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This volume contains 47 papers presented under the following four major categories: (1) "Visual Literacy and Education," including curriculum development, teaching visual literacy, thinking skills and visual literacy, teaching video production techniques, teacher education, cable television in public schools, using filmstrips in the classroom to develop comprehensive skills, alternative school assessment methods, visual literacy and the language arts, visual presentation and gender, interactive videodiscs, and computer assisted instruction and language arts (15 papers); (2) "Visual Literacy and Technology," including television technology, wayfinding and online public access catalogs, young publishers, desktop publishing, role of the electronic media, electronic texts, and scriptwriting (11 papers); (3) "Visual Literacy and Research and Theory," including investigation of narrative television technology, Einstein's perceptual theory in films; interpretation of image content, learning styles, cognitive style and contextual backgrounds, developmental techniques and the affective domain, hypertext, visual intelligence training, and television courses (10 papers); and (4) "Visual Literacy and Art, Aesthetics and Values," including art as instrument of vision, visual literacy for the non-artist, image creation, computer generated graphics, photography, ethics and visual literacy, and visual values (11 papers). Most of the papers contain references. (JLB)

This volume contains 47 papers presented under the following four major categories: (1) "Visual Literacy and Education," including curriculum development, teaching visual literacy, thinking skills and visual literacy, teaching video production techniques, teacher education, cable television in public schools, using filmstrips in the classroom to develop comprehensive skills, alternative school assessment methods, visual literacy and the language arts, visual presentation and gender, interactive videodiscs, and computer assisted instruction and language arts (15 papers); (2) "Visual Literacy and Technology," including television technology, wayfinding and online public access catalogs, young publishers, desktop publishing, role of the electronic media, electronic texts, and scriptwriting (11 papers); (3) "Visual Literacy and Research and Theory," including investigation of narrative television technology, Einstein's perceptual theory in films; interpretation of image content, learning styles, cognitive style and contextual backgrounds, developmental techniques and the affective domain, hypertext, visual intelligence training, and television courses (10 papers); and (4) "Visual Literacy and Art, Aesthetics and Values," including art as instrument of vision, visual literacy for the non-artist, image creation, computer generated graphics, photography, ethics and visual literacy, and visual values (11 papers). Most of the papers contain references. (JLB)

xvii, 182 p. : 29 cm

xvii, 182 p. : 29 cm

xvii, 182 p. : 29 cm

xvii, 182 p. : 29 cm

George Flattery continues his series on "The Jesus Way" from John 6:16-21.

George Flattery continues his series on "The Jesus Way" from John 6:16-21.

George Flattery continues his series on "The Jesus Way" from John 6:16-21.

xv, 209 p. : 28 cm

xv, 209 p. : 28 cm

Social and emotional (SE) skills are interpersonal, self-regulatory, and task-related behaviors that are important for adaption to and successful performance in educational and workplace settings (Casillas et al., 2015). Students can improve these skills through social and emotional learning (SEL) interventions or programming designed to teach SE skill development to students in school settings (e.g., Mahoney et al., 2018). While teaching SEL shows promising results and continues to gain traction in the K-12 education space, little consensus exists on what label should be used to describe this skill set (e.g., Berg et al., 2017). A recent study with parents suggests that, although support for social and emotional learning (SEL)-related skills is relatively strong, the label "social-emotional learning" itself is unfavorable (Tyner, 2021). This conclusion was based on a rank order item which presented parents with a list of 12 undefined, SEL-related program titles and asked them to select three school programs they would most and least want their child enrolled in. To test the assumption that respondents perceived all program labels as teaching SEL, the authors conducted a survey with parents and caregivers of ACT® test takers. Results from this follow-up study demonstrate that the program label "life skills" represents a far broader range of skills than those we would typically conceptualize as SE skills. While skills such as financial management, completing taxes, and taking care of homes and automobiles are undoubtedly important, they are fundamentally different from SE skills such as persistence, empathy, and resilience. [For the 2021 study "How to Sell SEL: Parents and the Politics of Social-Emotional Learning," see ED616280.]

