Skill Development of Undergraduate Students through International Exchange Program

The international exchange program plays an important role in improving soft skills and helps graduates’ to be confident to compete in job market. Student can achieve a vast academic knowledge apart from extracurricular and co-curricular skills through attending exchange program. Therefore, this paper aims at studying the development of students acquired soft skills and the barriers they face to achieve the goal through the exchange program. The data obtained from 87 students who participated in exchange program in different countries of Asia. Besides descriptive statistics, factor analysis was performed to identify the achieved soft skills through exchange program. This study reveals that, a large number of students were attending in exchange program at their own cost and most of the students were from the year 3. Indubitably, Exchange program helps to improve students’ soft skill such as; communication skill, team building ability, adaptability with situation. Moreover, lectures delivered to the students were found interesting. Results of the study are expected to motivate students attending in international exchange program and will offer a right direction to the students to get an expected outcome

Introduction
Learning is a complex process because of individual's ability to learn and way of learning. The goal of learning is to make sense of and using new information and experiences. During student life a student can get experience of new information from his/her institution as well as more experience of technology and culture can be gathered through exchange program. A student from developing country like Bangladesh some time spends a lot to attend in the exchange program for gather more experience and knowledge. However, there is no literature to find the impact of exchange program on the Bangladeshi students through exchange program. According to Earley et. al. (2006), cultural intelligence signifies the ability to adopt with new culture, gaining cross-cultural knowledge through knowing how things are operated nationally and internationally. Therefore, international experience is working as the key force of increasing cultural intelligence (Thomas & Inkson, 2004). Now days, Employers are focusing to recruit the employees who have a good understanding about cultural issues, ability to manage international relationships and know how to adopt with diverse workforce beside subjective knowledge [Earley, et. al. (2006), Atalar (2020), Ledwith & Seymor (2001), Mak, et.al. (1999)]. In the same way, academic institutions have acknowledged the significance of global perspective by introducing international elements into their program outcome [Harvey & Bowers-Brown (2004), Cranmer (2006)].
The term internationalization and globalization can be described in various ways (Knight, (1997(Knight, ( ), (2004]. In this study, internationalization is defined as a systematic way that required continuous efforts to strengthen the relationships between national cultures (Crossman & Clarke, 2010). On the other hand, globalization denotes itself as the world system (Marginson, 2000) that emphasizes upon homogeneity (Gosling & Mintzberg, 2003). Therefore, business is not limited within a nation and as a result, it has to be connected with international suppliers or customers and, the companies demanding employees who have such abilities [Gupta & House, (2004), Rossen, et.al. (2000), Stohl (2001)]. Literatures show that, the ability of working with intercultural teams has a positive impact on critical thinking, ability to solve problem, decision making, creative and innovation, communication, cooperation and financial managment [Cacioppe, (1998), De Anca & Va´zquez (2007, Dubrin, et. al. (2006), Tung & Thomas (2003)].
The universities are promoting internationalization of their curriculum for the students over the years (Webb, 2005). In order to create balanced graduate, universities as ''cultural institutions'' are adopting frequent changing context including culture for making positive contributions to students (Welch & Denman, 1997). However, the internationalization in universities has been exaggerated since 1950's to respond cultural forces associated with global economy [Welch & Denman (1997), Goby (2007)]. Among the area of internationalization, international exchange programs are being frequently promoted among the students of universities [Hermans (2007), Leask (2007)].
It was Australia who first introduced international exchange program to Asian nations as a symbol of peace and expansion of trade (Davis et al. 1999). ERASMUS was launched in 1987 with the hope of staff and student mobility within the universities (Welch & Denman, 1997). The Success of ERASMUS not only inspired Australia to offer University Mobility in Asia and the Pacific (UMAP) but also leaded Australian youth ambassadors for development programmer, AIESEC for better cultural understandings (Davis, et. al, 1999). Realizing the effect of exchange program, Colleges and Universities of USA also offered exchange program for the intensification of internationalization (Crossman & Clarke, 2010).
According to Hillage & Pollard (1998) and Rothwell & Arnold (2007), employability are the skills and abilities which help a graduate to be employee or continuing employment. Graduate employability is one of the key parameters to evaluate performance of both an individual and an institutional level (Harvey, 2001). That is why; the universities are focusing and offering subject oriented knowledge, effective knowledge, skills as well as attitudes to its graduates [Leckey & McGuigan (1997), Cox & King (2006)]. Effective knowledge is the responsibility, positive to work, ability to work as a team as well as independently and interpersonal skills (Cassidy, 2006). It is expected that, international exchange program would be able to increase knowledge transfer and develop competencies among the graduates [Di Pietro (2015), Chan & Dimmock (2008), Teichler (2004)]. Moreover, international experience has impact on learning and adoptability of cultural sensitivity, personal and professional development which influences employability (Osland, 2008). Przytuła et al. (2020) found that exchange program filled the cross culture gap. Handayani and Wienanda (2020) showed that international exchange program improved soft skill of the students. Roy et al. (2019) classified the learning outcome through exchange program into three categoriescultural outcome, personal outcome and career outcome.
Nowadays undergrad students from Bangladesh are also joining in various international exchange program. Bangladeshi students are attending international exchange program in a regular basis however, the impact of this program on the participants is still hardly found in literature. Thus, the objective of the study is to evaluate students' perception regarding acquisition of soft skill through international exchange program.

