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Relevant bibliographies by topics / Learning-based planning

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Academic literature on the topic 'Learning-based planning'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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Journal articles on the topic "Learning-based planning"

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PRAKASH, POONAM. "Critical Learning and Reflective Practice through Studio-based Learning in Planning and Architecture Education." Creative Space 3, no. 1 (July 2, 2015): 41–54. http://dx.doi.org/10.15415/cs.2015.31004.

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Sha’ari, Syireen Rose. "Ms Problem Based Learning in Media Planning." International Journal of Learning: Annual Review 15, no. 3 (2008): 279–88. http://dx.doi.org/10.18848/1447-9494/cgp/v15i03/45690.

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Park, S. C., M. T. Gervasio, M. J. Shaw, and G. F. DeJong. "Explanation-based learning for intelligent process planning." IEEE Transactions on Systems, Man, and Cybernetics 23, no. 6 (1993): 1597–616. http://dx.doi.org/10.1109/21.257757.

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Collins, Gregg, Lawrence Birnbaum, Bruce Krulwich, and Michael Freed. "Model-based integration of planning and learning." ACM SIGART Bulletin 2, no. 4 (July 1991): 56–60. http://dx.doi.org/10.1145/122344.122354.

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Tianfield, H. "Robot action planning via explanation-based learning." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 30, no. 2 (March 2000): 216–22. http://dx.doi.org/10.1109/3468.833104.

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Wang, Jiankun, Wenzheng Chi, Chenming Li, Chaoqun Wang, and Max Q. H. Meng. "Neural RRT*: Learning-Based Optimal Path Planning." IEEE Transactions on Automation Science and Engineering 17, no. 4 (October 2020): 1748–58. http://dx.doi.org/10.1109/tase.2020.2976560.

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Shepherd, Anne, and Bryna Cosgrif. "Problem-Based Learning: A Bridge between Planning Education and Planning Practice." Journal of Planning Education and Research 17, no. 4 (June 1998): 348–57. http://dx.doi.org/10.1177/0739456x9801700409.

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Whitley, Heather P., Edward Bell, Marty Eng, David G. Fuentes, Kristen L. Helms, Erik D. Maki, and Deepti Vyas. "Practical Team-Based Learning from Planning to Implementation." American Journal of Pharmaceutical Education 79, no. 10 (December 2015): 149. http://dx.doi.org/10.5688/ajpe7910149.

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Hwang, Kao-Shing, Wei-Cheng Jiang, and Yu-Jen Chen. "Pheromone-Based Planning Strategies in Dyna-Q Learning." IEEE Transactions on Industrial Informatics 13, no. 2 (April 2017): 424–35. http://dx.doi.org/10.1109/tii.2016.2602180.

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Grasas, Alex, and Helena Ramalhinho. "Teaching distribution planning: a problem-based learning approach." International Journal of Logistics Management 27, no. 2 (August 8, 2016): 377–94. http://dx.doi.org/10.1108/ijlm-05-2014-0075.

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Purpose – The purpose of this paper is to present a problem-based learning (PBL) activity that uses a decision support system (DSS) to teach one of the most fundamental topics in distribution planning: vehicle routing. Design/methodology/approach – The authors describe their teaching experience in a logistics and supply chain management (LSCM) course. In the PBL activity proposed, students need to solve a typical vehicle routing case with no previous theoretical background taught. The paper is written as a teaching guide for other instructors, detailing how the activity may be carried out in class. Findings – The PBL activity involved students from the very beginning, challenging them to solve a rather complicated problem. Its acceptance was very positive according to the student feedback survey conducted after the activity. Only when struggling with the difficulties of the case proposed, did students really appreciate the potential value of a DSS for making better decisions. Moreover, this activity raised concerns about how DSSs must be adapted for implementation in every business scenario. Originality/value – Teaching logistics management goes beyond lecturing on elemental concepts and tools; it is also about applying this knowledge to manage things. Although several PBL initiatives have been reported to be successful in the field of LSCM, this one incorporates a web-based DSS. The main issue in PBL activities is finding authentic and representative problems to develop transferable skills, and currently most logistics problems are solved using DSS.

