Pioneering quantitative assessment of questioning competency in elementary pre-service teachers using Likert-scale questions

利克特量表 比例(比率) 心理学 数学教育 多元方法论 定性研究 教师教育 半结构化面试 编码(社会科学) 定性性质 医学教育 教育学 计算机科学 医学 物理 发展心理学 社会学 机器学习 统计 量子力学 社会科学 数学
作者
Jianlan Wang,Yuanhua Wang,Shahin Shawn Kashef
出处
期刊:International Journal of Science Education [Taylor & Francis]
卷期号:: 1-24
标识
DOI:10.1080/09500693.2024.2439141
摘要

In pre-service teacher (PST) education, developing effective instructional practices like questioning is a crucial learning objective. Assessing PSTs' questioning competencies is essential, yet traditional qualitative methods (e.g., discourse analysis) limit large-scale analysis within PST preparation programs. Previously, we addressed this challenge by designing and validating instruments, including a video-coding scheme and free-response questions, to assess novice teachers' competencies in asking effective guiding questions to address student difficulties. We established a link between their questioning practices and performance on free-response questions. Building upon these efforts, this study aims to further enhance assessment efficiency by transforming pre-validated free-response questions into Likert-scale questions. In this approach, respondents rate provided options that represent various levels of questioning competencies, rather than providing their answers. Over two semesters, we administered Likert-scale questions to more than 100 PSTs each term to evaluate the feasibility and validity of this method. We identified five categories of options for Likert-scale questions and developed empirical equations to derive Pedagogical Content Knowledge in Questioning (PCK-Q) from the collected ratings. The findings support the use of Likert-scale questions as a promising tool for large-scale assessment of PCK-Q in PST education. We also discussed the application of Likert-scale questions in PST preparation.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
梦梦完成签到,获得积分10
刚刚
青年晚报完成签到,获得积分10
3秒前
彼岸发布了新的文献求助10
4秒前
扶摇发布了新的文献求助10
4秒前
张鹏飞完成签到,获得积分10
4秒前
6秒前
冻结完成签到 ,获得积分10
7秒前
作案不留痕应助Jimmy_King采纳,获得10
7秒前
10秒前
自然的剑封完成签到,获得积分10
10秒前
10秒前
pangpang发布了新的文献求助10
12秒前
12秒前
mia发布了新的文献求助10
12秒前
12秒前
14秒前
14秒前
mtt完成签到,获得积分10
16秒前
Kao应助JLnaruto采纳,获得10
16秒前
AISIR完成签到,获得积分20
17秒前
壮观艳发布了新的文献求助10
17秒前
hzz发布了新的文献求助10
18秒前
19秒前
gyq发布了新的文献求助10
19秒前
hancahngxiao发布了新的文献求助10
20秒前
Orange应助AISIR采纳,获得10
22秒前
酷波er应助是我呀吼采纳,获得10
22秒前
赘婿应助科研通管家采纳,获得10
23秒前
打工肥仔应助科研通管家采纳,获得10
23秒前
DUWEI应助科研通管家采纳,获得10
23秒前
23秒前
打工肥仔应助科研通管家采纳,获得10
23秒前
JamesPei应助科研通管家采纳,获得10
24秒前
今后应助科研通管家采纳,获得10
24秒前
24秒前
打工肥仔应助科研通管家采纳,获得10
24秒前
打工肥仔应助科研通管家采纳,获得10
24秒前
Momo01应助科研通管家采纳,获得10
24秒前
輓楓发布了新的文献求助10
24秒前
打工肥仔应助科研通管家采纳,获得10
24秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Cronologia da história de Macau 5000
Petrology and Plate Tectonics 800
Electrode Potentials 550
Matrix Methods in Data Mining and Pattern Recognition 510
Association of Reentry Well-Being with Psychological Distress, Employment, and Housing Instability 15-Months After Incarceration 500
Trees of tropical Asia : an illustrated guide to diversity 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7029367
求助须知:如何正确求助?哪些是违规求助? 8699326
关于积分的说明 18431655
捐赠科研通 6530035
什么是DOI,文献DOI怎么找? 3112131
关于科研通互助平台的介绍 2189973
邀请新用户注册赠送积分活动 2087666