心理学
清晰
适度
杠杆(统计)
调解
调解
结构方程建模
马尔科夫蒙特卡洛
数学教育
专业发展
贝叶斯概率
社会心理学
教育学
计算机科学
机器学习
生物化学
人工智能
化学
法学
政治学
作者
David Ansong,Moses Okumu,Emmanuel Owusu Amoako,Jamal Appiah‐Kubi,Abena Oforiwaa Ampomah,Isaac Koomson,Eric Hamilton
标识
DOI:10.1016/j.lindif.2023.102396
摘要
Teachers' impact on learning outcomes is well supported, but more robust evidence is needed on the complex indirect pathways through which teacher support impacts performance in low-versus high-stakes examinations. This study sought to understand the divergent mechanisms through which teachers' support for students affects their performance on low- and high-stakes assessments. Bayesian structural equation modeling with the Markov Chain Monte Carlo algorithm and data from Ghana were used to test mediated, unmediated, and moderated-mediated relationships. The results show that a typical student's high-stakes exam performance increases by approximately 13 % for each one-unit increase in teacher support. A chain mediation effect also exists through student homework behavior and low-stakes everyday performance. Student gender is also a moderator. These results could aid in identifying the malleable leverage points associated with low- and high-stakes assessments. Such empirical clarity would help education administrators to develop appropriate professional development programs that enhance teachers' support roles, enabling them to respond better to learning disparities and related challenges.
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