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2. Growing up Together: Sibling Correlation, Parental Influence, and Intergenerational Educational Mobility in Developing Countries. Policy Research Working Paper 10285
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World Bank, Development Research Group, Ahsan, Md. Nazmul, Emran, M. Shahe, Jiang, Hanchen, Han, Qingyang, and Shilpi, Forhad
- Abstract
This paper presents credible and comparable evidence on intergenerational educational mobility in 53 developing countries using sibling correlation as a measure, and data from 230 waves of Demographic and Health Surveys. It is the first paper to provide estimates of sibling correlation in schooling for a large number of developing countries using high quality standardized data. Sibling correlation is an omnibus measure of mobility as it captures observed and unobserved family and neighborhood factors shared by siblings when growing up together. The estimates suggest that sibling correlation in schooling in developing countries is much higher (average 0.59) than that in developed countries (average 0.41). There is substantial spatial heterogeneity across regions, with Latin America and Caribbean having the highest (0.65) and Europe and Central Asia the lowest (0.48) estimates. Country level heterogeneity within a region is more pronounced. The evolution of sibling correlation suggests a variety of mobility experiences, with some regions registering a monotonically declining trend from the 1970s birth cohort to the 1990s birth cohort (Latin America and the Caribbean and East Asia and Pacific), while others remained trapped in stagnancy (South Asia and Sub-Saharan Africa). The only region that experienced monotonically increasing sibling correlation is the Middle East and North Africa. The recent approach of Bingley and Cappellari (2019) is used to estimate the share of sibling correlation due to intergenerational transmission. The estimates show that when the homogeneity and independence assumptions implicit in the standard model of intergenerational transmission are relaxed, the estimated share is much larger. In the sample of countries, on average 74 percent of sibling correlation can be attributed to intergenerational transmission, while there are some countries where the share is more than 80 percent (most in Sub-Saharan Africa). This suggests a dominant role for parents in determining the educational opportunities of their children. Evidence on the evolution of the intergenerational share, however, suggests a declining importance of the intergenerational transmission component in many countries, but the pattern is diverse. In some cases, the trend in the intergenerational share is opposite to the trend in sibling correlation. [This report was prepared by the World Bank Group's Development Research Group, Development Economics.]
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- 2023
3. Out of the Gate, but Not Necessarily Teaching: A Descriptive Portrait of Early-Career Earnings for Those Who Are Credentialed to Teach. Working Paper No. 263-0422
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National Center for Analysis of Longitudinal Data in Education Research (CALDER) at American Institutes for Research, Goldhaber, Dan, Krieg, John, Liddle, Stephanie, and Theobald, Roddy
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Prior work on teacher candidates in Washington State has shown that about two thirds of individuals who trained to become teachers between 2005 and 2015 and received a teaching credential did not enter the state's public teaching workforce immediately after graduation, while about one third never entered a public teaching job in the state at all. In this analysis, we link data on these teacher candidates to unemployment insurance data in the state to provide a descriptive portrait of the future earnings and wages of these individuals inside and outside of public schools. Candidates who initially became public school teachers earned considerably more, on average, than candidates who were initially employed either in other education positions or in other sectors of the state's workforce. These differences persisted at least 10 years into the average career and across transitions into and out of teaching. There is therefore little evidence that teacher candidates who did not become teachers were lured into other professions by higher compensation. Instead, the patterns are consistent with demand-side constraints on teacher hiring during this time period that resulted in individuals who wanted to become teachers taking positions that offered lower wages but could lead to future teaching positions.
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- 2022
4. Research, Interrupted: Addressing Practical and Methodological Challenges under Turbulent Conditions. Working Paper
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RAND Education and Labor, Susan Bush-Mecenas, Jonathan Schweig, Megan Kuhfeld, Louis T. Mariano, and Melissa Kay Diliberti
- Abstract
The COVID-19 pandemic caused tremendous upheaval in schooling. In addition to its devasting effects on students' academic development, the disruptions to schooling had important consequences for researchers conducting effectiveness studies on educational programs during this era. Given the likelihood of future large-scale disruptions, it is important for researchers to plan resilient studies and think critically about possible adaptations when such turbulence arises. In this article, we utilize qualitative case study analysis to examine how researchers evaluating educational programs in the pandemic period adjusted to turbulent conditions through design pivots to ensure the feasibility of research. We find that researchers struggled to strike a balance between the evaluations that were intended and those that could realistically be accomplished. We identify how the challenges of the pandemic period and design pivots raised potential threats to validity, illuminate some promising practices that arose during the pandemic period, and provide recommendations for future research and evaluation programs focused on studying the effectiveness of educational programs during times of profound disruption.
