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2. The Impact of an Explicit Writing Intervention on EFL Students’ Short Story Writing

Author

Ioanna K. Tsiriotakis, Matthias Grünke, Ioannis Spantidakis, Eleni Vassilaki, and Nektarios A. M. Stavrou

Subjects
strategic behavior, metacognitive knowledge, EFL writing skills, procedural facilitative writing environments, explicit writing instruction, Education (General), L7-991
Abstract

Educational research has shown that a high ability to use effective strategies, a broad fount of metacognitive knowledge, and fostering of adaptive beliefs about writing lead to better text production performance. Explicit instruction enhances development in each of these areas. The aim of the present study was to examine the effects of a writing intervention program (based on the strategies “POW” and “WWW”) on the quality and length of stories composed by Greek grade 5 and 6 English Foreign Language (EFL) learners. The study was conducted with 177 participants from two Greek elementary schools, who were identified as below average, average, and above average writers, and who were assigned to one of two groups: the experimental group was provided with explicit instruction on narrative writing, the control group received no direct teaching and followed the guidelines outlined by a traditional writing program. It was postulated that explicit instruction would have a positive impact on students’ writing skills. Data analysis yielded statistically significant differences between experimental and control conditions. The students in the experimental group outperformed the ones in the control group in all writing assessments (pertaining to text quality and length). They also revealed a significant improvement in their writing quality and length, whereas no meaningful changes appeared in the control group. In addition, the improvement of writing quality was obvious for the below average, as well as, for the average and the above average students, supporting the notion that there was an improvement irrespective of the students’ level. These results speak to the practical effectiveness of explicit writing instruction to improve the story composition skills in grade 5 and 6 EFL learners. It is postulated and may be the subject of future research that the positive impact on students’ L2 composing skills will be transferable to their L1. Conclusions and pedagogical implications of the findings are discussed.

Published
2020
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7. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Author

