# Impact of Teaching Workload on Scientific Productivity: Multidimensional Analysis in the Complexity of a Mexican Private University

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## Abstract

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## 1. Introduction

#### 1.1. Multidimensional Factors in the Complexity of the Teaching Profession

#### 1.2. Scientific Productivity of University Professors

#### 1.3. Interrelation of Teaching and Research in University Professors

#### 1.4. Studies of the Impact of University Professors’ Time

## 2. Materials and Methods

#### 2.1. Data Collection

#### 2.2. Data Analysis

^{2}was used as an indicator of the model’s quality; the closer to 1, the better. We discarded models having multicollinearity, indicated by the parameter condition number with values higher than 2000.

**X**) learned by the regression model indicate the (positive or negative) influence of X

_{i}on the scientific productivity of the professor. The lack of X

_{i}’s significance in the regression model is considered proof of the null influence of the variable in the dependent variable (scientific productivity).

## 3. Results

#### 3.1. Data Exploration

#### 3.2. Statistical Analysis

#### 3.3. Regression Analysis

#### 3.3.1. Regression Model for all Professors

- y is the scientific production of the following year (DOCS_Y1);
- ${x}_{1}$ is the number of thesis students advised by the professor (TESISTAS_RESP);
- ${x}_{2}$ is 1 when the professor holds a researcher position, and it is 0 otherwise (RES_CONTRACT);
- ${x}_{3}$ is 1 when the professor holds an administrative position, and it is 0 otherwise (ADM_POS);
- ${x}_{4}$ is the current researcher proficiency level of the professor (SNI_VAL);
- ${x}_{5}$ is the professor’s age in years (AGE);
- ${x}_{6}$ is 1 if the professor belongs to the School of Engineering and Sciences, and it is 0 otherwise (EIC);
- ${x}_{7}$ is 1 if the professor belongs to the School of Medicine, and it is 0 otherwise (EMCS);
- ${x}_{8}$ is 1 if the professor belongs to the Business School, and it is 0 otherwise (EGADE);
- ${x}_{9}$ is 1 if the professor taught only to undergraduate students in that year, and it is 0 if they taught at least one (ONLY_UNDERGRAD).

^{2}of 0.491, which means it has high explanatory power (almost 50%); other factors explain the other 50% of scientific production. This model did not present multicollinearity issues (the condition number was 248). All the variables in this model were significant at 95% (p-value < 0.05), meanwhile the intercept (${\beta}_{0}$) was removed as long as it was not significant.

#### 3.3.2. Regression Model for SNI Professors

^{2}of 0.551, a little higher than the previous model. This model did not present multicollinearity issues (the condition number was 175). All the variables in this model were significant at 95% (p-value < 0.05); meanwhile, the intercept (${\beta}_{0}$) was removed as long as it was not significant.

#### 3.3.3. Regression Model for Non-SNI Professors

^{2}of 0.322, slightly higher than the previous model. This model did not present multicollinearity issues (the condition number was 223). All the variables in this model were significant at 95% (p-value < 0.05), meanwhile the intercept (${\beta}_{0}$) was removed as long as it was not significant.

#### 3.3.4. Analysis of the Regression Models

## 4. Discussion and Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**

**Distribution of**professors by gender and researcher proficiency level (0—No-SNI, C—SNI Candidate, 1—SNI Level 1, 2—SNI Level 2, 3—SNI Level 3).

**Figure 2.**Annual academic workload (taught groups) by researcher proficiency level (SNI) between 2014 and 2018.

**Figure 3.**Scientific productivity by researcher proficiency level (SNI). Note: SNI level 0 (Blue), SNI Candidate (Green), SNI level 1 (Orange), SNI level 2 (Red), and SNI level 3 (Aquamarine).

**Figure 4.**Scientific productivity by school (departments). Note: Engineering and Sciences (EIC) (Green), Medicine (EMCS) (Yellow), Business (EGADE) (Red), Humanities and Education (EHE) (Aquamarine), Architecture, Art and Design (EAAD) (Blue), Social Sciences and Government (ECSG) (Orange).

