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Dive into the research topics where Marsha Ing is active.

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Featured researches published by Marsha Ing.


American Educational Research Journal | 2011

The Influence of School Administrators on Teacher Retention Decisions

Donald Boyd; Pam Grossman; Marsha Ing; Hamilton Lankford; Susanna Loeb; James Wyckoff

This article explores the relationship between school contextual factors and teacher retention decisions in New York City. The methodological approach separates the effects of teacher characteristics from school characteristics by modeling the relationship between the assessments of school contextual factors by one set of teachers and the turnover decisions by other teachers in the same school. We find that teachers’ perceptions of the school administration has by far the greatest influence on teacher retention decisions. This effect of administration is consistent for first-year teachers and the full sample of teachers and is confirmed by a survey of teachers who have recently left teaching.


Journal of Teacher Education | 2009

Teacher Questioning to Elicit Students’ Mathematical Thinking in Elementary School Classrooms:

Megan L. Franke; Noreen M. Webb; Angela G. Chan; Marsha Ing; Deanna Freund; Dan Battey

Cognitively Guided Instruction (CGI) researchers have found that while teachers readily ask initial questions to elicit students’ mathematical thinking, they struggle with how to follow up on student ideas. This study examines the classrooms of three teachers who had engaged in algebraic reasoning CGI professional development. We detail teachers’ questions and how they relate to students’ making explicit their complete and correct explanations. We found that after the initial “How did you get that?” question, a great deal of variability existed among teachers’ questions and students’ responses.


Journal of Career Development | 2014

Can Parents Influence Children’s Mathematics Achievement and Persistence in STEM Careers?

Marsha Ing

This study explores the relationship between parental motivational practices, Children’s mathematics achievement trajectories, and persistence in science, technology, engineering, and mathematics (STEM) careers. Nationally representative longitudinal survey data were analyzed using latent growth curve analysis. Findings indicate that parents’ motivational practices influence their children’s mathematics achievement in terms of where the Children start in the 7th grade and how much mathematics achievement grows or changes through the 12th grade. Findings also indicate a positive relationship between mathematics-specific, intrinsically focused parental motivational practices and growth in mathematics achievement and persistence in STEM careers. These findings provide specific information about how different types of parental motivational practices influence long-term mathematics achievement and persistence in STEM careers.


Phi Delta Kappan | 2013

Boosting Student Interest in Science.

Pamela R. Aschbacher; Marsha Ing; Sherry M. Tsai

Adults could do much more to excite students about science as a subject and encourage their interest in science careers.


Elementary School Journal | 2015

Student Engagement with Others' Mathematical Ideas.

Megan L. Franke; Angela C. Turrou; Noreen M. Webb; Marsha Ing; Jacqueline Wong; Nami Shin; Cecilia H. Fernandez

Educators, researchers, and policy makers increasingly recognize that participation in classroom mathematics discussions, especially engaging with others’ ideas, can promote students’ mathematics understanding. How teachers can promote students’ high-level engagement with others’ ideas, and the challenges teachers face when trying to do so, have not often been studied, however. Using coding of videotaped whole-class and small-group discussions in 12 elementary school classrooms, we analyzed the level at which students engaged with each other’s mathematical ideas and the moves teachers used—both moves to invite student engagement and follow-up moves to encourage deeper engagement—to support student engagement. Teachers used a wide variety of invitation and follow-up moves to encourage student engagement and combined them in multiple ways in the moment to address the challenges students faced when trying to engage with others’ ideas. We show the limitations of teachers’ initial moves to stimulate engagement and the power of their follow-up moves to foster productive student struggle with the mathematics.


Educational Research and Evaluation | 2010

An Investigation of Early Parental Motivational Strategies on Mathematics Achievement by Ethnicity: A Latent Curve Model Approach.

