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Academic literature on the topic 'Relevance feedback'

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Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Relevance feedback"

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Raiber, Fiana, and Oren Kurland. "Relevance Feedback." ACM Transactions on Information Systems 37, no. 4 (December 10, 2019): 1–28. http://dx.doi.org/10.1145/3360487.

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Giorgi, D., P. Frosini, M. Spagnuolo, and B. Falcidieno. "3D relevance feedback via multilevel relevance judgements." Visual Computer 26, no. 10 (August 14, 2010): 1321–38. http://dx.doi.org/10.1007/s00371-010-0524-0.

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Zhou, Dong, Mark Truran, Jianxun Liu, and Sanrong Zhang. "Collaborative pseudo-relevance feedback." Expert Systems with Applications 40, no. 17 (December 2013): 6805–12. http://dx.doi.org/10.1016/j.eswa.2013.06.030.

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Premkumar, M., and R. Sowmya. "Interactive Content Based Image Retrieval using Multiuser Feedback." JOIV : International Journal on Informatics Visualization 1, no. 4 (December 1, 2017): 165. http://dx.doi.org/10.30630/joiv.1.4.57.

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Retrieving images from large databases becomes a difficult task. Content based image retrieval (CBIR) deals with retrieval of images based on their similarities in content (features) between the query image and the target image. But the similarities do not vary equally in all directions of feature space. Further the CBIR efforts have relatively ignored the two distinct characteristics of the CBIR systems: 1) The gap between high level concepts and low level features; 2) Subjectivity of human perception of visual content. Hence an interactive technique called the relevance feedback technique was used. These techniques used user’s feedback about the retrieved images to reformulate the query which retrieves more relevant images during next iterations. But those relevance feedback techniques are called hard relevance feedback techniques as they use only two level user annotation. It was very difficult for the user to give feedback for the retrieved images whether they are relevant to the query image or not. To better capture user’s intention soft relevance feedback technique is proposed. This technique uses multilevel user annotation. But it makes use of only single user feedback. Hence Soft association rule mining technique is also proposed to infer image relevance from the collective feedback. Feedbacks from multiple users are used to retrieve more relevant images improving the performance of the system. Here soft relevance feedback and association rule mining techniques are combined. During first iteration prior association rules about the given query image are retrieved to find out the relevant images and during next iteration the feedbacks are inserted into the database and relevance feedback techniques are activated to retrieve more relevant images. The number of association rules is kept minimum based on redundancy detection.
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Chen, Zhaofeng, Naixuan Guo, Jiu Sun, Yuanyuan Wang, Feng Zhou, Sen Xu, and Rugang Wang. "Pseudo-Relevance Feedback Method Based on the Topic Relevance Model." Mathematical Problems in Engineering 2022 (July 7, 2022): 1–6. http://dx.doi.org/10.1155/2022/1697950.

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In the field of information retrieval, most pseudo-relevance feedback models select candidate terms from the top k documents returned by the first-pass retrieval, but they cannot identify the reliability of these documents. This paper proposed a new approach to obtain feedback information more comprehensively by constructing four corresponding models. Firstly, the algorithm incorporated topic-based relevance information into the relevance model RM3 and constructed a topic-based relevance model, denoted as TopRM3, with two corresponding variants. TopRM3 estimated the reliability of a feedback document in language modeling when executing pseudo-relevance feedback from both term and topic-based perspectives. Secondly, the algorithm introduced topic-based relevance information into Rocchio’s model and constructed the corresponding model, denoted as TopRoc, with two corresponding variants. Experimental results on the five TREC collections show that the proposed TopRM3 and TopRoc are effective and generally superior to the state-of-the-art pseudo-relevance feedback models with optimal parameter settings in terms of mean average precision.
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Panyr, Jiri. "Conceptual Clustering and Relevance Feedback." KNOWLEDGE ORGANIZATION 14, no. 3 (1987): 133–37. http://dx.doi.org/10.5771/0943-7444-1987-3-133.

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Crouch, Carolyn J. "Relevance feedback at INEX 2005." ACM SIGIR Forum 40, no. 1 (June 2006): 58–59. http://dx.doi.org/10.1145/1147197.1147208.

