scholarly journals AN EXAMINATION OF THE CORRELATION BETWEEN SCIENCE AND TECHNOLOGY ATTITUDES SCALE, FREQUENCY OF SMARTPHONE USAGE SCALE AND LIFELONG LEARNING SCALE SCORES USING THE STRUCTURAL EQUATION MODEL

2017 ◽  
Vol 16 (1) ◽  
pp. 86-99
Author(s):  
Hakan Kör ◽  
Hasan Erbay ◽  
Melih Engin ◽  
Emre Dünder
Keyword(s):  
Lifelong Learning ◽  
Structural Equation Model ◽  
Structural Equation ◽  
Equation Model ◽  
Demographic Characteristics ◽  
Learning Technology ◽  
Digital Competence ◽  
Scale Scores ◽  
Technology Attitudes ◽  
Students Attitudes

Lifelong learning can be defined as all of the activities which aim to develop an individual’s skills, knowledge and abilities, socially, individually and professionally. Previous research on lifelong learning has been about using computers, digital competence and the correlation between demographic characteristics and intelligence. However, only one scale was used in this research, and, in general, only scores for demographic characteristics and lifelong learning were compared. In this research, the correlation between distance-learning students’ attitudes to technology, their frequency of use of smartphones and their attitudes to lifelong learning were examined. Reliability studies were carried out prior to the study and the Turkish adaptations of the scales published in international journals were administered with permission. The study sample consisted of 881 students studying in 12 different units of Hitit University: six Vocational Schools, four Faculties and two Graduate Schools. The data were analysed by creating a structural equation model on the open source R analysis program. According to the research results, there was a significant correlation between the three scales, and the correlation between the ‘lifelong learning’ scores and the ‘frequency of of smartphone usage’ scores was greater than the ‘technology attitudes’ scores. Key words: lifelong learning, technology attitudes, smartphone usage, technology leadership.

scholarly journals How basic psychological needs and motivation affect vitality and lifelong learning adaptability of pharmacists: a structural equation model

2018 ◽  
Vol 23 (3) ◽  
pp. 549-566 ◽  
Author(s):  
Sharon L. N. M. Tjin A Tsoi ◽  
Anthonius de Boer ◽  
Gerda Croiset ◽  
Andries S. Koster ◽  
Stéphanie van der Burgt ◽  
...  
Keyword(s):  
Lifelong Learning ◽  
Structural Equation Model ◽  
Structural Equation ◽  
Psychological Needs ◽  
Equation Model ◽  
Basic Psychological Needs

scholarly journals Predicting Students' Attitudes Toward Collaboration: Evidence From Structural Equation Model Trees and Forests

2021 ◽  
Vol 12 ◽  
Author(s):  
Jialing Li ◽  
Minqiang Zhang ◽  
Yixing Li ◽  
Feifei Huang ◽  
Wei Shao
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Structural Equation Model ◽  
Structural Equation ◽  
Equation Model ◽  
Data Driven ◽  
Group Model ◽  
Data Driven Decision Making ◽  
Model Trees ◽  
Students Attitudes

Numerous studies have shed some light on the importance of associated factors of collaborative attitudes. However, most previous studies aimed to explore the influence of these factors in isolation. With the strategy of data-driven decision making, the current study applied two data mining methods to elucidate the most significant factors of students' attitudes toward collaboration and group students to draw a concise model, which is beneficial for educators to focus on key factors and make effective interventions at a lower cost. Structural equation model trees (SEM trees) and structural equation model forests (SEM forests) were applied to the Program for International Student Assessment 2015 dataset (a total of 9,769 15-year-old students from China). By establishing the most important predictors and the splitting rules, these methods constructed multigroup common factor models of collaborative attitudes. The SEM trees showed that home educational resources (split by “above-average or not”), home possessions (split by “disadvantaged or not”), mother's education (split by “below high school or not”), and gender (split by “male or female”) were the most important predictors among the demographic variables, drawing a 5-group model. Among all the predictors, achievement motivation (split by “above-average or not”) and sense of belonging at school (split by “above-average or not” and “disadvantaged or not”) were the most important, drawing a 6-group model. The SEM forest findings proved the relative importance of these variables. This paper discusses various interpretations of these results and their implications for educators to formulate corresponding interventions. Methodologically, this research provides a data mining approach to discover important information from large-scale educational data, which might be a complementary approach to enhance data-driven decision making in education.

scholarly journals Does Postoperative Delirium Limit the Use of Patient-controlled Analgesia in Older Surgical Patients?

