Exploration of Predictors for Korean Teacher Job Satisfaction via a Machine Learning Technique, Group Mnet

Yoo, Jin Eun; Rho, Minjeong

doi:10.3389/fpsyg.2020.00441

Cited by 16 publications

(17 citation statements)

References 39 publications

(62 reference statements)

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“…Beyond producing interpretable models, the Enet and Mnet models of this study were comparable to RF models in terms of prediction. Likewise, multiple studies across diverse disciplines reported that linear models are comparable to RF (e.g., [53]- [55]) or even better than RF (e.g., [19], [20], [56]). These studies with ours have in common that the variables were pre-selected based on previous research.…”

Section: A Regularization and Learning Analyticsmentioning

confidence: 98%

“…The parameter γ , known as the concavity penalty, regulates the penalization rate depending on the size of the coefficients. When the coefficients are larger than the product of the two penalties, the rate of the MCP penalty quickly drops, thereby applying less shrinkage to the coefficients and yielding less biased estimates than LASSO does [19], [38]. Lastly, the ridge penalty is added to the Mnet equation, the parameter of which is λ 2 .…”

Section: A Regularization: Enet and Mnetmentioning

confidence: 99%

“…Regularization produces biased estimates, and significance testing is performed on unbiased estimates. Special techniques such as post-selection inference (e.g., [43]) are required to perform statistical testing after regularization, but currently only available with LASSO [19], [20]. Instead of statistical testing, we iterated data splitting and prediction modeling, and obtained selection counts as the criterion for variable importance; variables selected more often bear more importance than variables selected less often [19], [20].…”

Section: ) Cross-validation (Cv)mentioning

confidence: 99%

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Online Students’ Learning Behaviors and Academic Success: An Analysis of LMS Log Data From Flipped Classrooms via Regularization

2022

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The main purpose of this study was to demonstrate the uses of regularization, a machine learning technique, in exploring important predictors for online student success. We analyzed student and learning behavioral variables from undergraduate fully-online flipped classrooms. In particular, students' instructional video watching behaviors at an instructional unit level were extracted from LMS (learning management system) log data, and Enet (elastic net) and Mnet were employed among regularization. As results, regularization not only showed comparable prediction performance to random forest, a nonlinear method well-known for its prediction capabilities, but also produced interpretable prediction models as a linear method. Enet and Mnet selected 17 and 19 important predictors out of 159, respectively, and could identify potential low-performers as early as the first instructional week of the course. Important variables rarely recognized in previous studies included complete viewings of the first video before class and repeated complete viewings of challenging contents after in-class meetings. Unlike previous studies, aggregate measures of video lecture views were not important predictors. Variables less frequently studies in previous studies were the number of non-mandatory quiz-taking and mobile lecture watching frequencies. Variables in line with previous research were student attitudes towards the course, gender, grade level, and clicks on learning materials postings. Many students turned out not to watch lecture videos completely before class. Further research on regularization and exploration of these variables with other potentially important predictors can provide more insight into students' online learning from a comprehensive perspective.

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Section: A Regularization and Learning Analyticsmentioning

confidence: 98%

Section: A Regularization: Enet and Mnetmentioning

confidence: 99%

Section: ) Cross-validation (Cv)mentioning

confidence: 99%

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Online Students’ Learning Behaviors and Academic Success: An Analysis of LMS Log Data From Flipped Classrooms via Regularization

2022

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“…In the case of TALIS, teacher job satisfaction comprises satisfaction with the profession itself-including the role and work of a teacher-and also with the school environment. The variables representing job satisfaction for Korean teachers were categorized as either teacher or school characteristics, with the latter including variables associated with school demographics and the school climate [27].…”

Section: Teacher Job Satisfactionmentioning

confidence: 99%

Structural Model Analysis of Factors Affecting Sustainable Teacher Job Satisfaction in Korea: Evidence from TALIS 2018

Jung

Woo

2022

Sustainability

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This study aims to explore the relationship between the preparedness, self-efficacy, career motivation, and job satisfaction of Korean teachers with the 2018 data of TALIS (Teaching and Learning International Survey). For this purpose, 1266 Korean middle school teachers were selected, and an analysis of mediating effects was executed by utilizing structural equation modeling and phantom variables. The results are as follows: teacher preparedness had a significant and positive effect on teacher self-efficacy and career motivation. However, it failed to show a significant effect on teacher job satisfaction, which was instead significantly and positively affected by teacher self-efficacy and career motivation. In addition, based on the analysis of the estimate of mediating effects, it was discovered that teacher career motivation had a greater positive effect than that teacher self-efficacy on teacher job satisfaction. Taking these results into consideration, this study accordingly makes suggestions concerning the improvement of sustainable teacher job satisfaction, self-efficacy and career motivation inside teacher education courses. Furthermore, this study will propose measures such as further valuing the Teaching Personality and Aptitude Test and strengthening in-depth interviews in student teacher selection, extending teaching experiences through simulated instruction and peer supervision as well as providing well-organized teaching professional counseling opportunities for student teachers.

