Impact of the COVID-19 Pandemic on Higher Education: Characterizing the Psychosocial Context of the Positive and Negative Affective States Using Classification and Regression Trees

Our aim is to analyze the extent to which the psychosocial aspects can characterize the affective states of the teachers, administrative staff, and undergraduate and postgraduate students during the quarantine. A questionnaire was answered by 1,328 people from the community of the Universitat de Barcelona (UB), Spain. The survey was partially designed ad hoc, collecting indicators related to sociodemographic variables, the impact of COVID on the subjects or in their personal context, the psychosocial context of coexistence and perceived social support, characteristics related to the physical context during the quarantine, and labor conditions. Additionally, it included two validated instruments: the Survey Work-Home Interaction–Nijmegen for Spanish Speaking Countries (SWING-SSC) validated in Spanish and PANAS, the Positive and Negative Affect Schedule. Classification and Regression Trees (CART) were performed to identify which variables better characterize the participants' level of positive and negative affective states. Results according to groups showed that students are the ones who have suffered the most as a result of this situation (temporary employment regulation, higher scores in negative work-home and home-work interaction, lower scores in positive home-work interaction, and negative effects of teleworking). Additionally, they reported a higher mean score in interpersonal conflict and worse scores with regard to negative affective states. Based on sex, women were the ones whose environment was shown to be more frequently affected by the pandemic and who exhibited more negative effects of teleworking. In general terms, participants with the highest scores in negative affective states were those who perceived an increase in conflict and a high negative effect from work spilling over into their personal lives. On the contrary, participants with the highest levels of positive affective states were those with medium to low levels of negative home-work interaction, over 42.5 years old, and with medium to high levels of positive work-home interaction. Our results aim to help higher education to reflect on the need to adapt to this new reality, since the institutions that keep pace with evolving trends will be able to better attract, retain, and engage all the members of the university community in the years ahead.

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Classification Accuracy of Neural Networks vs. Discriminant Analysis, Logistic Regression, and Classification and Regression Trees

Methodology ◽

10.1027/1614-2241.3.2.47 ◽

2007 ◽

Vol 3 (2) ◽

pp. 47-57 ◽

Cited By ~ 17

Author(s):

Holmes Finch ◽

Mercedes K. Schneider

Keyword(s):

Neural Networks ◽

Logistic Regression ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Regression Trees ◽

Classification And Regression Trees ◽

Quadratic Discriminant Analysis ◽

Linear Discriminant ◽

Classification And Regression ◽

The Impact

Abstract. This paper compares the predictive accuracy of three commonly used parametric methods for group classification, linear discriminant analysis, quadratic discriminant analysis, and logistic regression, with two less common approaches, neural networks and classification and regression trees. The simulation study examined the impact of such factors as inequality of covariance matrices, distribution of predictors, and group size ratio (among others) on the performance of each method. Results indicate that quadratic discriminant analysis always performs as well as the other methods while neural networks behave very similarly to linear discriminant analysis and logistic regression.

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Predicting Student Performance using Classification and Regression Trees Algorithm

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.c8964.019320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 3349-3356 ◽

Cited By ~ 1

Keyword(s):

At Risk ◽

Student Performance ◽

Regression Trees ◽

Classification And Regression Trees ◽

Course Performance ◽

Model Classification ◽

Study Materials ◽

Classification And Regression ◽

Predicting Student Performance ◽

The Impact

Now a days Internet and Web technologies providing students opportunities for flexible interactivity with study materials, peers and instructors. And also generating large amounts of usage data that can be processed and reveal behavioral patterns of study and learning. In this paper, to predict course performance we extracted data from a Moodlebased blended learning course and build a student model. Classification and Regression Trees (CART) decision tree algorithm was used to classify students and predict those at risk, based on the impact of four online activities: message exchanging, group wiki content creation, course files opening and online quiz taking. The correct classifications in results prove that the model is sensitive to categorize very specific groups at risk.