Social and emotional (SE) skills are interpersonal, self-regulatory, and task-related behaviors that are important for adaption to and successful performance in educational and workplace settings (Casillas et al., 2015). Students can improve these skills through social and emotional learning (SEL) interventions or programming designed to teach SE skill development to students in school settings (e.g., Mahoney et al., 2018). While teaching SEL shows promising results and continues to gain traction in the K-12 education space, little consensus exists on what label should be used to describe this skill set (e.g., Berg et al., 2017). A recent study with parents suggests that, although support for social and emotional learning (SEL)-related skills is relatively strong, the label "social-emotional learning" itself is unfavorable (Tyner, 2021). This conclusion was based on a rank order item which presented parents with a list of 12 undefined, SEL-related program titles and asked them to select three school programs they would most and least want their child enrolled in. To test the assumption that respondents perceived all program labels as teaching SEL, the authors conducted a survey with parents and caregivers of ACT® test takers. Results from this follow-up study demonstrate that the program label "life skills" represents a far broader range of skills than those we would typically conceptualize as SE skills. While skills such as financial management, completing taxes, and taking care of homes and automobiles are undoubtedly important, they are fundamentally different from SE skills such as persistence, empathy, and resilience. [For the 2021 study "How to Sell SEL: Parents and the Politics of Social-Emotional Learning," see ED616280.]

Social and emotional (SE) skills are interpersonal, self-regulatory, and task-related behaviors that are important for adaption to and successful performance in educational and workplace settings (Casillas et al., 2015). Students can improve these skills through social and emotional learning (SEL) interventions or programming designed to teach SE skill development to students in school settings (e.g., Mahoney et al., 2018). While teaching SEL shows promising results and continues to gain traction in the K-12 education space, little consensus exists on what label should be used to describe this skill set (e.g., Berg et al., 2017). A recent study with parents suggests that, although support for social and emotional learning (SEL)-related skills is relatively strong, the label "social-emotional learning" itself is unfavorable (Tyner, 2021). This conclusion was based on a rank order item which presented parents with a list of 12 undefined, SEL-related program titles and asked them to select three school programs they would most and least want their child enrolled in. To test the assumption that respondents perceived all program labels as teaching SEL, the authors conducted a survey with parents and caregivers of ACT® test takers. Results from this follow-up study demonstrate that the program label "life skills" represents a far broader range of skills than those we would typically conceptualize as SE skills. While skills such as financial management, completing taxes, and taking care of homes and automobiles are undoubtedly important, they are fundamentally different from SE skills such as persistence, empathy, and resilience. [For the 2021 study "How to Sell SEL: Parents and the Politics of Social-Emotional Learning," see ED616280.]

Social and emotional (SE) skills are interpersonal, self-regulatory, and task-related behaviors that are important for adaption to and successful performance in educational and workplace settings (Casillas et al., 2015). Students can improve these skills through social and emotional learning (SEL) interventions or programming designed to teach SE skill development to students in school settings (e.g., Mahoney et al., 2018). While teaching SEL shows promising results and continues to gain traction in the K-12 education space, little consensus exists on what label should be used to describe this skill set (e.g., Berg et al., 2017). A recent study with parents suggests that, although support for social and emotional learning (SEL)-related skills is relatively strong, the label "social-emotional learning" itself is unfavorable (Tyner, 2021). This conclusion was based on a rank order item which presented parents with a list of 12 undefined, SEL-related program titles and asked them to select three school programs they would most and least want their child enrolled in. To test the assumption that respondents perceived all program labels as teaching SEL, the authors conducted a survey with parents and caregivers of ACT® test takers. Results from this follow-up study demonstrate that the program label "life skills" represents a far broader range of skills than those we would typically conceptualize as SE skills. While skills such as financial management, completing taxes, and taking care of homes and automobiles are undoubtedly important, they are fundamentally different from SE skills such as persistence, empathy, and resilience. [For the 2021 study "How to Sell SEL: Parents and the Politics of Social-Emotional Learning," see ED616280.]