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Algarni, Abdulmohsen, Yuefeng Li, Sheng-Tang Wu, and Yue Xu. "Text mining in negative relevance feedback." Web Intelligence and Agent Systems: An International Journal 10, no. 2 (2012): 151–63. http://dx.doi.org/10.3233/wia-2012-0238.

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Patil, Pushpa B., and Manesh Kokare. "Semantic Image Retrieval Using Relevance Feedback." International journal of Web & Semantic Technology 2, no. 4 (October 30, 2011): 139–48. http://dx.doi.org/10.5121/ijwest.2011.2411.

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Tao, Dacheng, Xuelong Li, and Stephen J. Maybank. "Negative Samples Analysis in Relevance Feedback." IEEE Transactions on Knowledge and Data Engineering 19, no. 4 (April 2007): 568–80. http://dx.doi.org/10.1109/tkde.2007.1003.

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Dissertations / Theses on the topic "Relevance feedback"

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Zhang, Peng. "Approximating true relevance model in relevance feedback." Thesis, Robert Gordon University, 2013. http://hdl.handle.net/10059/808.

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Relevance is an essential concept in information retrieval (IR) and relevance estimation is a fundamental IR task. It involves not only document relevance estimation, but also estimation of user's information need. Relevance-based language model aims to estimate a relevance model (i.e., a relevant query term distribution) from relevance feedback documents. The true relevance model should be generated from truly relevant documents. The ideal estimation of the true relevance model is expected to be not only effective in terms of mean retrieval performance (e.g., Mean Average Precision) over all the queries, but also stable in the sense that the performance is stable across different individual queries. In practice, however, in approximating/estimating the true relevance model, the improvement of retrieval effectiveness often sacrifices the retrieval stability, and vice versa. In this thesis, we propose to explore and analyze such effectiveness-stability tradeoff from a new perspective, i.e., the bias-variance tradeoff that is a fundamental theory in statistical estimation. We first formulate the bias, variance and the trade-off between them for retrieval performance as well as for query model estimation. We then analytically and empirically study a number of factors (e.g., query model complexity, query model combination, document weight smoothness and irrelevant documents removal) that can affect the bias and variance. Our study shows that the proposed bias-variance trade-off analysis can serve as an analytical framework for query model estimation. We then investigate in depth on two particular key factors: document weight smoothness and removal of irrelevant documents, in query model estimation, by proposing novel methods for document weight smoothing and irrelevance distribution separation, respectively. Systematic experimental evaluation on TREC collections shows that the proposed methods can improve both retrieval effectiveness and retrieval stability of query model estimation. In addition to the above main contributions, we also carry out initial exploration on two further directions: the formulation of bias-variance in personalization and looking at the query model estimation via a novel theoretical angle (i.e., Quantum theory) that has partially inspired our research.
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Xu, Zuobing. "Active relevance feedback algorithms /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2008. http://uclibs.org/PID/11984.

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Yamout, Fadi. "Relevance Feedback Using Weight Propagation." Thesis, University of Sunderland, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.485442.

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A new relevance feedback technique called Weight Propagation has been developed which provides greater retrieval effectiveness than previously described techniques. Documents judged relevant by the user propagate positive Weights to documents close by in vector similarity space, while documents judged not relevant propagate Negative Weights to such neighbouring documents. Variants of Weight Propagation are also described, namely WPI and WPR (inspired by Ide and Rocchio respectively), and WPY which is the main focus of this thesis, where only the maximum weight propagated to each document is counted. Weight Propagation was further enhanced by introducing a second-order propagation (documents that receive weights themselve!propagate weights to related documents) which increased the precision of the results. WPY is compared against the Rocchio and Ide techniques in the vector model based on the tf.idf weighting scheme, and against the Information-theoretic query expansion technique based on the Kullback-Leibler divergence measure using the DB2 weighting model of the Divergence From Randomness framework. Different RF models were employed such as pseudo relevance feedback in addition to both simulated positive and negative feedback using residual collection technique. The experiments are performed on different test collections such as MED, CISI, Cranfield, LISA, NPL, WTIOG and GOV. Small collections such as MED, CISI, and Cranfield were also tested in the semantic space using Latent Semantic Indexing and the optimal number of dimensions that captures the underlying semantics that exists between the documents is determined for these collections. Retrieval effectiveness is improved since the documents are treated as independent vectors rather than being merged into a single vector as is the case with traditional vector model relevance feedback techniques, or by determining the documents' relevancy based on the lengths of all the documents as with the Kullback-Leibler divergence measure used in traditional probabilistic relevance feedback techniques. In addition, the Weight Propagation technique does not expand terms as in the case with traditional approaches.
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Ruthven, Ian. "Abduction, explanation and relevance feedback." Thesis, University of Glasgow, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.392605.

