Relevant bibliographies by topics / Flexible Learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Flexible Learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Flexible Learning"

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Evans, David W., and Penelope J. Hood. "Flexible Learning." Management in Education 11, no. 4 (September 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 (September 6, 2011): 712–22. http://dx.doi.org/10.1108/00242531111166728.

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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 (June 1, 2012): 84–85. http://dx.doi.org/10.15373/22778179/sep2013/29.

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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 (September 1, 2006): 187–97. http://dx.doi.org/10.1007/s10755-006-9016-6.

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Talosa, Arlene D., Billy S. Javier, and Estela L. Dirain. "flexible-learning journey." Linguistics and Culture Review 5, S3 (October 23, 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 digital or technological self-efficacy are the two themes found as a learning opportunity with the current learning modality. Personal, Technological, Communication, Home Environment, and Teacher-related are the overarching themes that emerged as influencing factors. The findings of this study have generated several implications for teaching practice, technology experts, and other stakeholders that must be considered very seriously as they represent the opinion of the users of flexible learning.
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Richmond, Terry. "Making Learning Flexible." British Journal of Special Education 20, no. 1 (May 31, 2007): 20–23. http://dx.doi.org/10.1111/j.1467-8578.1993.tb00023.x.

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Nicoll, Katherine. "‘Flexible learning'—unsettling practices." Studies in Continuing Education 19, no. 2 (January 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 (June 1993): 127–28. http://dx.doi.org/10.1111/j.1365-2729.1993.tb00166.x.

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Harry, Keith. "Flexible and distance learning." Computers & Education 22, no. 4 (May 1994): 364–65. http://dx.doi.org/10.1016/0360-1315(94)90059-0.

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Murphy, David, Kym Fraser, and Len Webster. "'Wrestling with blancmange': A flexible flexible learning workshop." International Journal for Academic Development 5, no. 1 (January 2000): 54–60. http://dx.doi.org/10.1080/136014400410105.

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Dissertations / Theses on the topic "Flexible Learning"

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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 qualitative approach of narrative inquiry to analyse the learning culture of the Flexible Learning Centre (FLC) of Hollypark College, focusing on 15 learners’ and 2 tutors’ narratives elicited by episodic interviews. The narratives of learning biographies and work biographies of learners and tutors are explored, supplemented by quantitative data from College databases. The study evaluates the ways in which the pedagogical approach of selfdirected study with tutor support appears to be successful for predominantly mature learners wishing to acquire mainly Information Technology (IT) skills which may enhance their chances of employment or benefit them in other ways. Building on recent work on learning cultures, social capital, well-being and identity theories, the study gives a voice to these learners who are so far unheard and despite the demographic population shift to increasing numbers of older people, are not the focus of current government policy initiatives. Focus on employability which is equated with acquiring skills is seen as too narrow to encompass the wider needs met and benefits accrued by attending the FLC. The findings are that this FLC’s approach is particularly effective for older learners in general in acquiring IT skills. Those made redundant, the retired, those in work and carers who may have been out of the workforce for some time may flourish in this learning environment where mainstream courses cannot offer equivalent flexibility and opportunities to structure their own learning. The learning culture of the Flexible Learning Centre provides a space where such learners may not only acquire IT skills but may also increase their social and cultural capital while opening up new horizons for their future. Tutor/learner relationships are of key importance and the learning taking place should be re-evaluated by the Inspectorate and government alike.
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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 architecture capable of flexible automation at all levels of a management system. The work described in this thesis demonstrates the limitations of policy-based management, and illustrates how best to mitigate them through the adoption of self-learning techniques. The availability and suitability of today's learning algorithms for facilitating such automation is investigated, and where necessary, algorithmic enhancements to selected techniques are proposed and evaluated to explore relevant complexities. Validity of the architecture is demonstrated through two real -world trials. HYBRID has been applied to address distinct management problems, demonstrating on each occasion, 'how' the proposed architecture supports effective and safe exploitation of machine learning to enable greater behavioural flexibility within complex management systems.
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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, and restrictive modelling assumptions for complex data sets. This thesis addresses various aspects of both of these problems. The training cost for a GP has 0(N3) complexity, where N is the number of training data points. This is due to an inversion of the N x N covariance matrix. In this thesis we develop several new techniques to reduce this complexity to 0(NM2), where M is a user chosen number much smaller than N. The sparse approximation we use is based on a set of M 'pseudo-inputs' which are optimised together with hyperparameters at training time. We develop a further approximation based on clustering inputs that can be seen as a mixture of local and global approximations. Standard GPs assume a uniform noise variance. We use our sparse approximation described above as a way of relaxing this assumption. By making a modification of the sparse covariance function, we can model input dependent noise. To handle high dimensional data sets we use supervised linear dimensionality reduction. As another extension of the standard GP, we relax the Gaussianity assumption of the process by learning a nonlinear transformation of the output space. All these techniques further increase the applicability of GPs to real complex data sets. We present empirical comparisons of our algorithms with various competing techniques, and suggest problem dependent strategies to follow in practice.
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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 learning environments could be realized that significantly increase the performance of the architecture, both formally and ecologically, as well as ennobling students.
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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 concept of learning as negotiated practice. Data sources included a questionnaire with semi-structured and open-ended questions. Data collection and analysis was guided by the construct of third space theory (Guitierrez, 1999), which provided a frame for considering students’ DFL experiences as acts of negotiation that are situated in nested socio-cultural and socio-historical contexts. Two descriptive campus cases of learning are presented, one for each of the Lautoka and Labasa campuses. Within each case, data from a student group provides background to in-depth learning stories for focus of students at each campus. Given the socio-cultural, economical, political and the geographical difficulties that DFL students face this study shows that they also face constraints within the University learning contexts that impact the way they construct meaning from their learning experiences. This study draws attention to the need for teaching and learning in DFL that is learner focused and learner centred and that acknowledges learning as a social and cultural undertaking which is constructed as students draw on personal funds of knowledge to navigate unfamiliar terrain. Findings suggest that universities offering DFL courses would benefit students if the environments for learning constructed in DFL courses were developed in such a way that provides students with opportunities to draw on their unofficial personal and cultural resources in ways that enhance their ability to learn in the official world of University study. Third space theory is utilised to develop explanations of learning in DFL as a mediated act of negotiation between these official and unofficial spaces that learners inhabit. In the process of engaging and negotiating learning between the DFL learner’s two spaces, a third space or hybrid space is created. This space represents the negotiated solution to solving learning problems.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Education and Professional Studies
Arts, Education and Law
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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"