Social and emotional (SE) skills are interpersonal, self-regulatory, and task-related behaviors that are important for adaption to and successful performance in educational and workplace settings (Casillas et al., 2015). Students can improve these skills through social and emotional learning (SEL) interventions or programming designed to teach SE skill development to students in school settings (e.g., Mahoney et al., 2018). While teaching SEL shows promising results and continues to gain traction in the K-12 education space, little consensus exists on what label should be used to describe this skill set (e.g., Berg et al., 2017). A recent study with parents suggests that, although support for social and emotional learning (SEL)-related skills is relatively strong, the label "social-emotional learning" itself is unfavorable (Tyner, 2021). This conclusion was based on a rank order item which presented parents with a list of 12 undefined, SEL-related program titles and asked them to select three school programs they would most and least want their child enrolled in. To test the assumption that respondents perceived all program labels as teaching SEL, the authors conducted a survey with parents and caregivers of ACT® test takers. Results from this follow-up study demonstrate that the program label "life skills" represents a far broader range of skills than those we would typically conceptualize as SE skills. While skills such as financial management, completing taxes, and taking care of homes and automobiles are undoubtedly important, they are fundamentally different from SE skills such as persistence, empathy, and resilience. [For the 2021 study "How to Sell SEL: Parents and the Politics of Social-Emotional Learning," see ED616280.]

Social and emotional (SE) skills are interpersonal, self-regulatory, and task-related behaviors that are important for adaption to and successful performance in educational and workplace settings (Casillas et al., 2015). Students can improve these skills through social and emotional learning (SEL) interventions or programming designed to teach SE skill development to students in school settings (e.g., Mahoney et al., 2018). While teaching SEL shows promising results and continues to gain traction in the K-12 education space, little consensus exists on what label should be used to describe this skill set (e.g., Berg et al., 2017). A recent study with parents suggests that, although support for social and emotional learning (SEL)-related skills is relatively strong, the label "social-emotional learning" itself is unfavorable (Tyner, 2021). This conclusion was based on a rank order item which presented parents with a list of 12 undefined, SEL-related program titles and asked them to select three school programs they would most and least want their child enrolled in. To test the assumption that respondents perceived all program labels as teaching SEL, the authors conducted a survey with parents and caregivers of ACT® test takers. Results from this follow-up study demonstrate that the program label "life skills" represents a far broader range of skills than those we would typically conceptualize as SE skills. While skills such as financial management, completing taxes, and taking care of homes and automobiles are undoubtedly important, they are fundamentally different from SE skills such as persistence, empathy, and resilience. [For the 2021 study "How to Sell SEL: Parents and the Politics of Social-Emotional Learning," see ED616280.]

Social and emotional (SE) skills are interpersonal, self-regulatory, and task-related behaviors that are important for adaption to and successful performance in educational and workplace settings (Casillas et al., 2015). Students can improve these skills through social and emotional learning (SEL) interventions or programming designed to teach SE skill development to students in school settings (e.g., Mahoney et al., 2018). While teaching SEL shows promising results and continues to gain traction in the K-12 education space, little consensus exists on what label should be used to describe this skill set (e.g., Berg et al., 2017). A recent study with parents suggests that, although support for social and emotional learning (SEL)-related skills is relatively strong, the label "social-emotional learning" itself is unfavorable (Tyner, 2021). This conclusion was based on a rank order item which presented parents with a list of 12 undefined, SEL-related program titles and asked them to select three school programs they would most and least want their child enrolled in. To test the assumption that respondents perceived all program labels as teaching SEL, the authors conducted a survey with parents and caregivers of ACT® test takers. Results from this follow-up study demonstrate that the program label "life skills" represents a far broader range of skills than those we would typically conceptualize as SE skills. While skills such as financial management, completing taxes, and taking care of homes and automobiles are undoubtedly important, they are fundamentally different from SE skills such as persistence, empathy, and resilience. [For the 2021 study "How to Sell SEL: Parents and the Politics of Social-Emotional Learning," see ED616280.]

Social and emotional (SE) skills are interpersonal, self-regulatory, and task-related behaviors that are important for adaption to and successful performance in educational and workplace settings (Casillas et al., 2015). Students can improve these skills through social and emotional learning (SEL) interventions or programming designed to teach SE skill development to students in school settings (e.g., Mahoney et al., 2018). While teaching SEL shows promising results and continues to gain traction in the K-12 education space, little consensus exists on what label should be used to describe this skill set (e.g., Berg et al., 2017). A recent study with parents suggests that, although support for social and emotional learning (SEL)-related skills is relatively strong, the label "social-emotional learning" itself is unfavorable (Tyner, 2021). This conclusion was based on a rank order item which presented parents with a list of 12 undefined, SEL-related program titles and asked them to select three school programs they would most and least want their child enrolled in. To test the assumption that respondents perceived all program labels as teaching SEL, the authors conducted a survey with parents and caregivers of ACT® test takers. Results from this follow-up study demonstrate that the program label "life skills" represents a far broader range of skills than those we would typically conceptualize as SE skills. While skills such as financial management, completing taxes, and taking care of homes and automobiles are undoubtedly important, they are fundamentally different from SE skills such as persistence, empathy, and resilience. [For the 2021 study "How to Sell SEL: Parents and the Politics of Social-Emotional Learning," see ED616280.]

