within complex sensor network management systems. Policy-based management has often been the tool of choice for addressing such requirements, but is often only a partial solution, due to its reliance on end-user capacity for timely and accurate policy creation. A new systems architecture HYBRID, capable of autonomous system flexibility through user-independent adaptation, is therefore proposed. HYBRID combines policy based management with self-learning algorithms to realise a single a

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Snelson, Edward Lloyd. "Flexible and efficient Gaussian process models for machine learning." Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1445855/.

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Gaussian process (GP) models are widely used to perform Bayesian nonlinear regression and classification tasks that are central to many machine learning problems. A GP is nonparametric, meaning that the complexity of the model grows as more data points are received. Another attractive feature is the behaviour of the error bars. They naturally grow in regions away from training data where we have high uncertainty about the interpolating function. In their standard form GPs have several limitations, which can be divided into two broad categories: computational difficulties for large data sets, a

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Reves, Ian P. "New assemblies for learning : flexible construction systems aimed at new concepts of learning environments." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39616.

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The design and construction of American public high schools are forcibly influenced by ultra-cost effective techniques demanding simplicity in construction and durability of material. The inflexibility and banality of the architecture this paradigm typically delivers begs for exploration of the feasibility of innovative construction technologies. Technologies that influence both form and technique such as prefabrication of modular elements, utilization of CAD/CAM techniques to mill customized parts and pliable materials (i.e. plastics) crafted to achieve dynamic forms. More engaging, flexible

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Waqailiti, Ledua Meke. "Negotiating Learning in Distance and Flexible Learning at the University of the South Pacific." Thesis, Griffith University, 2011. http://hdl.handle.net/10072/365434.

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This is a thesis that presents a qualitative mixed method study of 30 students engaged in distance and flexible learning (DFL) courses of study across two campuses of the University of the South Pacific in Fiji. The goals of this research were to develop an account of the personal and cultural learning practices that students draw on and use when negotiating learning in their University studies and to understand the ways that University courses including teaching and learning interactions and course materials contribute to student learning and success. At the centre of this study is the concep

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Grüne, Markus [Verfasser]. "Flexible Abrechnungssysteme für digitale Produkte im E-Learning / Markus Grüne." Gießen : VVB Laufersweiler, 2007. http://d-nb.info/1001801261/34.

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Books on the topic "Flexible Learning"

1

Hall, Alexandra Linda. Flexible working, flexible learning. University of Salford, 1995.

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Tomlinson, Peter. Flexible learning, flexible teaching: The flexible learning framework and current educational theory. University of Leeds, School of Education, 1990.

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Tomlinson, Peter. Flexible learning, flexible teaching: The flexible learning framework and current educational theory. Employment Department, 1991.

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Fred, Percival, Craig David 1932-, Buglass Dorothy, Association for Educational and Training Technology., and Educational Technology International Conference (21st : 1986 : Edinburgh, Lothian), eds. Flexible learning systems. Kogan Page, 1987.

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Jackson, Rachael L. Flexible learning systems. De Montfort University, 1994.

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Great Britain. Employment Department Group., ed. Introducing flexible learning. Employment Group, 1990.

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Great Britain. Department of Employment. Learning Methods Branch., ed. Open & flexible learning: Learning methods. Learning Methods Branch, Employment Department, 1994.

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Yorkshire & Humberside Flexible Learning Project. and Wolds Enterprise Bureau, eds. Flexible teacher-flexible learner. Yorkshire & Humberside Flexible Learning Project, 1994.

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University, Thames Valley, ed. Establishing flexible learning programmes. Pitman Publishing, 1995.

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Great Britain. Further Education Unit. Flexible learning in perspective. Further Education Unit, 1989.

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Book chapters on the topic "Flexible Learning"

1

Rennie, Frank, and Keith Smyth. "Flexible learning." In Digital Learning: The Key Concepts, 2nd ed. Routledge, 2019. http://dx.doi.org/10.4324/9780429425240-86.

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Dallal, Ronnie. "Flexible Distance Learning." In Learning Technology in the European Communities. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2672-4_13.

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Pesch, Erwin. "Flexible Manufacturing Systems." In Learning in Automated Manufacturing. Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-49366-9_4.

