社区学院
心理学
医学教育
多级模型
教师发展
学生参与度
样品(材料)
高等教育
学业指导
学习型社区
测量数据收集
最佳实践
大学教师
教育学
测量仪器
学业成绩
数学教育
数据收集
职业发展
时间管理
学生事务
作者
Carol A. Lundberg,Young K. Kim,E. Michael Bohlig
标识
DOI:10.1177/00915521261428086
摘要
Research Questions: Using a sample of 56,807 community college students and 10,343 faculty from 105 community colleges, this study asked which faculty behaviors were associated with student learning, and if there were differences in those behaviors based on faculty type. Methods: Our two-stage analysis used data from the Community College Survey of Student Engagement (CCSSE) and the Community College Faculty Survey of Student Engagement (CCFSSE). We used hierarchical linear modeling (HLM) to identify faculty practices that predict student learning, with a random-effects ANOVA model and a means-as-outcomes model. After identifying the faculty practices that most strongly predicted learning, we identified differences in frequency of their use based on faculty type. Results: Three of the four strongest indicators of student learning were an emphasis on (a) support to help students succeed, (b) contact among students from different backgrounds, and (c) amount of study time. Talking about career plans with an instructor or advisor predicted career learning, academic learning, and personal development. Full-time faculty, faculty teaching career and technical education, developmental faculty, and faculty who had taught a course more than 20 times used these effective practices more than other faculty. Conclusions: Introducing students to institutional support for their success was the strongest predictor in the model, but part-time faculty, faculty teaching only college-level courses, and faculty with less experience in a course engaged in this behavior less often than their peers. Part-time faculty may need more information about support available to students, and college-level faculty could benefit by emphasizing support services.
科研通智能强力驱动
Strongly Powered by AbleSci AI