Methodology
This study is a cross sectional study to explore the skill achieved through exchange program.

Data Collection
Data were collected from the students who participated in different exchange programs through mail. A number of 87 students of different universities of Bangladesh were interviewed with a semi structured questionnaire. The questionnaire was finalized after pilot test and based on expert opinion.
A total of 500 students participated in exchange program were requested to participate in the survey, therefore, the response rate is over 17%. Email interview usually gets a low response [Manfreda, et. all. (2008), Tuten (1997), Jones & Pitt (1999)].

Analytical Tools
Descriptive statistical analysis was used to explore the personal determinants of attending in exchange program and factor analysis was used to explore the achieved soft skills through exchange program.

Factor Analysis
Factor analysis is a statistical analysis for reducing variables into fewer. Goal of the analysis is to maximizing the common variance from all variables and keep the variables into a common score. Though different types of methods can be applied to extract the factors, this study used principal component analysis.

Principal Component Analysis (PCA)
PCA, at first, formed a factor which maximizing variance of the variables. After that, the variance explained by the first factors is removed and then again maximizing variance of the variables to form second factor. The process continue till forming the last factor.

Factor loading
Factor loading is the correlation between the study variable and factor. Factor loading indicates the variance explained by the variable of the particular factor.

Eigenvalues
Eigenvalues is also called characteristic roots. Eigenvalues indicates variance explained by the factor out of the total variance.

Factor score
Factor score is the row and columns, which can be used as an index of all variables and can be used for further analysis.

Criteria for determining the number of factors
According to the Kaiser Criterion, the eigenvalue greater than 1 is a good criterion to select a factor and this study selected such 4 factors.

Rotation method
Among the different rotation method, varimax rotation method was used in the study for maximizing the squared correlation of items related to the factor.

Background characteristics of the participants
In Table 1 it is found that, among the respondents 54% were female and more than 56% participants were from first and second year of their academic session. About 87% participants were attendant in the less than 2 week's duration of exchange program.
Most of them (94.3%) have communication with their friends with whom introduce during the program. More than 54% respondents were attended using their own fund. About 57% respondents were using Facebook as a medium of communication with their friends of exchange program, whereas 26% wire using WhatsApp as a tool of communication (Fig 1). 44 Figure 1: Medium of communication used to connect with program friends Table 2 represents the adequateness of the sample and validity of factor analysis of the data. Kaiser Mayer Olkin measure represents that the sample size is adequate to run the factor analysis as the value is 0.839 (> 0.6). Bartlett's test indicates that correlation matrix is not an identity matrix as p value <0.01. There is no perfect correlation between the items (Appendix Table A1) though they may significant. Thus, all the criteria are indicating that, it is possible to run factor analysis for the data. Table 3 presents the percent of total variation which is explained by each component. It is observed that, first four variable has eigenvalue more than 1. And these variables can explain about 65% variation of the total. That is, the components can be reduced into four components. The observed variables are categories into the latent component based on the highest loadings for a component. The four components can be defined as personal skills teaching method collaboration and satisfaction were the achievement for attendee from an exchange program (Table 4). Timlin (2017) found that, the exchange program is developing students' personal, civic, academic, and professional skills. The personal skills are the linear combination of different culture acquisition, learn how to face real life problem, teamwork skill, adaptation with situation, understanding global job market requirement (Petzold, 2017), communication skills, self-esteem presentation skill. In exchange program, the participants found the classrooms a very interesting place of teaching and learning are filled with fun and enjoyed the lecture which was delivered with an appropriate example and eventually, students got interest in it. Miglietti (2015) also found that, international educational exchange had a significant impact on teaching in the classroom. According to their perception, this is a platform offers students' with great opportunity for collaboration that leads to friendship development group to solve problem and help each other. Sato, et. al. (2018) also expressed that international exchange among Asian youths would contribute to understand each other lifestyle. The participants were satisfied with the exchange program due to its' perfect arrangement and making the environment -a place of effective learning which raises interest in them to join again as well as to suggest their friend to join in exchange program.

