Profiling teacher pedagogical behaviours in plummeting postgraduate students’ anxiety in statistics

Asare, Prince Yeboah

doi:10.1080/2331186x.2023.2222656

Cited by 2 publications

(1 citation statement)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Aldahdouh et al [25] found that teachers' learning styles and background characteristics were related to their ability to implement teaching reforms during the COVID-19 pandemic. Asare [26] discovered that teachers' cognitive and emotional characteristics, as well as their teaching practices, had an impact on graduate students' statistical learning anxiety and attitudes. Marici et al [27] found that a teacher's appearance had an impact on students' learning attitudes and evaluations of the teacher, with students being more likely to accept teachers who were more attractive.…”

Section: Correlation Between Teacher Characteristics and Teaching Qua...mentioning

confidence: 99%

Identifying Key Factors Influencing Teaching Quality: A Computational Pedagogy Approach

Yao,

Lin

2023

Systems

View full text Add to dashboard Cite

Although previous research has explored the correlation between teacher characteristics and teaching quality, effective methods for identifying key factors that influence teaching quality are still lacking. This study aims to address this issue by developing an identification methodology based on a computational pedagogy research paradigm to identify the key characteristics of teachers and courses that influence their teaching quality. We developed quantitative models to quantify the characteristics of teaching quality, based on those identified in previous studies. Correlation and multiple correlation analyses were conducted to identify the key influencing characteristics, and grey correlation analysis was used to calculate the degree of correlation between these key characteristics and teaching quality. Our methodology was applied to 27 computer science discipline teachers and 82 courses, and validated with teaching data from eight additional teachers. Our findings demonstrate the effectiveness of our method in identifying the key influence characteristics of teachers and courses on teaching quality and confirm significant correlations between these key influential characteristics and teaching quality. This innovative approach provides new insights and tools for predicting and improving the teaching quality across disciplinary majors. Our research has significant implications for future education studies, particularly for the development of effective methods for identifying key factors that influence teaching quality. By providing a more comprehensive understanding of the key factors that influence teaching quality, our study can inform the development of evidence-based strategies to improve the teaching effectiveness for different disciplinary majors.

show abstract

Section: Correlation Between Teacher Characteristics and Teaching Qua...mentioning

confidence: 99%

Identifying Key Factors Influencing Teaching Quality: A Computational Pedagogy Approach

Yao,

Lin

2023

Systems

View full text Add to dashboard Cite

show abstract

Optimizing Learning: Predicting Research Competency via Statistical Proficiency

Wongvorachan,

Srisuttiyakorn,

Sriklaub

2024

Trends in Higher Education

View full text Add to dashboard Cite

In higher education, the cultivation of research competency is pivotal for students’ critical thinking development and their subsequent transition into the professional workforce. While statistics plays a fundamental role in supporting the completion of a research project, it is often perceived as challenging, particularly by students in majors outside mathematics or statistics. The connection between students’ statistical proficiency and their research competency remains unexplored despite its significance. To address this gap, we utilize the supervised machine learning approach to predict students’ research competency as represented by their performance in a research methods class, with predictors of students’ proficiency in statistical topics. Predictors relating to students’ learning behavior in a statistics course such as assignment completion and academic dishonesty are also included as auxiliary variables. Results indicate that the three primary categories of statistical skills—namely, the understanding of statistical concepts, proficiency in selecting appropriate statistical methods, and statistics interpretation skills—can be used to predict students’ research competency as demonstrated by their final course scores and letter grades. This study advocates for strategic emphasis on the identified influential topics to enhance efficiency in developing students’ research competency. The findings could inform instructors in adopting a strategic approach to teaching the statistical component of research for enhanced efficiency.

show abstract

Profiling teacher pedagogical behaviours in plummeting postgraduate students’ anxiety in statistics

Cited by 2 publications

References 38 publications

Identifying Key Factors Influencing Teaching Quality: A Computational Pedagogy Approach

Identifying Key Factors Influencing Teaching Quality: A Computational Pedagogy Approach

Optimizing Learning: Predicting Research Competency via Statistical Proficiency

Contact Info

Product

Resources

About