Estee Y. Cramer, Evan L. Ray, Velma K. Lopez, Johannes Bracher, Andrea Brennen, Alvaro J. Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Katie H. House, Yuxin Huang, Dasuni Jayawardena, Abdul H. Kanji, Ayush Khandelwal, Khoa Le, Anja Mühlemann, Jarad Niemi, Apurv Shah, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W. Zorn, Youyang Gu, Sansiddh Jain, Nayana Bannur, Ayush Deva, Mihir Kulkarni, Srujana Merugu, Alpan Raval, Siddhant Shingi, Avtansh Tiwari, Jerome White, Neil F. Abernethy, Spencer Woody, Maytal Dahan, Spencer Fox, Kelly Gaither, Michael Lachmann, Lauren Ancel Meyers, James G. Scott, Mauricio Tec, Ajitesh Srivastava, Glover E. George, Jeffrey C. Cegan, Ian D. Dettwiller, William P. England, Matthew W. Farthing, Robert H. Hunter, Brandon Lafferty, Igor Linkov, Michael L. Mayo, Matthew D. Parno, Michael A. Rowland, Benjamin D. Trump, Yanli Zhang-James, Samuel Chen, Stephen V. Faraone, Jonathan Hess, Christopher P. Morley, Asif Salekin, Dongliang Wang, Sabrina M. Corsetti, Thomas M. Baer, Marisa C. Eisenberg, Karl Falb, Yitao Huang, Emily T. Martin, Ella McCauley, Robert L. Myers, Tom Schwarz, Daniel Sheldon, Graham Casey Gibson, Rose Yu, Liyao Gao, Yian Ma, Dongxia Wu, Xifeng Yan, Xiaoyong Jin, Yu-Xiang Wang, YangQuan Chen, Lihong Guo, Yanting Zhao, Quanquan Gu, Jinghui Chen, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Hannah Biegel, Joceline Lega, Steve McConnell, V. P. Nagraj, Stephanie L. Guertin, Christopher Hulme-Lowe, Stephen D. Turner, Yunfeng Shi, Xuegang Ban, Robert Walraven, Qi-Jun Hong, Stanley Kong, Axel van de Walle, James A. Turtle, Michal Ben-Nun, Steven Riley, Pete Riley, Ugur Koyluoglu, David DesRoches, Pedro Forli, Bruce Hamory, Christina Kyriakides, Helen Leis, John Milliken, Michael Moloney, James Morgan, Ninad Nirgudkar, Gokce Ozcan, Noah Piwonka, Matt Ravi, Chris Schrader, Elizabeth Shakhnovich, Daniel Siegel, Ryan Spatz, Chris Stiefeling, Barrie Wilkinson, Alexander Wong, Sean Cavany, Guido España, Sean Moore, Rachel Oidtman, Alex Perkins, David Kraus, Andrea Kraus, Zhifeng Gao, Jiang Bian, Wei Cao, Juan Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Alessandro Vespignani, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana Pastore y Piontti, Xinyue Xiong, Andrew Zheng, Jackie Baek, Vivek Farias, Andreea Georgescu, Retsef Levi, Deeksha Sinha, Joshua Wilde, Georgia Perakis, Mohammed Amine Bennouna, David Nze-Ndong, Divya Singhvi, Ioannis Spantidakis, Leann Thayaparan, Asterios Tsiourvas, Arnab Sarker, Ali Jadbabaie, Devavrat Shah, Nicolas Della Penna, Leo A. Celi, Saketh Sundar, Russ Wolfinger, Dave Osthus, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dean Karlen, Matt Kinsey, Luke C. Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Elizabeth C. Lee, Juan Dent, Kyra H. Grantz, Alison L. Hill, Joshua Kaminsky, Kathryn Kaminsky, Lindsay T. Keegan, Stephen A. Lauer, Joseph C. Lemaitre, Justin Lessler, Hannah R. Meredith, Javier Perez-Saez, Sam Shah, Claire P. Smith, Shaun A. Truelove, Josh Wills, Maximilian Marshall, Lauren Gardner, Kristen Nixon, John C. Burant, Lily Wang, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Yueying Wang, Shan Yu, Robert C. Reiner, Ryan Barber, Emmanuela Gakidou, Simon I. Hay, Steve Lim, Chris Murray, David Pigott, Heidi L. Gurung, Prasith Baccam, Steven A. Stage, Bradley T. Suchoski, B. Aditya Prakash, Bijaya Adhikari, Jiaming Cui, Alexander Rodríguez, Anika Tabassum, Jiajia Xie, Pinar Keskinocak, John Asplund, Arden Baxter, Buse Eylul Oruc, Nicoleta Serban, Sercan O. Arik, Mike Dusenberry, Arkady Epshteyn, Elli Kanal, Long T. Le, Chun-Liang Li, Tomas Pfister, Dario Sava, Rajarishi Sinha, Thomas Tsai, Nate Yoder, Jinsung Yoon, Leyou Zhang, Sam Abbott, Nikos I. Bosse, Sebastian Funk, Joel Hellewell, Sophie R. Meakin, Katharine Sherratt, Mingyuan Zhou, Rahi Kalantari, Teresa K. Yamana, Sen Pei, Jeffrey Shaman, Michael L. Li, Dimitris Bertsimas, Omar Skali Lami, Saksham Soni, Hamza Tazi Bouardi, Turgay Ayer, Madeline Adee, Jagpreet Chhatwal, Ozden O. Dalgic, Mary A. Ladd, Benjamin P. Linas, Peter Mueller, Jade Xiao, Yuanjia Wang, Qinxia Wang, Shanghong Xie, Donglin Zeng, Alden Green, Jacob Bien, Logan Brooks, Addison J. Hu, Maria Jahja, Daniel McDonald, Balasubramanian Narasimhan, Collin Politsch, Samyak Rajanala, Aaron Rumack, Noah Simon, Ryan J. Tibshirani, Rob Tibshirani, Valerie Ventura, Larry Wasserman, Eamon B. O’Dea, John M. Drake, Robert Pagano, Quoc T. Tran, Lam Si Tung Ho, Huong Huynh, Jo W. Walker, Rachel B. Slayton, Michael A. Johansson, Matthew Biggerstaff, Nicholas G. Reich, Cramer, Estee Y [0000-0003-1373-3177], Ray, Evan L [0000-0003-4035-0243], Lopez, Velma K [0000-0003-2926-4010], Bracher, Johannes [0000-0002-3777-1410], Gneiting, Tilmann [0000-0001-9397-3271], Niemi, Jarad [0000-0002-5079-158X], White, Jerome [0000-0003-4148-8834], Woody, Spencer [0000-0002-2882-3450], Fox, Spencer [0000-0003-1969-3778], Gaither, Kelly [0000-0002-4272-175X], Meyers, Lauren Ancel [0000-0002-5828-8874], Tec, Mauricio [0000-0002-1853-5842], George, Glover E [0000-0003-4779-8702], Cegan, Jeffrey C [0000-0002-3065-3403], Hunter, Robert H [0000-0002-2382-7938], Lafferty, Brandon [0000-0002-2618-3787], Mayo, Michael L [0000-0001-9014-1859], Rowland, Michael A [0000-0002-6759-8225], Chen, Samuel [0000-0002-1070-9801], Salekin, Asif [0000-0002-0807-8967], Corsetti, Sabrina M [0000-0003-2216-2492], Falb, Karl [0000-0002-3465-3988], Huang, Yitao [0000-0001-7846-2174], Sheldon, Daniel [0000-0002-4257-2432], Guo, Lihong [0000-0003-4804-4005], Gu, Quanquan [0000-0001-9830-793X], Xu, Pan [0000-0002-2559-8622], Lega, Joceline [0000-0003-2064-229X], McConnell, Steve [0000-0002-0294-3737], Turner, Stephen D [0000-0001-9140-9028], Shi, Yunfeng [0000-0003-1700-6049], Walraven, Robert [0000-0002-5755-4325], van de Walle, Axel [0000-0002-3415-1494], Turtle, James A [0000-0003-0735-7769], Ben-Nun, Michal [0000-0002-9164-0008], Riley, Steven [0000-0001-7904-4804], Koyluoglu, Ugur [0000-0002-6286-351X], Cavany, Sean [0000-0002-2559-797X], España, Guido [0000-0002-9915-8056], Moore, Sean [0000-0001-9062-6100], Oidtman, Rachel [0000-0003-1773-9533], Perkins, Alex [0000-0002-7518-4014], Kraus, David [0000-0003-4376-3932], Cao, Wei [0000-0001-5640-0917], Lavista Ferres, Juan [0000-0002-9654-3178], Vespignani, Alessandro [0000-0003-3419-4205], Sinha, Deeksha [0000-0002-9788-728X], Perakis, Georgia [0000-0002-0888-9030], Bennouna, Mohammed Amine [0000-0002-9123-8588], Spantidakis, Ioannis [0000-0002-5149-6320], Tsiourvas, Asterios [0000-0002-2979-6300], Sarker, Arnab [0000-0003-1680-9421], Jadbabaie, Ali [0000-0003-1122-3069], Shah, Devavrat [0000-0003-0737-3259], Celi, Leo A [0000-0001-6712-6626], Osthus, Dave [0000-0002-4681-091X], Fairchild, Geoffrey [0000-0001-5500-8120], Mullany, Luke C [0000-0003-4668-9803], Rainwater-Lovett, Kaitlin [0000-0002-8707-7339], Lee, Elizabeth C [0000-0002-4156-9637], Dent, Juan [0000-0003-3154-0731], Hill, Alison L [0000-0002-6583-3623], Keegan, Lindsay T [0000-0002-8526-3007], Lemaitre, Joseph C [0000-0002-2677-6574], Truelove, Shaun A [0000-0003-0538-0607], Wills, Josh [0000-0001-7285-9349], Gao, Lei [0000-0002-4707-0933], Gu, Zhiling [0000-0002-8052-7608], Yu, Shan [0000-0002-0271-5726], Hay, Simon I [0000-0002-0611-7272], Murray, Chris [0000-0002-4930-9450], Stage, Steven A [0000-0001-5361-6464], Prakash, B Aditya [0000-0002-3252-455X], Rodríguez, Alexander [0000-0002-4313-9913], Xie, Jiajia [0000-0001-6530-2489], Keskinocak, Pinar [0000-0003-2686-546X], Baxter, Arden [0000-0002-6345-2229], Oruc, Buse Eylul [0000-0003-2431-3864], Sinha, Rajarishi [0000-0001-9157-674X], Yoder, Nate [0000-0003-4153-4722], Zhang, Leyou [0000-0002-2454-0082], Funk, Sebastian [0000-0002-2842-3406], Meakin, Sophie R [0000-0002-6385-2652], Sherratt, Katharine [0000-0003-2049-3423], Yamana, Teresa K [0000-0001-8349-3151], Pei, Sen [0000-0002-7072-2995], Shaman, Jeffrey [0000-0002-7216-7809], Li, Michael L [0000-0002-2456-4834], Bertsimas, Dimitris [0000-0002-1985-1003], Skali Lami, Omar [0000-0002-8208-3035], Soni, Saksham [0000-0002-8898-5726], Tazi Bouardi, Hamza [0000-0002-7871-325X], Wang, Yuanjia [0000-0002-1510-3315], McDonald, Daniel [0000-0002-0443-4282], Politsch, Collin [0000-0003-3727-9167], Rajanala, Samyak [0000-0002-5791-3789], Rumack, Aaron [0000-0002-9181-1794], Tibshirani, Ryan J [0000-0002-2158-8304], Drake, John M [0000-0003-4646-1235], Ho, Lam Si Tung [0000-0002-0453-8444], Slayton, Rachel B [0000-0003-4699-8040], Johansson, Michael A [0000-0002-5090-7722], Biggerstaff, Matthew [0000-0001-5108-8311], Reich, Nicholas G [0000-0003-3503-9899], and Apollo - University of Cambridge Repository