Conclusion
The social media should consider as a media of communication to collect information or data. Facebook, a very common and popular social media can play significant role to collect data or information quickly rather than any other digital communication media or traditional methods. Study reveals that, 58% of respond of exchange students' priority go to Facebook as communication media whereas, the priority of other respond is 26% through WhatsApp, 9% through email, 4% through Instagram and 3% for other way of communication.
It is found that 32.1% students are participated in exchange program through partial-funded and 13.7% students are fully-funded to participate international exchange program.
It should be mentioned here that, the 70% of the students of Daffodil International University (DIU) are from the rural areas of Bangladesh whose socio-economic status is not that much solvent.
That's why, many meritorious and insolvent students are not getting the opportunity to join the international exchange program due to pecuniary troubles. The findings of this research also express and indicate that, concentration should be given to the meritorious and insolvent students so that they could get the opportunity to attend in international exchange program with full funded scholarship or full grant from government or non-government organization for the betterment of the nation.
The significant achievement of student exchange program is to enhance the personal skills, interpersonal skills, communication skills, and soft skills, know the techniques and approaches and methods of teaching, collaboration or making friendship with the global student, increase their global network to learn foreign culture and experience. Therefore, it can be claimed that, if an institute or community wants to create graduate for global job market with soft skills along with their academic knowledge needs to involve their students with international exchange program.


s2fieldsofstudy: Education Psychology

text:
Symptomatology of attention deficit, hyperactivity and defiant behavior as predictors of academic achievement

Background It is essential to understand the factors that affect the academic achievement of schoolchildren, both in general and in terms of the major subsectors of each grade. Although symptoms of Attention Deficit Hyperactivity Disorder (ADHD) and Negative Defiant Disorder (NDD—which are commonly recognized as externalizing problems in childhood and adolescence—have been associated with lower academic achievement in the international literature, few studies have addressed this problem in Latin America. This study aimed to analyze the possible predictive relationship of attention problems, hyperactivity, and defiant behavior on academic achievement. Methods We recruited a sample of 4580 schoolchildren (50.9% female, 1754 belonging to primary school, and 2826 to secondary school, ranging from 9 to 18 years old). This cross-sectional study used the scales pertaining to attention problems, hyperactivity, and challenging behavior from the Child and Adolescent Evaluation System. Results The analysis showed that attention problems significantly affected all academic achievement areas, while hyperactivity and challenging behavior affected only some of them. The regression models explained 24% of the variability in overall academic achievement in primary school and 17% in secondary school. Other predictors included sex, age, socioeconomic level, and school attendance. Conclusions It is important to consider this symptomatology in the design of educational interventions.