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Dissertations / Theses on the topic "Learning-based planning"

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Grant, Timothy John. "Inductive learning of knowledge-based planning operators." [Maastricht : Maastricht : Rijksuniversiteit Limburg] ; University Library, Maastricht University [Host], 1996. http://arno.unimaas.nl/show.cgi?fid=6686.

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Kao, Hai Feng. "Optimal planning with approximate model-based reinforcement learning." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/39889.

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Model-based reinforcement learning methods make efficient use of samples by building a model of the environment and planning with it. Compared to model-free methods, they usually take fewer samples to converge to the optimal policy. Despite that efficiency, model-based methods may not learn the optimal policy due to structural modeling assumptions. In this thesis, we show that by combining model- based methods with hierarchically optimal recursive Q-learning (HORDQ) under a hierarchical reinforcement learning framework, the proposed approach learns the optimal policy even when the assumptions of the model are not all satisfied. The effectiveness of our approach is demonstrated with the Bus domain and Infinite Mario – a Java implementation of Nintendo’s Super Mario Brothers.

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Cervera, Mateu Enric. "Perception-Based Learning for Fine Motion Planning in Robot Manipulation." Doctoral thesis, Universitat Jaume I, 1997. http://hdl.handle.net/10803/10377.

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Robots must successfully execute tasks in the presence of uncertainty.
The main sources of uncertainty are modeling, sensing, and control. Fine motion problems involve a small-scale space and contact between objects.
Though modern manipulators are very precise and repetitive, complex tasks may be difficult --or even impossible-- to model at the desired degree of exactitude; moreover, in real-world situations, the environment is not known a-priori and visual sensing does not provide enough accuracy.
In order to develop successful strategies, it is necessary to understand what can be perceived, what action can be learnt --associated-- according to the perception, and how can the robot optimize its actions with regard to defined criteria.
The thesis describes a robot programming architecture for learning fine motion tasks.
Learning is an autonomous process of experience repetition, and the target is to achieve the goal in the minimum number of steps. Uncertainty in the location is assumed, and the robot is guided mainly by the sensory information acquired by a force sensor.
The sensor space is analyzed by an unsupervised process which extracts features related with the probability distribution of the input samples. Such features are used to build a discrete state of the task to which an optimal action is associated, according to the past experience. The thesis also includes simulations of different sensory-based tasks to illustrate some aspects of the learning processes.
The learning architecture is implemented on a real robot arm with force sensing capabilities. The task is a peg-in-hole insertion with both cylindrical and non-cylindrical workpieces.

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Nikolaev, Pavel. "Policy-based planning for student mobility support in e-Learning systems." Thesis, De Montfort University, 2014. http://hdl.handle.net/2086/10132.

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Student mobility in the area of Higher Education (HE) is gaining more attention nowadays. It is one of the cornerstones of the Bologna Process being promoted at both national and international levels. However, currently there is no technical system that would support student mobility processes and assist users in authoring educational curricula involving student mobility. In this study, the problem of student mobility programmes generation based on existing modules and programmes is considered. A similar problem is being solved in an Intelligent Tutoring Systems field using Curriculum generation techniques, but the student mobility area has a set of characteristics limiting their application to the considered problem. One of main limiting factors is that mobility programmes should be developed in an environment with heterogeneous regulations. In this environment, various established routines and regulations are used to control different aspects of the educational process. These regulations can be different in different domains and are supported by different authors independently. In this thesis, a novel framework was developed for generation of student mobility programmes in an environment with heterogeneous regulations. Two core technologies that were coherently combined in the framework are hierarchical planning and policy-based management. The policy-based planner was designed as a central engine for the framework. It extends the functionality of existing planning technologies and provides the means to carry out planning in environments with heterogeneous regulations, specified as policies. The policy-based planner enforces the policies during the planning and guarantees that the resultant plan is conformant with all policies applicable to it. The policies can be supported by different authors independently. Using them, policy authors can specify additional constraints on the execution of planning actions and extend the pre-specified task networks. Policies are enforced during the planning in a coordinated manner: situations when a policy can be enforced are defined by its scope, and the outcomes of policy evaluation are processed according to the specially defined procedures. For solving the problem of student mobility programme generation using the policy-based planner, the planning environment describing the student mobility problem area was designed and this problem was formalised as a planning task. Educational processes valid throughout the HE environment were formalised using Hierarchical Task Network planning constructs. Different mobility schemas were encoded as decomposition methods that can be combined to construct complex mobility scenarios satisfying the user requirements. New mobility programmes are developed as detailed educational processes carried out when students study according to these programmes. This provides the means to model their execution in the planning environment and guarantee that all relevant requirements are checked. The postponed policy enforcement mechanism was developed as an extension of the policy-based planner in order to improve the planning performance. In this mechanism, future dead-ends can be detected earlier during the planning using partial policy requests. The partial policy requests and an algorithm for their evaluation were introduced to examine policies for planning actions that should be executed in the future course of planning. The postponed policy enforcement mechanism was applied to the mobility programme generation problem within the descending policy evaluation technique. This technique was designed to optimise the process of programme components selection. Using it, policies for different domains can be evaluated independently in a descending order, gradually limiting the scope for the required component selection. The prototype of student mobility programme generation solution was developed. Two case studies were used to examine the process of student mobility programmes development and to analyse the role of policies in this process. Additionally, four series of experiments were carried out to analyse performance gains of the descending policy evaluation technique in planning environments with different characteristics.