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Krishnan, Sharenya. "Text-Based Information Retrieval Using Relevance Feedback." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-53603.

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Europeana, a freely accessible digital library with an idea to make Europe's cultural and scientific heritage available to the public was founded by the European Commission in 2008. The goal was to deliver a semantically enriched digital content with multilingual access to it. Even though they managed to increase the content of data they slowly faced the problem of retrieving information in an unstructured form. So to complement the Europeana portal services, ASSETS (Advanced Search Service and Enhanced Technological Solutions) was introduced with services that sought to improve the usability and accessibility of Europeana. My contribution is to study different text-based information retrieval models, their relevance feedback techniques and to implement one simple model. The thesis explains a detailed overview of the information retrieval process along with the implementation of the chosen strategy for relevance feedback that generates automatic query expansion. Finally, the thesis concludes with the analysis made using relevance feedback, discussion on the model implemented and then an assessment on future use of this model both as a continuation of my work and using this model in ASSETS.
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Vinay, V. "The relevance of feedback for text retrieval." Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1446146/.

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Relevance Feedback is a technique that helps an Information Retrieval system modify a query in response to relevance judgements provided by the user about individual results dis played after an initial retrieval. This thesis begins by proposing an evaluation framework for measuring the effectiveness of feedback algorithms. The simulation-based method in volves a brute force exploration of the outcome of every possible user action. Starting from an initial state, each available alternative is represented as a traversal along one branch of a user decision tree. The use of the framework is illustrated in two situations---searching on devices with small displays and for web search. Three well known RF algorithms, Rocchio, Robertson/Sparck-Jones (RSJ) and Bayesian, are compared for these applications. For small display devices, the algorithms are evaluated in conjunction with two strate gies for presenting search results: the top-D ranked documents and a document ranking that attempts to maximise information gain from the user's choices. Experimental results in dicate that for RSJ feedback which involves an explicit feature selection policy, the greedy top-D display is more appropriate. For the other two algorithms, the exploratory display that maximises information gain produces better results. A user study was conducted to evaluate the performance of the relevance feedback methods with real users and compare the results with the findings from the tree analysis. This comparison between the simulations and real user behaviour indicates that the Bayesian algorithm, coupled with the sampled display, is the most effective. For web-search, two possible representations for web-pages are considered---the textual content of the page and the anchor text of hyperlinks into this page. Results indicate that there is a significant variation in the upper-bound performance of the three RF algorithms and that the Bayesian algorithm approaches the best possible. The relative performance of the three algorithms differed in the two sets of experiments. All other factors being constant, this difference in effectiveness was attributed to the fact that the datasets used in the two cases were different. Also, at a more general level, a relationship was observed between the performance of the original query and benefits of subsequent relevance feedback. The remainder of the thesis looks at properties that characterise sets of documents with the particular aim of identifying measures that are predictive of future performance of statis tical algorithms on these document sets. The central hypothesis is that a set of points (corresponding to documents) are difficult if they lack structure. Three properties are identified---the clustering tendency, sensitivity to perturbation and the local intrinsic dimensionality. The clustering tendency reflects the presence or absence of natural groupings within the data. Perturbation analysis looks at the sensitivity of the similarity metric to small changes in the input. The correlation present in sets of points is measured by the local intrinsic dimensionality therefore indicating the randomness present in them. These properties are shown to be useful for two tasks, namely, measuring the complexity of text datasets and for query performance prediction.
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Gusenbauer, Stefan. "Relevance feedback in information retrieval a comparison including a practical evaluation of several approaches to relevance feedback in information retrieval." Saarbrücken VDM Verlag Dr. Müller, 2006. http://d-nb.info/989497259/04.