Subjects
model evaluation, Multidisciplinary, COVID-19, prediction, United States, Data Accuracy, 510 Mathematics, 360 Social problems & social services, weather, Humans, Public Health, ddc:510, ensemble forecast, Pandemics, Mathematics, Forecasting, Probability
Abstract

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Published
2022
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8. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Author

Estee Y Cramer, Evan L Ray, Velma K Lopez, Johannes Bracher, Andrea Brennen, Alvaro J Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Katie H House, Yuxin Huang, Dasuni Jayawardena, Abdul H Kanji, Ayush Khandelwal, Khoa Le, Anja Mühlemann, Jarad Niemi, Apurv Shah, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W Zorn, Youyang Gu, Sansiddh Jain, Nayana Bannur, Ayush Deva, Mihir Kulkarni, Srujana Merugu, Alpan Raval, Siddhant Shingi, Avtansh Tiwari, Jerome White, Neil F Abernethy, Spencer Woody, Maytal Dahan, Spencer Fox, Kelly Gaither, Michael Lachmann, Lauren Ancel Meyers, James G Scott, Mauricio Tec, Ajitesh Srivastava, Glover E George, Jeffrey C Cegan, Ian D Dettwiller, William P England, Matthew W Farthing, Robert H Hunter, Brandon Lafferty, Igor Linkov, Michael L Mayo, Matthew D Parno, Michael A Rowland, Benjamin D Trump, Yanli Zhang-James, Samuel Chen, Stephen V Faraone, Jonathan Hess, Christopher P Morley, Asif Salekin, Dongliang Wang, Sabrina M Corsetti, Thomas M Baer, Marisa C Eisenberg, Karl Falb, Yitao Huang, Emily T Martin, Ella McCauley, Robert L Myers, Tom Schwarz, Daniel Sheldon, Graham Casey Gibson, Rose Yu, Liyao Gao, Yian Ma, Dongxia Wu, Xifeng Yan, Xiaoyong Jin, Yu-Xiang Wang, YangQuan Chen, Lihong Guo, Yanting Zhao, Quanquan Gu, Jinghui Chen, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Hannah Biegel, Joceline Lega, Steve McConnell, VP Nagraj, Stephanie L Guertin, Christopher Hulme-Lowe, Stephen D Turner, Yunfeng Shi, Xuegang Ban, Robert Walraven, Qi-Jun Hong, Stanley Kong, Axel van de Walle, James A Turtle, Michal Ben-Nun, Steven Riley, Pete Riley, Ugur Koyluoglu, David DesRoches, Pedro Forli, Bruce Hamory, Christina Kyriakides, Helen Leis, John Milliken, Michael Moloney, James Morgan, Ninad Nirgudkar, Gokce Ozcan, Noah Piwonka, Matt Ravi, Chris Schrader, Elizabeth Shakhnovich, Daniel Siegel, Ryan Spatz, Chris Stiefeling, Barrie Wilkinson, Alexander Wong, Sean Cavany, Guido España, Sean Moore, Rachel Oidtman, Alex Perkins, David Kraus, Andrea Kraus, Zhifeng Gao, Jiang Bian, Wei Cao, Juan Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Alessandro Vespignani, Matteo Chinazzi, Jessica T Davis, Kunpeng Mu, Ana Pastore y Piontti, Xinyue Xiong, Andrew Zheng, Jackie Baek, Vivek Farias, Andreea Georgescu, Retsef Levi, Deeksha Sinha, Joshua Wilde, Georgia Perakis, Mohammed Amine Bennouna, David Nze-Ndong, Divya Singhvi, Ioannis Spantidakis, Leann Thayaparan, Asterios Tsiourvas, Arnab Sarker, Ali Jadbabaie, Devavrat Shah, Nicolas Della Penna, Leo A Celi, Saketh Sundar, Russ Wolfinger, Dave Osthus, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dean Karlen, Matt Kinsey, Luke C. Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Elizabeth C Lee, Juan Dent, Kyra H Grantz, Alison L Hill, Joshua Kaminsky, Kathryn Kaminsky, Lindsay T Keegan, Stephen A Lauer, Joseph C Lemaitre, Justin Lessler, Hannah R Meredith, Javier Perez-Saez, Sam Shah, Claire P Smith, Shaun A Truelove, Josh Wills, Maximilian Marshall, Lauren Gardner, Kristen Nixon, John C. Burant, Lily Wang, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Yueying Wang, Shan Yu, Robert C Reiner, Ryan Barber, Emmanuela Gakidou, Simon I. Hay, Steve Lim, Chris J.L. Murray, David Pigott, Heidi L Gurung, Prasith Baccam, Steven A Stage, Bradley T Suchoski, B. Aditya Prakash, Bijaya Adhikari, Jiaming Cui, Alexander Rodríguez, Anika Tabassum, Jiajia Xie, Pinar Keskinocak, John Asplund, Arden Baxter, Buse Eylul Oruc, Nicoleta Serban, Sercan O Arik, Mike Dusenberry, Arkady Epshteyn, Elli Kanal, Long T Le, Chun-Liang Li, Tomas Pfister, Dario Sava, Rajarishi Sinha, Thomas Tsai, Nate Yoder, Jinsung Yoon, Leyou Zhang, Sam Abbott, Nikos I Bosse, Sebastian Funk, Joel Hellewell, Sophie R Meakin, Katharine Sherratt, Mingyuan Zhou, Rahi Kalantari, Teresa K Yamana, Sen Pei, Jeffrey Shaman, Michael L Li, Dimitris Bertsimas, Omar Skali Lami, Saksham Soni, Hamza Tazi Bouardi, Turgay Ayer, Madeline Adee, Jagpreet Chhatwal, Ozden O Dalgic, Mary A Ladd, Benjamin P Linas, Peter Mueller, Jade Xiao, Yuanjia Wang, Qinxia Wang, Shanghong Xie, Donglin Zeng, Alden Green, Jacob Bien, Logan Brooks, Addison J Hu, Maria Jahja, Daniel McDonald, Balasubramanian Narasimhan, Collin Politsch, Samyak Rajanala, Aaron Rumack, Noah Simon, Ryan J Tibshirani, Rob Tibshirani, Valerie Ventura, Larry Wasserman, Eamon B O’Dea, John M Drake, Robert Pagano, Quoc T Tran, Lam Si Tung Ho, Huong Huynh, Jo W Walker, Rachel B Slayton, Michael A Johansson, Matthew Biggerstaff, and Nicholas G Reich