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Hartnett, Maggie. "Flexible and Distance Learning." In Encyclopedia of Education and Information Technologies. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-10576-1_67.

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Hartnett, Maggie. "Flexible and Distance Learning." In Encyclopedia of Education and Information Technologies. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-60013-0_67-1.

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Issa, Theodora. "Ethical Mindsets: Learning from Cultural Comparisons." In Flexible Systems Management. Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2834-9_7.

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Badiru, Adedeji B. "Learning Curve in the Supply Chain." In Flexible Supply Chain. CRC Press, 2024. http://dx.doi.org/10.1201/9781032620701-8.

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Khari, Chitra, and Shuchi Sinha. "Impact of Workplace Spirituality on Learning Commitment." In Flexible Systems Management. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4888-3_7.

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Subrahmanian, V. S. "Logic, Machine Learning, and Security." In Flexible Query Answering Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27629-4_1.

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Vančová, Martina Halás, and Zuzana Kovačičová. "Sharing Knowledge and Information Through Corporate e-Learning." In Flexible Systems Management. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3358-2_8.

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Conference papers on the topic "Flexible Learning"

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Kim, Juhyeon, and Hyunjung Shin. "Continual Learning with Flexible Preservation." In 2025 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2025. https://doi.org/10.1109/bigcomp64353.2025.00052.

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Kallab, Chadi, Samir Haddad, Jinan Charafeddine, Joseph Merhej, Nisrine Turkey, and Jinane Sayah. "Flexible Granular Deep Learning Voting Architecture." In 2024 International Conference on Computer and Applications (ICCA). IEEE, 2024. https://doi.org/10.1109/icca62237.2024.10927895.

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Paz-Pérez, Aurora, Anxo Tato, J. Joaquín Escudero-Garzás, and Felipe Gómez-Cuba. "Flexible Reinforcement Learning Scheduler for 5G Networks." In 2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN). IEEE, 2024. http://dx.doi.org/10.1109/icmlcn59089.2024.10625129.

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Mendonça, Marcele O. K., Flor G. Ortiz-Gomez, Jorge Querol, et al. "Flexible Payload Configuration for Satellites using Machine Learning." In 2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN). IEEE, 2024. http://dx.doi.org/10.1109/icmlcn59089.2024.10624839.

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Logar Avbelj, Polonca. "Flexible Furniture and Innovative Learning Environment." In Developing Effective Learning. University of Primorska Press, 2020. http://dx.doi.org/10.26493/978-961-293-002-8.42.

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Dar-EI, E., and H. Altman. "A DYNAMIC-B MODEL OF LEARNING." In Flexible Automation and Integrated Manufacturing 1994. Begellhouse, 2023. http://dx.doi.org/10.1615/faim1994.760.

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Hou, Kun, Ji-Gui Sun, Ming-Hao Yin, and Wei Yang. "Flexible Conformant Graphplan." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370682.

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Gorday, Paul, Nurgun Erdol, and Hanqi Zhuang. "Flexible FSK Learning Demodulator." In 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). IEEE, 2018. http://dx.doi.org/10.1109/uemcon.2018.8796649.

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"Facilitating flexible learning experiences." In Education and New Developments 2024. inScience Press, 2024. http://dx.doi.org/10.36315/2024v1end018.

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"Using flexible e-learning in the European retail: Flexible learning in European retail." In 2014 International Conference on Interactive Collaborative Learning (ICL). IEEE, 2014. http://dx.doi.org/10.1109/icl.2014.7017852.

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Reports on the topic "Flexible Learning"

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DeJong, Gerald F. Machine Learning for Flexible Robotics. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada252767.

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List, John, Ian Muir, and Gregory Sun. Using Machine Learning for Efficient Flexible Regression Adjustment in Economic Experiments. National Bureau of Economic Research, 2022. http://dx.doi.org/10.3386/w30756.

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Flaxman, Seth. Statistical Machine Learning for Researchers. Instats Inc., 2023. http://dx.doi.org/10.61700/3sz8pzpbpsg2i469.