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- 2023
5. Towards a Canvas for Defining and Structuring Analytics Projects
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Langer, Benedict, Schirrmacher, Viola, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, and Li, Shuliang, editor
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- 2024
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6. Leverage Data Security Policies Complexity for Users: An End-to-End Storage Service Management in the Cloud Based on ABAC Attributes
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Greneche, Nicolas, Andres, Frederic, Tanabe, Shihori, Pester, Andreas, Ali, Hesham H., Mahmoud, Amgad A., Bascle, Dominique, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Renault, Éric, editor, Boumerdassi, Selma, editor, and Mühlethaler, Paul, editor
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- 2024
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7. A Securing the Data Using a Novel Security Algorithm S-RKB-22
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Bagath Basha, C., Rajaprakash, S., Subapriya, V., Karthik, K., Jagadeesan, J., Sankar Ganesh, S., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nagar, Atulya K., editor, Jat, Dharm Singh, editor, Mishra, Durgesh, editor, and Joshi, Amit, editor
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- 2024
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8. Development of the Web-based Data-driven University Information Management System (UIMS) for the Inter-University Council for East Africa (IUCEA)
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Nshimiye, Abel, Ally, Mussa Dida, Mirau, Silas Steven, Ruhinda, Ben, Marx Gómez, Jorge, editor, Elikana Sam, Anael, editor, and Godfrey Nyambo, Devotha, editor
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- 2024
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9. Review of Research on the Commercialization of China’s Intelligent and Connected Vehicles Industry
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Zhao, Mengxu, Lu, Ruigang, Zhou, Boyang, Kang, Tianyi, China Society of Automotive Engineers, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, and Tan, Kay Chen, Series Editor
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- 2024
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10. Discussion paper: implications for the further development of the successfully in emergency medicine implemented AUD2IT-algorithm
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Christopher Przestrzelski, Antonina Jakob, Clemens Jakob, and Felix R. Hoffmann
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handover ,emergency medicine ,process management ,interoperability (IoP) ,data ,Medicine ,Public aspects of medicine ,RA1-1270 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
The AUD2IT-algorithm is a tool to structure the data, which is collected during an emergency treatment. The goal is on the one hand to structure the documentation of the data and on the other hand to give a standardised data structure for the report during handover of an emergency patient. AUD2IT-algorithm was developed to provide residents a documentation aid, which helps to structure the medical reports without getting lost in unimportant details or forgetting important information. The sequence of anamnesis, clinical examination, considering a differential diagnosis, technical diagnostics, interpretation and therapy is rather an academic classification than a description of the real workflow. In a real setting, most of these steps take place simultaneously. Therefore, the application of the AUD2IT-algorithm should also be carried out according to the real processes. A big advantage of the AUD2IT-algorithm is that it can be used as a structure for the entire treatment process and also is entirely usable as a handover protocol within this process to make sure, that the existing state of knowledge is ensured at each point of a team-timeout. PR-E-(AUD2IT)-algorithm makes it possible to document a treatment process that, in principle, does not have to be limited to the field of emergency medicine. Also, in the outpatient treatment the PR-E-(AUD2IT)-algorithm could be used and further developed. One example could be the preparation and allocation of needed resources at the general practitioner. The algorithm is a standardised tool that can be used by healthcare professionals of any level of training. It gives the user a sense of security in their daily work.
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- 2024
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11. Performance Analysis of CNN and Quantized CNN Model for Rheumatoid Arthritis Identification Using Thermal Image
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Mahesh Kumar, A. S., Mallikarjunaswamy, M. S., Chandrashekara, S., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Santosh, KC, editor, Goyal, Ayush, editor, Aouada, Djamila, editor, Makkar, Aaisha, editor, Chiang, Yao-Yi, editor, and Singh, Satish K, editor
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- 2023
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12. Data Paper as a Reward? Motivation, Consideration, and Perspective behind Data Paper Submission.
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Huang, Pao Pei and Jeng, Wei
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CITATION analysis ,PUBLICATIONS ,SCHOLARLY communication ,DATA ,SCHOLARLY publishing - Abstract
Data papers, as one of the channels to encourage researchers to open up research data under the open science movement, are expected to provide strong incentives through formal citations. However, few studies have investigated the drivers of this emerging type of publication. This study examines researchers' motivations, and considerations for data paper submission, as well as their perspectives on this scholarly publication. Through an in‐depth interview approach with ten data paper authors, our preliminary results found that, researchers are often driven by extrinsic factors to increase their publications, and data papers are sometimes viewed as territory claims before further research. Although the academic community widely recognizes the benefits of publishing data papers, some still cast a doubtful eye on its academic value and impact. We anticipate such insights on the driving forces and point of views of data papers could provide opportunities for stakeholders to fill gaps and strengthen the open science ecosystem. [ABSTRACT FROM AUTHOR]
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- 2022
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13. HS2 Phase One: Heritage GIS Digital Archive (Data paper)
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Fred Farshid Aryankhesal
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archaeology ,data ,archive ,historic environment ,hs2 ,Archaeology ,CC1-960 - Abstract
High Speed 2 (HS2) will be the largest programme of historic environment investigation and recording works ever undertaken in the UK. It is certain that the creation of HS2's historic environment physical and digital archive (High Speed Two Ltd. 2023) is an integral part of the lasting legacy of the programme, which presents an unprecedented opportunity for significant knowledge creation. HS2 historic environment works that have been undertaken for Phase One of HS2 between London to the West Midlands has resulted in a substantial digital archive, including Geographic Information Systems (GIS) data. This data paper highlights the GIS spatial datasets generated from the HS2 Phase One historic environment fieldwork programme. It explains the technical components of the datasets which are deposited with the Archaeology Data Service (ADS).
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- 2023
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14. SHMEM-ML: Leveraging OpenSHMEM and Apache Arrow for Scalable, Composable Machine Learning
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Grossman, Max, Poole, Steve, Pritchard, Howard, Sarkar, Vivek, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Poole, Stephen, editor, Hernandez, Oscar, editor, Baker, Matthew, editor, and Curtis, Tony, editor
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- 2022
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15. Luxury and Digital, Hindered Narratives?