Introduction
Although it may be debatable to consider academic achievement as the sole criterion for school success, evidence indicates that it can determine both the probability of completing formal education-and thus, the possibilities of developing one's own life project-as well as success in higher education and even in subsequent career development [1]. Growing evidence shows that externalizing mental health problems plays a key role in academic achievement during the school years [2]. Existing literature defines externalizing mental health problems as behaviors and psychological alterations in the behavioral domain, of which the manifestations may produce conflict or harm in the environment [3]. Borba and Marin demonstrated a relationship between externalizing problems and academic achievement, showing that these problems can lead students to experience negative relationships with teachers and peers, which consequently decreases their interest and motivation to learn [4].
Attention deficit hyperactivity disorder (ADHD), and negative defiant disorder (NDD) have been recognized for decades as two types of symptom configurations of externalizing problems. ADHD, as an externalizing problem, plays an important role in the relationship between Open Access *Correspondence: jflores@uta.cl 1 Escuela de Psicología y Filosofía, Universidad de Tarapacá & Centro de Justicia Educacional, CJE, Avenida 18 de Septiembre 2222, Arica, Chile Full list of author information is available at the end of the article children's mental health and academic achievement, whether considered on its own [5] or along with NDD as a comorbidity. Children and adolescents who suffer from NDD in addition to ADHD perform similarly to those with ADHD alone, but have more behavioral problems [6].
Globally, the prevalence of ADHD among children younger than 18 years old of age is estimated to be 7.2% [7]. Similarly, the prevalence of NDD varies between 3.1-3.3% [8,9]. The situation in Chile is alarming; the last national epidemiological study in Chile established that ADHD was the most prevalent mental health problem in the infant-juvenile population with a prevalence of 15% overall, 23.9% among children aged 4-11 years, and 5.4% among adolescents aged 12-18 years. More recently, another study estimated the prevalence of ADHD to be 6.4% among 3-17-year-olds [10]. Considering these high figures, compared with international literature, the possibility of overdiagnosis of ADHD among the Chilean child and adolescent population has been questioned [11]. Such a situation would start from the school system, with principals and teachers requesting referral and diagnostic confirmation to access additional resources for their care as students with transient special educational needs under the current Chilean educational regulations [12,13]. Meanwhile, NDD had an overall prevalence of 8.7%, broken down to 11.8 and 5.1% for each age group, respectively [14].
Existing research shows that males are more frequently diagnosed with ADHD than females. Although objective tests may not find significant differences in attention problems, parents tend to identify more attention problems among female children and teachers more disruptive behavior problems among male students [15]. NDD is also more common among males than females [16].
Previous studies find that these disorders have been associated with various negative outcomes, among both school children and adults. These outcomes include increased risk of involvement in delinquent acts, increased conflict in peer and family relationships, poorer academic achievement, dropping out of school, substance use, suicide ideation, traffic accidents, employment instability, and increased obesity [17,18]. All of this has been associated with a substantial economic cost to society, in part due to increased utilization of various public services relative to those without ADHD [19,20]. Another recent review of longitudinal studies included comorbidity with NDD. It finds that these children and adolescents experience problems related to psychosocial dysfunction with family, peers, and school, as well as issues of non-compliance with rules [21].
The symptoms of ADHD are continuously distributed within the population, extending ADHD-associated risk to be present among people who do not meet the diagnostic threshold [2]. It appears that when ADHD occurs in comorbidity with NDD, school psychosocial adjustment difficulties are increased among children and adolescents [6]. Females with ADHD have been found to perform better than males in language and mathematics [5]. A previous study found no differences in the areas of language and mathematics; however, it did find differences in the area of science, showing that males performed better than females [22]. Another investigation that included students with both ADHD and NDD symptomatology found no differences in academic achievement by sex [23].
Previous research considered academic achievement in different ways, mainly in terms of performance. Some used the final average score for each grade or subsector. Others used the score on standardized tests applied regularly at a national level [5]. Tests that measure academic achievement at the time of applying the test in one of the areas of interest have also been used [24]. Even parent or teacher ratings have been used as references for academic achievement [6,25]. A combination of two of the above criteria has also been used [26].
