The introduction to the learning styles concept, despite being criticized (see the further discussion on this issue), particularly the scale introduced by Kolb (1976) categorizing the student learning paths (divergers, assimilators, convergers, and accommodators), established that at least under certain circumstances and topics, students can organize their paths to receive information more efficiently (Cagiltay, 2008). Kolb's experiential learning cycle is a four-stage process depicting how we acquire and embed new knowledge. The teaching and evaluation designs should incorporate a broad spectrum of approaches to these categories, representatively collecting the outcomes for each type of student instead of only addressing some of them.

Subsequently, those categories were reformulated into four bipolar dimensions classifying the Learning that each student could be receiving: sensitive/intuitive (external/internal or also concrete/abstract), visual/auditory (visual/verbal), active/reflective (active/passive), and sequential/global (Felder and Silverman, 1988), and sometimes adding a fifth: deductive/inductive. Names differ because they could refer to teaching instead of learning. It is doubtful these approaches have been considered or represented even approximately throughout online lessons. Notably, the lack of higher-education teachers' specialization in those considerations and the lack of interaction did not promote the realistic perception of those needs in each student.

Each learning design delivered online showed characteristics of each instructor's conceptions of their teaching, sometimes more focused on some learning styles, only being able to privilege certain types. This affected the primordial construction of texts, images, demonstrations, and discussions based on facts, abstractions, concepts, or theory. The organization within a learning sequence and its relationship to the everyday world are equally important. In this study, Felder's dimensions (Felder and Silverman, 1988) were considered; although not the most modern, they are the most used in the literature for comparison; thus, we could categorize the study participants. Information was collected using some traditional methods described in the following sections (García-Cué et al., 2009).

The concept of Learning Styles, despite its popularity, has been recently questioned due to the mismatch between didactic practices and academic outcomes (Gleichgerrcht et al., 2015; Aslaksen and Lorås, 2018; Nancekivell et al., 2020). Although not universally accepted and considered partially inaccurate (Furey, 2020), this concept, when carefully interpreted, still provides valuable tools to close the gaps between interaction among learners, teachers, and educational content (Willingham et al., 2015). Rather than defining different ways to learn (Felder, 2020).

Personality traits are assumed to predispose the limiting or favoring of learning in specific disciplines. Many universities bet on identifying these personality traits because they can develop a better competitive differentiation in their students, a notable effort of sustainability in education. When some traits combine with stressful emergencies, such as the COVID-19 pandemic, they can interfere with students' Learning (Balkhi et al., 2020). Literature has widely established these facts unveiling the behaviors expressed in the pandemic confinement, particularly at the beginning. The crisis established abrupt changes in students' daily routines and learning performance (Nixon et al., 2021).

Statistical relationships between student personality traits and outcomes could be matched by moving students in the right direction toward their academic achievement via the cycle of generating student satisfaction/improving their performance (Pawlowska et al., 2014). Then, the teacher's knowledge about academic traits becomes vital for a better understanding of how students progress in each course, particularly to meet expectations (Pike, 2006). In this terrain, the University Personality Traits Scale (UBPS) comprises six dimensions (Rauschnabel et al., 2016), while the Myers-Briggs Type Indicator (MBTI) marks between four to sixteen personality types (Jessee et al., 2006). Despite criticisms (see the further discussion on the issue), the latter states an extensive, classical, and broad classification of personality traits. Analysis was established based on this scale.

MBTI has been criticized for its lack of reliability (Stein and Swan, 2019) and usability for decision-making (Bailey et al., 2018). Despite this, MBTI is still valuable to track some behavioral traits correlated with academic performance (Sipps et al., 2016). It becomes concomitant in specific learning contexts (Harvey, 1996) and it is also concurrent in personality traits (Sipps et al., 2016). MBTI is generally considered to be reliable for academic purposes (Randall et al., 2017).

The current research aims to correlate Learning styles and Personality traits with institutional initiatives based on methodologies and different approaches for delivering academic content. Both, Learning styles and Personality traits describe different stimuli perceived and shown by students. The surveyed student population can be characterized and correlated with successful tendencies amid learning activities.