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This workshop is designed to empower researchers with the fundamentals of machine learning using R. Participants will learn the key principles that make machine learning so effective, powering the modern AI and deep learning revolution. Through hands-on exercises, participants will gain experience applying a variety of flexible and scalable statistical machine learning methods to analyze datasets and build effective predictive models. An official Instats certificate of completion is provided along with 2 ECTS Equivalent points.

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Flaxman, Seth. Statistical Machine Learning for Researchers. Instats Inc., 2023. http://dx.doi.org/10.61700/wu1mihoap95h0469.

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This workshop is designed to empower researchers with the fundamentals of machine learning using R. Participants will learn the key principles that make machine learning so effective, powering the modern AI and deep learning revolution. Through hands-on exercises, participants will gain experience applying a variety of flexible and scalable statistical machine learning methods to analyze datasets and build effective predictive models. An official Instats certificate of completion is provided along with 2 ECTS Equivalent points.

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Goulet Coulombe, Philippe, Massimiliano Marcellino, and Dalibor Stevanovic. Panel Machine Learning with Mixed-Frequency Data: Monitoring State-Level Fiscal Variables. CIRANO, 2025. https://doi.org/10.54932/qgja3449.

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We study the nowcasting of U.S. state-level fiscal variables using machine learning (ML) models and mixed-frequency predictors within a panel framework. Neural networks with continuous and categorical embeddings consistently outperform both linear and nonlinear alternatives, especially when combined with pooled panel structures. These architectures flexibly capture differences across states while benefiting from shared patterns in the panel structure. Forecast gains are especially large for volatile variables like expenditures and deficits. Pooling enhances forecast stability, and ML models ar

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Streuli, Natalia, and Catherine M. Moleni. Education and HIV and AIDS in Malawi : the role of open, distance and flexible learning. Institute of Education, University of London, 2008. http://dx.doi.org/10.35648/20.500.12413/11781/ii083.

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Luke Luna, Christina, and Josh Weisgrau. Exponential Change Brings Exponential Choice: Navigating Life, Learning, and Career. Digital Promise, 2025. https://doi.org/10.51388/20.500.12265/244.

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Success in life and career is often viewed through a narrow lens, but what if we reimagined the pathways that lead there? This work explores a new vision for navigating education and career transitions, emphasizing adaptability, learner agency, and competency-based approaches. Through personal stories and key insights, we examine how K-12 and higher education institutions must evolve to support more flexible, individualized learning experiences. By recognizing the connections between skills, identity, and career growth, we can better equip learners for the complexities of the modern world.

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Qi, Fei, Zhaohui Xia, Gaoyang Tang, et al. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, 2020. http://dx.doi.org/10.37686/ser.v1i2.77.

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As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate th

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Kaffenberger, Michelle, and Jason Silberstein. Descriptive Learning Trajectories and Policy Simulations Using MICS6 Data. Research on Improving Systems of Education (RISE), 2022. http://dx.doi.org/10.35489/bsgrise-misc_2022/05.

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In recent years, scholars associated with the RISE Programme have analysed learning trajectories using a variety of global datasets to shed light on the global learning crisis and diagnose what might help address it (Crouch, Kaffenberger, and Savage, 2021). For those who may want to build and analyse learning trajectories, this note acts as a methodological guide for doing so using an important new dataset on foundational learning, the Multiple Indicator Cluster Surveys Round 6 (MICS6). We have applied the methods described in this note and, in partnership with the Global Education Monitoring

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Kaffenberger, Michelle, and Jason Silberstein. Descriptive Learning Trajectories and Policy Simulations Using MICS6 Data. Research on Improving Systems of Education (RISE), 2022. http://dx.doi.org/10.35489/bsg-rise-misc_2022/05.

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In recent years, scholars associated with the RISE Programme have analysed learning trajectories using a variety of global datasets to shed light on the global learning crisis and diagnose what might help address it (Crouch, Kaffenberger, and Savage, 2021). For those who may want to build and analyse learning trajectories, this note acts as a methodological guide for doing so using an important new dataset on foundational learning, the Multiple Indicator Cluster Surveys Round 6 (MICS6). We have applied the methods described in this note and, in partnership with the Global Education Monitoring

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