Socioeconomic status may have a chronic disadvantageous effect on students' academic achievement, as research consistently presents a negative relationship between socioeconomic status and academic achievement [27]. Additionally, there is evidence that professionals may experience subjective bias when dealing with minorities and vulnerable groups, diagnosing with NDD or behavioral disorder, rather than the actual ADHD presented [28].
Current evidence indicates that treatment for ADHD requires a multimodal approach [29], with cognitivebehavioral psychotherapy being consistently effective, especially in combination with pharmacotherapy [30,31]. A decisive aspect of ensuring success is to incorporate families and educators in treatment, to thereby help them to better understand the underlying symptomatology of this disorder [30][31][32]. Then they can collaborate optimally with health professionals.
Nevertheless, in educational terms, the emphasis on diagnosing ADHD may be counterproductive, as students with low levels of symptomatology may experience negative consequences, such as being stigmatized in the school system, which could outweigh the potential benefit of receiving treatment [33]. Recently, a systematic review on ADHD in children and adolescents [34] found a global increase in ADHD diagnosis between 1989 and 2017; this could be related to overdiagnosis and pharmacological overtreatment associated with the medicalization of childhood [35,36]. This systematic review of the literature identified several adverse effects associated with an ADHD diagnosis in various areas of children's lives, including academics performance [37,38]. In Chile, for example, a mixeddesign analysis of complaints related to mistreatment and discrimination at school reported by parents and guardians to the Superintendence of Education revealed that -both nationally and in all regions of the country-more than 50% of complaints were related to ADHD diagnoses; parents referred to different experiences of what they called "educational exclusion" because of having received the diagnosis [39].
Consequently, rather than an accurate diagnosis of these disorders in students, the presence of symptomatology related to attention problems, hyperactivity, and defiant behavior may be key to understanding the factors that affect academic achievement. An existing longitudinal study found that each symptoms separately and negatively affects academic achievement among students between the ages of 7 and 16 [2]. Among ADHD symptomatology, attention problems have been found to be the most relevant to academic achievement [25,26,40]. Conversely, hyperactivity has not always been found to be significantly associated with academic achievement [41].
In the past, there has been some focus on specific disciplinary areas of academic achievement. A review of studies involving ADHD and mathematics academic achievement, considering specific tests -rather than grade-level academic achievement-showed a significant effect [42]. Meanwhile, in terms of of language, a longitudinal study illuminated the complex relationship between ADHD and reading achievement; high ADHD symptoms at age 5 predicted low academic achievement around the age of 9 years. However, low academic achievement at age 5 also predicted high ADHD symptoms around the age of 9 years [43].
No other studies have been found that addressed this problem outside the United States or Europe. In Latin America, a significant relationship between ADHD and academic achievement has been found within the age range of 8-17 years old, by studies in Brazil [18], Colombia [44] and Argentina [45]. In Chile, a previous study addressed the relationship between ADHD and academic achievement in language tests, using a sample of 71 students in third and fourth grade of primary school (aged 8 to 10 years); it shows a significant relationship between the factors [46]. No studies have been found that addressed the relationship between ADHD/NDD symptomatology and academic achievement in primary or secondary grades or larger sample sizes. Knowledge on this relationship in a wide age range, while considering a diversity of areas of academic achievement, would contribute to the existing knowledge and would allow the design of interventions in the school setting in a timely manner.
In the present study, we used a screening instrument that does not directly address the diagnosis of ADHD, which allows us to assess the presence of common symptomatology of this disorder, such as attention problems and hyperactivity. The same is true with respect to NDD. It should be noted that NDD is also known as Oppositional Defiant Disorder.
Consequently, the aim of this study was to analyze the possible predictive relationship between attention problems, hyperactivity, and defiant behavior on academic achievement. The research hypotheses were as follows: a) the symptomatology of these externalizing problems predicts academic achievement in different disciplinary areas, and b) attention problems are the most relevant predictors.