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Jin, Lan. "Relevance feedback in the retrieval of reusable software components." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq22831.pdf.

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Djordjevic, Divna. "User relevance feedback, search and retrieval of visual content." Thesis, Queen Mary, University of London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432897.

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Ng, Chee Un. "Image retrieval using experience-based relevance feedback and visualisation." Thesis, University of Warwick, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425556.

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Books on the topic "Relevance feedback"

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Koenemann-Belliveau, Jürgen. Relevance feedback: Usage, usability, utility. Ann Arbor, Mich: UMI Dissertation Services, 2001.

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Lavrenko, Victor. A generative theory of relevance. Berlin: Springer, 2009.

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Kelly, Barry. Information retrieval on the WWW and the issue of relevance feedback. [s.l: The Author], 1998.

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E, Robertson S. An operational evaluation of weighting, ranking, and relevance feedback, via a front-end system. London: City University, Dept. of Information Science, 1987.

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Lavrenko, Victor. Generative Theory of Relevance. Springer, 2010.

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Lavrenko, Victor. Generative Theory of Relevance. Springer London, Limited, 2008.

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Enhancing performance of full-text retrieval systems using relevance feedback. Ann Arbor, Mich: University Microfilms International, 1993.

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Rangayyan, Rangaraj, and Paulo Mazzoncini de Azevedo-Marques. Content-Based Retrieval of Medical Images: Landmarking, Indexing, and Relevance Feedback. Springer International Publishing AG, 2012.

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Rangayyan, Rangaraj, and Paulo Mazzoncini de Azevedo-Marques. Content-Based Retrieval of Medical Images: Landmarking, Indexing, and Relevance Feedback. Morgan & Claypool Publishers, 2013.

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de, Paulo Mazzoncini, and Rangaraj Mandayam Rangayyan. Content-Based Retrieval of Medical Images: Landmarking, Indexing, and Relevance Feedback. Morgan & Claypool Publishers, 2013.

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Book chapters on the topic "Relevance feedback"

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He, Ben. "Relevance Feedback." In Encyclopedia of Database Systems, 2378–79. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_931.

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Buhmann, M. D., Prem Melville, Vikas Sindhwani, Novi Quadrianto, Wray L. Buntine, Luís Torgo, Xinhua Zhang, et al. "Relevance Feedback." In Encyclopedia of Machine Learning, 862–63. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_724.

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Fang, Hui, and Cheng Xiang Zhai. "Web Search Relevance Feedback." In Encyclopedia of Database Systems, 1–5. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_462-2.

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Fang, Hui, and ChengXiang Zhai. "Web Search Relevance Feedback." In Encyclopedia of Database Systems, 3493–97. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_462.

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Zhang, Hongjiang, and Zhong Su. "Relevance Feedback in CBIR." In Visual and Multimedia Information Management, 21–35. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-0-387-35592-4_3.

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Fang, Hui, and Cheng Xiang Zhai. "Web Search Relevance Feedback." In Encyclopedia of Database Systems, 4646–50. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_462.

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Vechtomova, Olga. "Relevance Feedback for Text Retrieval." In Encyclopedia of Database Systems, 1–2. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_949-2.

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Vechtomova, Olga. "Relevance Feedback for Text Retrieval." In Encyclopedia of Database Systems, 2381–82. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_949.

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Pan, Hanglin. "Relevance Feedback in XML Retrieval." In Current Trends in Database Technology - EDBT 2004 Workshops, 187–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30192-9_18.

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Crucianu, Michel. "Image Retrieval and Relevance Feedback." In Encyclopedia of Database Systems, 1384–89. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_1016.

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Conference papers on the topic "Relevance feedback"

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Haas, Micha, Joachim Rijsdam, Bart Thomee, and Michael S. Lew. "Relevance feedback." In the 6th ACM SIGMM international workshop. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1026711.1026737.

