# The Impact of Teacher Quality on Student Motivation, Achievement, and Persistence in Science and Mathematics

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## Abstract

**:**

## 1. Introduction

## 2. Theoretical Frameworks

#### 2.1. Social Cognitive Career Theory (SCCT)

#### 2.2. Teacher Quality Framework (TQF)

## 3. A Brief Review of Current Literature

#### 3.1. Student Factors and Student STEM Outcomes

#### 3.2. Teacher Factors and Student STEM Outcomes

#### 3.3. Limitations of the Extant Literature

## 4. Research Questions

- To what extent do math and science teacher quality factors relate to high school students’ motivational beliefs (i.e., self-efficacy, utility, interest) for STEM?
- To what extent do math and science teacher quality factors relate to high school students’ STEM achievement and persistence (i.e., advanced course-taking, mathematics test performance)?
- To what extent do math and science teacher quality factors relate to high school students’ career plans in STEM while controlling for students’ motivational beliefs and achievement in STEM?

## 5. Conceptual Framework for the Study

## 6. Methodology

#### 6.1. Data Set

#### 6.2. Measures

#### 6.2.1. Student Variables

^{th}grade in 2011). The other achievement-related student outcomes were advanced course-taking in science and mathematics. Advanced course-taking data is retrieved from U13 and HS cycles (collected in 2013 and 2014, respectively). Advanced course taking in science and math (two variables) were created using a binary code of 1 if a student took any IB, AP, or dual credit course(s) in the subject matter and 0 if none were taken. The last student variable is the STEM career variable: STEM career expectation at the age of 30 years old. STEM career expectation variable was last collected in the F2 cycle when regular track students were in their second year in college.

#### 6.2.2. Teacher Variables

#### 6.2.3. Dimension Reduction for Teaching Practices

#### 6.3. Analytic Techniques

## 7. Results

**Research**

**Question**

**1:**

_{1}through β

_{6}); and teacher-level factors are colored in green (terms whose coefficients are from β

_{8}through β

_{12}). Teacher degree implies whether a science teacher has a degree in science or a math teacher has a degree in mathematics. Teacher practice 1 and 2 correspond to understand and connection for math teachers and inquiry and connection for science teachers, respectively.

^{2}= 0.04), their ninth-grade math teachers’ self-efficacy (β = 0.03, p < 0.01) and emphasis on conceptual understanding (math understand; β = 0.04, p < 0.01) emerged as significant predictors. For the regression model with students’ 10th grade utility value for math as the outcome, F(11, 8592) = 11.02, R

^{2}= 0.01, the degree to which their ninth-grade mathematics teachers emphasized increasing students’ interest in math and connecting math real-life applications (connection; β = 0.03, p < 0.05), was a significant predictor. Finally, for the model with students’ 10th grade math interest as the outcome, F(11, 7352) = 19.87, R

^{2}= 0.03, the teacher understand variable again emerged as a significant predictor (β = 0.03, p < 0.05). In sum, students who were taught by teachers that focused on connecting mathematics ideas and put more emphasis on developing problem-solving skills, mathematical reasoning, and conceptual understanding of mathematics, had higher levels of mathematics motivational beliefs at the end of 10th grade than those taught by teachers who did not focus on these areas.

^{2}= 0.02, science inquiry teaching practice was a significant predictor of students’ science self-efficacy (β = 0.03, p < 0.05). For the model where students’ science 10th grade utility value regressed on student demographics and science teacher characteristics F(11, 7906) = 17.60, R

^{2}= 0.02, the connect variable emerged as a significant predictor of students’ science utility value (β = 0.03, p < 0.05). Finally, for the model where students’ science interest regressed on student demographics and science teacher characteristics F(11, 6232) = 5.91, R

^{2}= 0.01, the only predictor that emerged was whether teachers had obtained a science degree (β = 0.03, p < 0.05).

**Research**

**Question**

**2:**

^{th}grade after controlling for student demographics (see Table 4). Again, the teacher characteristics entered in the models included their qualifications and instructional practices. This multiple linear regression model was statistically significant F(11, 8845) = 276.08, R

^{2}= 0.26. Teacher qualifications such as whether math teachers had a math teacher certification (β = 0.03, p < 0.01) and their years of experience teaching math (β = 0.06, p < 0.001) were all statistically significant predictors of students’ math achievement. Moreover, the degree to which math teachers focused a deeper conceptual understanding of mathematics in their instruction emerged as the strongest significant predictor of math achievement (β = 0.14, p < 0.001). The connection variable, however, had a significant but negative association with students’ math achievement (β = −0.03, p < 0.01). This negative association was unexpected. Perhaps, there was a disconnect between the HSLS’ mathematics test and their relation to real-life contexts. Unfortunately, NCES keeps the items confidential for use in future; therefore, analysis of the test items to see if our explanation holds are not possible.