Methodology
This is a cross-sectional observational study looking for correlations between ADHD/NDD symptomatology and measures of academic performance [47]..

Sample
The sample comprised 4580 students. Participants were divided into primary and secondary school groups, and the primary and secondary samples were considered separately because different versions of the Child and Adolescent Evaluation System (Sistema de Evaluación de Niños y Adolescentes, SENA) instrument were applied.. The baseline sociodemographic statistics are presented in Table 1. The educational dependence of the school refers to the origin of its source of funding to operation. This can be fully provided by the state (public), partially provided by the state (subsidized) or fully provided by the parents (private).
The primary school sample consisted of 1754 primary school students from public, subsidized, and private schools in Arica. A total of 49.1% were male, and 50.9% were female. The mean age was 10.1, and the standard deviation (SD) was ±0.9, with an age range of 9-12 years. It included students from grades 4-6.
The secondary school sample consisted of 2826 high school students from public, subsidized, and private schools in Arica. Of the participants, 51.1% were male, and 48.9% were female. The mean age was 14.4 (SD ±1.7), with an age range of 12-18 years. It included students from grades 7-12.

Procedure
This study was approved by the Ethics Committee of the Universidad de Tarapacá. Next, 35 educational establishments in the city of Arica were invited to participate in the study. Informed consent was obtained from the parents after explaining the purpose and scope of the study, after which the students themselves were asked for their consent. In terms of evaluation, questionnaires were performed in groups within each grade. At least two trained interviewers answered questions, together with the teacher of the same course. The duration was approximately 45 min.

Instruments
The SENA system, mentioned earlier, was used as an instrument in this study [48]. This instrument was developed by specialists in psychopathology and psychological assessment to help in the detection of a wide range of emotional and behavioral problems among children from 3 to 18 years old. It is noteworthy that it was developed and validated entirely in Spanish, which makes it easier for children participating in this study to understand and complete. Three scales of externalizing problems were used: attention problems, hyperactivity-impulsivity, and defiant behavior. This study used the self-reporting versions for both the primary school and secondary school groups.
Answers were provided on a Likert-type scale from 1 -"never or almost never" to 5 -"always or almost always. "The total of each dimension is the average of the dimension responses and can vary from 1 to 5. Examples are: "I find it hard to be quiet doing things" (hyperactivity), "I am easily distracted" (attention problems), "I talk back to my parents or teachers" (defiant behavior). Some items vary slightly in wording between the two versions.
The hyperactivity-impulsivity scales in both primary and secondary school questionnaires each have 10 items, while the defiant behavior scale has four items in the primary school and three in the secondary school questionnaire. In the present study, the reliability of these scales, both in Alpha and Omega, were .81 for hyperactivityimpulsivity, .83 for attention problems, and .71 for defiant behavior in primary school. Meanwhile, for secondary school, it was .80, .87 and .68 respectively. This coincides with the reliability obtained by Sánchez-Sánchez research team [49]. The SENA can be used to classify students into four zones: low risk, caution, clinical significance and extreme. Regarding the percentages in each zone, the broad results of this project are described in another study, which also includes a larger sample [50].