The general research objective was to analyze how behavioral, environmental, and academic factors conditioned educational transitions during the COVID-19 pandemic. To achieve this goal, the tools mentioned in Section 2 were combined independently to collect information about learning styles and personality traits declared by each participant. Also, each student's perception of the university teaching orientation was collected. The technological skills of each student are part of their hidden curriculum, so a skills self-evaluation was compiled, together with their perception of their teachers' mastery of technology. A specific question included the self-perception about the current educational context and its relevance to their careers during the period they were surveyed. The final sections gathered their self-perception of academic performance and adaptation during confinement and subsequently during the “New Normal” (corresponding to the semester in which this instrument was applied: summer 2022). Students were also asked about the online learning received during confinement regarding the value and their preference between online and face-to-face instruction. Information was also collected if each student experienced the transition from high school to university during the confinement. The instrument was previously used and reported as an insight into a limited group of students enrolled in the introductory Physics course (Delgado, 2022) (see the Supplementary material as reference). Each question was identified with a Code label with format Qx, x being the response being gathered. Each question gathered the answer on a number scale indicated in each response section. Questions Q31 through Q38 corresponded to academics and the COVID-19 facts. Table 1 shows the statistical mode for some relevant questions and each cluster of students synthetically. Note that question Q32 for males in the Tec20 model was multi-modal.

In agreement with the first research objective, the demographic correlations collected were analyzed. Then, per the second objective, the causal impact was observed on the performance and adaptation reported by each student. For the third objective, researchers analyzed the existing correlations of self-perception on the transition to the “New Normal” education.

The questionnaire was sent out as a Google Form inviting the sample target to fill in the form. A couple of reminders were sent during May 2022, which was the period during the semester when the lockdown finished in Mexico and students were compelled to go back to university on a 100% face-to-face basis. At that time, the semester was also finishing, so, it was possible for students to compare both scenarios. The type of research pursued was Primary Quantitative Research, oriented to reach conclusions through certain statistical and correlational analyses. Information on students was gathered in a file in the format of comma-separated values and then uploaded to a notebook in Mathematica. It allowed the creation of specialized methods to analyze information statistically, graphically, or correlational, and particularly programming own algorithms of analysis to compare and plot the outcomes, which was valuable because of the wide number of variables intended to become involved as causal issues.

Thus, for the initial multivariable analysis, the use of multivariable graphical correlations was preferred with the ability to print plots. Such plots, despite being complex in some cases, provide a glance at the comparison between variables to find possible primary correlations, which were selectively analyzed using inference tests. For this reason, the use of clustering of data in discrete categories for each dimension being analyzed was considered. Once some correlations were noticed in the primary analysis, more concrete inference tests were performed as a two-way analysis of variance (ANOVA) test to set more conclusive outcomes for those detected correlations noticed in the first graphical insight, and in addition, getting a double check for the statistical meaningfulness.

Figure 2 presents the sample distribution outcomes for each learning style declared by students (questions Q1–Q5) compared with the perceived university orientation in the same category (questions Q6–Q10). Each dot graphically corresponds with one student in each comparative quadrant, they are presented as that for aesthetic purposes. Absolute percentages for each crossed-over group (institution vs. student) are shown correspondingly in red at the center of each figure. Thus, Figures 2A–E report the learning styles: external (Co)/internal(Ab), visual (Vi)/auditory-verbal (Au), deductive (De)/inductive (In), active(Ac)/reflexive(Re), and sequential(Se)/global(Gl), respectively.

Figure 2A shows that the surveyed population mainly uses factual information to achieve Learning instead of abstract facts. Their knowledge is based on facts rather than theories or indirect information, which characterizes a practical learning orientation (Mena et al., 2019). However, a few students combined facts with abstractions (principles, formulas, diagrams). Notably, few students perceived themselves as demanding a more profound comprehension; instead, they just followed the facts, agreeing with Kolb (2014). The teaching style was perceived as balanced between concrete and abstract knowledge, despite some students preferring factual information vs. the perceived abstract instruction.