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“…Penalized regression has begun to emerge as an approach to predictive modeling in diverse fields, including bioinformatics (e.g., Liu et al, 2018;Zeng et al, 2020) and engineering (e.g., Nabian & Meidani, 2020;Zhang et al, 2017). With regard to large-scale educational assessment data, elastic net (Yoo, 2018) and group mnet (Yoo & Rho, 2020) have been employed in analyzing data from the Trends in International Mathematics and Science Study (National Center for Education Statistics, n.d. https:// nces. ed.…”

Section: Introductionmentioning

confidence: 99%

Machine learning procedures for predictor variable selection for schoolwork-related anxiety: evidence from PISA 2015 mathematics, reading, and science assessments

Immekus

Jeong

Yoo

2022

Large-scale Assess Educ

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Background Large-scale international studies offer researchers a rich source of data to examine the relationship among variables. Machine learning embodies a range of flexible statistical procedures to identify key indicators of a response variable among a collection of hundreds or even thousands of potential predictor variables. Among these, penalized regression approaches, including least absolute selection and shrinkage operator (LASSO) and elastic net (Enet), have been advanced as useful tools capable of handling large number of predictors for variable selection for model generation. While the utility of penalized regression within educational research is emerging, less application of these machine learning methods, including random forest, to predictor variable selection in large-scale international data appears in the literature. In response, this study compared LASSO, Enet, and random forest for predictor variable selection, including the traditional forward stepwise (FS) regression approach, for students’ test anxiety or, more specifically, schoolwork-related anxiety based on PISA 2015 data. Methods Prediction of the three machine learning methods were compared for variable selection of 188 indicators of schoolwork-related anxiety. Data were based on US students (N = 5593) who participated in PISA 2015. With the exception of FS, LASSO, Enet, and random forest were iterated 100 times to consider the bias resulting from data-splitting to determine the selection or non-selection of each predictor. This resulted in the reporting of number of selected variables into the following five count categories: 1 or more, 25 or more, 50 or more, 75 or more, and all 100 iterations. Results LASSO and Enet both outperformed random forest but did not differ from one another in terms of prediction performance in 100 iterations of modeling. Correspondingly, LASSO was compared to FS in which, of the 188 predictors, 27 were identified as key indicators of schoolwork-related anxiety across 100 iterations, and 26 variables were also statistically significant with FS regression. Aligned with previous research, key indicators included personal, situational, and mathematics and reading achievement. Further, LASSO identified 28 variables (14.89%) statistically unrelated to schoolwork-related anxiety, which included indicators aligned to students’ academic- and non-academic behaviors. Conclusions LASSO and Enet outperformed random forest and yielded comparable results in which determinants of schoolwork-related anxiety included personal and environmental factors, including achievement goals, sense of belonging, and confidence to explain scientific phenomenon. LASSO and FS also identified similar predictor variables related, as well as unrelated, to schoolwork-related anxiety. Aligned with previous research, females reported higher schoolwork-related anxiety than males. Mathematics achievement was negatively related to anxiety, whereas reading performance was positively associated with anxiety. This study also bears significance as one of the first penalized regression studies to incorporate sampling weights and reflect the complex sampling schemes of large-scale educational assessment data.

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Exploration of Predictors for Korean Teacher Job Satisfaction via a Machine Learning Technique, Group Mnet

Cited by 16 publications

References 39 publications

Online Students’ Learning Behaviors and Academic Success: An Analysis of LMS Log Data From Flipped Classrooms via Regularization

Online Students’ Learning Behaviors and Academic Success: An Analysis of LMS Log Data From Flipped Classrooms via Regularization

Structural Model Analysis of Factors Affecting Sustainable Teacher Job Satisfaction in Korea: Evidence from TALIS 2018

Machine learning procedures for predictor variable selection for schoolwork-related anxiety: evidence from PISA 2015 mathematics, reading, and science assessments

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