Chilean Ministry of Education SIGE database
The school management system database of the Ministry of Education collects annual data on students throughout Chile. Access to this database is restricted and students are assigned an individual code to maintain their anonymity.
The data used from this database were age, grade, sex, school attendance, school dependence, socioeconomic level, and academic achievement. Both the average school academic achievement and the averages of the main subsectors were considered to carry out a more detailed analysis. The averages per subsector considered were: 1) mathematics; 2) language; 3) natural sciences; 4) social sciences; 5) visual arts; and 6) physical education.
The socioeconomic level was classified according to the priority in the allocation of school subsidy resources granted to the schools for each student by the Ministry of Education. This has three options: first priority (priority), second priority (preferential), and non-priority Priority was 30% and refers to students belonging to the most vulnerable families who live in extreme poverty, while preferential students belong to the 80% most vulnerable [51]. The coding of the educational dependence of the school was public as 1, subsidized as 2 and private as 3.

Statistical analysis
A descriptive analysis was performed using the study variables. Subsequently, correlation and multiple linear regression analyses were performed using SPSS version 22 sofware. Seven regression models were analyzed for each school group: primary and secondary. The only change was the dependent variable. In the first model the dependent variable is the overall performance average, while in the following six, the averages of each of the subsectors were used: language, mathematics, natural sciences, social sciences, visual arts, and physical education.
The predictors considered in all regresion models were attention problems, hyperactivity-impulsivity, defiant behavior, school attendance, age, socioeconomic level, and sex (1 = male, 2 = female). Demographic variables (age, sex and socioeconomic level) and school attendance were introduced to control for their effects. This allowed us to know whether the predictors contributed in all the regression models to a similar extent.

Results
The skewness and kurtosis of the variables were within the normal distribution range for both primary and secondary schools [52]. Table 2 presents the correlations and the means (and SDs) of the primary school group variables. It shows that the correlation of each of the externalizing problems scales was higher for general academic achievement than for each of the subsectors. The most significant association was the inverse correlation between hyperactivity and general academic achievement. Overall academic achievement was not significantly correlated with the educational dependence of the school; however, it was positively correlated with socioeconomic level, school attendance, and students' age. None of the externalizing problems scales were significantly correlated with socioeconomic level. The inverse correlation between defiant behavior and educational dependency was found only for the latter variable. Both attention problems and hyperactivity-impulsivity were positively correlated with age. School attendance was negatively correlated with the two aforementioned symptoms.. Table 3 presents the correlations and the means (and SDs) of each variable in the secondary school group. The sample size (n) used for each variable is specified, since depending on the schools' curricula, the subsectors may vary in the number of students who took these subjects.
The correlations of externalizing problems with overall academic achievement were all negative and significant, with attention problems again being the highest. However, they were lower in magnitude than in the primary school group. Academic achievement did not correlate significantly with age, but correlated positively with the educational dependence of the school, school attendance, and socioeconomic level. None of the externalizing problems correlated significantly with age or socioeconomic level. However, all of these factors correlated positively and significantly with educational dependence. School attendance correlated negatively with attention problems and hyperactivity-impulsivity, as well as with defiant behavior.
Finally, seven regression models were estimated for overall academic achievement and the main subsectors in each group. In both primary and secondary schools, academic achievement (overall or by subsector) was correlated with age, educational dependence, Table 2 Correlations and descriptive statistics of behavioral problems and achievement in primary school socioeconomic level, and school attendance, which were controlled for in all regression analyses by including them as predictors. Sex was also controlled for. Attention problems, hyperactivity-impulsivity, defiant behavior were considered as predictors of overall academic achievement and of each subsector separately (see Table 4).
In the primary school group, the results show that, with the exception of hyperactivity-impulsivity, the predictors explain 23.7% of the variance in overall academic achievement. Although hyperactivity-impulsivity does not contribute to explaining overall academic achievement, it is present in the prediction models of mathematics, languages, and social sciences. Attention problems are the most important predictor in almost all models, except for visual arts and physical education, where age is of greater magnitude. Defiant behavior is present in the general achievement model, as well as in language and social studies. Apart from the general achievement model, sex is only a factor in languages and visual arts, with female students obtaining higher achievements. The socioeconomic level affects all models; this shows that a higher socioeconomic level predicts better academic achievement. Age also appears in all models, with a negative predictive relationship with academic achievement. The predictive capacity of the regression models in each subsector varies from 11.2% in physical education to 17.8% for both language and social sciences.
In the secondary school group, overall academic achievement can be explained by 17.1% of all predictors. Hyperactivity-impulsivity appears in all models, except for the visual arts and physical education. Again, attention problems were the most important predictor in the first five models. Defiant behavior is present in the general achievement model, as well as in mathematics and social studies. Sex is present again in the general achievement model, where its magnitude doubles, as well as in language and visual arts. However, unlike in the primary school group, it is also present in the natural science and social science regression models. In all of them, being female predicts better academic achievement. Unlike in primary school, age appears to have a positive predictive relationship in the models, but only in the mathematics and physical education subsectors. The socioeconomic level was present in all models. The predictive capacity of the models in each subsector varied from 5.7% in physical education to 15% in languages.
The presence of collinearity between the independent variables was ruled out by the inflated variance factor, which was < 2 in all of them, which is well below the usually accepted cut-off [53].

