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The present work analyzes the academic performance of students from at-risk groups from the perspective of Social Network Analysis (SNA), studying the academic and interaction information of 45 students belonging to at-risk groups who attended a pilot socio-academic course during one academic term. This information was used to create a sociogram, which served as the basis for determining the centrality metrics of the SNA. The relationships between these metrics and the academic variables were then studied by means of correlation analysis and linear regression with LASSO standardization. As a preview of the results, it was determined that the academic performance of the students in the pilot course was influenced, on the one hand, by their academic knowledge prior to being admitted to the university, represented by the score on the Mathematics and Geometry section of the diagnostic test, and on the other hand, by the dynamics of the social network in which they interacted in the classroom, represented by the eigenvector centrality. These results have significant potential for explaining the academic performance according to SNA metrics, and they provide evidence to support the implementation of practices that promote a healthy social environment in an academic context.
Tarquino Sanchez; David Naranjo; Jack Vidal; Diego Salazar; Cristina Pérez; Marianela Jaramillo. Analysis of academic performance based on sociograms: A case study with students from at risk groups. Journal of Technology and Science Education 2021, 11, 167 -179.
AMA StyleTarquino Sanchez, David Naranjo, Jack Vidal, Diego Salazar, Cristina Pérez, Marianela Jaramillo. Analysis of academic performance based on sociograms: A case study with students from at risk groups. Journal of Technology and Science Education. 2021; 11 (1):167-179.
Chicago/Turabian StyleTarquino Sanchez; David Naranjo; Jack Vidal; Diego Salazar; Cristina Pérez; Marianela Jaramillo. 2021. "Analysis of academic performance based on sociograms: A case study with students from at risk groups." Journal of Technology and Science Education 11, no. 1: 167-179.
Universities are committed to offering quality education; however, a high rate of academic failure is often observed in the first year of studies. Considering the impact that motivation and emotional aspects can have on students’ commitment to study and therefore on their academic performance, achievement, and well-being, this study aims to identify the factors associated with academic success or failure in 1071 students entering the National Polytechnic School (Quito, Ecuador). The data were compiled from the existing computer records of the university with the permission of the responsible administrative staff. A predictive model has been used and a binary logistic regression analysis was carried out through the step-forward regression procedure based on the Wald statistic to analyze the predictive capacity of the variables related to emotional intelligence, motivational and self- regulated socio-cognitive skills, goal orientation, and prior academic achievement (measured by university entrance marks and through a knowledge test carried out at the beginning of the university academic year). To determine the cut-off point for the best discriminatory power of each of the variables, a Receiver Operating Characteristics (ROC) curve analysis has been used. The results indicate that the variables that are significant in the prediction of academic success or failure are the two academic performance measures: the emotional attention variable, and the performance-approach goals and the motivational self-efficacy variable. Additionally, the highest predictive power is displayed by the prior academic performance measure obtained through the knowledge test conducted at the beginning of the university course.
Raquel Gilar-Corbi; Teresa Pozo-Rico; Juan-Luis Castejón; Tarquino Sánchez; Ivan Sandoval-Palis; Jack Vidal. Academic Achievement and Failure in University Studies: Motivational and Emotional Factors. Sustainability 2020, 12, 9798 .
AMA StyleRaquel Gilar-Corbi, Teresa Pozo-Rico, Juan-Luis Castejón, Tarquino Sánchez, Ivan Sandoval-Palis, Jack Vidal. Academic Achievement and Failure in University Studies: Motivational and Emotional Factors. Sustainability. 2020; 12 (23):9798.
Chicago/Turabian StyleRaquel Gilar-Corbi; Teresa Pozo-Rico; Juan-Luis Castejón; Tarquino Sánchez; Ivan Sandoval-Palis; Jack Vidal. 2020. "Academic Achievement and Failure in University Studies: Motivational and Emotional Factors." Sustainability 12, no. 23: 9798.