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Tsala Effa, Didier, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rauterberg, Matthias, editor, Fui-Hoon Nah, Fiona, editor, Siau, Keng, editor, Krömker, Heidi, editor, Wei, June, editor, and Salvendy, Gavriel, editor
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- 2022
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16. Using KullBack-Liebler Divergence Based Meta-learning Algorithm for Few-Shot Skin Cancer Image Classification: Literature Review and a Conceptual Framework
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Akinrinade, Olusoji B., Du, Chunglin, Ajila, Samuel, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Singh, Mayank, editor, Tyagi, Vipin, editor, Gupta, P. K., editor, Flusser, Jan, editor, and Ören, Tuncer, editor
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- 2022
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17. A Method for Data Compression and Personal Information Suppressing in Columnar Databases
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Meena, Gaurav, Mahrishi, Mehul, Choudhary, Ravi Raj, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Balas, Valentina E., editor, Sinha, G. R., editor, Agarwal, Basant, editor, Sharma, Tarun Kumar, editor, Dadheech, Pankaj, editor, and Mahrishi, Mehul, editor
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- 2022
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18. Augmented Reality Data Analysis for Human Needs: Subsistence, Protection, and Leisure
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Yahya, Manal A., Dahanayake, Ajantha, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kabashkin, Igor, editor, Yatskiv, Irina, editor, and Prentkovskis, Olegas, editor
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- 2022
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19. Coordination of Models for Describing and Making Agro-Technological Decisions
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Ereshko, Felix, Budzko, Vladimir, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Taratukhin, Victor, editor, Matveev, Mikhail, editor, Becker, Jörg, editor, and Kupriyanov, Yury, editor
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- 2022
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20. Decision Modelling in Timed Dynamic Condition Response Graphs with Data
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Hildebrandt, Thomas T., Normann, Håkon, Marquard, Morten, Debois, Søren, Slaats, Tijs, van der Aalst, Wil, Series Editor, Mylopoulos, John, Series Editor, Ram, Sudha, Series Editor, Rosemann, Michael, Series Editor, Szyperski, Clemens, Series Editor, Marrella, Andrea, editor, and Weber, Barbara, editor
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- 2022
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21. Cybersecurity in Smart Cities: Technology and Data Security in Intelligent Transport Systems
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Galego, Nuno Miguel Carvalho, Pascoal, Rui Miguesl, Howlett, Robert J., Series Editor, Jain, Lakhmi C., Series Editor, Mesquita, Anabela, editor, Abreu, António, editor, and Carvalho, João Vidal, editor
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- 2022
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22. Metalurgija Journal 1962-2022 y – List of Published Papers
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I. Mamuzić
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metallurgy ,journal ,articles ,list ,data ,Mining engineering. Metallurgy ,TN1-997 - Abstract
For the interval 1962-2022 y, during the continuos publication last 60 years, in Metalurgija Journal Authors from 40 countries from Mexico to China (all 5 continent ) have publish. The goal of the Article is give List of Papers published in this interval, 199 issues or 238 numbers, 2721 scientific and technical, and 287 contributions (total 3008 papers ) of Authors whose investigation results and ideas have been examined and found on the pages of this Journal. Thanks for all.
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- 2022
23. Can't Inflate Data? Let the Models Unite and Vote: Data-Agnostic Method to Avoid Overfit with Small Data
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Shimmei, Machi and Matsuda, Noboru
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We propose an innovative, effective, and data-agnostic method to train a deep-neural network model with an extremely small training dataset, called VELR (Voting-based Ensemble Learning with Rejection). In educational research and practice, providing valid labels for a sufficient amount of data to be used for supervised learning can be very costly and often impractical. The shortage of training data often results in deep neural networks being overfitting. There are many methods to avoid overfitting such as data augmentation and regularization. Though, data augmentation is considerably data dependent and does not usually work well for natural language processing tasks. Moreover, regularization is often quite task specific and costly. To address this issue, we propose an ensemble of overfitting models with uncertainty-based rejection. We hypothesize that misclassification can be identified by estimating the distribution of the class-posterior probability P(y|x) as a random variable. The proposed VELR method is data independent, and it does not require changes to the model structure or the re-training of the model. Empirical studies demonstrated that VELR achieved classification accuracy of 0.7 with only 200 samples per class on the CIFAR-10 dataset, but 75% of input samples were rejected. VELR was also applied to a question generation task using a BERT language model with only 350 training data points, which resulted in generating questions that are indistinguishable from human-generated questions. The paper concludes that VELR has potential applications to a broad range of real-world problems where misclassification is very costly, which is quite common in the educational domain. [For the complete proceedings, see ED630829.]
- Published
- 2023
24. A Shared Lens around Sensemaking in Learning Analytics: What Activity Theory, Definition of a Situation and Affordances Can Offer
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Oleksandra Poquet
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The paper argues that learning analytics as a research field can benefit from a theory-informed shared language to describe sensemaking of learning and teaching data. To make the case for such shared language, first, I critically review prominent sensemaking theories to then demonstrate how studies in learning analytics do not use coherent descriptions of sensemaking, eclectically combining the paradigms that have underlying differences. I then propose a conceptualization of sensemaking that overcomes the differences between these theories and explains how the concepts of "activity system," the "definition of the situation" and "affordances" can be used to capture individual differences in sensemaking. The paper concludes with a preliminary framework and examples demonstrating its utility in raising new theoretical questions, informing design principles and providing shared language for researchers in learning analytics.
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- 2024
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25. "Paper More Precious Than Blood": Chinese Exclusion Era Identity Documentation Processes and Racialization of Identity Data.