In Figure 2B, the students are again grouped mainly around visual Learning instead of auditory. This most frequent orientation within engineering programs also involves active and reflective styles (analyzed below). Note that teaching was again perceived as balanced, combining theory, simulation, and practice (Katsioloudis and Fantz, 2012). However, some students perceived teachers as delivering excessive knowledge through writing and lecturing. Figure 2C shows a broader diversity. With a balanced orientation within the group of students (and presumably in the population), they slightly perceive themselves as less deductive (47.1%) than inductive (52.9%). Meanwhile, the university was perceived as slightly less deductive (39.2%) than inductive (60.8%). The even distribution of both styles is notable but leaves out 52.3% of students from their natural learning style.

Figure 2D confirms that engineering students perceive themselves as primarily active rather than reflective: experimentation over reflection, although the faculty orientation appears notably balanced. Despite this, as Felder and Silverman (1988) pointed out, students gradually mature and recognize that mere discussion or execution of activities is not enough; introspection or abstraction is always required for meaningful Learning. Then, it is a correct institutional balance. Figure 2E notably exhibits a recognized pattern: engineering students are generally structured and like to follow algorithms, 81.7 vs. 19.3%. Interestingly, those students who self-declared as global learners do not recognize such a faculty orientation.

This subsection describes the statistical method regarding the previous results presented on radar plots of frequency. Figure 3A resumes the learning styles distribution for the student sample. Learning styles are concentrated in 22 combinations (from 32 possible), and six stand out: CoViInAcSe, CoViDeAcSe, AbViInAcSe, CoViDeReSe, CoViInAcGl, and CoViDeAcGl. They mainly share four characteristics (Concrete, Visual, Active, and Sequential), differing only in how the students catch information: Inductive or Deductive. Note particularly that in the six main combinations, Visual style was recurrent. When learning styles are broken down by gender in Figure 3B, only five stand out, and two are dominant. Women: CoViInAcSe, CoViDeAcSe, and AbViInAcSe. Men: CoViInAcSe, CoViDeAcSe, and CoViDeReSe. The first two dominant combinations are the same in both genders, being more dominant in women than men. Women are grouped in three main combinations and men in four. Among these, they differ only in Inductive or Deductive. Notably, women agree more with the abstract, sequential, and inductive styles, while men are inclined toward concrete and global.

In another trend, Figure 3C shows the 30 combinations of learning styles that the faculty focus on. Seven stand out, but only three are dominant: AbAuInReSe, CoViDeAcSe, and CoViInReSe. It highlights that the Verbal and Passive styles do not appear to be those students prefer, despite teachers most frequently using them. In the seven groups standing out, the Sequential style is recurrent. The five learning styles dimensions should be interpreted as reference styles, which, combined, set a wide range of Learning styles (Honey and Mumford, 1989). However, learning styles prevail within a classroom, and the teaching style is also decisive, becoming tied in the teaching-learning process (Honey and Mumford, 1989). Some similar results were exposed by Kolb (2007), although doing a different description than Honey and Mumford (1989).

The statistical distribution of outcomes for the MBTI (Jessee et al., 2006) is shown in Figure 4, coming from questions Q11–Q14. Those plots represent the frequency of occurrence for the personality traits shown by the group of students surveyed. Figure 4A reports the main personality traits. From the 15 expressed, just five stand out: ExSeThPe, InSeThPe, InItThJu, InItThPe, and ExItThPe. The dominant group: ExSeThPe, focuses on the world around them, taking outside information. They think before acting, willing to change if something impulses them. Students prefer introversion over extroversion in the second group but agree with the first group on the remaining characteristics. The third and fourth groups agree on the first three characteristics. Figure 4B shows the personality distribution by gender. Men are mainly in five groups, while women are in six. Thus, for women: ExSeThJu, ExSeThPe, InItThJu, InSeThPe, InSeThJu, and ExSeFePe; and men: ExSeThPe, InSeThPe, InItThJu, InItThPe, and ExItThPe. Due to the gender balance in the sample (and population), the men's distribution is almost dominant, noting a radical change in that of women, where greater diversity is present.

