Evaluación de indicadores para la enseñanza superior: desarrollo de herramientas

Wegner, Rubia Cristina; Alves Junior, Antonio José; Fernandes, Marcelo Pereira

doi:10.1590/1982-57652024v29id27881828

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Article • Avaliação (Campinas) 29 • 2024 • https://doi.org/10.1590/1982-57652024v29id27881828 linkcopiar

Evaluación de indicadores para la enseñanza superior: desarrollo de herramientas

Autoría SCIMAGO INSTITUTIONS RANKINGS

El objetivo principal de este artículo es contribuir a la última valoración de que la inteligencia artificial puede ayudar en la recogida, integración y seguimiento de datos e indicadores de los programas de grado, como el abandono y la retención. Su punto de partida es la constatación de que el fenómeno del abandono no es una mera decisión individual del estudiante, sino que tiene lugar en el contexto académico, personal y profesional del estudiante que abandona. Además, la retención es un indicador que puede servir para predecir el fenómeno del abandono. De esta forma, la metodología consiste en describir el desarrollo de una herramienta de base de datos interactiva, es decir, de análisis de datos y observación de datos e indicadores de un curso de grado de una universidad pública federal. Entre los principales resultados, cabe destacar que, aunque parezca fácil construir una herramienta de este tipo, su éxito en la lucha contra el abandono y la retención requiere una política institucional adoptada por la universidad.

Palabras clave:
análisis de datos; indicadores de abandono; graduación; Institución Federal de Enseñanza Superior (IFES)

Dimension	Elements
Student Skills and Interests	Socioeconomic family level
	Family influences
	Level of career satisfaction
	Deficient basic education
	Lack of information about the program and profession
	Low academic performance
	Level of integration and interaction in academic life
	Lack of vocational guidance
Internal Environment	Priority given to research over teaching
	Lack of adaptation process to university life
	Limited interaction: students with each other and with faculty
	Pedagogical and didactic organization
	Low attendance and student retention
	Long and outdated curricula
	Devaluation of teaching and passive acceptance of dropout
External Environment	Financial difficulties
	Professional commitments
	Family commitments
	Personal and health issues
	Job market

References	Definition	Calculation
Paredes (1994)	Uses the "concept of course performance," calculated as the ratio between graduates and available spots.	$% E = 100 % - \frac{graduates}{spots}$
Silva Filho et al. (2007)	Total dropout measures the number of students who, having entered a certain course, institution, or education system, did not obtain a diploma after a certain number of years.	$E_{n} = 1 - \frac{(M_{n} - I_{n})}{(M_{n - 1} - C_{n - 1})}$
Special Commission on Dropout Studies - ANDIFES (1996)	Students who, at the end of a complete generation period, had not graduated and were no longer enrolled in the course.	$% E = \frac{N_{i} - N_{d} - N_{r}}{N_{i}} * 100$
Serpa; Pinto (2000)	Dropouts as the variation in enrollments excluding new entrants and graduates.	$E_{x} = I_{x + 1} - (M_{x + 1} - M_{x}) - C_{x}$
Braga; Peixoto; Bogutchi (2003)	Data from cohorts still within the maximum time for completion, referred to by these authors as an "incomplete generation”.	$\begin{array}{c} % E = 100 % - % graduates - 0, 06 \\ (100 % - % graduates) \end{array}$
FORPLAD (2015)	Considers the number of new entrants enrolled in a given period and the enrolled and graduated students from the previous period, based on course duration. This is the most widely used indicator by institutions, as it is referenced in official control body documents.	$E v_{p} = (1 - \frac{M_{p} - I_{p}}{M_{p - 1} - C_{p - 1}}) * 100$
Teixeira; Quito (2021)	Flow methodology that allows for adjustments to the maximum and minimum time limits for completion of credit hours.	$\begin{aligned} {Minimum Longitudinal Dropout Rate}_{i, n, p} \\ \begin{aligned} = \frac{\sum_{i = 2010}^{n p} E v a_{T, n, p}}{i n g_{i = T}^{n}} \\ * 100 \end{aligned} \end{aligned}$ $\begin{aligned} {Minimum Longitudinal Dropout Rate}_{i, n, p} \\ \begin{aligned} = \frac{\sum_{i = 2010}^{1, 5 p} E v a_{T, n, p}}{i n g_{i = T}^{n}} \\ * 100 \end{aligned} \end{aligned}$

Institution	Program - description	Main results or features	Year of creation - implementation and involved Units
UFSM - Federal University of Santa Maria	Integra - Application	Tracks students' academic journey from enrollment to departure. Course coordinators can assess students at higher risk of not completing the course and access the course observatory.	2018 Undergraduate Dean’s Office (PROGRAD), Planning Dean’s Office (PROPLAN), and Data Processing Center (CPD)
UFG - Federal University of Goiás	SISSA - AI-based Platform	Integrates data to predict students' academic performance ('success'), provides data analysis and indicator formulation, academic support through tutor training, and student assistance, including chatbot interaction.	Funded by MEC, SETEC, and SESU. Implemented by UFG and CEIA. 11 other IFES use the platform: IFBA, IFRO, IFSP, IFB, IFSUL, UNIPAMPA, UNIFEI, UFSCar, UFPA, and UFERSA.
UFRPE - Federal Rural University of Pernambuco	Sabiá (System of Academic Business Intelligence and Analytics)	Centralizes and integrates data, enabling the extraction of various summary reports of indicators for undergraduate courses and the university, including infographics.	UFRPE - data Observatory coordination laboratory
MEC - Federal Network for Professional, Scientific, and Technological Education	Nilo Peçanha Platform - PBI Dashboard	Gathers and publishes statistical data and indicators. Unlike previous examples, it is not solely focused on dropout and retention indicators.	Entire federal network of profes- sional, scientific, and technological education
MEC, INEP, and DEED	Higher Education Census Database for Longitudinal Academic Path Indicators - School Flow Indicators	Supports discussions on higher education outcomes by presenting indicators chronologically and based on student status - retention, withdrawal, and completion.	Educational Statistics Department/INEP/MEC, using Higher Education Census data since 2009. Not a tool like the others, but provides replicable concepts and indicators.

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