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

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

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

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Brande, Lieve van den. Flexible and distance learning. Chichester: Wiley, 1993.

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

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

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Flexible and distance learning. Chichester, England: Wiley, 1992.

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Race, Phil. Developing flexible learning quality. Pontypridd: University of Glamorgan, 1992.

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Implementing and managing flexible learning. London: Pitman, 1995.

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Book chapters on the topic "Flexible Learning"

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Dallal, Ronnie. "Flexible Distance Learning." In Learning Technology in the European Communities, 169–75. Dordrecht: 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, 135–217. Heidelberg: 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, 769–79. Cham: 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, 1–11. Cham: 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, 105–17. New Delhi: Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2834-9_7.

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Khari, Chitra, and Shuchi Sinha. "Impact of Workplace Spirituality on Learning Commitment." In Flexible Systems Management, 99–110. Singapore: 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, 3–6. Cham: 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, 255–74. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3358-2_8.

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Vinnakota, Tirumala R. "An Exploration of Holistic Learning in Different Contexts." In Flexible Systems Management, 37–49. New Delhi: Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2834-9_3.

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Gerteis, Wolfgang, and Michael Altenhofen. "Flexible Instruktionsstrategien in L3." In E-Learning in der Praxis, 253–76. Wiesbaden: Gabler Verlag, 2002. http://dx.doi.org/10.1007/978-3-322-82416-5_15.

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Conference papers on the topic "Flexible Learning"

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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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Cooper, Clayton, Dongdong Liu, Jianjing Zhang, and Robert X. Gao. "Feature-Based Transfer Learning for Bearing Fault Recognition Without Available Fault Data." In 2020 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/isfa2020-9636.

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Abstract Machine learning has demonstrated its effectiveness in fault recognition for mechanical systems. However, sufficient data for establishing accurate and reliable fault detection methods is not always available in real-world applications. Transfer learning leverages the knowledge learned from a source domain in order to bypass limitations in data availability and facilitate effective analysis in a target domain. For mechanical fault recognition, existing transfer learning methods mainly focus on transferring knowledge between different operating conditions which require training samples corresponding to all desired fault conditions from the target domain in order to realize domain adaptation. However faulted data in real applications is usually unavailable and impractical to collect. In this paper, a transfer learning-based cross-machine bearing fault recognition method is investigated. This new method sees domain adaptation take place without faulted data being available in the target domain, and thus alleviates data availability limitations. The effectiveness of the method is demonstrated in a case study in which the bearing diagnostic method is transferred from an electric motor to a wind turbine.
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Chujo, Taichi, Kosei Nishida, and Tatsushi Nishi. "A Conflict-Free Routing Method for Automated Guided Vehicles Using Reinforcement Learning." In 2020 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/isfa2020-9620.