The transition from online to face-to-face education after the confinement showed that the teaching/learning process did not favor all learning or teaching styles. Also, it did not work for all personality types. Teaching was not perceived as uniform, generating differences in student commitment, even if teachers and students aligned in the same teaching and learning styles (Mansor and Ismail, 2012). The personalities of both were a factor playing a decisive role in the evolution of education during the confinement.

Complete outcomes for the perception of teaching practices are presented in Figure 6 as normalized radar charts, including, as before, dashed lines for dispersion. Students perceived teaching strategies as diversified and balanced overall. Possibly, due to the diversity of topics studied each semester, students recognized that teachers managed the Learning neatly. Also, they perceived feedback, Social Learning, and motivation as being promoted. Practices and metacognition were still the weakest resources. Those scores, by the student, define his Inclusivity index implemented as a possible causal factor in the subsequent analysis.

Outcomes in the next sections present several variables together to find correlations between their categorical values. Two variables are commonly used and presented on each axis, either directly from the questionnaire or calculated in the same way as some of the indexes introduced in Section 4.3. The third variable is commonly reported in color to cross over variables in agreement with the corresponding bar at the center-bottom of the plot. Individual dots presented make reference to individual students classified in clusters. Figures 7A, B exhibit the relation between Affinity and Inclusivity indexes with the quartile distribution of declared performances by students (questions Q32 and Q36). Tiny horizontal displacements of dots (each one corresponding to one student) around the Affinity values are non-meaningful, just appearing to represent all students analyzed. Performance quartile belonging is shown in color in agreement with the color bar. Both plots show that during the online teaching and face-to-face periods in the “New Normal,” increased Affinity and Inclusivity played a crucial role in the performance (red dots), despite the lowest performance in both cases not appearing completely related to them.

Figure 8A compares Adaptation during the lockdown under online education (Q33) and the “New Normal” under face-to-face education (Q37) with other related variables. The scale used for Adaptation is 1–4 (in agreement with the color bar below it). Figure 8A compares Adaptation vs. Teaching quality (Q34) on a scale of 0–3 (worst-better) and Online performance (Q32) on a scale of 1–6 (worst-better). Figure 8B compares Adaptation vs. face-to-face education preference (Q38) on a scale of 0–3 (lower-higher) and also the face-to-face performance (Q36) using a scale of 1–6 (worst-better). Performances are reported in color in agreement with the color bar below it. The dashed white lines split the regions with the highest/lowest Adaptation vs. Teaching quality or face-to-face preference. Note that students form clusters where the small displacements are non-meaningful; the purpose is to show each student separately.

Figure 8A shows that students with better Adaptation exhibit better performance during the online learning period, while students with the lowest Adaptation had lower online performance. The latter students evaluated online Teaching quality as the lowest. For the “New Normal period,” students perceiving the highest performance clearly preferred face-to-face teaching and had the highest Adaptation for returning. Still, students with the lowest performance preferred the face-to-face learning approach. The last analysis shows most students' clear preference for the face-to-face model, independently from their performance in any period. In the online learning period, Teaching quality perception was down independently from the level of Adaptation. Comparisons between the two types of Adaptation are presented and discussed below.

Compared performances in each pandemic period (online and face-to-face teaching) are presented here. A third variable is included in Figures 9A–C, respectively: face-to-face preference over online preference, Gender, or Program plan (Tec20 or Tec21). The dashed white line splits the cases with the highest/lowest performances during each period as a reference.