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Nham, Kai
- Subjects
- *
RACIALIZATION ,CHINESE Exclusion Act of 1882 - Abstract
This project interrogates the United States' national fixation on the answer to the question: Who are you? In this article, it is posed that identity documentation practices arising out of the Chinese Exclusion Act era cast identity as an empirical and immutable phenomenon, specifically in response to the racialization of American-born Chinese settlers as duplicitous, through the mechanisms that information is collected, the actual information itself, and the cross-references or connections created between cases. Through tracing this lineage, racialized identification data is identified and theorized as part of hegemonic data regimes. [ABSTRACT FROM AUTHOR]
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- 2023
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26. Sanctity of Digital Privacy and Personal Data during COVID-19: Are Youths Enough Digitally Literate to Deal with It?
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Ghosh, Swagata, Chauhan, Gajendra Singh, and Kotwal, Renu
- Abstract
The COVID-19 pandemic has fast-tracked the development of digital applications and inspired everyone to adapt to the technologies to curb the spread of outbreak. As this crisis intensifies, the rapid usage of digital devices and apps has echoed the serious concerns about civil liberties, privacy, and data protection. Considering the situation, this research aimed to explore the internet using habits of the youths of West Bengal, a state in eastern India, during COVID-19. Besides, the paper explored their experiences of using various digital applications, the fundamental digital literacy and how safely they protect data from breaches. Thus, the paper presents the results by conducting an online survey among the youths in West Bengal. The result, from 215 participants, highlighted that the increased use of these digital applications has not matched the demand for digital privacy literacy among the young generation of the state. While this pandemic has raised their concerns over digital privacy and data protection, yet they do not undertake any strong protection mechanisms to safeguard them digitally. Besides, this paper suggests suitable plans to raise awareness among this generation and form a healthy digital citizenship with a proper regulatory framework as it is the need of the hour.
- Published
- 2023
27. How Big Is a Leaf? Using Cognitive Tuning to Explore a Teacher's Communication Processes to Elicit Children's Emerging Ideas about Data
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Mathematics Education Research Group of Australasia (MERGA), Fry, Kym, English, Lyn, and Makar, Katie
- Abstract
The intangible concept of data, as part of statistical literacy, can be complex for young children to grasp. Inquiry as a pedagogy has potential for supporting student development of statistical literacy as the investigation process is driven by the inquiry question. The aim of this paper is to gain insight into how a teacher's communication processes with her students supported their emerging understandings about the abstract concept of data. In this exploratory case study, we present data from a Year 4 classroom (age 9) in a guided mathematical inquiry within the STEM context of agricultural science. The inquiry question the students addressed was, "How big is a leaf?" The inquiry focused on linking data to the real-life context the data represented.
- Published
- 2022
28. Psychological Testing at Entrance Exam at 'Dunarea de Jos' University of Galati, Romania
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Andrei, Mihaela and Pricopie-Filip, Alina
- Abstract
The university admission test comes after the high school graduation exam - the baccalaureate. The baccalaureate results of each candidate must be known by the university admissions committee. They provide information on the degree of intelligence, the skills acquired up to this date, but also the presence of inclinations and skills indispensable to the fulfillment of professional aspirations. The university entrance exam should not be focused only on quantity and quality of knowledge. Besides that, one of the objectives of this exam must be to test the interest in completing the studies through the university level for which he opts, but also the candidate's skills that "offer" him the productive and satisfying course of the entire cycle of higher education, even the perspective of future achievements. To realize that three psychological investigation tools of candidates (tests) can be used, necessary to highlight: (1) personality profile of the candidate; (2) interest profile, motivational; and (3) aptitude profile. The paper proposes a new admission methodology: the data collected through the proposed tests and correlated with the high school graduation data can accept the candidates, as admitted to the profile they opted for, or can redirect them to choose the right path. [For the full proceedings, see ED630948.]
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- 2022
29. Towards Real Interpretability of Student Success Prediction Combining Methods of XAI and Social Science
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Cohausz, Lea
- Abstract
Despite calls to increase the focus on explainability and interpretability in EDM and, in particular, student success prediction, so that it becomes useful for personalized intervention systems, only few efforts have been undertaken in that direction so far. In this paper, we argue that this is mainly due to the limitations of current Explainable Artificial Intelligence (XAI) approaches regarding interpretability. We further argue that the issue, thus, calls for a a combination of AI and social science methods utilizing the strengths of both. For this, we introduce a step-wise model of interpretability where the first step constitutes of knowing important features, the second step of understanding counterfactuals regarding a particular person's prediction, and the third step of uncovering causal relations relevant for a set of similar students. We show that LIME, a current XAI method, reaches the first but not subsequent steps. To reach step two, we propose an extension to LIME, Minimal Counterfactual-LIME, finding the smallest number of changes necessary to change a prediction. Reaching step three, however, is more involved and additionally requires theoretical and causal reasoning - to this end, we construct an easily applicable framework. Using artificial data, we showcase that our methods can recover connections among features; additionally, we demonstrate its applicability on real-life data. Limitations of our methods are discussed and collaborations with social scientists encouraged. [For the full proceedings, see ED623995.]
