How the Entry Profiles and Early Study Habits Are Related to First-Year Academic Performance in Engineering Programs

Abstract

This paper explores how the entry profiles of engineering students are related to their academic performance during the first year of university in a sample of 255 first-year engineering students (77 females and 178 males) at a university in Northeast Mexico. The predictors used were the high school grade point average (HSGPA), Scholastic Aptitude Test (SAT) results, the first admission test, and a Spanish adaptation of the Survey of Study Habits and Attitudes Test (SSHA) from Brown and Holtzman. The SSHA adaptation was tested for internal consistency reliabilities via Cronbach’s alpha globally (0.92) and for the following categories: delay avoidance (DA: 0.79), work methods (WM: 0.81), teacher approval (TA: 0.89), and educational acceptance (EA: 0.74). The results were compared with those of other studies to validate their consistency. To assess the different entry profiles between high- and low-achieving students, we performed a Kruskal–Wallis test and found significant differences (p < 0.001) between both profiles for all variables. We then measured the relationships between the variables and academic success by constructing a correlation table, where HSGPA, SAT, and DA showed the highest correlations: 0.61, 0.40, and 0.36, respectively. With these outcomes, a predictive model via a logistic regression ($R^2=0.52$) was built to forecast first year academic performance in the specific context.

1. Introduction

In response to Sustainable Development Goal (SDG) 4: Quality Education of UNESCO [1], this work aims to contribute to SDG 4.3 (equal access to higher education) by increasing and expanding retention rates in higher education. Worldwide, according to the Organization for Economic Cooperation and Development (OECD) [2], the dropout rate is about 24%. In Latin America and the Caribbean, the gross enrolment rate in higher education increased to 54%, with a 22% dropout rate [3,4]. In Mexico, only 21.6% of the population has higher education [5], with the national dropout rate being 8.2% [6]. Nuevo Leon is one of the states with the lowest dropout rate at only 0.5%, which is well below the national percentage [7]. This has decreased in recent years due to the constant strategies applied in universities for this purpose. For this reason, an easy-to-apply tool is proposed so that administrators and teachers can effectively distinguish low-achieving and high-achieving students. To create strategies that allow these students to successfully carry out their university careers, it is important to pay attention early in a student’s first year to implement the necessary strategies to avoid negative experiences in their university life [8] and to eliminate school dropouts.

The first year of university constitutes a critical period that significantly influences a successful or irregular career and school dropout [9]. Academic success during this time is considered a multidimensional concept that includes successful results in various areas; it is crucial because the students’ experiences in it are the foundation of their future psychological wellbeing and academic paths [10].

Tinto [11] found that approximately 75% of the students who left their university studies did so during the first two years of the degree. This finding helps us determine that students in their first year of university have the highest probability of dropping out, and this period is considered a critical period for achieving academic success and ensuring retention. The author also mentioned that the first year is considered an adaptation phase.

The grade point average (GPA) obtained during the first semester in university is the main determinant of early dropouts [12]. In addition, some authors [13] emphasized that achieving high grades in science courses, such as physics, chemistry, and calculus, is directly related to retention predictions in engineering programs.

It has also been observed that academic skills, confidence, time management [14], independent learning [15], and emotional factors [16] are generally related to the the student’s ability to adapt to university [17,18].

For this reason, assessing the factors involved in academic success during the first year is essential when acquiring data from students. These can serve as a great support for the development of actions aimed at support and timely intervention for those students who require it, such that successful retention is ensured during the first year, leading to academic success and, therefore, to an increase in the graduation rates of students in the field of engineering.

Aim and Structure of the Study

The objective of this research was to discover how the entry profiles of engineering students are related to their academic performance during the first year of university, which can lead to the design of appropriate care and early intervention strategies and an increase in retention, thus ensuring higher graduation rates.

For this task, we chose the Survey of Study Habits and Attitudes (SSHA) developed by Brown and Holtzman [19], which was applied at the beginning of the semester. This allowed us to measure categories such as: (a) delay avoidance (DA), which is focused on measuring the degree of students’ procrastination, that is, their ability to manage time and perform tasks on time;

(b) work methods (WM) for levels of effective study skills;

(c) teacher approval (TA) for a student’s opinions about a teacher’s behavior and methods; and (d) educational acceptance (EA) for measuring a student’s educational objectives.

We also analyzed the variables of study habits (SH) and study attitudes (SA), which are defined as follows:

(1)$\mathrm{SH}=\mathrm{DA}+\mathrm{WM},$

(2)$\mathrm{SA}=\mathrm{TA}+\mathrm{EA}.$

Additionally, we included information on admission metrics:

• -. SAT as an entry requirement; students take a Spanish version of the SAT that was developed by the College Board [20] to determine their mathematical and verbal abilities;

• -. High School Grade Point Average (HSGPA) to have a measure of the student’s previous academic performance.

We measured academic performance in the first year using the grades in a differential calculus course as an output variable. Based on these grades, we categorized the students as high achievers (HAs) and low achievers (LAs). More details can be found in Section 2.4. Afterwards we analyzed how the other variables are related to this outcome and could possibly predict it.

To resolve if there were significant differences among students with different performances in the first year, we studied the students’ profiles in both categories (HAs and LAs) estimated with the variables of the SSHA, HSGPA, and their results on the SAT test, as shown graphically in Figure 1. In Section 2, we present the details of the materials used, the characteristics of the sample, and our statistical analyses. In Section 3, we show the results obtained from our research, which we discuss in Section 4, and we end with our conclusions in Section 5.

2. Materials and Methods

This study aimed to determine how the entry profiles of engineering students are related to their academic performance during the first year of university so that early attention strategies can be designed and retention can be increased and, particularly, in order to provide a simple prediction model that academic administrators can implement. Therefore, the research questions (RQs) were:

• RQ1.. How different are the entry profiles and early study habits of HAs and LAs?

• RQ2.. How is the first-year academic performance related to the entry profile and early study habits?

• RQ3.. How well can admission metrics and early study habits measurements predict student performance in the first year of engineering programs?

2.1. Participants and Application Procedure

This study involved the participation of 255 first-year engineering students (77 females and 178 males) from a private university in Northeast Mexico in the fall of 2019. For the sample, we selected the whole cohort of students enrolled in the differential calculus course and, later, in the integral calculus course.

2.2. Questionnaires

2.2.1. The Survey of Study Habits and Attitudes Test (SSHA)

Brown and Holtzman developed the SSHA and designed it for use as a screening and diagnostic tool, a teaching aid, and a research tool [19]. It is one of the most widely used inventories, and it is focused on distinguishing between levels of study habits, skills, and attitudes. The SSHA was originally designed to provide a single score that measures effectiveness in academic achievement. Since it measures achievement through the habits and attitudes of students, its scale, with 100 items, is also considered an accurate predictor [21] of a student’s academic outcomes.