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Abstract In a modern large-scale fabrication, hundreds of vehicles are used for transportation. Since traffic conditions are changing rapidly, the routing of automated guided vehicles (AGV) needs to be changed according to the change in traffic conditions. We propose a conflict-free routing method for AGVs using reinforcement learning in dynamic transportation. An advantage of the proposed method is that a change in the state can be obtained as an evaluation function. Therefore, the action can be selected according to the states. A deadlock avoidance method in bidirectional transport systems is developed using reinforcement learning. The effectiveness of the proposed method is demonstrated by comparing the performance with the conventional Q learning algorithm from computational results.
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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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Russell, Matthew, and Peng Wang. "Transferable Deep Learning for In-Situ Tool Wear Diagnosis." In 2020 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/isfa2020-9643.

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Abstract Emerging deep learning (DL) techniques, which have demonstrated the superior capability to learn complex patterns and interrelations from multivariate data, provide promising solutions to characterize and model complex system that cannot be accurately described by conventional machine learning techniques. Hence, DL techniques have been extensively studied for condition monitoring, diagnosis, and remaining life prediction of manufacturing machine and components. One challenge associated with DL techniques is that the accuracy and reliability of DL models would vary significantly with the data amount, variety, and machine operating scenarios that are used to train the models. If the trained model is applied beyond the training scenarios, an internal covariate shift problem would occur and thereby damage the model reliability. To address this challenge, the DL models should not only extract hierarchical features from the input data, but also study the similarities and differences among data collected from different scenarios and include the discovered similarities in the feature extraction mechanism to generalize models to a broad application. This paper presents a trial to develop a transferable convolutional neural network (CNN) for in-situ diagnosis tool wear severity under different operating conditions.
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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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Wang, Zihan, Hongyi Xu, and Yang Li. "Material Model Calibration by Deep Learning for Additively Manufactured Alloys." In 2020 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/isfa2020-9640.

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Abstract Metal parts manufactured via Powder Bed Fusion (PBF) process show great potential in industrial applications. Hierarchical, heterogeneous microstructure characteristics of the PBF-built alloys pose a significant challenge to the prediction of structural performances. To enable computational engineering of this type of materials, multiscale microstructure modeling framework has been proposed to predict the stochastic material properties. AlSi10Mg built by Selective Laser Melting (SLM) is selected as the demonstrative example. At the microscale, the epitaxial granular structures are reconstructed based on Scanning Electron Microscopic Electron Backscatter Diffraction (SEM EBSD) images. The microscale analysis provides property inputs for the mesoscale model, which captures the fish scale like melt pools at the millimeter scale. The predicted material properties are compared with the experimental data for further calibration of the material constitutive models. One critical challenge is that some parameters in material models cannot be directly obtained from experimental tests. In this work, we establish a machine learning-based model calibration framework to predict the unknown material parameters. Furthermore, several machine learning methods are compared to shed lights on their capability of capturing the relation between input parameter values and the resultant prediction errors.
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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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Ding, Baoqing, Xuefeng Chen, Xingwu Zhang, Yu Zhang, and Ruqiang Yan. "SVD-based dictionary learning for bearing fault diagnosis." In 2016 International Symposium on Flexible Automation (ISFA). IEEE, 2016. http://dx.doi.org/10.1109/isfa.2016.7790126.

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Itano, Keiko, Koji Ochiai, Koichi Takahashi, Takahide Matsushima, and Hiroshi Asahara. "An Automated Image Analysis and Cell Identification System Using Machine Learning Methods." In 2020 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/isfa2020-9630.

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Abstract In many biological laboratories, biologists analyze images and identify cell or organ states manually. There are some problems: lack of human resource and high experimental costs, among others. Identification results vary according to the person. To solve these problems, the process automation of biologists’ operations and quantitative identification are needed. Here, a cell-foci-phenotype identification system is developed by applying image processing and machine learning methods to fluorescent cell images. With this system, cell-foci-phenotype with high accuracy can be predicted and biologists’ efforts in doing image analysis can be reduced.
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Reports on the topic "Flexible Learning"

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DeJong, Gerald F. Machine Learning for Flexible Robotics. Fort Belvoir, VA: Defense Technical Information Center, December 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. Cambridge, MA: National Bureau of Economic Research, December 2022. http://dx.doi.org/10.3386/w30756.