Figure 9A shows that most students with medium or higher performances mainly prefer face-to-face Learning. Just a few students surveyed exhibited low performances in both periods together. Considering most of the surveyed students were men, Figure 9B does not reveal apparent differences by gender in perceptions. Note the little groups of women and men with low online performances after having a better face-to-face performance. Figure 9C compares the two educational models, noting that most students surveyed fell under the Tec21 model, as expected (see Table 1). Only a tiny segment of Tec21 students exhibited low perceptions during online Learning but after notably higher face-to-face perceptions (red dots on the left side and in the upper region). By comparing the data in more detail, Figure 9C highlights that 20% of the students of the Tec20 plan did not have the expected performance in either of the two learning models. Meanwhile, Tec21 students (75 of 108 had not experienced the face-to-face model in the university) also perceived their performance was inadequate in both models.

Relations suggested by Figure 10A between Personality traits and academic performance should be analyzed quantitatively. Performances at each stage were compared in agreement with the survey outcomes classified by a Personality trait, subjecting this to a two-way ANOVA test (Cohen, 1988; Gelman, 2005) to decide the real variability through each dimension. Note that Performances in each stage have different scales (level 1 in Q32 is not present in Q36 due to the short period of face-to-face education). Performance values were normalized on the scale [0, 1], becoming barely comparable. Then, considering the Personality trait as a first factor and the pandemic stage as a second factor, the normalized Performance was compared using the test with a significance of α = 0.05. The test is shown in Table 2. The p-values do not indicate statistically meaningful differences by Personality trait or Pandemic stage (α < p) despite, under a different context, a limited impact is observed between personality and engagement in the online courses during the lockdown (Quigley et al., 2022).

Repeating the procedure, this time using the Learning style as the first factor, it was found that the test did not indicate meaningful differences either (Table 3). This outcome could be explained due to the impact of Affinity, as observed in Figure 7, possibly overshadowing the direct effect on Performance because of the adaptability exhibited. Such findings are interesting because discipline could maintain students' efforts through the transition. Such outcomes are positive; personality traits and learning styles did not play a fundamental role in the Performances cluster, possibly to the Affinity with the primary learning styles and the broad Inclusivity of the leading academic Personality traits. Outcomes were compatible with those observed in Figure 9, impacting most students.

From the findings in Figure 8, denoting the impact of Adaptation on Performance, a quantitative analysis was developed. Researchers assessed a meaningful transition by considering the declared Adaptation index in each stage, first considering Personality traits (Table 4) and then Learning styles (Table 5) as the first factor, respectively. The support given to teachers adapting their courses to an online version promoted the use of technological resources. Also, the institutional knowledge acquired during the confinement, the follow-up given to students, and the continuous assessment boosted the continuous improvement of the blended teaching approach, as observed in similar contexts (Akram et al., 2021).

In this case, only the Personality trait exhibited as a statistically significant factor for Adaptation (α = 0.05 < p = 0.0224), interestingly not for Learning styles. It suggests that Adaptation could be more related to academic Personality than Learning styles. Also, there is no significant change in Adaptation going to the “New Normal.” This fact is also positive and already guessed from Figure 8, where most students exhibited satisfactory Adaptation only differentiated by Teaching quality or Teaching preference.

The outcomes observed in Figures 8A, 9A suggest that teaching quality is well evaluated by students with good Online performances, despite their preference for the face-to-face model. Students were asked about the Online teaching quality (Q34) and face-to-face education preference (Q38). If both questions are well evaluated, there is no clear evidence about a real preference for the face-to-face approach. If they are not concordant (either poor online education with face-to-face preference or high online education with poor face-to-face preference), it means a double check for the preference on one of the models (face-to-face or online, respectively). To perform such a comparison, Figure 10B shows the number of students (vertical axis) fulfilling with each pair of outcomes: Teaching online quality (Q34) and face-to-face education preference (Q38); then with the average Online performance reported at the top of each bar, and calculated for each subset of students.