The original version of the test was developed in English many decades ago; thus, we made a translation and a revised version in Spanish to adapt it to the modern Mexican context. We have included it in Appendix A. One semester prior to when this study was performed, we conducted a pilot test to determine its validity through Cronbach’s Alpha with a group of 62 students enrolled in an engineering program at the same university. As a result, we found the values of Cronbach’s Alpha—presented in Table 1—which confirmed the validity of the test to be used in this work. According to the criteria of George and Mallery [22] for Cronbach’s Alpha, we can see in Table 1 that the results obtained in the pilot test ranged from acceptable to excellent in individual constructs, while Cronbach’s Alpha for the general test resulted in an excellent coefficient. For this reason, it can be concluded that the update of the SSHA survey for the research carried out is reliable and valid.

2.2.2. The Scholastic Aptitude Test (SAT)

The Scholastic Aptitude Test was developed by the College Board [20], and it measures what a student is capable of achieving with the knowledge acquired in their years of study [23,24,25,26,27,28] and assesses the academic potential of the student in the continuation of university studies.

SAT scores [29] are reported on the College Board scale. The total score is a number between 400 and 1600. Any score above 1050 is above average and indicates that the student will be accepted by many colleges [30]. There are two steps for designating scores:

• One determines the adjusted scores on the verbal and math sections by counting the number of exercises answered correctly and subtracting the fraction of exercises answered incorrectly;

• One then translates the adjusted scores to the corresponding ones on the College Board scale.

Currently, some private universities in Mexico apply the SAT to determine the admission profiles of their students.

2.3. Percent Grade and Grade Point Average Scores

In general, for students, academic achievement is an essential factor in their university careers. This motivates most institutions to request a given level of academic performance and a threshold score in their grades to allow students to continue their studies in their first year and the remaining years.

Commonly, universities focus on measuring success with numerical grades and semester-by-semester course accreditation. Table 2 shows the equivalence of the grade scales that are mainly used by universities according to their context. For example, Mexico primarily uses a percent scale.

The high-school GPA (HSGPA) is the final average obtained in high school upon graduation and is measured on the same scale as the university average.

2.4. Output Variable of Academic Achievement (ACH)—LAs and HAs Classification

Budny et al. [31] analyzed how student retention decreases with lower first-year science and math course grades. Table 3 shows the relationship between the retention rate of students and the grades obtained by students in their courses, such as calculus, chemistry, and physics.

Students who obtain an average above 2.5 on the 4.0 scale have improved academic performance rates in engineering, exceeding a 50% retention rate. We could see that a high average was obtained by students in the first year.

Private universities use the percent scale, for which an average higher than 2.5 would be equivalent to a grade higher than 80. Based on this, in order to measure academic performance, a categorical variable, ACH, was defined by using the final grade in the differential calculus course, and the threshold grade of 80 was used as the separation between LAs and HAs:

(3)$\mathrm{ACH}=\left\{\begin{array}{ccc}\mathrm{HAs}\hfill & if& \mathrm{GPA}\ge 80.\\ \\ \mathrm{LAs}\hfill & if& \mathrm{GPA}<80.\end{array}\right.$

2.5. Statistical Analysis and Data Processing

For data analysis, we used the statistical software R [32]. For the SSHA adaptation, internal consistency reliabilities were tested by using Cronbach’s Alpha globally and for each of the categories. The correlations of the HSGPA, SAT, and SSHA variables with academic performance were measured. With these results, we propose a predictive model through logistic regression that can be used to predict academic performance in the first year. We also verified the AIC, Nagelkerke $R^2$, accuracy, sensitivity, and specificity.

3. Results

3.1. RQ1: Kruskal–Wallis Analysis

To address RQ1, we performed a Kruskal–Wallis test to resolve the discriminatory capability of the variables and, thus, be able to distinguish between high-achievers (HAs) and low-achievers (LAs), with a sample of 136 HAs and 116 LAs.

As shown by the Kruskal–Wallis test (Table 4), all the variables were significant in separating the LAs and HAs. The variables that showed a greater size effect were HSGPA (${\eta }_H$ = 0.390, large) and SAT (${\eta }_H$ = 0.144, large). Those that had a moderate size effect were DA (${\eta }_H$ = 0.128, moderate), WM (${\eta }_H$ = 0.106, moderate), and EA (${\eta }_H$ = 0.0836, moderate), and TA showed a small size effect (${\eta }_H$ = 0.0580, small). Therefore, we selected HSGPA, SAT, and DA for our main model.

Analyzing the means (Table 5) of the profiles of the HAs and LAs in terms of the variables of the SSHA test, DA was the variable with the lowest mean values, with 23.17 (HA) and 16.65 (LA), followed by TA, where HAs had a higher mean score (25.29) compared to that of LAs (20.49) and EA, with a mean of 26.07 for HAs and 20.82 for LAs. WM was the variable with the highest mean values: 27.10 for HAs and 20.96 for LAs. The SH and SA composite variables showed means of 50.26 and 51.36 for HAs and 37.61 and 41.30 for LAs, respectively.

The results indicate a significant difference between HAs and LAs for all tested variables. Based on this, we claim that the SSHA, HSGPA, and the updated and revised version of the SAT are pertinent as predictive instruments for students’ academic performance. These results allowed us to partially answer RQ1, with evidence that HAs have significantly better attitudes, study habits, and SAT scores than those of LAs.

3.2. RQ2: Correlation Analysis

To answer RQ3, we performed a correlation test. Table 6 shows the correlations between all the variables considered in this analysis. For this purpose, HAs = 1 and LAs = 0. Correlations were classified according to [33]. Scores between 0.3 and 0.6 in the social sciences correspond to a moderate to strong correlation. The variables that were most correlated with academic performance were HSGPA, SAT, DA, and WM (0.61, 0.40, 0.36, and 0.34, respectively).

These results allowed us to answer RQ3, with evidence that the variable with the greatest correlation was HSGPA.

3.3. RQ3: Model Analysis

Based on the results obtained above, to determine the best model for predicting academic performance assembled with the variables studied in this work, we used Akaike’s information criterion (AIC) [34]. The model that included the most variables was Model 2, which included the independent variables HSGPA, SAT, and SH = DA + WM. In each of the other models, one or more of the variables were omitted to match the criteria for the eligibility of each university. The models were organized according to the AIC value. We present our results in Table 7; a lower AIC value corresponds to greater compatibility of the model with the data. We also included the Nagelkerke $R^2$ for each model. Both findings imply that using a combination of HSGPA, SAT, and DA would be the most suitable academic predictor.

We can see that, as the value of the AIC increased, the value of $R^2$ decreased, which was consistent with the choice of Model 1 as the best predictor. Model 1 (HSGPA + SAT + DA) had the lowest AIC with a value of 234.71 and the highest $R^2$, which was $0.52$. Although Model 2 had all four of the variables that had the highest correlations with the output variable (HSGPA and SAT + DA + WM), the AIC value increased to 237.49 and the $R^2$ decreased to 0.51, so we do not recommend including WM in the final model. In the case of reducing the variables analyzed in Models 3 to 6, we could see that they were still good predictors, since the value of $R^2$ ranged from 0.36 to 0.50. Table 8 presents the statistics for each model.