viii, 246 p. : 22 cm

Despite an emphasis in government strategy on the provision of later life learning, little is known about the experiences of older people in their pursuit of such opportunities. This paper explores the extent to which middle-class retirees' learning needs are catered for in Northern Ireland. Data from semi-structured interviews suggest that the older learner has clearly defined learning needs. However, there is a perceived lack of support and understanding of these needs by those organisations involved in policy development and policy delivery.

Despite an emphasis in government strategy on the provision of later life learning, little is known about the experiences of older people in their pursuit of such opportunities. This paper explores the extent to which middle-class retirees' learning needs are catered for in Northern Ireland. Data from semi-structured interviews suggest that the older learner has clearly defined learning needs. However, there is a perceived lack of support and understanding of these needs by those organisations involved in policy development and policy delivery.

pages

The life of John Philip Baratier, the prodigy of this age for genius and learning; ... By Mr. Formey, ... Translated from the French. Being the History of the works of the learned for October, 1743. 1745.. Digitized from IA40316201-27 . Previous issue: bim_eighteenth-century_owens-new-book-of-roads_owen-w-william_1784 . Next issue: bim_eighteenth-century_the-life-of-mary-stewart_boisguillebert-pierre-l_1729 .

An analysis of office work (OW) highlights the relationship between formal vocational qualifications and tacit knowledge gained through experience. In OW, "abstracted" skills (typewriting, correspondence) and theory are taught in schools out of their practical context and can become obsolete because of technological change. Some types of "abstract knowledge"(social competencies, personal attitudes) need to be learned in a work context. Historically, women were socialized to supportive roles and routinized work and were able to supply the "abstract knowledge" component of OW using their life experiences and roles. Changes in technology, work organization and career expectations (especially for women) require rethinking the role of school-based skills and workplace practice. General qualifications need to include the ability to cope with open situations in a non-routinized way. Skills, such as empathy, that were part of female gender roles should be incorporated into the formal qualifications for OW. The function of schooling should not mainly be to transfer technical skills and prescriptive knowledge, but to provide a space for reflective and critical training together with background and overview insights. (CG)

An analysis of office work (OW) highlights the relationship between formal vocational qualifications and tacit knowledge gained through experience. In OW, "abstracted" skills (typewriting, correspondence) and theory are taught in schools out of their practical context and can become obsolete because of technological change. Some types of "abstract knowledge"(social competencies, personal attitudes) need to be learned in a work context. Historically, women were socialized to supportive roles and routinized work and were able to supply the "abstract knowledge" component of OW using their life experiences and roles. Changes in technology, work organization and career expectations (especially for women) require rethinking the role of school-based skills and workplace practice. General qualifications need to include the ability to cope with open situations in a non-routinized way. Skills, such as empathy, that were part of female gender roles should be incorporated into the formal qualifications for OW. The function of schooling should not mainly be to transfer technical skills and prescriptive knowledge, but to provide a space for reflective and critical training together with background and overview insights. (CG)

An analysis of office work (OW) highlights the relationship between formal vocational qualifications and tacit knowledge gained through experience. In OW, "abstracted" skills (typewriting, correspondence) and theory are taught in schools out of their practical context and can become obsolete because of technological change. Some types of "abstract knowledge"(social competencies, personal attitudes) need to be learned in a work context. Historically, women were socialized to supportive roles and routinized work and were able to supply the "abstract knowledge" component of OW using their life experiences and roles. Changes in technology, work organization and career expectations (especially for women) require rethinking the role of school-based skills and workplace practice. General qualifications need to include the ability to cope with open situations in a non-routinized way. Skills, such as empathy, that were part of female gender roles should be incorporated into the formal qualifications for OW. The function of schooling should not mainly be to transfer technical skills and prescriptive knowledge, but to provide a space for reflective and critical training together with background and overview insights. (CG)