Stefanie R. McDonald; Marsha Ing; George A. Marcoulides

This study examined the developmental effects of early parental intrinsic and extrinsic motivational strategies on mathematics achievement scores obtained from White students compared to underrepresented minority students. A latent growth curve model was fit to data from the Longitudinal Study of American Youth (LSAY) with mathematics achievement assessed from 7th grade through 12th grade. Intrinsic parental motivational strategies were found to positively predict initial levels of mathematics achievement scores for White students but not underrepresented minority students. Extrinsic parental motivational strategies did not predict initial levels of mathematics achievement scores for either group of students. For White students, growth rate was found to be positively influenced by intrinsic and extrinsic parental motivational strategies. However, there is no evidence of this relationship for underrepresented minority students. This research contributes to an emerging literature relating developmental change processes to mathematics achievement.


Journal of Pre-College Engineering Education Research | 2014

Gender Differences in the Consistency of Middle School Students’ Interest in Engineering and Science Careers

Marsha Ing; Pamela R. Aschbacher; Sherry M. Tsai

This longitudinal study analyzes survey responses in seventh, eighth, and ninth grade from diverse public school students (n = 482) to explore gender differences in engineering and science career preferences. Females were far more likely to express interest in a science career (31%) than an engineering career (13%), while the reverse was true for males (58% in engineering, 39% in science). After controlling for student and school demographic characteristics, females were as consistent as males in their science career interests during the three years of the study but less consistent in their engineering career interests. Knowing an engineer significantly predicted consistent career interest in engineering for males but not for females. Childhood interest in science and engineering was related to whether females and males expressed any interest in those subjects. Females and males both showed interest for careers where they can discover new things that help the environment or people’s health; females were less interested in designing and inventing, solving problems, and using technology. These findings suggest that increasing the number of diverse students who pursue engineering careers may require introducing students from early elementary to middle school to engineering as an array of careers that can improve health, happiness, and safety, and make the world a better place.


Applied Measurement in Education | 2016

Initial Considerations When Applying an Instructional Sensitivity Framework: Partitioning the Variation Between and Within Classrooms for Two Mathematics Assessments

Marsha Ing

ABSTRACT Drawing inferences about the extent to which student performance reflects instructional opportunities relies on the premise that the measure of student performance is reflective of instructional opportunities. An instructional sensitivity framework suggests that some assessments are more sensitive to detecting differences in instructional opportunities compared to other assessments. This study applies an instructional sensitivity framework to compare student performance on two different mathematics achievement measures across five states and three grade levels. Results suggest a range of variation in student performance among teachers on the same mathematics achievement measure, variation between the two different mathematics achievement measures, and variation between grade levels within the same state. Findings highlight initial considerations for educators interested in selecting and evaluating measures of student performance that are reflective of instructional opportunities.


Archive | 2015

Addressing Measurement Issues in Two Large-Scale Mathematics Classroom Observation Protocols

Jeffrey C. Shih; Marsha Ing; James E. Tarr

In large-scale studies of mathematics achievement, many important variables are at the classroom level and therefore require the collection of classroom observation data. However, in these studies, collecting observation data is expensive, and collecting numerous observations can be cost prohibitive. In addition to these practical considerations, there are methodological and conceptual considerations to measuring instruction. This chapter raises several of these issues around measuring mathematics classroom instruction by using empirical data from two prominent observational protocols.


Educational and Psychological Measurement | 2018

What About the “Instruction” in Instructional Sensitivity? Raising a Validity Issue in Research on Instructional Sensitivity:

Marsha Ing

In instructional sensitivity research, it is important to evaluate the validity argument about the extent to which student performance on the assessment can be used to infer differences in instructional experiences. This study examines whether three different measures of mathematics instruction consistently identify mathematics assessments as being sensitive to instruction. Mixed findings across fourth-grade (n = 8,298) and fifth-grade (n = 9,336) students and their teachers across three school districts raise questions as to whether different ways of measuring instruction provide similar inferences about the instructional sensitivity of assessments. This raises validity concerns about the quality of inferences based on different measures of instruction.

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Noreen M. Webb

University of California

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Pamela R. Aschbacher

California Institute of Technology

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Nami Shin

University of California

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Nida Denson

University of Western Sydney

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