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- 2022
30. Can Population-Based Engagement Improve Personalisation? A Novel Dataset and Experiments
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Bulathwela, Sahan, Verma, Meghana, Pérez-Ortiz, María, Yilmaz, Emine, and Shawe-Taylor, John
- Abstract
This work explores how population-based engagement prediction can address cold-start at scale in large learning resource collections. The paper introduces: (1) VLE, a novel dataset that consists of content and video based features extracted from publicly available scientific video lectures coupled with implicit and explicit signals related to learner engagement; (2) two standard tasks related to predicting and ranking context-agnostic engagement in video lectures with preliminary baselines; and (3) a set of experiments that validate the usefulness of the proposed dataset. Our experimental results indicate that the newly proposed VLE dataset leads to building context-agnostic engagement prediction models that are significantly performant than ones based on previous datasets, mainly attributing to the increase of training examples. VLE dataset's suitability in building models towards Computer Science/ Artificial Intelligence education focused on e-learning/MOOC use-cases is also evidenced. Further experiments in combining the built model with a personalising algorithm show promising improvements in addressing the cold-start problem encountered in educational recommenders. This is the largest and most diverse publicly available dataset to our knowledge that deals with learner engagement prediction tasks. The dataset, helper tools, descriptive statistics and example code snippets are available publicly. [For the full proceedings, see ED623995.]
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- 2022
31. DerSql, Generating SQL from an Entity-Relation Diagram
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Andrea Domínguez-Lara and Wulfrano Arturo Luna-Ramírez
- Abstract
The automatic code generation is the process of generating source code snippets from a program, i.e., code for generating code. Its importance lies in facilitating software development, particularly important is helping in the implementation of software designs such as engineering diagrams, in such a case, automatic code generation copes with the problem of how to obtain code from a graphic representation, for instance an UML diagram or a Relational Diagram. Some advantages of automatic code generation are: a) to obtain the source code more quickly and to do it with lower margins of error; b) it is promising to be applied in teaching contexts, whilst provide instructors with a tool to teach, the expected results of assignments can be assessed by comparing the results of students and the automatic generated code. Furthermore, one of the most frequently tasks in classrooms when teaching relational databases is the design of Entity-Relationship Diagrams which eventually become SQL code. The manual transition from an Entity-Relationship Diagram to SQL code is a time-consuming process and requires of a skilled eye to be successfully performed. In this paper, we present "DerSql," an extension of the DIA Diagrammer, a well-known free software engineering tool, to automatically generate SQL code from an Entity-Relationship Diagrams. The results are tested for the case of 1 -- 1 and 1 -- n arities relationships. We consider that "DerSql" represents a remarkable tool for teaching while it is a promising advance in developing DIA as a 4th Generation software engineering application. [For the full proceedings, see ED638044.]
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- 2022
32. Skill up Tennessee: Job Training That Works
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Sneed, Christopher T., Upendram, Sreedhar, Cummings, Clint, and Fox, Janet E.
- Abstract
Employment and training services offered through Extension are part of and continue a long tradition of policy-focused employment and job training. This paper chronicles the successes of UT Extension's work as a third-party partner in the delivery of workforce development programming geared toward individuals receiving Supplemental Nutrition Assistance Program (SNAP) benefits. The paper begins with an overview of the federal program and a discussion of how Tennessee forged a state-level partnership for the delivery of workforce services. Data showing program success including number of participants served, supportive services offered, and economic impact are highlighted. Finally, lessons learned are outlined.
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- 2023
33. Privacy Harm and Non-Compliance from a Legal Perspective
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Suvineetha Herath, Haywood Gelman, and Lisa Mckee
- Abstract
In today's data-sharing paradigm, personal data has become a valuable resource that intensifies the risk of unauthorized access and data breach. Increased data mining techniques used to analyze big data have posed significant risks to data security and privacy. Consequently, data breaches are a significant threat to individual privacy. Privacy is a multifaceted concept covering many areas, including the right to access, erasure, and rectify personal data. This paper explores the legal aspects of privacy harm and how they transform into legal action. Privacy harm is the negative impact to an individual as a result of the unauthorized release, gathering, distillation, or expropriation of personal information. Privacy Enhancing Technologies (PETs) emerged as a solution to address data privacy issues and minimize the risk of privacy harm. It is essential to implement privacy enhancement mechanisms to protect Personally Identifiable Information (PII) from unlawful use or access. FIPPs (Fair Information Practice Principles), based on the 1973 Code of Fair Information Practice (CFIP), and the Organization for Economic Cooperation and Development (OECD), are a collection of widely accepted, influential US codes that agencies use when evaluating information systems, processes, programs, and activities affecting individual privacy. Regulatory compliance places a responsibility on organizations to follow best practices to ensure the protection of individual data privacy rights. This paper will focus on FIPPs, relevance to US state privacy laws, their influence on OECD, and reference to the EU General Data Processing Regulation. (GDPR).
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- 2023
34. The measure of a quality research paper
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Wanjari, Priya D. and Rohankar, Akshara
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- 2022
35. Classroom Equity Data Inquiry for Racial Equity
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Rebekah Sidman-Taveau
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Longstanding inequities exist in community colleges across the United States. To address these inequities, California Community Colleges educators have engaged in a variety of practices including the writing of equity plans and participation in equity data inquiry. However, there is an urgent need for greater focus on racial equity and for more faculty involvement in equity work at the classroom level. This paper presents a teacher case study exploring Classroom Equity Data Inquiry (CEDI), a tool for faculty professional learning focused on equitable student outcomes. In CEDI, professors examine their disaggregated classroom data, reflect on their class equity gaps, and pursue relevant professional development. They implement targeted interventions and then assess those interventions. This paper describes the author's sustained CEDI utilizing six years of equity data in her English as a Second Language classes at a small northern California community college. First, it provides a definition and rationale for CEDI. Second, it details the author's CEDI process and challenges. Third, it shares the author's changes in thinking and practice including high impact interventions the author implemented to reduce equity gaps for men of color in her classes. Fourth, the article describes positive qualitative student data and increased success and retention rates for Hispanic and multi-race males following the interventions. The article concludes that CEDI requires training, support, and time, but that the approach merits further research. More research is needed on CEDI methods and their possible impact on racial equity in the classroom.
