An Individual-Intellectual Model of Students’ Academic Achievement (Based on Humanitarian Specializations)

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Abstract

Any educational institution implementing the Federal State Educational Standards (FSES) is faced with the task of forming the necessary competencies in students. The level of competence formation is reflected, among other things, in academic achievement. Despite the interest in this topic among scientists, the indirect effects of multilevel individual traits on students’ academic achievements have not yet considered through intelligence and creativity in detail. In this study, individual-intellectual models tested students’ academic achievement. The sample consisted of 415 students of Perm city universities aged 17 to 22 years, 293 female and 122 male. Structural equation modeling was in use for shared and partial groups. The main obtained results were as follows. In the shared models, none of psychometric intelligence and psychometric creativity variables served mediators between the individual traits and academic achievement. In the partial models, fluid intelligence and fluency also did not operate as mediators. Three partial models were fit the data in respect with the mediation structure. Crystallized intelligence, originality, and flexibility acted as separate mediators. The mediator models entered the individual traits: excitation (nervous system), activity (temperament), open-mindedness, belonged self (personality). Mediator effects were observed under different Compositions and combinations of individual traits. Thus, a number of individual-intellectual integrations received empirical support for students’ academic achievement.

General Information

Keywords: traits of individuality, psychometric intelligence, psychometric creativity, academic achievement, mediation model

Journal rubric: Educational Psychology

Article type: scientific article

DOI: https://doi.org/10.17759/pse.2022270407

Funding. The reported study was funded by the Russian Foundation for Basic Research (RFBR), project number 19-29-07046.

Received: 02.02.2021

Accepted:

For citation: Dorfman L.Y., Kalugin A.Yu. An Individual-Intellectual Model of Students’ Academic Achievement (Based on Humanitarian Specializations). Psikhologicheskaya nauka i obrazovanie = Psychological Science and Education, 2022. Vol. 27, no. 4, pp. 68–76. DOI: 10.17759/pse.2022270407.

Full text

Introduction

The problem of nurturing academic achievement among students is multidimensional; many factors are involved. Academic achievement comes in different types: grade point average; the results of subject Olympiads; the results of the Unified State Exam; initial, intermediate, and final assessments (in the form of seminars, tests, exams), etc. Nevertheless, various internal and external factors can be predictors of students’ academic achievement. Internal factors include motivation toward achievement and academic motivation [15], intelligence level [10; 17], critical, reflective, and creative thinking [13], academic self-efficacy [20], personality traits [17; 20], hope and optimism [18], psychological maturity [17], etc. External factors include socioeconomic status and type of school [21], upbringing [16], parental involvement [22], etc.

Individual traits, psychometric intelligence, and psychometric creativity as joint predictors of students’ academic achievement remain important, but problematic and insufficiently studied. These constructs are heterogeneous, have different theoretical backgrounds, and there are conceptual barriers between them. In order to include them in a joint study, it is necessary to find out the conditions under which they can fit into a common theoretical background [4]. One of the prerequisites for posing this problem is likely cross-theoretical integration [Ibid.].

The theoretical basis of this empirical study is an integration of the theories of V.S. Merlin [8] and D.V. Ushakov [12] (see details [5]). The mechanism of mediation is the locus of integration between the two theories. Psychometric intelligence and psychometric creativity act as mediating links between students’ individual traits and academic achievement. Although there are studies devoted to some aspects of the relationship between the indicators mentioned [7; 10; 17; 20], these studies affect only some aspects of individuality. The cumulative effect of multilevel traits has not actually been tested.

Some studies use complex mediator models to uncover the factors supporting academic achievement [14]. Nevertheless, the mediative function that both psychometric intelligence and creativity have between students’ individual multilevel traits and academic achievement remains largely beyond researchers’ attention.

The aim of the study was to build and examine an individual-intellectual model of the academic achievement of university students who were engaged in humanitarian work.

The following empirical hypotheses were tested:

  1. Psychometric intelligence and psychometric (verbal) creativity selectively mediate between students’ individual multilevel traits and their academic achievement.
  2. Individual multilevel traits are included in varieties of mediator models selectively.
  3. Psychometric intelligence and psychometric (verbal) creativity provide not one, but several ways to jointly activate the mediators between the students’ individual multilevel traits and their academic achievement.

Method

Participants

The study involved 415 students from higher educational institutions in Perm, including 293 females and 122 males aged 17 to 22 years (M = 18.6, SD = 1.0).