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Harper, David J., and Diane Kelly. "Contextual relevance feedback." In the 1st international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1164820.1164847.

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Harman, Donna. "Relevance feedback revisited." In the 15th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/133160.133167.

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Aalbersberg, IJsbrand Jan. "Incremental relevance feedback." In the 15th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/133160.133169.

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Crane, Matt. "Diversified relevance feedback." In SIGIR '13: The 36th International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2484028.2484227.

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Lv, Yuanhua, and ChengXiang Zhai. "Positional relevance model for pseudo-relevance feedback." In Proceeding of the 33rd international ACM SIGIR conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1835449.1835546.

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Brondwine, Elinor, Anna Shtok, and Oren Kurland. "Utilizing Focused Relevance Feedback." In SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2911451.2914695.

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Amati, G. "Learning by examples as relevance feedback and relevance feedback as learning by examples." In IEE Two-day Seminar. Searching for Information: Artificial Intelligence and Information Retrieval Approaches. IEE, 1999. http://dx.doi.org/10.1049/ic:19990889.

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Iwayama, Makoto. "Relevance feedback with a small number of relevance judgements." In the 23rd annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/345508.345538.

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Gemmell, Carlos, Federico Rossetto, and Jeffrey Dalton. "Relevance Transformer: Generating Concise Code Snippets with Relevance Feedback." In SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3397271.3401215.

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Reports on the topic "Relevance feedback"

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Demner-Fushman, Dina, Daqing He, and Douglas W. Oard. Exploring Interactive Relevance Feedback With a Two-Pass Study Design. Fort Belvoir, VA: Defense Technical Information Center, October 2004. http://dx.doi.org/10.21236/ada453566.

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Diaz, Fernando, and James Allan. When Less is More: Relevance Feedback Falls Short and Term Expansion Succeeds at HARD 2005. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada444098.

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van Gestel, Natasja, Kees Jan van Groenigen, Craig Osenberg, Jeffrey Dukes, and Paul Dijkstra. Biogeochemical Responses and Feedbacks to Climate Change: Synthetic Meta-Analyses Relevant to Earth System Models. Office of Scientific and Technical Information (OSTI), March 2018. http://dx.doi.org/10.2172/1429337.

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Hedge, Jerry W., Walter C. Borman, Mark J. Bourne, and Mark C. Butler. A Review of Navy Survey Data Relevant to Implementation and Evaluation of the Human Performance Feedback and Development Model. Fort Belvoir, VA: Defense Technical Information Center, September 2004. http://dx.doi.org/10.21236/ada524847.

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Coultas, Mimi, Ruhil Iyer, and Jamie Myers. Handwashing Compendium for Low Resource Settings: A Living Document, Edition 3. Institute of Development Studies (IDS), October 2020. http://dx.doi.org/10.19088/slh.2020.001.

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Frequent and proper handwashing with soap is vital to prevent the spread of COVID-19. In response, the Sanitation Learning Hub at the Institute of Development Studies has developed this Handwashing Compendium for Low Resource Settings. Bringing existing information from different organisations into one place, the compendium provides guidance, local examples of and further resources on accessible low-cost handwashing facilities, environmental cues and physically distanced hygiene promotion. The compendium is a living document which will be updated regularly as learning emerges. It has been developed and disseminated quickly so immediate, relevant and timely actions can be taken. Please share feedback and contributions via email (SLH@ids.ac.uk) or Twitter (@SanLearningHub). For those with limited internet access, individual sections of the compendium can also be sent upon request.
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Coultas, Mimi, Ruhil Iyer, and Jamie Myers. Handwashing Compendium for Low Resource Settings: A Living Document, Edition 3. Institute of Development Studies (IDS), October 2020. http://dx.doi.org/10.19088/slh.2020.008.