Table 8 shows the models that used standardized variables, since it was then possible to directly observe the variables that had the greatest impact on the models. We could observe in Models 1–5 that HSGPA generally had the highest $\beta$ coefficient and the lowest p-value, so it was more impactful. In the case of Model 6, when we did not use HSGPA, the aptitude test was the variable with the greatest impact on the model. The variance inflation factor (VIF) in all of the models was close to 1, so we can assume that the correlation between the predictor variables in the model was not large enough to require attention.

The accuracy, specificity, and sensitivity of the prediction of academic performance according to the predictors of each model are shown in Table 9.

Table 9 presents the prediction accuracies of the above models. The prediction rates of LAs as (0) were 74%, 74%, 73%, 75%, 76%, and 69% for each model, respectively. The prediction rates of HAs as (1) were 84%, 82%, 82%, 81%, 82%, and 76%, respectively. Finally, the proportions of predictions identical to the current category among all of the data were 79% for Models 1 and 5, 78% for Models 2, 3, and 4, and 73% for Model 6. Model 1, which had greater sensitivity, allowed us to better identify HAs, as it was the most useful model for student selectivity. Where only the HSGPA was known, we could observe that Model 5 was useful for early warning models, as it had a higher specificity. Model 1 allowed us to further answer RQ3 by showing that the HSGPA, academic aptitudes (SAT), and study habits (SSHA) could be used to predict the academic performance of students in a first-year college program, as shown graphically in Figure 2. It was observed that the other models were still useful.

4. Discussion

The main objective of this study was to observe how the entry profiles of engineering students are related to their first-year academic performance. For this purpose, their HSGPA SAT scores and their entry Delay Avoidance DA were considered. First, the profiles of the students were observed to see if there were significant differences between HAs and LAs. Afterward, correlations between variables were performed to analyze which were more related to academic performance. Finally, regression models were used to observe the predictive power of the entry profile variables. Based on the study results, we observed that HAs and LAs have significantly different entry profiles in terms of HSGPA, SAT, and DA. Moreover, the observed differences in Delay Avoidance are consistent with studies undertaken in similar contexts Table 10 [35]; therefore, measuring the student’s study habits on their entrance to the university provides valuable information about their expected academic performance.

We compared the results of a similar study performed by Aquino (2011) [35] with our results. Table 5 presents the standard deviations and mean values of entry profiles for low and high achievers in both studies. It can be seen that there were no significant differences between the results obtained in both studies; furthermore, we computed a more accurate comparison based on the confidence intervals for the mean value at 95% for each of the variables. Our results can be seen in Table 10. We found that the mean confidence intervals for all variables were consistent for HAs, but only those of DA were consistent for LAs. Moreover, because the DA intervals were always consistent, one may infer that the observed confidence intervals of DA (or a given DA threshold) may be used to classify students as HAs or LAs according to their entry profiles. Nevertheless, one may consider that both the study by Aquino and this study were conducted in similar contexts.

For comparison, Table 11 presents our results and those of previous studies with similar models and variables, where the correlation between academic performance in a student’s first year and the student’s entry profile was considered. Since the studies used different statistical methods, we compared the correlations between the output variable, i.e., academic performance, and the input variables of the students, i.e., their entry profiles.

In this study, as in those presented by other authors (Table 11), a high tendency was clearly seen in the correlation between the level of academic performance and the variable of previous performance. Similar to the other studies, the HSGPA showed the highest correlation. In our case, the high school average was 0.41–0.67. The second highest correlation was with the SAT or entrance exam (0.30–0.44). In turn, a correlation was also observed with the academic variables focused on study habits (0.17–0.44), which are tools that students need to develop to be successful in their studies. Finally, attitudinal variables showed considerable differences between studies, and there were very low correlations between academic performance (r < 0.3) and the output variable. This was expected, since attitudinal variables are difficult to measure, so using them as academic predictors is not recommended. In addition to the above, it was found, in the present study, as in the others analyzed, that the attitudinal variables did not present a significant correlation (0.04–0.28); for this reason, the category corresponding to the attitudes will not be taken into account as part of the final model concerning the output variable. For comparison, we list the variables of this work with those of other studies in Table 12. The variables in the predictive models, such as the HSGPA, are frequently found to be significant variables for predictive models. As shown in the table, when models include the HSGPA and SAT, the beta values are below 0.62 and 0.35, respectively. Other variables, such as gender, intrinsic and extrinsic motivation, and resilience, have also been reviewed, as can be seen in Table 12.

The HSGPA and SAT were proven to be the best predictors of first-year academic performance and, therefore, student retention. This is in agreement with the literature [41,42,43,44]. Nevertheless, ref. [45] states that, from previous studies, GPA may be the most promising predictor, but it is sensitive to the particular institution of origin. Regarding the SAT, students with scores lower than 890 had the lowest retention rate, i.e., 63.8%. Similarly, of the students who had an HSGPA of C- or lower, only 40.0% returned for their second year of college [42]. This allows us to confirm that our model is a good predictor of academic performance of engineering in the first year of university students in similar contexts.

Limitations

Regarding the limitations of our research, contextual considerations should be made. There are factors that could have affected academic dropout that were not analyzed, such as those related to the socioeconomic levels of the students (given that a private university was taken as the object of study) and external personal and familial factors. Other factors that were not measured but could also be considered in future studies include the levels of motivation of the students, the reasons for which they chose to study engineering, and whether they had a financial scholarship at the university (see, for instance, [27,28,38,39,40]; for a systematic review, see [46] and the references therein).

Note that these results may not be entirely transferable for all contexts. For instance, [47] found that study attitudes and habits were not significant in predicting high school academic success. Moreover, a study of engineering studies in a different context [48] did not observe a significant effect of HSGPA on students’ academic performance. Therefore, the present outcomes should be taken with caution on the particularities of each case, even their origin institution [45].

5. Conclusions

In this work, we studied a sample of 255 first-year engineering students at a university in Northeast Mexico. Our aim was to determine the factors that influenced their academic performance in their first year of university so as to determine appropriate academic strategies and provide evidence for a simple prediction model that academic administrators can use.

By studying academic data, such as the SAT, HSGPA, study habits, and study attitudes, and by using a Kruskal–Wallis test, we found that high achievers had more positive attitudes, more effective study habits, and significantly higher SAT scores than those of LAs.

Furthermore, the results of our correlations showed that the HSGPA was strongly related to first-year academic performance, which allowed us to conclude that, by focusing on the HSGPA, SAT, and DA variables (or any other procrastination variable), which had proven predictive power with respect to student’s academic performance, certain variables can be used to forecast students’ academic performance in a first-year university program through the students’ admission profiles. While measuring procrastination sometimes requires the application of additional surveys, using only the HSGPA and SAT still provides a good approximation, and most institutions have such data available.

The SSHA survey was confirmed to be an excellent tool for measuring study habits and attitudes. We have provided an updated and reviewed Spanish version of this test.