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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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Samji, Salimah, and Mansi Kapoor. Funda Wande through the Lens of PDIA: Showcasing a Flexible and Iterative Learning Approach to Improving Educational Outcomes. Research on Improving Systems of Education (RISE), January 2022. http://dx.doi.org/10.35489/bsg-rise-ri_2022/036.

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Funda Wande has adopted a ‘learning by doing’ strategy that is similar to the Problem Driven Iterative Adaptation (PDIA) approach to solving complex problems. PDIA is a high-impact process of innovation that helps organisations develop the capability to solve complex problems while they are solving such problems. It is a step-by-step framework that helps break down problems into their root causes, identify entry points, search for possible solutions, take action, reflect upon what is learned, adapt, and then act again. Its dynamic process and tight feedback loops enable teams to find and fit solutions to the local context. This case provides a narrative of the Funda Wande story with boxes illustrating how PDIA principles and tools like problem construction, deconstruction, entry point analysis, iteration, and building authorisation would have been applied in practice. The sources of this case include a literature review of education in South Africa, related research documents, and conversations with staff at Funda Wande.
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Qi, Fei, Zhaohui Xia, Gaoyang Tang, Hang Yang, Yu Song, Guangrui Qian, Xiong An, Chunhuan Lin, and Guangming Shi. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, December 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 the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.
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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), October 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 Report (GEMR), developed a tool to showcase the results. The resulting “Learning Trajectories” webpage serves as an interactive introduction to learning trajectories and related policy simulations, and features a flexible data explorer for those who want to conveniently build, analyse, and apply learning trajectories and policy simulations to their own work and context.
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7

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

Full text
Abstract:
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 Report (GEMR), developed a tool to showcase the results. The resulting “Learning Trajectories” webpage serves as an interactive introduction to learning trajectories and related policy simulations, and features a flexible data explorer for those who want to conveniently build, analyse, and apply learning trajectories and policy simulations to their own work and context.
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8

Ahmed AlGarf, Yasmine. From Self-Awareness to Purposeful Employment: Guiding Egyptian youth using arts-based learning. Oxfam IBIS, August 2021. http://dx.doi.org/10.21201/2021.7932.

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Alwan wa Awtar (A&A), a partner of the Youth Participation and Employment (YPE) programme, implements a youth programme supporting young people to shape their prospects through professional and soft skill development, safe learning space and non-formal education. A&A has learned important lessons throughout its journey. A safe learning environment, flexible learning techniques, visual and performing arts in education and participatory management are key approaches for successful youth programmes. During the COVID-19 period, many of the professional development programmes have been delivered online, which was a good example of adaptation to changing circumstances that ensured the sustainability and continued effectiveness of the programme.
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Cilliers, Jacobus, Eric Dunford, and James Habyarimana. What Do Local Government Education Managers Do to Boost Learning Outcomes? Research on Improving Systems of Education (RISE), March 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/064.

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Decentralization reforms have shifted responsibility for public service delivery to local government, yet little is known about how their management practices or behavior shape performance. We conducted a comprehensive management survey of mid-level education bureaucrats and their staff in every district in Tanzania, and employ flexible machine learning techniques to identify important management practices associated with learning outcomes. We find that management practices explain 10 percent of variation in a district's exam performance. The three management practices most predictive of performance are: i) the frequency of school visits; ii) school and teacher incentives administered by the district manager; and iii) performance review of staff. Although the model is not causal, these findings suggest the importance of robust systems to motivate district staff, schools, and teachers, that include frequent monitoring of schools. They also show the importance of surveying subordinates of managers, in order to produce richer information on management practices.
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Medhurst, Marijne, Maya Conway, and Kathryn Richardson. Remote learning for students with a disability: Game changer or moment in time? Literature Review. Australian Council for Educational Research, November 2022. http://dx.doi.org/10.37517/978-1-74286-683-3.

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This literature review draws from Australian and international research into the impact of remote learning for students with disability, published between March 2020 and April 2022. The literature relates to pedagogical services provided by early childhood services and schools to support students with disability, rather than therapeutic services. The social implications for students are reviewed along with educational factors, and implications for inclusion and support by schools. Following an overview of the legal and policy frameworks supporting the education of students with disability, this review investigates benefits, challenges and opportunities for both remote learning and transition back to in-person educational settings for students and their families. The themes emerging include flexible approaches to learning, connectedness and wellbeing.
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