**Research**

**Question**

**3:**

## 8. Discussion

^{th}grade compared to students who received instruction from teachers who did not place emphasis in these areas in ninth grade. This finding provides further support for student-centered teaching approaches (informed by constructivist philosophy) that are foundational to reform-based teaching within the mathematics education community [82].

#### 8.1. Limitations

^{th}grade). Thus, any math and science teacher-changes in 10th and 11th grades cannot be accounted for. For example, after ninth grade, some students may have been taught by different math and science teachers who affected students’ STEM outcomes in different ways than the original ninth grade teacher. However, it is still safe to assume that every student has been taught for at least one year by their math and science teachers who participated in the study. Finally, although no causal inferences can be made from this study because of the non-random study design of HSLS:09 [93,94,95], some causal hypotheses can still be made based on the results and prior research after having reduced the selection bias to the extent possible by inclusion of analytic weights [69,94] (see [96]). Our hope, however, is this study’s findings serve as sound hypotheses related to teacher effects that can be utilized and tested by more robust research designs such as random experiments (which are not possible in NCES’s large-scale studies) or propensity score matching (PSM) [97] to approximate a random experiment design using HSLS:09 with a treatment condition based on teacher factors. This provides opportunities to expand on our research and to develop causal inferences as a continuation of this study.

#### 8.2. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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Items | Factor Loading | |
---|---|---|

Math Understand | Math Connect | |

M1concepts | 0.74 | −0.01 |

M1problem | 0.56 | 0.13 |

M1reason | 0.57 | 0.10 |

M1ideas | 0.69 | 0.21 |

M1prepare | 0.63 | 0.08 |

M1logic | 0.58 | 0.04 |

M1interest | −0.07 | 0.71 |

M1history | 0.03 | 0.82 |

M1explain | 0.25 | 0.67 |

M1business | 0.09 | 0.69 |

M1algorithm | 0.14 | -0.01 |

M1compskills | −0.02 | 0.07 |

M1compute | 0.03 | 0.13 |

M1test | −0.07 | 0.12 |

Items | Factor Loading | |
---|---|---|

Science Inquiry | Science Connect | |

N1skills | 0.59 | 0.03 |

N1prepare | 0.58 | −0.06 |

N1evidence | 0.63 | 0.11 |

N1ideas | 0.69 | 0.07 |

N1interest | 0.12 | 0.71 |

N1business | −0.08 | 0.83 |

N1society | 0.09 | 0.58 |

N1history | 0.03 | 0.64 |

N1concepts | 0.13 | 0.09 |

N1terms | 0.10 | −0.06 |

N1test | 0.08 | 0.01 |

**Table 3.**Summary of Multiple Linear Regression Analyses Predicting Motivational Beliefs about Math and Science.

Mathematics ^{b} | Science ^{c} | |||||
---|---|---|---|---|---|---|

Variable | Self-Efficacy | Utility | Interest | Self-Efficacy | Utility | Interest |

β | β | β | β | β | β | |

Male | 0.10 *** | 0.04 *** | 0.01 | 0.09 *** | -0.02 * | 0.02 |

Black | 0.07 *** | 0.07 *** | 0.04 * | 0.03 * | 0.04 ** | 0.01 |

Asian | 0.06 *** | 0.08 *** | 0.11 *** | −0.00 | 0.11 *** | 0.05 *** |

Hispanic | 0.05 *** | 0.05 *** | 0.08 *** | −0.03 * | −0.01 | −0.03 |

SES | 0.14 *** | 0.03 * | 0.10 *** | 0.10 *** | 0.07 *** | 0.05 *** |

Teacher self-efficacy ^{a} | 0.03 ** | −0.01 | 0.02 | 0.01 | 0.00 | −0.00 |

Teacher certification ^{a} | 0.01 | −0.01 | 0.01 | −0.01 | −0.02 * | −0.01 |

Teacher degree ^{a} | 0.00 | −0.01 | −0.01 | 0.01 | 0.02 | 0.03 * |

Teacher experience ^{a} | 0.01 | 0.01 | 0.02 | 0.00 | 0.01 | 0.01 |

Understand (math) | 0.04 ** | 0.00 | 0.03 * | - | - | - |

Connection (math) | 0.01 | 0.03 * | 0.02 | - | - | - |

Inquiry (science) | - | - | - | 0.03 * | 0.02 | 0.01 |

Connection (science) | - | - | - | −0.01 | 0.03 * | 0.01 |

R-square | 0.04 *** | 0.01 *** | 0.03 *** | 0.02 | 0.02 *** | 0.01 |

n | 8534 | 8604 | 7363 | 7809 | 7918 | 6244 |

^{a}Corresponds to math teacher for math outcomes, science teacher for science outcomes.