Finally, we recommend the following:

• This study should be adapted to the context of each institution depending on their needs and experiences, and corresponding intervention actions should be taken for students whose academic performance is unfavorable or could be improved so as to ensure long-term academic performance;

• It is recommended that each institution choose an instrument that helps them determine students’ academic skills. In the present study, an adaptation of the SSHA test was selected given the context of the institution;

• Taking into account all of the above, an accurate classification system that allows for the implementation of support strategies focused on students’ needs can be developed to support the development of students’ academic performance, thus ensuring higher retention rates and much higher graduation rates;

• We also recommend considering each student’s personal threshold so that they can achieve academic performance in a healthy manner.

Thus, in this work, we have shown that a model with a few variables is sufficient to produce a robust predictive model. A natural enhancement of this study would be to extend it into other disciplines, particularly those with different gender proportions, in order to explore any relevant demographic effects. Such broader investigations, which are beyond the scope of the present work, would help to explore the wider transferability of these findings.

Author Contributions

Conceptualization: O.A.G., A.S.-G. and W.X.C.-G. Methodology, investigation, resources, writing—original draft preparation, writing—review and editing, and visualization: D.M.H.T., O.A.G., A.S.-G., W.X.C.-G., H.M.-H. and R.K.G.; software and data curation: O.A.G., D.M.H.T., A.S.-G., W.X.C.-G. and H.M.-H. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:

GPA	Grade Point Averange
SSHA	Survey of Study Habits and Attitudes
DA	Delay Avoidance
WM	Work Methods
TA	Teacher Approval
EA	Educational Acceptance
LAs	Low Achievers
HAs	High Achievers
SAT	Academic Attitude Test

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Figures and Tables

Figure 1. Graphic model of academic performance and its predictors.

Figure 2. Final graphic model of academic performance.

Table 1

Cronbach’s Alpha of the revised and Spanish version of the SSHA that we used in this study. We have included it in Appendix A.

Category	$\mathit{\alpha }$	Consistency
SSHA	0.92	Excellent
DA	0.79	Acceptable
WM	0.81	Good
TA	0.89	Good
EA	0.74	Acceptable

Table 2

Comparison of GPA scales.

Percent Grade	4.0 Scale	Letter Grade
97–100	4.0	A+
93–96	4.0	A
90–92	3.7	A−
87–89	3.3	B+
83–86	3.0	B
80–82	2.7	B−
77–79	2.3	C+
73–76	2.0	C
70–72	1.7	C−
67–69	1.3	D+
65–66	1.0	D
60–64	1.0	D−
59 or less	0.0	F

Table 3

GPA courses vs. retention rate.

			LAs			HAs
Course GPA	0–1.0	1.0–1.5	1.5–2.0	2.0–2.5	2.5–3.0	3.0–3.5	3.5–4.0
Retention rate	7.0	18.2	30.6	47.4	62.0	77.0	87.0

Table 4

Kruskal–Wallis test.

	K	DF	p	${\mathit{\eta }}_{\mathit{H}}$
HSGPA	99.6	1	1.86 × 10−23	0.390 (large)
SAT	37.5	1	8.91 × 10−10	0.144 (large)
DA	33.4	1	7.44 × 10−9	0.128 (moderate)
WM	27.8	1	1.37 × 10−7	0.106 (moderate)
TA	15.7	1	7.56 × 10−5	0.0580 (small)
EA	22.2	1	2.51 × 10−6	0.0836 (moderate)
SH	35.8	1	2.21 × 10−9	0.137 (moderate)
SA	19.7	1	9.14 × 10−6	0.0738 (moderate)

Table 5

Means and standard deviations of entry profiles for low achievers and high achievers.

		HAs			LAs
	n	Mean	sd	n	Mean	sd
DA	136	23.17	8.49	119	16.65	8.37
WM	136	27.10	8.66	119	20.96	8.58
TA	136	25.29	9.65	119	20.49	9.37
EA	136	26.07	8.88	119	20.82	8.93
SH	136	50.26	15.67	119	37.61	14.96
SA	136	51.36	18.10	119	41.30	17.97

Table 6

Predictors and response correlation analysis.

	ACH	HSGPA	SAT	DA	WM	EA	TA
ACH	1	0.61 *	0.40 *	0.36 *	0.34 *	0.28	0.24
HSGPA		1	0.55	0.37	0.42	0.32	0.26
SAT			1	0.06	0.24	0.08	0.09
DA				1	0.66	0.53	0.49
WM					1	0.49	0.51
EA						1	0.92
TA							1

* High Correlation.

Table 7

Comparison of the Akaike information criterion (AIC) and Nagelkerke $R^2$ between models.

	Model	AIC	Nagelkerke ${\mathit{R}}^2$
1	HSGPA + SAT + DA	234.71	0.52
2	HSGPA + SAT + SH *	237.49	0.51
3	HSGPA + DA	238.39	0.50
4	HSGPA + SAT	242.43	0.49
5	HSGPA	243.52	0.48
6	SAT + DA	277.11	0.36

* SH = DA + WM.

Table 8

Comparison among the models. (*) stands for moderate, (**) medium, and (***) large significance.

	Model	Variable	$\mathit{\beta }$	SE	p-Value	VIF
1	HSGPA + SAT + DA	(Intercept)	0.10	0.17	0.54
		HSGPA	1.45	0.25	6.44 × 10−9 ***	1.13
		SAT	0.49	0.21	0.02 *	1.16
		DA	0.57	0.19	0.002 **	1.09
2	HSGPA + SAT + SH	(Intercept)	0.10	0.16	0.54
		HSGPA	1.47	0.25	3.97 × 10−9 ***	1.14
		SAT	0.41	0.20	0.04 *	1.11
		SH	0.49	0.19	0.01 **	1.06
3	HSGPA + DA	(Intercept)	0.12	0.16	0.46
		HSGPA	1.68	0.23	5.33 × 10−13 ***	1.02
		DA	0.47	0.18	0.008 **	1.02
4	HSGPA + SAT	(Intercept)	0.10	0.16	0.55
		HSGPA	1.67	0.24	4.74 × 10−12 ***	1.09
		SAT	0.34	0.20	0.08	1.09
5	HSGPA	(Intercept)	0.11	0.16	0.51
		HSGPA	1.82	0.23	2.08 × 10−15 ***	1.00
6	SAT + DA	(Intercept)	0.13	0.15	0.37
		SAT	1.07	0.18	4.05 × 10−9 ***	1.07
		DA	0.93	0.17	3.34 × 10−8 ***	1.07

Table 9

Specificity, sensitivity, and accuracy of each model.

	Model 1					Model 2
	Observed	Predicted		% Correct		Observed	Predicted		% Correct
		0	1				0	1
Success	0 (LA)	88	31	Specificity	0.74	0	88	31	Specificity	0.74
	1 (HA)	22	114	Sensitivity	0.84	1	24	112	Sensitivity	0.82
				Accuracy	0.79				Accuracy	0.78
	Model 3					Model 4
	Observed	Predicted		% Correct		Observed	Predicted		% Correct
		0	1				0	1
Success	0	87	32	Specificity	0.73	0	89	30	Specificity	0.74
	1	25	111	Sensitivity	0.82	1	26	110	Sensitivity	0.81
				Accuracy	0.78				Accuracy	0.78
	Model 5					Model 6
	Observed	Predicted		% Correct		Observed	Predicted		% Correct
		0	1				0	1
Success	0	90	29	Specificity	0.76	0	82	37	Specificity	0.69
	1	24	112	Sensitivity	0.82	1	32	104	Sensitivity	0.76
				Accuracy	0.79				Accuracy	0.73

Table 10

SSHA validation.