Discussion
The present study aimed to analyze the possible predictive relationship of attention problems, hyperactivity, and defiant behavior on academic achievement. Our results completely or partially supported the hypotheses of the study. The main finding was that the variable most closely related to academic achievement was attention problems. This was followed by hyperactivity-impulsivity, and finally, defiant behavior. However, the latter two are not always predictors. In this relationship, demographic variables (sex, age, and socioeconomic level), school attendance, and the school's educational dependence were controlled for in the analyses.
One strength of this study is its the large sample size. Another strength was the wide age range of participants, as we considered both primary and secondary school students. Another favorable aspect was the analysis of academic performance in a broader spectrum than usual, including various subsectors of academic performance. That allows for a more complete picture of the relationship.
The analysis results partially support the first hypothesis, as each variable separately was significant in predicting some of the areas of academic achievement. Our analysis showed that attention problems is the most relevant predictor associated with overall academic achievement in some learning subsectors. This confirms the second hypothesis, and is in line with existing international literature that denotes attention problems as one of the most important externalizing mental health factors with respect to academic achievement [41]. Attention problems was the only predictor that was present in all regression models. These results are consistent with those of previous research [4,25,26,44]. The conceptual explanation for why attention problems affect academic achievement requires further research; however, it is likely related to a different use of attentional resources [54] than what regular classroom teachers assume and expect among students. In contrast, hyperactivityimpulsivity was not present in all primary or secondary regression models, which is consistent with the results of previous international studies [41]. Defiant behavior was also not constant in these models, although it was constant in both primary and secondary models of overall academic achievement.
The regression model explained 6.6% more in the primary school sample than in the secondary school sample. It is possible that this is because symptomatology is more prevalent in primary school than in secondary school. It is also possible that increased academic difficulty and greater complexity in social relationships play a role in this difference [23,55]. It is notable that different achievement areas that are not usually considered in achievement research, such as social studies, natural sciences, and especially visual arts, showed similar results to language and mathematics. The variance explained between them in the primary school sample is lower than 3%. Meanwhile, physical education remains lower, but still has predictive capacity. In the secondary school sample, the same areas also behave similarly, not varying by more than 3% among themselves. Once again, the only area that is notably different is physical education. This suggests that the academic difficulties experienced by students with ADHD/NDD symptomatology are not limited to subsectors that require greater logical-deductive resources, such as mathematics, but extend to other areas of academic achievement that require the development of sensitive competencies and fine motor skills, such as visual arts. However, the difference generated in each subsector in the predictive capacity of the models-for both primary and secondary schools-decreases by no more than 3% in mathematics, language, and natural sciences. In the social sciences, it decreases by 6.2% and in physical education by 5.5%. Meanwhile, visual arts had exactly the same value in both school groups.
It may be considered insufficient to use the final average of each subject and the general average of each grade as academic achievement indicators, rather than standardized tests that measure specific skills. However, students' grades are those that they face in their daily context, along with their actions and reactions in their interpersonal relationships with their teachers and parents. Moreover, starting in secondary school, these grades are accumulated as part of their grade for higher education, at least in Chile.
The results suggest that, in addition to the mental health predictors in its externalizing symptomatology, there are sociodemographic factors that affect academic achievement, although not all of them in the same way and in the same directionality, referring to primary and secondary school. In line with the existing literature [27], socioeconomic level was significantly and negatively associated with academic achievement at both levels of education. Globally, this coincides with previous research that shows a positive relationship with academic achievement [43]. When analyzing its effect in terms of primary and secondary school, we find that, although it is significant in all primary and secondary models, it decreases in the latter group in terms of overall academic achievement.
In terms of students' sex, in the overall academic achievement model, the results coincide with previous research that shows that female students experience better academic achievement than male students [15]. However, it is notable that, in the mathematical regression models of both groups, sex does not generate significant differences, which contradicts the results of a previous study that found that female students perform better in this area; we must keep in mind that, in this latter study, a mathematics-specific test was used [5]. This could indicate that there is a differential effect in terms of  sex, depending on which academic achievement is being assessed. However, we must also take into account that in secondary school, a significant effect related to students' sex appears in two models in which it did not appear in primary school (natural sciences and social sciences); moreover, its magnitude doubles in the general model. This could be explained by biological maturation as well as a cultural socialization effect [23,55].
Age also shows complex behavior as a variable, since it appears as a significant predictor in all primary models in a negative relationship with academic achievement, indicating that, as primary students advance in their level of education, they obtain progressively worse academic grades. In contrast, in secondary school, the relationship between age and school academic achievement appears to be non-linear; in four models, it presents a positive correlation with academic achievement, but continues with a negative relationship in the natural sciences model. Mathematically, it does not appear to be a significant predictor.
It is notable that age is positively and significantly related to hyperactivity-impulsivity in the primary school group but has no relationship in the high school group. This suggests that there is a cut-off point beyond which symptoms may decline. These results coincide with those found in a previous study [56], where children diagnosed with ADHD were almost twice as high in the 10-11-year-old group than in the 8-9 year-old group. However, this study only included primary school students up to 11 years old. This is consistent with the lower prevalence of ADHD in adolescents than in children [10].
The results of this study provide clear evidence of the association between greater self-reporting of attentional difficulties and students' own perception and lower academic achievement. In the school context, and given the negative implications associated with the (over) diagnosis of ADHD [34], the symptomatology associated with the self-perception of attentional difficulties needs to be considered on its own [2].
Despite its strengths and contributions, this study has several limitations. First, the sampling was not random, which reduces the generalizability of the results. Second, our sample was limited to a single region of northern Chile. Third, only one source of information was used; the self-reported questionnaires completed by the students themselves. However, it is necessary to consider that, from students' self-reports, this study identified symptoms related to attention problems, hyperactivity-impulsivity, and defiant behaviors, without approaching the configuration of these symptoms as disorders or the study of their comorbidities. Finally, although there were several months' difference between the application of the externalizing problems scales and the academic achievement results of each subsector, in general, causality in the relationship cannot be assumed.
Considering our findings, future research should include more geographical locations with randomly selected samples and more sources of information. Additionally, longitudinal studies would be desirable to understand the complex evolution of the relationships between reports of these problems from the perspective of students, as well as their teachers and parents, that challenge classroom normalization and pedagogical authority [57] and their effects on students' academic achievement and school trajectories. This should be viewed from the perspective of their permanence, promotion, and graduation from primary and secondary education [58,59]. However, it would be interesting to incorporate other variables that have received less attention in relation to achievement, such as the study habits of children and adolescents, as evidence has been found to differ significantly in students with and without ADHD [60].

Conclusion
The symptomatology of externalizing problems considered in this study-attention problems, hyperactivity-impulsivity, and defiant behavior-have a significant relationship with academic achievement in general, as well as with some learning subsectors. After controlling for the effect of students' socioeconomic level, age, sex, and school attendance, as well as hyperactivity problems and defiant behavior, self-reported attention problems were the most significant and consistent predictors of overall school academic achievement, both in primary and in secondary school. It is suggested that self-identified attention problems be addressed by the students themselves, by designing interventions within the school context that incorporate the diverse ways in which students distribute their attentional resources in the pedagogical activities planned by teachers. On the contrary, overdiagnosis of ADHD is not recommended, given the adverse effects of the consequent pharmacological overtreatment and stigmatization in the school context.