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- 2024
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36. Toward Redefining Library Research Support Services in Australia and Aotearoa New Zealand: An Evidence-Based Practice Approach
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Alisa Howlett, Eleanor Colla, and Rebecca Joyce
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An increasingly complex and demanding research landscape has seen university libraries rapidly evolve their services. While research data management, bibliometrics, and research impact services have predominantly featured in the literature to date, the full scope of support libraries are currently providing to their institutions is unknown. This paper aims to present an up-to-date view of the scope and extent of research support services by university libraries across Australia and Aotearoa New Zealand. A coding process analyzed content data from university library websites. Eleven research support areas were identified. Service delivery is split between synchronous and asynchronous modes. This paper describes a lived experience of an evidence-based library and information practice approach to improving research support services at two Australian university libraries, and while it highlights continued maturation of research support services, more research is needed to better understand influences on service development.
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- 2024
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37. Law Case Teaching Combining Big Data Environment with SPSS Statistics
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Zhao Wang
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This paper proposes an online learning platform learner DM method based on the improved fuzzy C clustering (FCM) algorithm, constructs a learner feature database, and combines clustering analysis and SPSS statistical methods to statistically summarize the big data of law, thus improving the deficiencies of static and absolute classification of students in the student model. In the experiment paper, the improved algorithm is implemented and the experimental data is analyzed. The results show that the learner behavior feature extraction model in this paper has fewer errors and higher recall rate. Compared with the traditional CF algorithm, the error rate is reduced by 19.64% and the recall rate is increased by 22.85%. This study provides better targeted teaching programs and case resources for legal case teaching and promotes the innovation of legal case teaching mode.
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- 2024
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38. The Data Awareness Framework as Part of Data Literacies in K-12 Education
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Lukas Höper and Carsten Schulte
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Purpose: In today's digital world, data-driven digital artefacts pose challenges for education, as many students lack an understanding of data and feel powerless when interacting with them. This paper aims to address these challenges and introduces the data awareness framework. It focuses on understanding data-driven technologies and reflecting on the role of data in everyday life. The paper also presents an empirical study on young school students' data awareness. Design/methodology/approach: The study involves a teaching unit on data awareness framed by a pre- and post-test design using a questionnaire on students' awareness and understanding of and reflection on data practices of data-driven digital artefacts. Findings: The study's findings indicate that the data awareness framework supports students in understanding data practices of data-driven digital artefacts. The findings also suggest that the framework encourages students to reflect on these data practices and think about their daily behaviour. Originality/value: Students learn a model about interactions with data-driven digital artefacts and use it to analyse data-driven applications. This approach appears to enable students to understand these artefacts from everyday life and reflect on these interactions. The work contributes to research on data and artificial intelligence literacies and suggests a way to support students in developing self-determination and agency during interactions with data-driven digital artefacts.
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- 2024
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39. Understanding Privacy and Data Protection Issues in Learning Analytics Using a Systematic Review
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Liu, Qinyi and Khalil, Mohammad
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The field of learning analytics has advanced from infancy stages into a more practical domain, where tangible solutions are being implemented. Nevertheless, the field has encountered numerous privacy and data protection issues that have garnered significant and growing attention. In this systematic review, four databases were searched concerning privacy and data protection issues of learning analytics. A final corpus of 47 papers published in top educational technology journals was selected after running an eligibility check. An analysis of the final corpus was carried out to answer the following three research questions: (1) What are the privacy and data protection issues in learning analytics? (2) What are the similarities and differences between the views of stakeholders from different backgrounds on privacy and data protection issues in learning analytics? (3) How have previous approaches attempted to address privacy and data protection issues? The results of the systematic review show that there are eight distinct, intertwined privacy and data protection issues that cut across the learning analytics cycle. There are both cross-regional similarities and three sets of differences in stakeholder perceptions towards privacy and data protection in learning analytics. With regard to previous attempts to approach privacy and data protection issues in learning analytics, there is a notable dearth of applied evidence, which impedes the assessment of their effectiveness. The findings of our paper suggest that privacy and data protection issues should not be relaxed at any point in the implementation of learning analytics, as these issues persist throughout the learning analytics development cycle. One key implication of this review suggests that solutions to privacy and data protection issues in learning analytics should be more evidence-based, thereby increasing the trustworthiness of learning analytics and its usefulness.
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- 2023
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40. Success in Education by Defying Great Odds: A Positive Deviance Analysis of Educational Policies
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Eva Ponte
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Education is seen as a resource at a global level but is currently considered to be in crisis in many parts of the world. This constitutes a significant drawback in terms of humanity's prosperity and well-being since education is the key not only to an educated workforce but also to humane, collaborative, and caring societies. Even within this dim landscape, there are certain educational systems that defy the odds and perform significantly higher than their otherwise comparable systems. This paper proposes using an unusual lens for educational policy comparative studies, that of positive deviance, to aid us in progressing towards a more stable educational state of affairs. Using a positive deviance methodology, which focuses on learning what is working well in systems that defy and overcome substantial challenges, this study investigates the patterns, attitudes, and actions of three selected cases: Massachusetts as a positive deviant in the US, Estonia as a positive deviant in Europe, and Castile-Leon as a positive deviant in Spain. The purpose is, by analysing educational policies, laws, and other related documents, to find commonalities that explain why these systems outperform others. The results of the comparative analysis pinpoint areas and strategies informative to those leading struggling educational systems, such as a strong commitment to equity and justice, placing teachers at the centre of reforms, using assessment as a tool for process monitoring and summative inquiry, and making preschool education accessible to all.