		HAs			LAs
	This Work	Aquino (2011)	Consistent	This Work	Aquino (2011)	Consistent
DA	21.7–24.6	16.8–23.6	Yes	15.1–18.2	14.9–16.4	Yes
WM	25.6–28.6	22.6–31.9	Yes	19.4–22.5	16.2–17.9	No (Small difference)
TA	23.7–26.9	17.2–24	Yes	18.8–22.2	14.3–15.7	No (Small difference)
EA	24.6–27.6	23–31.7	Yes	19.2–22.4	16.7–18.4	No (Small difference)
SH	47.6–52.9	39.8–55.1	Yes	34.9–40.3	31.2–34.2	No (Small difference)
SA	48.3–54.4	40.7–55.2	Yes	38.1–44.5	31.2–34	No (Small difference)

Table 11

Comparison of correlations with other studies.

Study	Variable Output	Academic Variables				Non-Academic Variables
	Performance Level	Previous Performance	Skills	Study Habits		Attitudinal
Our Study	HAs or LAs	HSGPA 0.61	SAT 0.40	DA 0.36	WM 0.34	EA 0.28	TA 0.24
Alhadabi et al. [36]	GPA			Mastery Goals 0.26	Self-efficacy 0.22	Consistency of interest 0.19
DeBerard et al. [23]	GPA	HSGPA 0.67	SAT 0.30			Mental health −0.01
Allen et al. [10]	GPA	HSGPA 0.51	ACT 0.44			Commitment to College −0.04
Olani [24]	GPA	HSGPA 0.41	Entrance exam score 0.35	Skills and Efficiency 0.17		Motivation 0.11
Van der Zanden et al. [37]	GPA	HSGPA 0.62		academic adjustment 0.44		Social adjustment −0.09

Table 12

Summary of the comparison of the studies.

Refs.	Model	Variables	Beta	SE	t	F	R	R2	R2 Change
This Work								0.52
		HSGPA	0.23	0.04 (p ∼6 × 10−9
		SAT	0.003	0.001 (p ∼0.02)
		DA	0.06	0.02 (p = 0.002)
Krumrei-Mancuso et al. (2013) [38]	Psychosocial Factors Predicting First-YearCollege Student Success (M3)						0.69	478	0.023 (p < 0.01)
		Gender	−0.1
		Ethnicity	0.6
		1st semester GPA	0.62 (p < 0.1)
		Academic Self-Efficacy	0.17 (p < 0.1)
		Organization and Attention to Study	0.07
		Stress and Time Press	−0.06
		Involvement with College Activity	−0.04
		Emotional Satisfaction with Academics	−0.08
		Class Communication	−0.01
T. Coyle et al. (2011) [39]	Regressions of GPA on SAT and g (multiple-aptitude test)					41.98 (p < 0.01)		0.15
		SAT	0.35		6.89 (p < 0.01)
		g	0.03		0.61
		SATxg	0.03		1.80 (p < 0.1)
E. Cohn et al. (2004) [27]	Partial regression coefficients and t-ratios (in parentheses). Dependent variable: college GPA	Intercept	−0.041 (−0.23)			55.70 (p < 0.1)		0.39
		Percent Rank	0.005 * (2.85)
		High School GPA	0.231 * (4.03)
		SAT	0.0017 * (10.02)
		White female	0.163 * (3.21)
		Nonwhite male	−0.006 (−0.08)
		Nonwhite female	0.029 (0.46)
Reynolds and Weigand (2010) [40]	Predicting GPA							0.08
		Self-efficacy	−0.08	−0.05 (0.08)
		Academic resilience	0.49 (p < 0.01)	0.56 (0.17)
	Predicting GPA							0.09	0.01
		University environment	0.12
	Predicting GPA							0.09	0.01
		IMO	−0.11	−0.06 (0.06)
	Predicting GPA							0.1	0.00
		EMO	−0.07	−0.05 (0.08)
	Predicting GPA							0.12	0.01
		Amotivation	−0.12	−0.08 (0.06)
	Predicting GPA							0.22	0.01
		Race	−0.34 p < 0.001	−0.61 (0.15)
Wolfe and Johnson (1995) [28]	All Forward Multiple Regression Analyses							0.38 (p < 0.01)
	JPI scales	High school averages	0.43	p < 0.01					0.19
		Organization	0.27	p < 0.01					0.07
		SAT total	0.26	p < 0.01					0.05
		Infrequency	−0.16	p < 0.05					0.03
		Risk taking	−0.17	p < 0.05					0.02
		Innovation	0.16	p < 0.05					0.02
	Big 3							0.35 (p < 0.01)
		High school average	0.43	p < 0.01					0.19
		Control	0.32	p < 0.01					0.09
		SAT total	0.24	p < 0.01					0.05
		Well-being	0.13	p < 0.05					0.02
	Big5							0.32 (p < 0.01)
		High school average	0.43	p < 0.01					0.19
		Conscientiousness	0.31	p < 0.01					0.09
		SAT total	0.23	p < 0.01					0.04
	Other predictors							0.32 (p < 0.01)
		High school average	0.43	p < 0.01					0.19
		Self-efficacy	0.27	p < 0.01					0.08
		SAT total	0.21	p < 0.01					0.03
		Attendance	0.16	p < 0.05					0.02
	All predictors							.35 (p < 0.01)
		High school average	0.43	p < 0.01					0.19
		Control (from Big 3)	0.32	p < 0.01					0.09
		SAT total	0.24	p < 0.01					0.05
		Infrequency (from JPI)	−0.14	p < 0.05					0.02

Appendix A

A reviewed and updated Spanish version of the SSHA [19] was used in this work.

Instructions (Spanish): Decide cómo te sientes acerca de cada una de las declaraciones y marca tu respuesta en la hoja de respuestas proporcionada. Elige una de las cinco posibles opciones de respuesta:

1—Raramente, 2—A veces, 3—Frecuentemente, 4—Por lo general, 5—Casi siempre.

1. Cuando la tarea que me encargan es demasiado larga o muy difícil, renuncio o estudio sólo las partes más fáciles de la lección.

2. Cuando hago reportes, preparo un tema o cualquier otro trabajo escrito, me aseguro de comprender lo que me están pidiendo antes de empezar.

3. Siento que los profesores no comprenden las necesidades e intereses de los estudiantes.

4. Mi desagrado por ciertos profesores me hace descuidar mi trabajo escolar.

5. Si me tengo que ausentar de clases, me aseguro de reponer las actividades perdidas sin que el profesor me tenga que recordar.