Regarding the previous remarks, most students preferring face-to-face education had low perceptions of online Teaching quality. Interestingly, such students expressed almost the lowest Online performance (3.8 in red). It possibly means those students were academically affected by the type of education during the confinement. Students in the middle, the second most, indicated a medium online Teaching quality and no preference between online and face-to-face education, with moderately high Online performance (4.4 in orange). Thus, such students undoubtedly correspond to all those indifferent to the teaching model, probably because they had no notable academic losses. The less frequent group, students considering the online model as the best quality, notably had the second-highest Online performance (5.5), which agreed with their preference. Two other groups are located on each side. The first was those expressing their preference for the face-to-face model but evaluating their experience with the Online model very well. They exhibited the highest Online performance (5.9 in black), undoubtedly corresponding with the best students academically. The other group, those poorly evaluating the Online model but also preferring it, exhibited the lowest Online performance (3.3 in purple). Such a group certainly experienced more academic difficulties and obstacles.

The first two groups represent most of the students, it was found that the entire population was mainly split between those easily adapting to both education scenarios and those preferring the face-to-face model because their outcomes or performance under the online model was average or lower. For the latter students, education under the “New Normal” opened opportunities to overcome the confinement experience. In similar studies, the preference for online education appeared multivariate despite the technological conditions (Muthuprasad et al., 2021), or quality, flexibility, self-paced classes, or productivity (Gaba et al., 2021).

It was found in our engineering students, on average, the sustained discipline to maintain their academic performance through the lockdown. Our university was prepared to a great extent for such contingency in terms of technology culture, online and blended courses as part of the standard academic offering, teacher training in educational technologies and didactic methodologies by discipline, and a well-settled infrastructure for internet services, resources, support services, equipment for teachers, and technological culture. All that intangible value was already present to face the COVID-19 crisis with a rapid response and a sustained, but never before experienced, online education (Kaqinari et al., 2022).

Preferences expressed by students for face-to-face education should be interpreted as situations reflecting their effective learning and performance. In the analysis, it was observed that some students genuinely adapted to both models regarding performance but still preferred the face-to-face approach. Other groups favored one or the other models, and then expressed their opinion as a function of this fact. Such diversity, again, is the expression of the narrowed scenario for the sudden and emergent online education imposed during the COVID-19 confinement, but also accompanied by a set of institutional scaffolding actions. Those, together with previously developed culture, training, and facilities for online and blended learning (Mayers et al., 2022), could be compared with other experiences (Andreou et al., 2022).

In the present research, technological skills assess an element of academic success during confinement (Lorente-Rodríguez and Pulido-Montes, 2022). Students recognized themselves as competent in four of the five domains. Problem-solving is a skill the teacher/institution needs to support more. Notably, this is the lowest domain in women. Figure 6 also reflects the need for practical activities, class activities, and motivation using technology and combining individual and team didactic strategies. These actions may support more students who otherwise would drop courses or fail.

The outcomes in Figure 2 show a good relationship between Learning styles and teaching in the community on average but also show evidence of the necessity to increase active learning, reflection, auditory attention, and ability to simplify, doing what is necessary to use several communication channels to give instructions. For engineering students, during the transition from online to face-to-face classes under the “New Normal,” the academy must adapt activities to be carried out in the classroom or the university facilities considering the combinations of learning and teaching styles.

A no less important, permanent, and relevant aspect is the ability of each teacher to self-assess their didactic strategies considering a possible characterization of their students. The ability of each teacher to understand the causes and effects of their design decisions becomes relevant, not only to determine how they impact each type of student but also to understand the evolution of generations, adapting to the external impositions of the institution or society.

This aspect requires recurrent design activities to be carried out in the classroom or within the university facilities to boost the construction of interpersonal relationships and promote teamwork or peer-to-peer work. Interactive learning experiences should be encouraged, and the importance of the contents to be applied should be explained to reduce the inertia of overconfidence and comprehend the time to perform them.

It is recommended that the faculty accentuates and uses the pillars of affinity and inclusion to motivate student participation and make them aware of self-regulation. Communicating by writing and verbally what is expected of students in their deliverables and providing feedback will result in the desired student success and graduation profile. This will overcome the absence of recorded classes and the lack of other resources during the online model. The knowledge and application of diverse teaching and learning methodologies must be strengthened to strengthen academic inclusion (Jaegler, 2022).