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Richardson, Karen Work. "Looking at/looking through: Teachers planning for curriculum -based learning with technology." W&M ScholarWorks, 2009. https://scholarworks.wm.edu/etd/1550154152.

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Pengelly, M. "Principled decision-making for tutoring : a rational construction of planning and decision-making from instructional principles." Thesis, University of Exeter, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235986.

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Eriksson, Jan, and Dag Øyvind Tornes. "Learning to play Starcraft with Case-based Reasoning : Investigating issues in large-scale case-based planning." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18720.

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In this master thesis we describe our work in creating a planner for the real-time strategy game Starcraft using case-based reasoning. Our work has been focused on the challenges in creating a usable casebase, and the resulting issues arising from scaling up the casebase.First, we present an agent designed to play Starcraft using plans from our CBR planner, and its architecture. We then move on to describe how this planner works, and how it overcomes the challenges in scaling up.We then present several experiments designed to measure how well our approach works given the limitations we have set. Finally, we discuss our results, and provide some interesting unsolved challenges which may benefit from further investigation.

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Robinson, Eric John S. M. Massachusetts Institute of Technology. "Coordinated planning of air and space assets : an optimization and learning based approach." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/84185.

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Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2013.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
"June 2013." Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 155-157).
collect information. This may include taking pictures of the ground, gathering infrared photos, taking atmospheric pressure measurements, or any conceivable form of data collection. Often these separate organizations have overlapping collection interests or flight plans that are sending sensors into similar regions. However, they tend to be controlled by separate planning systems which operate on asynchronous scheduling cycles. We present a method for coordinating various collection tasks between the planning systems in order to vastly increase the utility that can be gained from these assets. This method focuses on allocation of collection requests to scheduling systems rather than complete centralized planning over the entire system so that the current planning infrastructure can be maintained without changing any aspects of the schedulers. We expand on previous work in this area by inclusion of a learning method to capture information about the uncertainty pertaining to the completion of collection tasks, and subsequently utilize this information in a mathematical programming method for resource allocation. An analysis of results and improvements as compared to current operations is presented at the end.
by Eric John Robinson.
S.M.

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Johansson, Åke, and Joel Wikner. "Learning-Based Motion Planning and Control of a UGV With Unknown and Changing Dynamics." Thesis, Linköpings universitet, Reglerteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176923.