6. Tengo problemas para expresar lo que quiero decir en exámenes, reportes y cualquier otro trabajo a entregar.

7. Mis profesores hacen su clases interesantes y significativas para mi.

8. Siento que estudiaría más duro, si me dieran libertad de escoger las clases que me gustan.

9. Estar soñando despierto distrae mi atención de los temas cuando estoy estudiando.

10. Mis profesores critican mi trabajo escrito por estar mal planificado o escrito apresuradamente.

11. Siento que los profesores permiten que su agrado o desagrado por los estudiantes influyan demasiado en las calificaciones.

12. A pesar de que no me guste alguna materia, trabajo duro para obtener buenas calificaciones.

13. Aunque una tarea sea aburrida, me permanezco haciéndola hasta que la complete.

14. Pongo especial atención a la limpieza de los reportes, informes y otros trabajos que se entregarán.

15. Creo que la forma más fácil de sacar buenas calificaciones es estar de acuerdo con todo lo que dicen los profesores.

16. Pierdo interés en mis estudios después de los primeros días de escuela.

17. Mantengo todo mi trabajo para cada clase junto y cuidadosamente organizado en una agenda.

18. Memorizo reglas de ortografía, definiciones de palabras, reglas de gramática, etc., sin entenderlas realmente.

19. Creo que a los profesores les gusta mostrar demasiado quién es el que manda.

20. Creo que los profesores realmente quieren caerle bien a sus estudiantes.

21. Cuando tengo problemas con mi trabajo escolar, trato de hablarlo con el profesor.

22. Dudo en pedirle a un profesor una explicación más detallada de una tarea que no me queda clara.

23. Siento que los profesores son de mente demasiado estrecha y se quedan en su idea.

24. Siento que los estudiantes no tienen suficiente libertad para seleccionar sus propios temas para ensayos e informes.

25. No me molesto en corregir los errores en los reportes que mis profesores me han calificado y devuelto.

26. Me pongo nervioso y confundido cuando tomo un examen y no respondo las preguntas tan bien como podría hacerlo.

27. Creo que los profesores esperan que los estudiantes estudien demasiado fuera de clase.

28. La falta de interés en mi trabajo escolar me dificulta mantener mi atención en mis tareas de lectura.

29. Mi lugar de estudio en casa lo mantengo limpio y profesional.

30. Tengo problemas con la ortografía, la gramática y la puntuación al escribir ensayos y reportes.

31. Cuando explican una lección o responden preguntas, mis profesores usan palabras que no entiendo.

32. A menos que realmente me guste una materia, creo en hacer sólo lo suficiente para obtener una calificación aprobatoria.

33. Las interrupciones distraen mis estudios cuando estoy estudiando en casa.

34. Al tomar notas, tiendo a escribir cosas que luego resultan ser poco importantes.

35. Mis profesores no dan suficiente explicación de las cosas que están tratando de enseñar.

36. Me siento confundido e indeciso sobre lo que quiero estudiar en la escuela y lo que quiero hacer después de salir de la escuela.

37. Me cuesta trabajo prepararme para ponerme a estudiar.

38. Me va mal en los exámenes porque me resulta difícil pensar con claridad y planificar mi trabajo en un periodo corto de tiempo.

39. Siento que los profesores son demasiado estrictos y sabelotodo al tratar con los estudiantes.

40. Parte de mi trabajo escolar es tan poco interesante que tengo que obligarme a hacer las tareas.

41. No puedo estudiar bien porque me pongo inquieto, de mal humor o tengo depresión.

42. Me salto las figuras, gráficos y tablas en las tareas de lectura.

43. Creo que los profesores secretamente disfrutan hacer pasar a sus estudiantes por “momentos difíciles”.

44. Creo que pasar un buen momento y divertirme en la vida es más importante que estudiar.

45. Dejo mis tareas escritas para el último minuto.

46. Después de leer varias páginas de un tema, soy incapaz de poder recordar lo que acabo de leer.

47. Creo que los profesores tienden a hablar demasiado.

48. Creo que los profesores tienden a evitar discutir problemas y acontecimientos actuales con los estudiantes.

49. Cuando me siento a estudiar, me siento demasiado cansado, aburrido o somnoliento para estudiar correctamente.

50. Se me dificulta encontrar los puntos más importantes de una lectura, los cuales luego vienen en los exámenes.

51. Siento que los profesores intentan dar la misma atención y ayuda a todos sus alumnos.

52. Siento que mis calificaciones muestran realmente lo que puedo lograr.

53. Perder mucho tiempo hablando por teléfono o mandando mensajes, viendo TV y redes sociales, escuchando música, etc., afecta mis estudios.

54. Cuando tengo dudas acerca del formato para un ensayo, busco otro que me sirva de modelo o guía.

55. Las ilustraciones, ejemplos y explicaciones que me da mi profesor son aburridas y difíciles de entender.

56. Creo que no vale la pena el tiempo, dinero y esfuerzo que se tiene que invertir para la universidad.

57. Mi estudio en casa se hace de una manera no planificada.

58. Cuando leo una lectura muy larga, me detengo de vez en cuando para intentar recordar lo que voy leyendo.

59. Siento que los profesores tienden a despreciar a los estudiantes que sacan calificaciones bajas y burlarse de sus errores.

60. Algunas de mis clases son tan aburridas que paso el tiempo en clase dibujando, viendo redes sociales (whatsapp, facebook, instagram, etc.) o soñando despierto, en lugar de escuchar al profesor.

61. Tener tantas otras cosas que hacer causa que me retrase en la escuela.

62. Parece que logro muy poco para toda la cantidad de tiempo que paso estudiando.

63. Siento que los profesores hacen sus materias demasiado difíciles para el alumno promedio.

64. Siento que estoy llevando materias que me beneficiarán muy poco.

65. Intento hacer mi tarea en la escuela para reducir lo que me llevo a casa.

66. Puedo leer una lectura de una tarea solo por poco tiempo, antes de que las palabras dejen de tener sentido.

67. Pienso que los profesores de clases extracurriculares o cocurriculares, hacen más por la vida escolar que los profesores de clases regulares.

68. Creo que el principal trabajo de la escuela es enseñar a sus estudiantes cosas que le ayudarán a ganarse la vida.

69. Problemas fuera de la escuela: con amigos, pareja o problemas en casa, me hacen descuidar mi trabajo escolar.

70. Copio los diagramas, dibujos, tablas y otras ilustraciones que el profesor pone en el pizarrón.

71. Siento que los profesores piensan más en las calificaciones que en el verdadero propósito de la escuela.

72. Trato realmente de interesarme en todas las materias que llevo.

73. Termino mis tareas a tiempo.

74. Pierdo puntos en los exámenes porque cambio mi primer respuesta, sólo para más tarde descubrir que tenía razón en la primera.

75. Pienso que los estudiantes que hacen muchas preguntas y participan en clase, sólo intentan quedar bien con el profesor.

76. Siento que la principal razón por la cual se asiste a la universidad es para ser admirado y envidiado por otros.

77. Me gusta tener la música, televisión o series puestas (Netflix) y/o redes sociales cuando estudio.

78. Cuando me preparo para un examen, organizo lo que tengo que estudiar en un orden planificado: orden de importancia, orden en que se enseñó en clase, etc.

