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This study deals with making the strength of material courses taught at mechanical, civil, and aerospace departments more effective by incorporating a series of personalized projects. These projects are parametrized by some entries that are made personal depending on the serial number of the student in the class. Cooperation is encouraged along with the need to evaluate the results that depend on the student's serial number. The correlation with the so-called IKEA effect is demonstrated. It appears that the results are extremely encouraging, leading to a much better understanding of issues. The project-based teaching has the aim of enhancing involvement and promoting interest and collaboration among students while discouraging cheating. Despite the harder effort required along the course, this method would also increase comprehension and related horizontal engineering skills by increasing motivation and valorizing each student's work thanks to the “IKEA Effect.” Students’ feedback is reported. 

Previous studies have established that there is a relationship between efficacy beliefs and procrastination. Theory and research on motivation suggest that visual imagery (the capacity to create vivid mental images) may be implicated in this relationship and in the general tendency to procrastinate. This study’s aim was to build on prior work by examining the role of visual imagery, as well as roles of other specific personal and affective factors, in predicting academic procrastination. Self-efficacy for self regulatory behavior was observed to be the strongest predictor, predicting lower rates of academic procrastination, though this effect was significantly greater for individuals who scored higher on a measure of visual imagery. Visual imagery predicted higher levels of academic procrastination when included in a regression model with other significant factors, though this relationship did not hold for individuals who scored higher on self regulatory self-efficacy, suggesting that this self-belief may shield individuals who would otherwise be disposed to procrastination behavior. Negative affect was observed to predict higher levels of academic procrastination, contrary to a previous finding. This result highlightsthe importance of considering social contextual issues that may influence emotional states, such as those surrounding the Covid-19 epidemic, in studies of procrastination. 

Abstract This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %. Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease’s ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual’s exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper. 

Purchasing a home is one of the largest investments most people make. House price prediction allows individuals to be informed about their asset wealth. Transparent pricing on homes allows for a more efficient market and economy. We report the performance of machine learning models trained with structured tabular representations and unstructured text descriptions. We collected a dataset of 200 descriptions of houses which include meta-information, as well as text descriptions. We test logistic regression and multi-layer perceptron (MLP) classifiers on dividing these houses into binary buckets based on fixed price thresholds. We present an exploration into strategies to represent unstructured text descriptions of houses as inputs for machine learning models. This includes a comparison of term frequency-inverse document frequency (TF-IDF), bag-of-words (BoW), and zero-shot inference with large language models. We find the best predictive performance with TF-IDF representations of house descriptions. Readers will gain an understanding of how to use machine learning models optimized with structured and unstructured text data to predict house prices.