An analysis of office work (OW) highlights the relationship between formal vocational qualifications and tacit knowledge gained through experience. In OW, "abstracted" skills (typewriting, correspondence) and theory are taught in schools out of their practical context and can become obsolete because of technological change. Some types of "abstract knowledge"(social competencies, personal attitudes) need to be learned in a work context. Historically, women were socialized to supportive roles and routinized work and were able to supply the "abstract knowledge" component of OW using their life experiences and roles. Changes in technology, work organization and career expectations (especially for women) require rethinking the role of school-based skills and workplace practice. General qualifications need to include the ability to cope with open situations in a non-routinized way. Skills, such as empathy, that were part of female gender roles should be incorporated into the formal qualifications for OW. The function of schooling should not mainly be to transfer technical skills and prescriptive knowledge, but to provide a space for reflective and critical training together with background and overview insights. (CG)

An analysis of office work (OW) highlights the relationship between formal vocational qualifications and tacit knowledge gained through experience. In OW, "abstracted" skills (typewriting, correspondence) and theory are taught in schools out of their practical context and can become obsolete because of technological change. Some types of "abstract knowledge"(social competencies, personal attitudes) need to be learned in a work context. Historically, women were socialized to supportive roles and routinized work and were able to supply the "abstract knowledge" component of OW using their life experiences and roles. Changes in technology, work organization and career expectations (especially for women) require rethinking the role of school-based skills and workplace practice. General qualifications need to include the ability to cope with open situations in a non-routinized way. Skills, such as empathy, that were part of female gender roles should be incorporated into the formal qualifications for OW. The function of schooling should not mainly be to transfer technical skills and prescriptive knowledge, but to provide a space for reflective and critical training together with background and overview insights. (CG)

The collection presents six articles giving international perspectives on learning disabled (LD) adults. G. Gray begins with an analysis of "Vocational Guidance for Dyslexic School Leavers and Adults in Great Britain," in which he notes coping strategies to be used in the workplace and describes the evolution of societal attitudes to LD persons in the United Kingdom. B. Dyssegaard follows with a look at "The Transition from School to Society in Denmark.""Educational and Vocational Issues: Learning Disabled Adults in the Federal Republic of Germany" is the topic of the paper by U. Bleidick which comments upon that country's organizational flexibility as exemplified in the extension of school attendance to a voluntary 10th year. Among the "Canadian Perspectives on Youth & Adults with Learning Disabilities--1985" considered by C. Smith are views of vocational rehabilitation and barriers to success for LD people. M. Bruck adds a review of data on "The Long-Term Prognosis of Childhood Learning Disabilities: A View from Canada." In the final paper, "Learning Disabled Adults in the Netherlands: A Problem for the Future?" A. Wilmink and R. deGroot examine effects of structural provisions for LD students in that country. (CL)

The collection presents six articles giving international perspectives on learning disabled (LD) adults. G. Gray begins with an analysis of "Vocational Guidance for Dyslexic School Leavers and Adults in Great Britain," in which he notes coping strategies to be used in the workplace and describes the evolution of societal attitudes to LD persons in the United Kingdom. B. Dyssegaard follows with a look at "The Transition from School to Society in Denmark.""Educational and Vocational Issues: Learning Disabled Adults in the Federal Republic of Germany" is the topic of the paper by U. Bleidick which comments upon that country's organizational flexibility as exemplified in the extension of school attendance to a voluntary 10th year. Among the "Canadian Perspectives on Youth & Adults with Learning Disabilities--1985" considered by C. Smith are views of vocational rehabilitation and barriers to success for LD people. M. Bruck adds a review of data on "The Long-Term Prognosis of Childhood Learning Disabilities: A View from Canada." In the final paper, "Learning Disabled Adults in the Netherlands: A Problem for the Future?" A. Wilmink and R. deGroot examine effects of structural provisions for LD students in that country. (CL)

Series 1 Matthew Syed explores how we can all get better at learning from our mistakes.

Series 1 Matthew Syed explores how we can all get better at learning from our mistakes.

Series 1 Matthew Syed explores how we can all get better at learning from our mistakes.