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- 2024
41. Choosing American Colleges from Afar: Chinese Students' Perspectives
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Yefei Xue, Siguo Li, and Liang Ding
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Chinese students studying abroad have been increasing rapidly in the past decades and become a significant financial contribution to receiving countries. Accordingly, understanding their enrollment choice is essential to facilitate college marketing and admission strategies. Though the decision process is believed to be different from domestic students, empirical analysis of Chinese students' enrollment choices is still lacking. This paper fills the void by examining the influential factors of Chinese students' enrollment choice with novel student-level data. We find that in addition to factors domestic students typically consider, such as financial aid and academic quality, Chinese students particularly emphasize college ranking, reputation, and location in their decision process. Furthermore, unlike domestic students who usually prefer colleges with proximity to home, Chinese students' location preference is linked to job prosperity. We also find that the impact of the factors varies for students from different regions of China, which can be attributable to uneven economic development within the country.
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- 2024
42. Normative Challenges in Data Governance: Insights from Global Health Research
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Mathew Mercuri and Claudia I. Emerson
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Many important questions in health professions education require datasets that are built from several sources, in some cases using data collected for a different purpose. In building and maintaining these datasets, project leaders will need to make decisions about the data. While such decisions are often construed as technical, there are several normative concerns, such as who should have access, how the data will be used, how products resulting from the data will be shared, and how to ensure privacy of the individuals the data is about is respected, etc. Establishing a framework for data governance can help project leaders in avoiding problems, related to such matters, that could limit what can be learned from the data or that might put the project (or future projects) at risk. In this paper, we highlight several normative challenges to be addressed when determining a data governance framework. Drawing from lessons in global health, we illustrate three kinds of normative challenges for projects that rely on data from multiple sources or involved partnerships across institutions or jurisdictions: (1) legal and regulatory requirements, (2) consent, and (3) equitable sharing and fair distribution.
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- 2024
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43. Persona Journey Mapping to Drive Equity during an LMS Transition
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Kam Moi Lee, Megan Mcfarland, and Kari Goin Kono
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One way to achieve equitable design is to directly include users who will be impacted the most in the planning and facilitation of a project. Common financial, logistical, and/or temporal constraints reveal that direct inclusion of the people most impacted is not always possible. If this barrier arises, one promising alternative is the creation and use of personas. Using a vignette and case study qualitative methodological approach, three researchers at a large urban university in the Pacific Northwest detail personas and journey mapping as an equitable design practice during a LMS migration on a rapid development timeline. This paper details how personas were created using empirical data, how journey mapping impacted various teams, and how centering equity better prepared staff to support instructors throughout the migration while addressing the student learning impact.
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- 2023
44. A review paper on characteristics of blockchain
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Khanna, Roma
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- 2021
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45. 2022 BenchCouncil International Symposium on benchmarking, measuring and optimizing (Bench 2022) call for papers.
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Chunjie Luo and Wanling Gao
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BENCHMARKING (Management) ,DATA management ,HARDWARE ,COMPUTER software ,DATA - Published
- 2022
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46. Using Markup Languages for Accessible Scientific, Technical, and Scholarly Document Creation
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White, Jason J. G.
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In using software to write a scientific, technical, or other scholarly document, authors have essentially two options. They can either write it in a 'what you see is what you get' (WYSIWYG) editor such as a word processor, or write it in a text editor using a markup language such as HTML, LATEX, Markdown, or AsciiDoc. This paper gives an overview of the latter approach, focusing on both the non-visual accessibility of the writing process, and that of the documents produced. Currently popular markup languages and established tools associated with them are introduced. Support for mathematical notation is considered. In addition, domain-specific programming languages for constructing various types of diagrams can be well integrated into the document production process. These languages offer interesting potential to facilitate the non-visual creation of graphical content, while raising insufficiently explored research questions. The flexibility with which documents written in current markup languages can be converted to different output formats is emphasized. These formats include HTML, EPUB, and PDF, as well as file formats used by contemporary word processors. Such conversion facilities can serve as means of enhancing the accessibility of a document both for the author (during the editing and proofreading process) and for those among the document's recipients who use assistive technologies, such as screen readers and screen magnifiers. Current developments associated with markup languages and the accessibility of scientific or technical documents are described. The paper concludes with general commentary, together with a summary of opportunities for further research and software development.
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- 2022
47. Communities of Practice: Aligning K-12 and Postsecondary Education
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State Higher Education Executive Officers (SHEEO), Colorado, Jessica, Klein, Carrie, and Whitfield, Christina
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The State Higher Education Executive Officers Association's (SHEEO) "Communities of Practice" project builds upon SHEEO's ongoing efforts to measure the capacity and effective use of state postsecondary data systems and provides states with opportunities to develop solutions to common issues with those systems. The sixth Community of Practice convening, "Aligning K-12 and Postsecondary Education," was held December 7-8, 2021, in Denver, Colorado. The two-day meeting included representatives from 13 states: Delaware, Georgia, Hawaii, Idaho, Kentucky, Louisiana, Maryland, Missouri, Pennsylvania, Rhode Island, Tennessee, Utah, and Washington. Teams included representatives from SHEEO agencies, state K-12 agencies, P-20 partnership organizations, state longitudinal data systems, and others. The Community of Practice also addressed the impacts of the COVID-19 pandemic on student transitions and how state data systems can be used to promote equitable outcomes for low-income students and students of color. During the convening, teams explored practical uses of state P-20 data for improving college access, equity, and success and how state postsecondary data can better inform the K-12 to postsecondary pipeline. This white paper highlights key themes and findings of the convening, including challenges and lessons learned from the participating state teams and suggestions of topics for further consideration. Case studies describing ongoing efforts in Georgia and Pennsylvania are included in the appendices. Presentations from the December 2021 convening are available on SHEEO's state postsecondary data website.