^{b}A brief paper with preliminary results of a similar analysis was presented elsewhere before [87]

^{c}A brief paper with preliminary results of a similar analysis was presented elsewhere before [88].

Variable | Achievement ^{a} |
---|---|

β | |

Male | 0.00 |

Black | −0.09 *** |

Asian | 0.14 *** |

Hispanic | −0.02 * |

SES | 0.39 *** |

Math teacher self-efficacy | 0.01 |

Math teacher certification | 0.03 ** |

Math teacher degree in math | 0.02 |

Math teacher experience | 0.06 *** |

Understand (math) | 0.14 *** |

Connection (math) | −0.03 ** |

R-square | 0.25 *** |

^{a}A brief paper with preliminary results of a similar analysis was presented elsewhere before [87].

Variable | Advanced Math Course-Taking ^{b} | Advanced Science Course-Taking |
---|---|---|

Exp(β) | Exp(β) | |

Male | 1.28 *** | 1.14 * |

Black | 1.17 | 1.18 |

Asian | 1.58 *** | 2.22 *** |

Hispanic | 0.96 | 0.95 |

SES | 0.97 | 1.25 *** |

Teacher self-efficacy ^{a} | 0.99 | 1.02 |

Teacher certification ^{a} | 1.11 | 0.86 |

Teacher degree ^{a} | 1.00 | 1.15 * |

Teacher experience ^{a} | 1.02 *** | 1.01 * |

Understand (math) | 1.51 ** | - |

Connection (math) | 1.03 | - |

Inquiry (science) | - | 1.08 |

Connection (science) | - | 1.06 |

Pseudo R-square | 0.02 *** | 0.03 *** |

^{a}Corresponds to math teacher for math outcomes, science teacher for science outcomes.

^{b}A brief paper with preliminary results of a similar analysis was presented elsewhere before [87].

Variable | STEM Career Plans Math Predictors | STEM Career Plans Science Predictors |
---|---|---|

Exp (β) | Exp (β) | |

Male | 0.67 *** | 0.60 *** |

Black | 0.97 | 0.96 |

Asian | 1.54 *** | 1.55 *** |

Hispanic | 1.20 | 1.07 |

SES | 1.09 | 1.16 ** |

Math self-efficacy | 1.13 * | - |

Math interest | 1.21 *** | - |

Math achievement | 1.02 *** | - |

Math advanced course-taking | 1.08 | - |

Science self-efficacy | - | 1.16 ** |

Science interest | - | 1.23 *** |

Science advanced course-taking | - | 1.59 *** |

Teacher self-efficacy ^{a} | 0.93 | 0.93 |

Teacher certification ^{a} | 1.00 | 0.88 |

Teacher degree ^{a} | 1.05 | 0.88 |

Teacher experience ^{a} | 1.01 | 1.01 |

Understand (math) | 0.78 | - |

Connection (math) | 1.13 | - |

Inquiry (science) | - | 1.10 |

Connection (science) | - | 1.13 |

Pseudo R-square | 0.05 *** | 0.05 *** |

^{a}Corresponds to math teacher for math predictors, science teacher for science predictors.

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**MDPI and ACS Style**

Ekmekci, A.; Serrano, D.M.
The Impact of Teacher Quality on Student Motivation, Achievement, and Persistence in Science and Mathematics. *Educ. Sci.* **2022**, *12*, 649.
https://doi.org/10.3390/educsci12100649

**AMA Style**

Ekmekci A, Serrano DM.
The Impact of Teacher Quality on Student Motivation, Achievement, and Persistence in Science and Mathematics. *Education Sciences*. 2022; 12(10):649.
https://doi.org/10.3390/educsci12100649

**Chicago/Turabian Style**

Ekmekci, Adem, and Danya Marie Serrano.
2022. "The Impact of Teacher Quality on Student Motivation, Achievement, and Persistence in Science and Mathematics" *Education Sciences* 12, no. 10: 649.
https://doi.org/10.3390/educsci12100649