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Research about unmanned ground vehicles (UGVs) has received an increased amount of attention in recent years, partly due to the many applications of UGVs in areas where it is inconvenient or impossible to have human operators, such as in mines or urban search and rescue. Two closely linked problems that arise when developing such vehicles are motion planning and control of the UGV. This thesis explores these subjects for a UGV with an unknown, and possibly time-variant, dynamical model. A framework is developed that includes three components: a machine learning algorithm to estimate the unknown dynamical model of the UGV, a motion planner that plans a feasible path for the vehicle and a controller making the UGV follow the planned path. The motion planner used in the framework is a lattice-based planner based on input sampling. It uses a dynamical model of the UGV together with motion primitives, defined as a sequence of states and control signals, which are concatenated online in order to plan a feasible path between states. Furthermore, the controller that makes the vehicle follow this path is a model predictive control (MPC) controller, capable of taking the time-varying dynamics of the UGV into account as well as imposing constraints on the states and control signals. Since the dynamical model is unknown, the machine learning algorithm Bayesian linear regression (BLR) is used to continuously estimate the model parameters online during a run. The parameter estimates are then used by the MPC controller and the motion planner in order to improve the performance of the UGV. The performance of the proposed motion planning and control framework is evaluated by conducting a series of experiments in a simulation study. Two different simulation environments, containing obstacles, are used in the framework to simulate the UGV, where the performance measures considered are the deviation from the planned path, the average velocity of the UGV and the time to plan the path. The simulations are either performed with a time-invariant model, or a model where the parameters change during the run. The results show that the performance is improved when combining the motion planner and the MPC controller with the estimated model parameters from the BLR algorithm. With an improved model, the vehicle is capable of maintaining a higher average velocity, meaning that the plan can be executed faster. Furthermore, it can also track the path more precisely compared to when using a less accurate model, which is crucial in an environment with many obstacles. Finally, the use of the BLR algorithm to continuously estimate the model parameters allows the vehicle to adapt to changes in its model. This makes it possible for the UGV to stay operational in cases of, e.g., actuator malfunctions.

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Albert, Christian [Verfasser]. "Scenario-based landscape planning : influencing decision-making through substantive outputs and social learning / Christian Albert." Hannover : Technische Informationsbibliothek und Universitätsbibliothek Hannover (TIB), 2012. http://d-nb.info/1022753908/34.

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Books on the topic "Learning-based planning"

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Barbara, Workman, ed. Planning and reviewing work based learning: A practical guide. Faringdon, Oxfordshire: Libri Publishing, 2010.

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Shaw, Michael. Incorporating machine learning in knowledge-based process planning systems: An explanation-based approach. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1990.

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Parker, Diane. Planning for inquiry: It's not an oxymoron! Urbana, Ill: National Council of Teachers of English, 2007.

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Higgins, Marilyn. Work-based learning within planning education: A good practice guide. London: University of Westminster Press for the Discipline Network in Town Planning., 1997.

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Karen, Hammons, ed. Curriculum for integrated learning: A lesson-based approach. Albany, N.Y: Delmar Publishers, 1998.

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1961-, Sage Sara, ed. Problems as possibilities: Problem-based learning for K-12 education. Alexandria, Va: Association for Supervision and Curriculum Development, 1998.

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1961-, Sage Sara, ed. Problems as possibilities: Problem-based learning for K-16 education. 2nd ed. Alexandria, Va: Association for Supervision and Curriculum Development, 2002.

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Ullrich, Carsten. Pedagogically founded courseware generation for web-based learning: An HTN-planning-based approach implemented in PAIGOS. Berlin: Springer, 2008.

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Ullrich, Carsten. Pedagogically founded courseware generation for web-based learning: An HTN-planning-based approach implemented in PAIGOS. Berlin: Springer, 2008.

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Pedagogically founded courseware generation for web-based learning: An HTN-planning-based approach implemented in PAIGOS. Berlin: Springer, 2008.

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Book chapters on the topic "Learning-based planning"

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Davidson-Shivers, Gayle V., Karen L. Rasmussen, and Patrick R. Lowenthal. "Planning the Evaluation of Online Instruction." In Web-Based Learning, 141–82. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67840-5_5.

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Davidson-Shivers, Gayle V., Karen L. Rasmussen, and Patrick R. Lowenthal. "Concurrent Design: Instructional and Motivational Strategy Planning." In Web-Based Learning, 215–57. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67840-5_7.

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Langford, John, Xinhua Zhang, Gavin Brown, Indrajit Bhattacharya, Lise Getoor, Thomas Zeugmann, Thomas Zeugmann, et al. "Explanation-Based Learning for Planning." In Encyclopedia of Machine Learning, 392–96. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_297.

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Kambhampati, Subbarao, and Sungwook Yoon. "Explanation-Based Learning for Planning." In Encyclopedia of Machine Learning and Data Mining, 1–7. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-1-4899-7502-7_97-1.