137 p. ; 19 cm

1 online resource

1 online resource

1 online resource

The paper commences by reviewing some of the issues currently being raised with respect to the use of technology in undergraduate mathematics teaching and learning. Selected material from three research projects is used to address a series of questions. The questions relate to the use of symbolic manipulators in tertiary mathematics, to undergraduate student attitudes toward the use of computers in learning mathematics, and to outcomes of using technology in collaborative student activity in pre-university classrooms. Results suggest that teaching demands are increased rather than decreased by the use of technology, that attitudes to mathematics and to computers occupy different dimensions, and that students adopt different preferences in the way they utilize available resources. These outcomes are reflected back on the literature, and implications for teaching, learning, and research are discussed. (Contains 49 references.) (Author)

Ryan reads today's daily meditation and talks to author Jack Weatherford about his books Genghis Khan and the Making of the Modern World and Indian Givers: How Native Americans Transformed the World (which you can get at The Painted Porch), why we should learn from history and implement new solutions based on past failures, and more. Jack Weatherford is the New York Times bestselling author of Genghis Khan and the Making of the Modern World, which sold over 300,000 copies and has been optioned by Wolf Films (producer of Law and Order), Indian Givers: How the Indians of the Americas Transformed The world, his first national bestseller, and The History of Money, among other acclaimed books that have been published in more than twenty-five languages.In 2006 he spoke at the United Nations to honor the 800th anniversary of the founding of the Mongol nation by Genghis Khan. In 2007 President Enkhbayar of Mongolia awarded him Mongolia's highest honor for military or civilian service. Although the original Spanish edition of Indian Givers was banned in some parts of Latin America, nearly a quarter of a century later Bolivia honored him for his work on the indigenous people of the Americas. A specialist in tribal peoples, he taught for twenty-nine years at Macalester College in Saint Paul, Minnesota, where he held the DeWitt Wallace Distinguished Chair of Anthropology.Father's Day is just two weeks away! The leather editions of The Daily Stoic and The Obstacle is the Way make the perfect gift for any of the fathers in your life. InsideTracker provides you with a personalized plan to improve your metabolism, reduce stress, improve sleep, and optimize your health for the long haul. For a limited time, get 20% off the entire InsideTracker store. Just go to insidetracker.com/STOIC to claim this deal.The Jordan Harbinger Show is one of the most interesting podcasts on the web, with guests like Kobe Bryant, Mark Manson, Eric Schmidt, and more. Listen to one of Ryan's episodes right now (1, 2), and subscribe to the Jordan Harbinger Show today.Since 2007, MyBodyTutor's daily accountability and 1:1 coaching has been the most effective way to get healthy and stay fit. To save $50 all you have to do is go to MyBodyTutor.com, join, and mention Daily Stoic when they ask how you heard about them.80,000 Hours is a nonprofit that provides free research and support to help people have a positive impact with their career. To get started planning a career that works on one of the world's most pressing problems, sign up now at 80000hours.org/stoic.Sign up for the Daily Stoic email: https://dailystoic.com/dailyemail

Ryan reads today's daily meditation and talks to author Jack Weatherford about his books Genghis Khan and the Making of the Modern World and Indian Givers: How Native Americans Transformed the World (which you can get at The Painted Porch), why we should learn from history and implement new solutions based on past failures, and more. Jack Weatherford is the New York Times bestselling author of Genghis Khan and the Making of the Modern World, which sold over 300,000 copies and has been optioned by Wolf Films (producer of Law and Order), Indian Givers: How the Indians of the Americas Transformed The world, his first national bestseller, and The History of Money, among other acclaimed books that have been published in more than twenty-five languages.In 2006 he spoke at the United Nations to honor the 800th anniversary of the founding of the Mongol nation by Genghis Khan. In 2007 President Enkhbayar of Mongolia awarded him Mongolia's highest honor for military or civilian service. Although the original Spanish edition of Indian Givers was banned in some parts of Latin America, nearly a quarter of a century later Bolivia honored him for his work on the indigenous people of the Americas. A specialist in tribal peoples, he taught for twenty-nine years at Macalester College in Saint Paul, Minnesota, where he held the DeWitt Wallace Distinguished Chair of Anthropology.Father's Day is just two weeks away! The leather editions of The Daily Stoic and The Obstacle is the Way make the perfect gift for any of the fathers in your life. InsideTracker provides you with a personalized plan to improve your metabolism, reduce stress, improve sleep, and optimize your health for the long haul. For a limited time, get 20% off the entire InsideTracker store. Just go to insidetracker.com/STOIC to claim this deal.The Jordan Harbinger Show is one of the most interesting podcasts on the web, with guests like Kobe Bryant, Mark Manson, Eric Schmidt, and more. Listen to one of Ryan's episodes right now (1, 2), and subscribe to the Jordan Harbinger Show today.Since 2007, MyBodyTutor's daily accountability and 1:1 coaching has been the most effective way to get healthy and stay fit. To save $50 all you have to do is go to MyBodyTutor.com, join, and mention Daily Stoic when they ask how you heard about them.80,000 Hours is a nonprofit that provides free research and support to help people have a positive impact with their career. To get started planning a career that works on one of the world's most pressing problems, sign up now at 80000hours.org/stoic.Sign up for the Daily Stoic email: https://dailystoic.com/dailyemail