Subjects
Geospatial analysis, Coronavirus disease 2019 (COVID-19), Computer science, business.industry, Probabilistic logic, Staffing, computer.software_genre, Scientific modelling, Health care, Econometrics, National level, business, computer, Independent research
Abstract

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance Statement This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

Published
2021
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9. The Power of Analytics in Epidemiology for COVID 19

Author

Shane Weisberg, Divya Singhvi, Asterios Tsiourvas, Ioannis Spantidakis, Mohammed Amine Bennouna, David Alexandre Nze Ndong, Leann Thayaparan, Omar Skali Lami, and Georgia Perakis

Subjects
medicine.medical_specialty, education.field_of_study, Coronavirus disease 2019 (COVID-19), business.industry, Computer science, Process (engineering), Population, Distribution (economics), Data science, Ranking, Analytics, Pandemic, Epidemiology, medicine, business, education
Abstract

Mitigating the COVID-19 pandemic poses a series of unprecedented challenges, including predicting new cases and deaths, understanding the true prevalence, and allocating the different vaccines across regions. In this paper, we describe our efforts to tackle these issues. We first discuss the methods we developed for predicting cases and deaths using a novel ML based method we call MIT-Cassandra. MIT-Cassandra is currently being used by the CDC and is consistently among the top 10 methods in accuracy, often ranking 1st amongst all submitted methods. We then use this prediction to model the true prevalence of COVID 19 and incorporate this prevalence into an optimization model for fair vaccine allocation. The latter part of the paper also gives insights on how prevalence and exposure of the disease in different parts of the population can affect the distribution of different vaccine doses in a fairway. Finally, and importantly, our work has specifically been used as part of a collaboration with MIT's Quest for Intelligence and as part of MIT's process to reopen the institute.

Published
2021
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10. Training undergraduate engineering students to read research articles: A qualitative think-aloud study

Author

Ioannis Spantidakis and Emmanouela Seiradakis

Subjects
media_common.quotation_subject, 05 social sciences, Behavior change, 050301 education, Metacognition, Strategy training, Training (civil), Reading, Reading (process), Research article, 0502 economics and business, ComputingMilieux_COMPUTERSANDEDUCATION, Mathematics education, Engineering students, Think-Aloud, Undergraduate engineering, Genre, Psychology, Think aloud protocol, 0503 education, 050203 business & management, media_common
Abstract

Summarization: The ability to read research articles is considered an essential skill for undergraduate students. However, previous works on explicitly training engineering undergraduates in reading the specific genre is scarce. This paper presents the findings of a qualitative pre-Test/post-Test think-Aloud study that aimed to explore the possible reading behavior changes of four Greek undergraduate engineering students who received genre analysis and metacognitive strategy training for one academic semester. Our findings suggest that the course enhanced students' familiarity with the research article genre and benefited their reading strategy use. Presented on

Published
2018
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11. EFL Engineering Students’ Research Article Genre Knowledge Development through Concept Mapping Tasks: A Qualitative Interview-based Study

Author

Ioannis Spantidakis and Emmanouela Seiradakis

Subjects
Conceptualization, Process (engineering), Concept map, ComputingMilieux_COMPUTERSANDEDUCATION, Mathematics education, Rhetorical question, Metacognition, Psychology, Construct (philosophy), Qualitative research, Primary research
Abstract

This qualitative study explores how the use of concept mapping can function as a genre knowledge scaffold within an experimental course that aimed to teach EFL undergraduate students how to read primary research articles in their discipline. Using semi-structured student interviews, the study explored the development of the rhetorical, formal, process and content research article genre facets of three second-year Electrical and Computer Engineering students after working collaboratively on three specially designed concept mapping tasks underpinned by the theories of genre analysis and metacognition. Our data suggest that the process of visual conceptualization encouraged students to engage in deeper forms of genre analysis and explore the different dimensions of the multifaced research article genre construct.