**Figure 5.**The average number of coauthors for professors in every school (department). Note: Engineering and Sciences (EIC) (Green), Medicine (EMCS) (Yellow), Business (EGADE) (Red), Humanities and Education (EHE) (Aquamarine), Architecture, Art and Design (EAAD) (Blue), Social Sciences and Government (ECSG) (Orange).

**Figure 7.**Correlogram between independent variables and the dependent variable. Note: Variables listed by row/column are: GENDER, ADM_POS, RES_CONTRACT, AGE, SNI_VAL, TESISTAS_RESP, GROUPS_GRAD, GROUPS_UNDERGRAD, GROUPS, ONLY_UNDERGRAD, and DOCS_Y1.

**Figure 8.**The correlation between groups taught at undergraduate and graduate levels on scientific productivity is broken down by researcher proficiency level.

Variable | Definition | Values |
---|---|---|

GENDER | Indicates whether the professor’s gender is male (1) or female (0) | 0/1 |

ADM_POS | Indicates if the professor holds an administrative position at the institution during the year (1) or not (0). | 0/1 |

RES_CONTRACT | Indicates if the professor had a research-only (or primarily) contract during the year (1) or not (0). | 0/1 |

AGE | The age of the professor at the beginning of the calendar year. | 21–76 |

SCHOOL | The school where the professor taught more classes that year. | EIC, EMCS, EGADE, ECSG, EHE, EAAD, OTHER |

SNI_VAL | The researcher proficiency level of the professor in the year. Not being in the researcher systems is encoded as 0, being Candidate as 1, Level 1 as 2, Level 2 as 3, and Level 3 (maximum) as 4. | 0–4 |

TESISTAS_RESP | The number of theses (undergraduate and graduate) students advised by the professor during the year. Co-advised students are multiplied by a responsibility percentage. | 0–130 |

GROUPS_GRAD * | The number of groups taught at the graduate level during the year. | 0–15.84 |

GROUPS_UNDERGRAD * | The number of groups taught at the undergraduate level during the year. | 0–64.26 |

GROUPS * | The total number of groups taught during the year. | 0–65.61 |

ONLY_UNDERGRAD | Flag indicating whether the professor taught only to undergraduate students throughout the entire year (1) or attended graduate students as well (0). | 0/1 |

DOCS_Y1 | The number of Scopus-indexed papers published by the professor in the calendar year after the analyzed workload. | 0–61 |

X | Variable | All Professors | SNI Professors | Non-SNI Professors |
---|---|---|---|---|

1 | TESISTAS_RESP | 0.061 | 0.043 | 0.059 |

2 | RES_CONTRACT | 0.922 | 0.771 | 1.905 |

3 | ADM_POS | 0.731 | 1.203 | 0.417 |

4 | SNI_VAL | 1.000 | 1.829 | - |

5 | AGE | −0.015 | −0.058 | 0.003 |

6 | EIC | 1.750 | 2.346 | 0.569 |

7 | EMCS | 1.329 | 1.189 | 0.544 |

8 | EGADE | 0.520 | - | - |

9 | UNDERGRAD_ONLY | −0.258 | −0.544 | - |

Adjusted R^{2} | 0.489 | 0.551 | 0.322 |

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**MDPI and ACS Style**

Ramirez-Montoya, M.S.; Ceballos, H.G.; Martínez-Pérez, S.; Romero-Rodríguez, L.M.
Impact of Teaching Workload on Scientific Productivity: Multidimensional Analysis in the Complexity of a Mexican Private University. *Publications* **2023**, *11*, 27.
https://doi.org/10.3390/publications11020027

**AMA Style**

Ramirez-Montoya MS, Ceballos HG, Martínez-Pérez S, Romero-Rodríguez LM.
Impact of Teaching Workload on Scientific Productivity: Multidimensional Analysis in the Complexity of a Mexican Private University. *Publications*. 2023; 11(2):27.
https://doi.org/10.3390/publications11020027

**Chicago/Turabian Style**

Ramirez-Montoya, Maria Soledad, Hector G. Ceballos, Sandra Martínez-Pérez, and Luis M. Romero-Rodríguez.
2023. "Impact of Teaching Workload on Scientific Productivity: Multidimensional Analysis in the Complexity of a Mexican Private University" *Publications* 11, no. 2: 27.
https://doi.org/10.3390/publications11020027