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Preoperative hematocrit and platelet count are associated with blood loss during spinal fusion for children with neuromuscular scoliosis

Journal of Perioperative Practice ◽

10.1177/1750458920962634 ◽

2021 ◽

pp. 175045892096263

Author(s):

Margaret O Lewen ◽

Jay Berry ◽

Connor Johnson ◽

Rachael Grace ◽

Laurie Glader ◽

...

Keyword(s):

Platelet Count ◽

Blood Loss ◽

Spinal Fusion ◽

Regression Trees ◽

Classification And Regression Trees ◽

Neuromuscular Scoliosis ◽

Independent Variables ◽

Estimated Blood Loss ◽

Laboratory Results ◽

Classification And Regression

Aim To assess the relationship of preoperative hematology laboratory results with intraoperative estimated blood loss and transfusion volumes during posterior spinal fusion for pediatric neuromuscular scoliosis. Methods Retrospective chart review of 179 children with neuromuscular scoliosis undergoing spinal fusion at a tertiary children’s hospital between 2012 and 2017. The main outcome measure was estimated blood loss. Secondary outcomes were volumes of packed red blood cells, fresh frozen plasma, and platelets transfused intraoperatively. Independent variables were preoperative blood counts, coagulation studies, and demographic and surgical characteristics. Relationships between estimated blood loss, transfusion volumes, and independent variables were assessed using bivariable analyses. Classification and Regression Trees were used to identify variables most strongly correlated with outcomes. Results In bivariable analyses, increased estimated blood loss was significantly associated with higher preoperative hematocrit and lower preoperative platelet count but not with abnormal coagulation studies. Preoperative laboratory results were not associated with intraoperative transfusion volumes. In Classification and Regression Trees analysis, binary splits associated with the largest increase in estimated blood loss were hematocrit ≥44% vs. <44% and platelets ≥308 vs. <308 × 109/L. Conclusions Preoperative blood counts may identify patients at risk of increased bleeding, though do not predict intraoperative transfusion requirements. Abnormal coagulation studies often prompted preoperative intervention but were not associated with increased intraoperative bleeding or transfusion needs.

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Regional Mapping of Groundwater Potential in Ar Rub Al Khali, Arabian Peninsula Using the Classification and Regression Trees Model

Remote Sensing ◽

10.3390/rs13122300 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2300

Author(s):

Samy Elmahdy ◽

Tarig Ali ◽

Mohamed Mohamed

Keyword(s):

Machine Learning ◽

Regional Scale ◽

Regression Trees ◽

Classification And Regression Trees ◽

Groundwater Potential ◽

Machine Learning Algorithms ◽

Conditioning Factors ◽

Potential Mapping ◽

Classification And Regression ◽

Groundwater Potential Mapping

Mapping of groundwater potential in remote arid and semi-arid regions underneath sand sheets over a very regional scale is a challenge and requires an accurate classifier. The Classification and Regression Trees (CART) model is a robust machine learning classifier used in groundwater potential mapping over a very regional scale. Ten essential groundwater conditioning factors (GWCFs) were constructed using remote sensing data. The spatial relationship between these conditioning factors and the observed groundwater wells locations was optimized and identified by using the chi-square method. A total of 185 groundwater well locations were randomly divided into 129 (70%) for training the model and 56 (30%) for validation. The model was applied for groundwater potential mapping by using optimal parameters values for additive trees were 186, the value for the learning rate was 0.1, and the maximum size of the tree was five. The validation result demonstrated that the area under the curve (AUC) of the CART was 0.920, which represents a predictive accuracy of 92%. The resulting map demonstrated that the depressions of Mondafan, Khujaymah and Wajid Mutaridah depression and the southern gulf salt basin (SGSB) near Saudi Arabia, Oman and the United Arab Emirates (UAE) borders reserve fresh fossil groundwater as indicated from the observed lakes and recovered paleolakes. The proposed model and the new maps are effective at enhancing the mapping of groundwater potential over a very regional scale obtained using machine learning algorithms, which are used rarely in the literature and can be applied to the Sahara and the Kalahari Desert.

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