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Frequent and proper handwashing with soap is vital to prevent the spread of COVID-19. In response, the Sanitation Learning Hub at the Institute of Development Studies has developed this Handwashing Compendium for Low Resource Settings. Bringing existing information from different organisations into one place, the compendium provides guidance, local examples of and further resources on accessible low-cost handwashing facilities, environmental cues and physically distanced hygiene promotion. The compendium is a living document which will be updated regularly as learning emerges. It has been developed and disseminated quickly so immediate, relevant and timely actions can be taken. Please share feedback and contributions via email (SLH@ids.ac.uk) or Twitter (@SanLearningHub). For those with limited internet access, individual sections of the compendium can also be sent upon request.
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Pereiro, Diego, Oleg Belyaev Korolev, Martha B. Dunbar, Garbriel Navarro, and Caroline Cusack. Best practice on creating "Extreme Marine Events" Hazard maps & forecasts Report. EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d6.3.

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This report presents steps for the design and implementation of a marine observatory providing current and forecasted oceanic conditions relevant to the aquaculture sector, with particular focus on “Extreme Marine Events”. Examples of successful implementation of these guidelines in the framework of the EuroSea project are presented for two aquaculture sites: Deenish Island in Ireland and El Campello in Spain. The process starts with stakeholder interaction to understand their main needs and concerns and is followed by the design of the software architecture that carries out the data acquisition, post-processing and visualisation in an open-access web platform. User feedback is of paramount importance during the whole process to ensure the services offered match the needs of the aquaculture sector. (EuroSea Deliverable, D6.3)
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Agrawal, Asha Weinstein, Jon Canapary, Carol Anne Carroll, Tam Guy, and Anastasia Loukaitou-Sideris. Learning about Street Harassment on Transit: A Survey Instrument for Transit Agencies. Mineta Transporation Institute, December 2023. http://dx.doi.org/10.31979/mti.2023.2301.

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This study was conducted in accordance with California Senate Bill 1161 (2022), legislation that directed the Mineta Transportation Institute to develop a reliable, easy-to-use survey instrument for collecting information from public transit passengers about the extent, location, and characteristics of any street harassment they experience. Although the legislation is focused on large California transit agencies, the study findings are equally relevant to smaller agencies and agencies outside the state. To develop the recommended questionnaire, we reviewed literature about street harassment, analyzed transit passenger surveys to explore the questions asked about safety and harassment, received feedback on iterative drafts of the questionnaire from numerous transit riders and experts, and pilot-tested the survey with 329 bus and train riders. The recommended questionnaire is available on the MTI website in fourteen languages, including English, Spanish, and Chinese.
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Carter, Becky. Integrating Local Voices into Programme Governance in Fragile and Conflict-Affected Settings. Institute of Development Studies, July 2022. http://dx.doi.org/10.19088/k4d.2022.110.

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This rapid literature review explores how local people’s views and perspectives on their concerns, needs and capabilities (beyond asking about their experiences with aid) have been integrated into the national-level governance mechanisms of humanitarian assistance and development programmes in fragile and conflict-affected settings. There is limited systematic evidence available on this topic. There are a few cases of including civil society in national-level programme or sector governance bodies; there is more documented experience of including local actors in humanitarian response coordination. There is also relevant learning from feedback mechanisms, analysis and research, and people-centred approaches to aid planning and management more generally. The literature highlights the importance of conflict-sensitive approaches underpinned by regular conflict and political economy analysis; consulting with local actors on how they want to communicate and engage, and setting up safe and effective spaces for engagement; investing in long-term partnerships and capacity building to strengthen local organisations; and undertaking participatory, qualitative research that starts from ‘people’s own reading of how their lives are changing over time’ (Daigle, 2022: 15).
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10

Ahmed, Badrun Nessa, and Rizwana Islam. TEACHING AND LEARNING EXPERIENCE AT THE NATIONAL UNIVERSITY AFFILIATED TERTIARY COLLEGES IN BANGLADESH. Bangladesh Institute of Development Studies, March 2024. http://dx.doi.org/10.57138/axvn7639.