The school-dropout problem is a serious issue that affects both a country’s education system and its economy, given the substantial investment in education made by national governments. One strategy for counteracting the problem at an early stage is to identify students at risk of dropping out. The present study introduces a model to predict student dropout rates in the Escuela Politécnica Nacional leveling course. Data related to 2097 higher education students were analyzed; a logistic regression model and an artificial neural network model were trained using four variables, which incorporated student academic and socio-economic information. After comparing the two models, the neural network, with an experimentally defined architecture of 4–7–1 architecture and a logistic activation function, was selected as the model that should be applied to early predict dropout in the leveling course. The study findings show that students with the highest risk of dropping out are those in vulnerable situations, with low application grades, from the Costa regime, who are enrolled in the leveling course for technical degrees. This model can be used by the university authorities to identify possible dropout cases, as well as to establish policies to reduce university dropout and failure rates.
Iván Sandoval-Palis; David Naranjo; Jack Vidal; Raquel Gilar-Corbi. Early Dropout Prediction Model: A Case Study of University Leveling Course Students. Sustainability 2020, 12, 9314 .
AMA StyleIván Sandoval-Palis, David Naranjo, Jack Vidal, Raquel Gilar-Corbi. Early Dropout Prediction Model: A Case Study of University Leveling Course Students. Sustainability. 2020; 12 (22):9314.
Chicago/Turabian StyleIván Sandoval-Palis; David Naranjo; Jack Vidal; Raquel Gilar-Corbi. 2020. "Early Dropout Prediction Model: A Case Study of University Leveling Course Students." Sustainability 12, no. 22: 9314.
This paper addresses the relationship between student evaluation of teaching (SET) and academic achievement in higher education. Meta-analytic studies on teaching effectiveness show a wide range of results, ranging from small to medium correlations between SET and student achievement, based on diverse methodological approaches, sample size studies, and contexts. This work aimed to relate SET, prior academic achievement, and academic achievement in a large sample of higher education students and teachers, using different methodological procedures, which consider as distinct units of analysis the group class and the individuals, the variability between students within classes, and the variability between group-class means, simultaneously. The data analysis included the calculation of group-class means and its relationship with the group-class mean academic achievement, through correlation and hierarchical regression techniques; additionally, a multilevel path analysis was applied to the relationship between prior academic achievement, SET, and their academic achievement, considering the variability among group classes. A multisection analysis was also carried out in those course disciplines in which there was more than one class group (section). The results of individual and group-class analysis revealed that SET was moderately low but related to academic achievement in a significant way once the effect of previous academic achievement was controlled. In addition, multilevel path analysis revealed the effect of SET on achievement, both within and between group-class levels. The results of the analysis carried out in the course disciplines with different sections, according to a multisection design, yielded similar results to the individual and aggregated data analyses. Taken together, the results revealed that SET was low related to academic achievement, once the effect of previous academic achievement was controlled. From these results, it follows that the use of SET as a measure of teachers’ effectiveness for making administrative decisions remains controversial.
Tarquino Sánchez; Raquel Gilar-Corbi; Juan Luis Castejón; Jack Vidal; Jaime León. Students’ Evaluation of Teaching and Their Academic Achievement in a Higher Education Institution of Ecuador. Frontiers in Psychology 2020, 11, 233 .
AMA StyleTarquino Sánchez, Raquel Gilar-Corbi, Juan Luis Castejón, Jack Vidal, Jaime León. Students’ Evaluation of Teaching and Their Academic Achievement in a Higher Education Institution of Ecuador. Frontiers in Psychology. 2020; 11 ():233.
Chicago/Turabian StyleTarquino Sánchez; Raquel Gilar-Corbi; Juan Luis Castejón; Jack Vidal; Jaime León. 2020. "Students’ Evaluation of Teaching and Their Academic Achievement in a Higher Education Institution of Ecuador." Frontiers in Psychology 11, no. : 233.