2009 ◽  
Vol 111 (3) ◽  
pp. 625-631 ◽  
Author(s):  
Jacqueline M. Leung ◽  
Laura P. Sands ◽  
Sudeshna Paul ◽  
Tim Joseph ◽  
Sakura Kinjo ◽  
...  
Keyword(s):  
Structural Equation Model ◽  
Visual Analog Scale ◽  
Structural Equation ◽  
Postoperative Delirium ◽  
Assessment Method ◽  
Equation Model ◽  
Opioid Use ◽  
Patient Controlled Analgesia ◽  
Analog Scale ◽  
Scale Scores

Unlabelled BACKGROUNDPostoperative pain Is an independent predictor of postoperative delirium. Whether postoperative delirium limits patient-controlled analgesia (PCA) use has not been determined. Methods The authors conducted a nested cohort study in older patients undergoing noncardiac surgery and used PCA for postoperative analgesia. Delirium was measured by using the Confusion Assessment Method. The authors computed a structural equation model to determine the effects of pain and opioid consumption on delirium status and the effect of delirium on opioid use. Results Of 335 patients, 108 (32.2%) developed delirium on postoperative day (POD) 1, and 120 (35.8%) on POD 2. Postoperative delirium did not limit the use of PCA. Patients with postoperative delirium used more PCA in a 24-h period (POD 2) compared to those without delirium (mean dose of hydromorphone +/- SE adjusted for covariates was 2.24 +/- 0.71 mg vs. 1.25 +/- 0.67 mg, P = 0.02). Despite more opioid use, patients with delirium reported higher Visual Analogue Scale scores than those without delirium (POD 1: mean visual analog scale +/- SE at rest 4.2 +/- 0.23 vs. 3.3 +/- 0.22, P = 0.0051; POD 2: 3.3 +/- 0.23 vs. 2.5 +/- 0.19, P = 0.004). Path coefficients from structural equation model revealed that pain and opioid use affect delirium status, but delirium does not affect subsequent opioid dose. Conclusions Postoperative delirium did not limit PCA use. Despite more opioid use, visual analog scale scores were higher in patients with delirium. Future studies on delirium should consider the role of pain and pain management as potential etiologic factors.

scholarly journals DETERMINANTS OF INTEREST IN USING TRAVEL VLOGS ON YOUTUBE AS A REFERENCE FOR TRAVELING

2020 ◽  
Vol 18 (4) ◽  
pp. 622-631
Author(s):  
Dore Rhendy Mamori ◽  
◽  
Mukhamad Najib ◽  
Agus Maulana ◽  
◽  
...  
Keyword(s):  
Structural Equation Model ◽  
Structural Equation ◽  
Equation Model ◽  
Demographic Characteristics ◽  
Analytical Tool ◽  
Perceived Usefulness ◽  
Reference Information ◽  
Usage Behavior

This study aims to analyze the demographic characteristics, usage behavior, and things that have an impact on attracting viewers to watch and utilize travel vlogs on YouTube as a travel reference. respondents in this study 131 were selected based on criteria aged 18 years and over and had watched travel vlogs on youtube at least once in 3 months to find travel reference information. The analytical tool used in this study is the Structural Equation Model (SEM) using LISREL software. The results of this study are to find out things that affect interest in using travel vlogs on YouTube based on nine hypotheses. First, the popularity of the travel vlog variable does not affect the credibility of the perception variable. Variable perception of interest, attitude variables towards use have a significant effect. Second, the video characteristic variable influences the perception variable of credibility. Third, the video characteristic variable influences the perception of benefits. Fourth, the credibility perception variable influences perceived usefulness. Fifth, video characteristics influence attitudes towards use. Sixth, the credibility variable influences attitudes toward use. Seventh, perceived usefulness variables influence attitudes toward use. Eighth, the perception of video characteristics influences the use of interest. Ninth, Attitudes toward use affect the interest to use.

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