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The Government of Bangladesh is currently implementing the College Education Development Project (CEDP) to improve participating colleges' teaching and learning environment and strengthen the strategic planning and management capacity of National University (NU) affiliated tertiary colleges in Bangladesh. The focus of CEDP is to improve the capacity of the National University College system to plan, manage, implement, and monitor institutional programs, as well as strengthen the foundation for the next phase of development activities. CEDP promotes institution-led activities that focus on creating quality teaching-learning environments in government and non-government colleges through the availability of competitive grants. The achievement of the College Education Development Project (CEDP) is the satisfaction level of students, teachers, and employers in terms of the quality and relevance of teaching. To measure the satisfaction level of the relevant stakeholders (i.e., students, teachers, and employers), three beneficiary feedback surveys (i.e., baseline, mid-term, and endline) are planned to be conducted, among which the baseline was carried out in 2019. The Bangladesh Institute of Development Studies (BIDS) conducted the Mid-term Satisfaction Survey in May-June 2022. The mid-term survey is the second of the three planned surveys of the CEDP, measuring the mid-term satisfaction level of the stakeholders, students and teachers of National University-affiliated colleges, and employers of NU graduates. This study uses data from the Mid-term Satisfaction Survey to assess the mid-term satisfaction level of students, teachers, and employers. The study was designed using a mixed-method approach, both quantitative and qualitative, to address the objectives of this study. Data analysis has used both the baseline data collected in 2019 and the mid-term data collected in this study. Using the baseline and mid-term data, a two-round panel data was constructed at the college level. Depending on the specific indicators, the program's effect at the college level was calculated. We compare the overall satisfaction level regarding all the relevant indicators by stakeholder types, i.e., principals, teachers, and students, and observe differences among the average satisfaction levels. The overall teaching and learning environment satisfaction level is 3.81 among college principals, 2.95 among teachers, and 2.57 among students. A similar pattern is also found for other indicators except the collaboration of colleges with industries. The satisfaction level regarding the collaboration of colleges with industries is noted as the lowest for principals (1.62) and teachers (1.76), and for students, it is slightly higher (2.10 on a scale of 5). The lowest satisfaction level among students is recorded for connectivity through the internet (1.89), and the highest for teaching skills (3.92). The regression results show that for the full sample, the Difference-in-Difference (DiD) of the satisfaction scores on the quality of academic infrastructure, the quality of internet connection, and the quality of facilities for students’ soft skill improvement are statistically significant. The DiD for the other two satisfaction scores, namely, the teaching and learning environment and the degree of industry linkage, are not statistically significantly different from zero. These results show that the colleges that received Institutional Development Grants (IDGs) have made a positive and statistically significant impact on the improvement of the quality of academic infrastructure, quality of internet connection and other related facilities, and quality of facilities for students’ soft skill compared to those who did not receive this grant. However, the grant has made some changes in the teaching and learning environment and the degree of industry linkage between IDG awarded colleges and IDG non-recipient colleges. These changes are not statistically significant. The overall findings from the mid-term satisfaction survey highlighted that: (1) Institutional Development Grant (IDG) has made positive and statistically significant impact on the improvement of quality of academic infrastructure, quality of internet connection and other related facilities, and quality of facilities for students’ soft skill compared to those who did not receive this grant; (2) The grant has made some changes in the teaching and learning environment and the degree of industry linkage between IDG-awarded colleges and IDG non-recipient colleges. These changes are not significant enough to increase the satisfaction level of the students, teachers, and principals. Therefore, this study proposes these recommendations for increasing the overall satisfaction level of all stakeholders: (1) The poor level of industry collaboration has been highlighted by all types of beneficiaries. To facilitate industry collaboration, job fairs should be organised every year, preferably at the district level; (2) Introducing short course facilities can increase the job market opportunities of the NU-affiliated colleges; (3) Subject-based pedagogical training for the NU teachers is highly recommended; (4) The interrelation and collaboration between NU-affiliated colleges and universities should be increased. The colleges that are not well equipped with enough facilities can collaborate with the universities to share their equipment, such as computer labs, libraries, scientific labs, etc. This will help the less privileged colleges provide quality teaching and learning facilities to the students; (5) Forming and activating the activities of Alumni Associations in the NU-affiliated colleges; (6) There should be funds available for the renovation of old academic buildings, addition to an existing building, and upgrading labs and research facilities for teachers wherever appropriate, (7) There should be some provision of need-based funds/emergency grant that might be used or made available to the college authorities in case of sudden emergency or need (e.g., a sudden flash flood in Sylhet division)
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