Published
2019
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12. The Examination of the Effects of Writing Strategy-Based Procedural Facilitative Environments on Students' English Foreign Language Writing Anxiety Levels

Author

Nektarios A. Stavrou, Ioannis Spantidakis, Ioanna K. Tsiriotakis, and Eleni Vassilaki

Subjects
media_common.quotation_subject, Foreign language, education, Metacognition, Developmental psychology, medicine, Cognitive apprenticeship, Psychology, procedural facilitative writing environments, General Psychology, media_common, Original Research, 060201 languages & linguistics, Second language writing, 05 social sciences, 050301 education, Cognition, 06 humanities and the arts, EFL writing anxiety, Language acquisition, cognitive apprenticeship, strategy-based procedural facilitation, Feeling, 0602 languages and literature, Anxiety, medicine.symptom, 0503 education, metacognition
Abstract

Empirical studies have shown that anxiety and negative emotion can hinder language acquisition. The present study implemented a writing instructional model so as to investigate its effects on the writing anxiety levels of English Foreign Language learners. The study was conducted with 177 participants, who were administered the Second Language Writing Anxiety Inventory (SLWAI; Cheng, 2004) that assesses somatic, cognitive and behavioral anxiety, both at baseline and following the implementation of a writing instructional model. The hypothesis stated that the participant’s writing anxiety levels would lessen following the provision of a writing strategy-based procedural facilitative environment that fosters cognitive apprenticeship. The initial hypothesis was supported by the findings. Specifically, in the final measurement statistical significant differences appeared where participants in the experimental group showed notable lower mean values of the three factors of anxiety, a factor that largely can be attributed to the content of the intervention program applied to this specific group. The findings validate that Foreign Language writing anxiety negatively effects Foreign Language learning and performance. The findings also support the effectiveness of strategy-based procedural facilitative writing environments that foster cognitive apprenticeship, so as to enhance language skill development and reduce feelings of Foreign Language writing anxiety.

Published
2016

13. ONLINE COURSE DESIGN AND MATERIALS DEVELOPMENT FOR TEACHING READING OF RESEARCH ARTICLES TO EFL UNDERGRADUATE STUDENTS AT A GREEK TECHNICAL UNIVERSITY

Author

Emmanuela Vardis Seiradakis and Ioannis Spantidakis

Subjects
Grammar, media_common.quotation_subject, 05 social sciences, Lifelong learning, Metacognition, 010501 environmental sciences, Procedural knowledge, 01 natural sciences, Education, Terminology, Reading (process), 0502 economics and business, ComputingMilieux_COMPUTERSANDEDUCATION, Cognitive apprenticeship, Mathematics education, Rhetorical question, Psychology, 050203 business & management, 0105 earth and related environmental sciences, media_common
Abstract

Recent research findings suggest that reading research articles (RAs) enhances undergraduate engineering students’ technical knowledge and fosters their lifelong learning skills. Nevertheless, the RA genre inherently displays challenging features for novice readers, especially EFL readers. Previous works on developing materials for teaching the reading of RAs to undergraduate students are limited and mostly report on the effectiveness of interventions rather than on course design and materials development. This paper presents the design and development of online materials for a Moodle-based, English for Specific Academic Purposes course that aimed to help Greek undergraduate Electrical and Computer Engineering (ECE) students to learn how to read RAs within their field. The materials design was based on the theories of genre analysis, metacognition and cognitive apprenticeship. Initially, a small RA corpus consisting of thirty RAs from high-ranking ECE journals and conferences from IEEE, ACM, Elsevier and Springer was created in cooperation with the ECE faculty. Subsequently, a move analysis was performed based on a simplified coding scheme of rhetorical moves in the target genre adjusted to the needs of novice Greek EFL readers. The results from our corpus analysis were used as the foundation of the genre-based materials that aimed at fostering learners’ declarative, procedural and conditional genre knowledge and included various examples of move structures and patterns, terminology, grammar as well as weekly genre analysis reflective tasks. We then created materials that intended to provide further support so that students could convert their newly acquired genre knowledge into procedural knowledge and explicitly taught top-down RA expeditious reading strategies and conditional knowledge by including metacognitive strategy training that intended to raise their awareness of when and why they should use the taught strategies. In an attempt to further tailor the materials to the needs of our students we included audiovisual enhancements in both L1 and L2 for presentation and feedback purposes, metacognitive prompts, online dictionaries and concordancers.

Published
2018
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