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Kambhampati, Subbarao, and Sungwook Yoon. "Explanation-Based Learning for Planning." In Encyclopedia of Machine Learning and Data Mining, 492–96. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_97.

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Hooker, Elaine, and Ruth Helyer. "Planning and negotiating your learning." In The Work-based Learning Student Handbook, 120–41. London: Macmillan Education UK, 2015. http://dx.doi.org/10.1007/978-1-137-41384-0_7.

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Möbus, Claus, Heinz-Jürgen Thole, and Olaf Schröder. "Diagnosis of Intentions and Interactive Support of Planning in a Functional, Visual Programming Language." In Simulation-Based Experiential Learning, 61–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78539-9_5.

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Rosenbloom, Paul S., Soowon Lee, and Amy Unruh. "Bias in Planning and Explanation-Based Learning." In Foundations of Knowledge Acquisition: Cognitive Models of Complex Learning, 269–307. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-3172-2_8.

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Demir, Mustafa, and Nilay Keskin Samanci. "Planning Process of Argumentation-Based Science Learning." In More Voices from the Classroom, 17–34. Rotterdam: SensePublishers, 2017. http://dx.doi.org/10.1007/978-94-6351-095-0_2.

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Pan, Jia, Sachin Chitta, and Dinesh Manocha. "Faster Sample-Based Motion Planning Using Instance-Based Learning." In Springer Tracts in Advanced Robotics, 381–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36279-8_23.

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Conference papers on the topic "Learning-based planning"

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YANG, LIN. "E-LEARNING PLANNING PERSPECTIVE." In Proceedings of the Third International Conference on Web-based Learning (ICWL 2004). WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702494_0010.

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"A Preliminary Study of A Business-Management/Strategic-Planning Board Game with Situated Learning Mechanisms." In 13th EuropeanConference on Game Based Learning. ACI, 2020. http://dx.doi.org/10.34190/gbl.20.039.

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Alizadeh, Ali, Nazmia Humaira, and Emre Koyuncu. "Learning-based Aircraft Trajectory Planning Enhancement." In AIAA Scitech 2019 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2019. http://dx.doi.org/10.2514/6.2019-0138.

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Pandey, Ashutosh, Bradley Schmerl, and David Garlan. "Instance-Based Learning for Hybrid Planning." In 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W). IEEE, 2017. http://dx.doi.org/10.1109/fas-w.2017.122.

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Sharma, Avinash, Kanika Gupta, Anirudha Kumar, Aishwarya Sharma, and Rajesh Kumar. "Model based path planning using Q-Learning." In 2017 IEEE International Conference on Industrial Technology (ICIT). IEEE, 2017. http://dx.doi.org/10.1109/icit.2017.7915468.

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Loula, Joao, Kelsey Allen, Tom Silver, and Josh Tenenbaum. "Learning constraint-based planning models from demonstrations." In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. http://dx.doi.org/10.1109/iros45743.2020.9341535.

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Ng, Jun Hao Alvin, and Ronald P. A. Petrick. "Incremental Learning of Planning Actions in Model-Based Reinforcement Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/443.

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The soundness and optimality of a plan depends on the correctness of the domain model. Specifying complete domain models can be difficult when interactions between an agent and its environment are complex. We propose a model-based reinforcement learning (MBRL) approach to solve planning problems with unknown models. The model is learned incrementally over episodes using only experiences from the current episode which suits non-stationary environments. We introduce the novel concept of reliability as an intrinsic motivation for MBRL, and a method to learn from failure to prevent repeated instances of similar failures. Our motivation is to improve the learning efficiency and goal-directedness of MBRL. We evaluate our work with experimental results for three planning domains.

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Hun Woo, Jong, Young In Cho, Sang Hyeon Yu, So Hyun Nam, Haoyu Zhu, Dong Hoon Kwak, and Jong-Ho Nam. "Machine Learning (Reinforcement Learning)-Based Steel Stock Yard Planning Algorithm." In 2020 Winter Simulation Conference (WSC). IEEE, 2020. http://dx.doi.org/10.1109/wsc48552.2020.9384049.