79. Creo que los profesores intencionalmente ponen exámenes los días posteriores a fiestas o partidos de fútbol o eventos culturales importantes.

80. Creo que tener un equipo de fútbol ganador en la universidad o grupos estudiantiles sobresalientes, es igual de importante que aprender historia o matemáticas.

81. Conmigo, estudiar es algo impredecible, dependiendo del estado de ánimo en el que me encuentre.

82. Soy descuidado con la gramática, signos de puntuación y la ortografía cuando respondo preguntas de un examen.

83. Creo que una buena manera de obtener buenas calificaciones es haciendo halagos a los maestros.

84. Pienso que sería mejor para mi dejar la universidad y conseguir un trabajo.

85. Todos los días estudio una hora o más fuera de la escuela.

86. Aunque estudio hasta el último minuto posible, no puedo terminar los exámenes dentro del tiempo permitido.

87. Siento que es casi imposible para el alumno promedio terminar toda la tarea.

88. Siento que las cosas que enseñan en la escuela, no te enseñan a enfrentar problemas de la vida adulta.

89. Mantengo mis tareas al día, haciendo mi trabajo del día a día de forma regular y constante.

90. Si me queda tiempo, me tomo unos minutos para revisar las respuestas de mi examen antes de entregarlo.

91. Siento que las tareas ridículas que encargan los profesores, son la principal razón de que los estudiantes se copien.

92. Leer o estudiar mucho me da dolor de cabeza.

93. Prefiero estudiar solo, que con otros.

94. Cuando devuelven los exámenes, encuentro que mi calificación ha bajado por errores descuidados.

95. Siento que no se puede esperar que a todos los estudiantes les caigan bien todos los profesores.

96. Prefiero faltar a la escuela cuando hay alguna otra cosa que quiero hacer.

97. Antes de una clase planeo lo que haré para aprovechar al máximo mi tiempo.

98. Durante exámenes olvido nombres, fechas, fórmulas y otros detalles que en realidad sí sé.

99. Creo que los maestros se dedican a dar clases principalmente porque lo disfrutan.

100. Creo que las calificaciones más altas se le dan a los estudiantes que pueden memorizar hechos, en lugar de aquellos que saben razonar.

References

1. UNESCO. Moving Forward the 2030 Agenda for Sustainable Development; UNESCO: Paris, France, 2017; Available online: https://en.unesco.org/creativity/sites/creativity/files/247785en.pdf (accessed on 30 March 2022).

2. OECD. Education at a Glance 2021: OECD Indicators; OECD Publishing: Paris, France, 2021; [DOI: https://dx.doi.org/10.1787/b35a14e5-en]

3. Heredia-Jiménez, V.; Jiménez, A.; Ortiz-Rojas, M.; Marín, J.I.; Moreno-Marcos, P.M.; Muñoz-Merino, P.J.; Kloos, C.D. An Early Warning Dropout Model in Higher Education Degree Programs: A Case Study in Ecuador; LAUR@ EC-TEL: Hannover, Germany, 2020.

4. OECD. Enrolment Rate in Secondary and Tertiary Education (Indicator); OECD Publishing: Paris, France, 2022; [DOI: https://dx.doi.org/10.1787/1d7e7216-en]

5. INEGI. Características Educativas de la Población. Available online: https://www.inegi.org.mx/temas/educacion/ (accessed on 22 May 2022).

6. INEGI Tasa de Abandono Escolar por Entidad Federativa Según Nivel Educativo. Available online: https://www.inegi.org.mx/app/tabulados/interactivos/?pxq=9171df60-8e9e-4417-932e-9b80593216ee (accessed on 22 May 2022).

7. Secretaria de Educación Publica. Principales Cifras del Sistema Educativo Nacional. Available online: https://www.planeacion.sep.gob.mx/Doc/estadistica_e_indicadores/principales_cifras/principales_cifras_2020_2021_bolsillo.pdf (accessed on 22 May 2022).

8. Lowe, H.; Cook, A. Mind the Gap: Are students prepared for higher education?. J. Furth. High. Educ.; 2003; 27, pp. 53-76. [DOI: https://dx.doi.org/10.1080/03098770305629]

9. Silva Laya, M. El primer año universitario: Un tramo crítico para el éxito académico. Perfiles Educ.; 2011; 33, pp. 102-114.

10. Allen, J.; Robbins, S.B. Prediction of college major persistence based on vocational interests, academic preparation, and first-year academic performance. Res. High. Educ.; 2008; 49, pp. 62-79. [DOI: https://dx.doi.org/10.1007/s11162-007-9064-5]

11. Tinto, V. Leaving College: Rethinking the Causes and Cures of Student Attrition; 2nd ed. University of Chicago Press: Chicago, IL, USA, 1993.

12. McGrath, M.; Braunstein, A. The prediction of freshmen attrition: An examination of the importance of certain demographic, academic, financial and social factors. Coll. Stud. J.; 1997; Available online: https://psycnet.apa.org/record/1997-42938-010 (accessed on 13 November 2022).

13. Geisinger, B.N.; Raman, D.R. Why they leave: Understanding student attrition from engineering majors. Int. J. Eng. Educ.; 2013; 29, 914.

14. den Broeck, L.V.; Laet, T.D.; Lacante, M.; Pinxten, M.; Soom, C.V.; Langie, G. The effectiveness of a MOOC in basic mathematics and time management training for transfer students in engineering. Eur. J. Eng.; 2020; 45, pp. 534-549. [DOI: https://dx.doi.org/10.1080/03043797.2019.1641692]

15. Thompson, M.; Pawson, C.; Evans, B. Navigating entry into higher education: The transition to independent learning and living. J. Furth. Higher Educ.; 2021; 45, pp. 1398-1410. [DOI: https://dx.doi.org/10.1080/0309877X.2021.1933400]

16. Young, E.; Thompson, R.; Sharp, J.; Bosmans, D. Emotional transitions? Exploring the student experience of entering higher education in a widening- participation HE-in-FE setting. J. Furth. High. Educ.; 2020; 44, pp. 1349-1363. [DOI: https://dx.doi.org/10.1080/0309877X.2019.1688264]

17. Schütze, H.; Bartyn, J.; Tapsell, A. Increasing self-efficacy to improve the transition to university: An Australian case study. J. Furth. Higher Educ.; 2021; 45, pp. 845-856. [DOI: https://dx.doi.org/10.1080/0309877X.2020.1826034]

18. Money, J.; Nixon, S.; Graham, L. Do educational experiences in school prepare students for university? A teachers’ perspective. J. Furth. Higher Educ.; 2020; 44, pp. 554-567. [DOI: https://dx.doi.org/10.1080/0309877X.2019.1595547]

19. Brown, W.; Holtzman, W. Survey of Study Habits and Attitudes; The Psychological Corporation: New York, NY, USA, 1967.

20. College Board. SAT Suite of Assessments. Available online: https://satsuite.collegeboard.org/sat (accessed on 22 May 2022).