"The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors."

The study addresses the prediction of quality of life, leveraging machine learning models with a focus on health, socioeconomics, subjective well-being, and environmental indicators. Thus, this study aims to evaluate the efficacy of machine learning in quality-of-life prediction based on property crime and temperature. Five machine learning algorithms were used to be empirically compared namely generalized linear model (GLM), random forest (RF), decision tree (DT), gradient boosted tree (GBT) and support vector machine (SVM) are compared empirically. The performance of each machine learning algorithm in predicting the quality of life has been observed based on the attributes of property crime and tropical climate (temperature). Despite initial low correlation with quality of life, temperature significantly contributes to specific algorithms, enhancing predictive accuracy. This shows the complexity of machine learning impacts. SVM emerges as the best-performing algorithm, followed by RF and DT. The findings highlight the importance of seemingly unrelated factors in prediction outcomes. This paper presents a fundamental research framework useful for helping educators and researchers to explore in depth quality of life prediction with using property crime and temperature as a factor.

The study addresses the prediction of quality of life, leveraging machine learning models with a focus on health, socioeconomics, subjective well-being, and environmental indicators. Thus, this study aims to evaluate the efficacy of machine learning in quality-of-life prediction based on property crime and temperature. Five machine learning algorithms were used to be empirically compared namely generalized linear model (GLM), random forest (RF), decision tree (DT), gradient boosted tree (GBT) and support vector machine (SVM) are compared empirically. The performance of each machine learning algorithm in predicting the quality of life has been observed based on the attributes of property crime and tropical climate (temperature). Despite initial low correlation with quality of life, temperature significantly contributes to specific algorithms, enhancing predictive accuracy. This shows the complexity of machine learning impacts. SVM emerges as the best-performing algorithm, followed by RF and DT. The findings highlight the importance of seemingly unrelated factors in prediction outcomes. This paper presents a fundamental research framework useful for helping educators and researchers to explore in depth quality of life prediction with using property crime and temperature as a factor.

The study addresses the prediction of quality of life, leveraging machine learning models with a focus on health, socioeconomics, subjective well-being, and environmental indicators. Thus, this study aims to evaluate the efficacy of machine learning in quality-of-life prediction based on property crime and temperature. Five machine learning algorithms were used to be empirically compared namely generalized linear model (GLM), random forest (RF), decision tree (DT), gradient boosted tree (GBT) and support vector machine (SVM) are compared empirically. The performance of each machine learning algorithm in predicting the quality of life has been observed based on the attributes of property crime and tropical climate (temperature). Despite initial low correlation with quality of life, temperature significantly contributes to specific algorithms, enhancing predictive accuracy. This shows the complexity of machine learning impacts. SVM emerges as the best-performing algorithm, followed by RF and DT. The findings highlight the importance of seemingly unrelated factors in prediction outcomes. This paper presents a fundamental research framework useful for helping educators and researchers to explore in depth quality of life prediction with using property crime and temperature as a factor.