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- 2022
48. Using Community-Based Problems to Increase Motivation in a Data Science Virtual Internship
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Johnson, Jillian C. and Olney, Andrew M.
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Typical data science instruction uses generic datasets like survival rates on the Titanic, which may not be motivating for students. Will introducing real-life data science problems fill this motivational deficit? To analyze this question, we contrasted learning with generic datasets and artificial problems (Phase 1) with a community-sourced dataset and authentic problems (Phase 2) in the context of an 8-week virtual internship. Retrospective survey questions indicated interns experienced increased motivation in Phase 2. Additionally, analysis of intern discourse using Linguistic Inquiry and Word Count (LIWC) indicated a significant difference in linguistic measures between the two phases. Phase 1 had significantly greater measures of pronouns with a small-medium effect size, 2nd person words with a medium-large effect size, positive emotion with a medium effect size, inter-rogations with a medium-large effect size, question marks with a medium-large effect size, risk with a medium-large effect size, and causal words with a medium effect size. These results in conjunction with a retrospective survey suggest that phase 1 had more questions asked, more causal relationships defined, and included linguistic features of success and failure. Results from Phase 2 indicated that community-sourced data and problems may increase motivation for learning data science. [For the full proceedings, see ED623995.]
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- 2022
49. The Impact of the Pandemic on IRT Model/Data Fit
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Plackner, Christie and Kim, Dong-In
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The application of item response theory (IRT) is almost universal in the development, implementation, and maintenance of large-scale assessments. Therefore, establishing the fit of IRT models to data is essential as the viability of calibration and equating implementations depend on it. In a typical test administration situation, measurement disturbances that influence model data fit are expected. Unfortunately, test administrations nationwide experienced new measurement disturbances because of the COVID-19 pandemic. Given the substantial disruption in education, did the response patterns of test takers change enough that model data fit is threatened and the degree of confidence in applying IRT analyses diminished? Using data from a large-scale state assessment system's 2019 and 2021 administration of the same test forms, model and data fit statistics for items and test takers were evaluated. The summary item fit index Q[subscript 1] (Yen, 1993) and the person fit statistic l[subscript z] (Choi, 2010; Drasgow et. al., 1985) were used for the analyses. Results from the study provide evidence that there wasn't a greater risk to the use of IRT models in 2021 than in previous years, despite the measurement disturbances introduced by the COVID-19 pandemic.
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- 2022
50. Reporting transparency and completeness in trials: Paper 4 - Reporting of randomised controlled trials conducted using routinely collected electronic records - room for improvement
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Merrick Zwarenstein, Margaret Sampson, Lars G. Hemkens, Chris Gale, Stephen J. McCall, Clare Relton, Mahrukh Imran, Ole Fröbert, Sinead Langan, Linda Kwakkenbos, David Moher, Kimberly A. Mc Cord, Brett D. Thombs, Edmund Juszczak, Sena Jawad, Group, CONSORT Extension for Trials Conducted Using Cohorts and RoutinelyCollected Data, and Canadian Institutes of Health Research
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medicine.medical_specialty ,Epidemiology ,MEDLINE ,Health records ,law.invention ,Healthcare improvement science Radboud Institute for Health Sciences [Radboudumc 18] ,Experimental Psychopathology and Treatment ,CONSORT Extension for Trials Conducted Using Cohorts and RoutinelyCollected Data Group ,Electronic records ,Randomized controlled trial ,Extension ,law ,medicine ,Electronic Health Records ,Humans ,01 Mathematical Sciences ,11 Medical and Health Sciences ,Randomized Controlled Trials as Topic ,Data ,business.industry ,Collected ,Transparency (behavior) ,humanities ,Routinely ,Health ,Research Design ,Family medicine ,CONSORT-ROUTINE ,Electronics ,business - Abstract
Contains fulltext : 237219.pdf (Publisher’s version ) (Open Access) Objective: To describe characteristics of randomised controlled trials (RCTs) conducted using electronic health records (EHRs), including completeness and transparency of reporting assessed against the 2021 CONSORT Extension for RCTs Conducted Using Cohorts and Routinely Collected Data (CONSORT-ROUTINE) criteria. Study design: MEDLINE and Cochrane Methodology Register were searched for a sample of RCTs published from 2011–2018. Completeness of reporting was assessed in a random sample using a pre-defined coding form. Results 183 RCT publications were identified; 122 (67%) used EHRs to identify eligible participants, 139 (76%) used the EHR as part of the intervention and 137 (75%) to ascertain outcomes. When 60 publications were evaluated against the CONSORT 2010 item and the corresponding extension for the 8 modified items, four items were 'adequately reported' for the majority of trials. Five new reporting items were identified for the CONSORT-ROUTINE extension; when evaluated, one was 'adequately reported', three were reported 'inadequately or not at all', the other 'partially'. There were, however, some encouraging signs with adequate and partial reporting of many important items, including descriptions of trial design, the consent process, outcome ascertainment and interpretation. Conclusion: Aspects of RCTs using EHRs are sub-optimally reported. Uptake of the CONSORT-ROUTINE Extension may improve reporting. 12 p.
- Published
- 2022
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