21. Memis, A.D.; Hülya, K. The Relationship between the Study Habits and Attitudes and Metacognitive Reading Comprehension Self-Awareness, Reading Comprehension, Reading Attitudes. World J. Educ.; 2019; 9, pp. 133-145. [DOI: https://dx.doi.org/10.5430/wje.v9n4p133]

22. George, D.; Mallery, P. SPSS for Windows Step by Step: A Simple Guide and Reference; 11.0 update Allyn & Bacon: Boston, MA, USA, 2003.

23. DeBerard, M.S.; Spielmans, G.I.; Julka, D.L. Predictors of academic achievement and retention among college freshmen: A longitudinal study. Coll. Stud. J.; 2004; 38, pp. 66-81.

24. Olani, A. Predicting first year university students’ academic success. Electron. J. Res. Educ. Psychol.; 2009; 7, pp. 1053-1072. [DOI: https://dx.doi.org/10.25115/ejrep.v7i19.1351]

25. Ting, S.R. Predicting Academic Success of First-Year Engineering Students from Standardized Test Scores and Psychosocial Variables. Int. J. Eng. Educ.; 2001; 17, pp. 75-80.

26. Zwick, R.; Himelfarb, I. The Effect of High School Socioeconomic Status on the Predictive Validity of SAT Scores and High School Grade-Point Average. J. Educ. Meas.; 2011; 48, pp. 101-121. [DOI: https://dx.doi.org/10.1111/j.1745-3984.2011.00136.x]

27. Elchanan, C.; Cohn, S.; Balch, D.C.; Bradley, J. Determinants of Undergraduate GPAs: SAT Scores, High-School GPA and High-School Rank. Econ. Educ. Rev.; 2004; 23, pp. 577-586.

28. Wolfe, R.N.; Johnson, S.D. Personality as a Predictor of College Performance. Educational and Psychological Measurement. Educ. Psychol. Meas.; 1995; 55, pp. 177-185. [DOI: https://dx.doi.org/10.1177/0013164495055002002]

29. College Board. What Is a Good SAT Score?. 2022; Available online: https://satsuite.collegeboard.org/sat/scores/understanding-scores/what-is-a-good-score (accessed on 13 November 2022).

30. College Board. How Scores Are Calculated. 2022; Available online: https://satsuite.collegeboard.org/sat/scores/understanding-scores/how-scores-are-calculated (accessed on 13 November 2022).

31. Budny, D.; Bjedov, G.; LeBold, W. Assessment of the impact of the freshman engineering courses. Proceedings of the Frontiers in Education 1997 27th Annual Conference. Teaching and Learning in an Era of Change; Pittsburgh, PA, USA, 5–8 November 1997; Volume 2, pp. 1100-1106.

32. Foundation, T.R. The R Project for Statistical Computing. Available online: https://www.r-project.org/ (accessed on 29 April 2022).

33. Akoglu, H. User’s guide to correlation coefficients. Turk. J. Emerg. Med.; 2018; 18, pp. 91-93. [DOI: https://dx.doi.org/10.1016/j.tjem.2018.08.001]

34. Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control.; 1974; 19, pp. 716-723. [DOI: https://dx.doi.org/10.1109/TAC.1974.1100705]

35. Aquino, L.B. Study habits and attitudes of freshmen students: Implications for academic intervention programs. J. Lang. Teach. Res.; 2011; 2, 1116. [DOI: https://dx.doi.org/10.4304/jltr.2.5.1116-1121]

36. Alhadabi, A.; Karpinski, A.C. Grit, self-efficacy, achievement orientation goals, and academic performance in University students. Int. J. Adolesc. Youth; 2022; 25, pp. 519-535. [DOI: https://dx.doi.org/10.1080/02673843.2019.1679202]

37. Van der Zanden, P.J.; Denessen, E.; Cillessen, A.H.; Meijer, P.C. Patterns of success: First-year student success in multiple domains. Stud. High. Educ.; 2009; 44, pp. 2081-2095. [DOI: https://dx.doi.org/10.1080/03075079.2018.1493097]

38. Krumrei-Mancuso, E.J.; Newton, F.B.; Kim, E.; Wilcox, D. Psychosocial factors predicting first-year college student success. J. Coll. Stud.; 2013; 54, pp. 247-266. [DOI: https://dx.doi.org/10.1353/csd.2013.0034]

39. Coyle, T.; Snyder, A.; Pillow, D.; Kochunov, P. SAT predicts GPA better for high ability subjects: Implications for spearman’s law of diminishing returns. Personal. Individ. Differ.; 2011; 50, pp. 470-474. [DOI: https://dx.doi.org/10.1016/j.paid.2010.11.009] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21562615]

40. Reynolds, A.L.; Weig, M.J. The relationships among academic attitudes, psychological attitudes, and the first-semester academic achievement of first-year college students. J. Stud. Aff. Res. Pract.; 2010; 47, pp. 175-195. [DOI: https://dx.doi.org/10.2202/1949-6605.6004]

41. Marini, J.; Westrick, P.; Young, L.; Ng, H.; Shaw, E. Examining the Validity of the SAT at Higher Education Institutions Outside of the U.S.; College Board: New York, NY, USA, 2020; pp. 1-10.

42. Marini, J.P.; Westrick, P.A.; Young, L.; Ng, H.; Shmueli, D.; Shaw, E.J. Differential Validity and Prediction of the SAT®: Examining First-Year Grades and Retention to the Second Year; College Board: New York, NY, USA, 2019.

43. Kobrin, J.L.; Camara, W.J.; Milewski, G.B. Students with Discrepant High School GPA and SAT® I Scores; Research Notes. RN-15 College Board: New York, NY, USA, 2022.

44. Allensworth, E.M.; Clark, K. High School GPAs and ACT Scores as Predictors of College Completion: Examining Assumptions About Consistency Across High Schools. Educ. Res.; 2020; 49, pp. 198-211. [DOI: https://dx.doi.org/10.3102/0013189X20902110]

45. Verostek, M.; Miller, C.W.; Zwickl, B. Analyzing admissions metrics as predictors of graduate GPA and whether graduate GPA mediates Ph.D. completion. Phys. Rev. Phys. Educ. Res.; 2021; 17, 020115. [DOI: https://dx.doi.org/10.1103/PhysRevPhysEducRes.17.020115]

46. van der Zanden, P.J.A.C.; Denessen, E.; Cillessen, A.H.N.; Meijer, P.C. Domains and predictors of first-year student success: A systematic review. Educ. Res. Rev.; 2018; 23, pp. 57-77. [DOI: https://dx.doi.org/10.1016/j.edurev.2018.01.001]

47. Jhoselle, T. The Influence of Study Attitudes and Study Habits on the Academic Performance of the Students. 2020; Available online: https://ssrn.com/abstract=3717274 (accessed on 13 November 2022).

48. Huang, C.-L. Self-efficacy in the Prediction of Academic Performance of Engineering Students. Proceedings of the 2003 ASEE Gulf-Southwest Annual Conference; Nashville, TN, USA, 22–25 June 2003; The University of Texas at Arlington: Arlington, TX, USA, 2003.