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Jacak, Witold, and Karin Pröll. "Q-Learning Based Therapy Planning Decision Support System." In 2008 Third International Conference on Broadband Communications, Information Technology & Biomedical Applications. IEEE, 2008. http://dx.doi.org/10.1109/broadcom.2008.26.

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Liao, Xiaofei, Yang Wang, Yiliang Xuan, and Dequan Wu. "AGV Path Planning Model based on Reinforcement Learning." In 2020 Chinese Automation Congress (CAC). IEEE, 2020. http://dx.doi.org/10.1109/cac51589.2020.9326742.

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Reports on the topic "Learning-based planning"

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Rosenbloom, Paul S., Soowon Lee, and Amy Unruh. Bias in Planning and Explanation-Based Learning. Fort Belvoir, VA: Defense Technical Information Center, May 1993. http://dx.doi.org/10.21236/ada269608.

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Brinkerhoff, Derick W., Sarah Frazer, and Lisa McGregor-Mirghani. Adapting to Learn and Learning to Adapt: Practical Insights from International Development Projects. RTI Press, January 2018. http://dx.doi.org/10.3768/rtipress.2018.pb.0015.1801.

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Adaptive programming and management principles focused on learning, experimentation, and evidence-based decision making are gaining traction with donor agencies and implementing partners in international development. Adaptation calls for using learning to inform adjustments during project implementation. This requires information gathering methods that promote reflection, learning, and adaption, beyond reporting on pre-specified data. A focus on adaptation changes traditional thinking about program cycle. It both erases the boundaries between design, implementation, and evaluation and reframes thinking to consider the complexity of development problems and nonlinear change pathways.Supportive management structures and processes are crucial for fostering adaptive management. Implementers and donors are experimenting with how procurement, contracting, work planning, and reporting can be modified to foster adaptive programming. Well-designed monitoring, evaluation, and learning systems can go beyond meeting accountability and reporting requirements to produce data and learning for evidence-based decision making and adaptive management. It is important to continue experimenting and learning to integrate adaptive programming and management into the operational policies and practices of donor agencies, country partners, and implementers. We need to devote ongoing effort to build the evidence base for the contributions of adaptive management to achieving international development results.

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Johnson, Mark, and John Wachen. Examining Equity in Remote Learning Plans: A Content Analysis of State Responses to COVID-19. The Learning Partnership, November 2020. http://dx.doi.org/10.51420/report.2020.2.

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In this technical report, the authors present a content analysis of state guidance on remote learning from the 2019-20 school year. As schools across the country closed in response to COVID-19, state education agencies (SEAs) developed guidance for use by districts on how to ensure the continuation of education during the pandemic. The described analysis applied an equity framework that was developed based on concepts drawn from a literature review to examine the extent to which SEAs addressed issues of equity in their remote learning recommendations. The analysis revealed variation in the extent to which states explicitly focused on equity in their guidance. The analysis also identified exemplar states that encouraged local educators to keep equity at the forefront of their planning.

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Robledo, Ana, and Amber Gove. What Works in Early Reading Materials. RTI Press, February 2019. http://dx.doi.org/10.3768/rtipress.2018.op.0058.1902.

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Access to books is key to learning to read and sustaining a love of reading. Yet many low- and middle-income countries struggle to provide their students with reading materials of sufficient quality and quantity. Since 2008, RTI International has provided technical assistance in early reading assessment and instruction to ministries of education in dozens of low- and middle-income countries. The central objective of many of these programs has been to improve learning outcomes—in particular, reading—for students in the early grades of primary school. Under these programs, RTI has partnered with ministry staff to produce and distribute evidence-based instructional materials at a regional or national scale, in quantities that increase the likelihood that children will have ample opportunities to practice reading skills, and at a cost that can be sustained in the long term by the education system. In this paper, we seek to capture the practices RTI has developed and refined over the last decade, particularly in response to the challenges inherent in contexts with high linguistic diversity and low operational capacity for producing and distributing instructional materials. These practices constitute our approach to developing and producing instructional materials for early grade literacy. We also touch upon effective planning for printing and distribution procurement, but we do not consider the printing and distribution processes in depth in this paper. We expect this volume will be useful for donors, policymakers, and practitioners interested in improving access to cost-effective, high-quality teaching and learning materials for the early grades.

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