The study addresses the prediction of quality of life, leveraging machine learning models with a focus on health, socioeconomics, subjective well-being, and environmental indicators. Thus, this study aims to evaluate the efficacy of machine learning in quality-of-life prediction based on property crime and temperature. Five machine learning algorithms were used to be empirically compared namely generalized linear model (GLM), random forest (RF), decision tree (DT), gradient boosted tree (GBT) and support vector machine (SVM) are compared empirically. The performance of each machine learning algorithm in predicting the quality of life has been observed based on the attributes of property crime and tropical climate (temperature). Despite initial low correlation with quality of life, temperature significantly contributes to specific algorithms, enhancing predictive accuracy. This shows the complexity of machine learning impacts. SVM emerges as the best-performing algorithm, followed by RF and DT. The findings highlight the importance of seemingly unrelated factors in prediction outcomes. This paper presents a fundamental research framework useful for helping educators and researchers to explore in depth quality of life prediction with using property crime and temperature as a factor.

The study addresses the prediction of quality of life, leveraging machine learning models with a focus on health, socioeconomics, subjective well-being, and environmental indicators. Thus, this study aims to evaluate the efficacy of machine learning in quality-of-life prediction based on property crime and temperature. Five machine learning algorithms were used to be empirically compared namely generalized linear model (GLM), random forest (RF), decision tree (DT), gradient boosted tree (GBT) and support vector machine (SVM) are compared empirically. The performance of each machine learning algorithm in predicting the quality of life has been observed based on the attributes of property crime and tropical climate (temperature). Despite initial low correlation with quality of life, temperature significantly contributes to specific algorithms, enhancing predictive accuracy. This shows the complexity of machine learning impacts. SVM emerges as the best-performing algorithm, followed by RF and DT. The findings highlight the importance of seemingly unrelated factors in prediction outcomes. This paper presents a fundamental research framework useful for helping educators and researchers to explore in depth quality of life prediction with using property crime and temperature as a factor.

The study addresses the prediction of quality of life, leveraging machine learning models with a focus on health, socioeconomics, subjective well-being, and environmental indicators. Thus, this study aims to evaluate the efficacy of machine learning in quality-of-life prediction based on property crime and temperature. Five machine learning algorithms were used to be empirically compared namely generalized linear model (GLM), random forest (RF), decision tree (DT), gradient boosted tree (GBT) and support vector machine (SVM) are compared empirically. The performance of each machine learning algorithm in predicting the quality of life has been observed based on the attributes of property crime and tropical climate (temperature). Despite initial low correlation with quality of life, temperature significantly contributes to specific algorithms, enhancing predictive accuracy. This shows the complexity of machine learning impacts. SVM emerges as the best-performing algorithm, followed by RF and DT. The findings highlight the importance of seemingly unrelated factors in prediction outcomes. This paper presents a fundamental research framework useful for helping educators and researchers to explore in depth quality of life prediction with using property crime and temperature as a factor.

Internalising mental health difficulties, characterised by disturbances in mood and/or emotion, significantly impair quality of life, and their prevalence increases significantly during the transition from childhood to adolescence. The objective of this parallel cluster randomised controlled trial was to determine whether a universal, school-based social and emotional learning intervention (Passport: Skills for Life) could reduce internalising symptoms (primary outcome) and related outcomes compared to the usual school curriculum (control). Passport is implemented by trained class teachers, who deliver 18 weekly lessons across 5 modules (emotions; relationships and helping each other; difficult situations; fairness, justice and what is right; and, change and loss). Mainstream primary schools (clusters) were the unit of randomisation. Allocation took place following ​completion of baseline measures, with minimisation ensuring balance across trial arms in school size and the proportion of children eligible for free school meals.  A total of k = 62 schools (N = 2242 children, aged 8-9) were randomly allocated to intervention (Passport; k = XX; N = XXXX [to be reported in Stage 2 submission]) or control (k = YY; N = YYYY [to be reported in Stage 2 submission]) conditions. The trial’s lead data analyst was masked to school allocation, but trial participants, schools, and the remaining authors were not. The intention-to-treat analysis of the primary outcome (internalising symptoms) revealed that control and intervention schools differed by [estimated effect size and its precision to be reported in Stage 2 submission]. Secondary outcome intention-to-treat analyses revealed [estimated effect sizes and their precision to be reported in Stage 2 submission]. [Statement on adverse events to be reported in Stage 2 submission]. [Statement on general interpretation of results to be reported in Stage 2 submission].