Measures

We studied the nervous system, temperament, and personality as multilevel traits of integral individuality [8]. A Russian adaptation of the Pavlovian Temperament Survey by J. Strelau was used to measure the nervous system [3]. A Russian adaptation of the Formal Characteristics of Behaviour — Temperament Inventory by J. Strelau was used to measure temperament [11]. A Russian adaptation of the Big Five Inventory-2 by C.J. Soto and O.P. John was used to measure personality traits [19]. The Four-Factor Self Questionnaire by L.Ya. Dorfman [6] was used to measure the self-concept.

A Russian adaptation of Guilford’s Alternate Uses was used to assess psychometric (verbal) creativity [1]. Raven’s Progressive Matrices [9] was used to measure fluid intelligence. The Universal Intellectual Test by N. A. Baturin and N. A. Kurgansky [2] was used to measure crystallized intelligence.

The average of students’ annual grades in all disciplines was computed to determine academic achievement. A five-point grading scale (exams) was used.

Data analysis

Individual multilevel traits were included in models as exogenous variables, psychometric intelligence and psychometric (verbal) creativity as mediator variables, and academic achievement as an endogenous variable. In addition, the covariances of the exogenous variables was entered into the models.

All individual traits initially were included in the model and then, one by one, those that least related to the mediator were excluded from the model. In the final model, there were only significant paths between variables.

The models for shared and partial groups differed. Shared models included 3 feasible candidate mediators: a) crystallized and fluid intelligence together (M1); b) fluency, flexibility, and originality of creativity jointly (M2); and c) intelligence (crystallized and fluid) and verbal creativity (fluency, flexibility, originality) jointly (M3). The partial models included crystallized (M4) and fluid (M5) intelligence, fluency (M6), flexibility (M7), and originality (M8) creativity separately as candidates for mediators.

Model fit indices were the chi-square statistic (χ2), the chi-square to df ratio (χ2/df), the Comparative Fit Index (CFI), and the Root Mean Square Error of Approximation (RMSEA). Additionally, the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were used.

Structural equation modeling data was tested in IBM SPSS AMOS v.22.

Results

When testing the shared models, M1 and M3 were low fit indices according to the ratio of chi-square statistics to degrees of freedom (χ2/df > 2). Hence, these models are not mediator models. The M2 model fit perfectly (RMSEA < 0.05, CFI > 0.95). The path coefficients from exogenous variables to candidate mediator variables were significant, but non-significant from the former to the endogenous variable. This means that this model cannot be as a mediator model either.

When the partial models were tested, models M4 and M5 were perfect fit indices (RMSEA < 0.05, CFI > 0.95). In these models, the path coefficients from exogenous variables to fluid intelligence and fluency were significant, but non-significant from the former to the endogenous variable. So, fluid intelligence and fluency did not serve as mediators in these models.

The M6, M7, and M8 models were perfectly fit as Table 1 shows.

In these models, the path coefficients from exogenous variables to crystallized intelligence, originality, and flexibility were significant, and also significant from the former to the endogenous variable. Hence, crystallized intelligence, originality, and flexibility served as mediators in these models (Fig. 1—3).

Table 1 Partial model fit indices М6, М7, М8

Partial models

Model fit indices

χ2

df

p

χ2 / df

CFI

RMSEA

AIC

BIC

М6. Crystallized intelligence as a mediator

1.99

6

0.92

0.33

1.00

0.001

32.0

92.4

М7. Originality as a mediator

0.60

1

0.44

0.60

1.00

0.001

10.6

30.7

М8. Flexibility as a mediator

2.30

2

0.32

1.15

0.99

0.02

18.3

50.5

Note: χ2 — chi-square statistic value; df — degrees of freedom; p — significance level; χ2 / df — relative chi-square; CFI — Comparative Fit Index; RMSEA — Root Mean Square Error of Approximation; AIC — Akaike Information Criterion; BIC — Bayesian Information Criterion.

Fig. 1. The M6 model with crystallized intelligence as a mediator:

Crystal_Int — crystallized intelligence; Academ_Ach — academic achievement (grade point average); solid lines with arrows — paths with significant positive coefficients; dashed lines with arrows — paths with significant negative coefficients; solid arcs with arrows — significant positive correlations between personality traits; dashed arcs with arrows — significant negative correlations between personality traits; * — p<0.05, ** — p<0.01, *** — p<0.001.

Fig. 2. Model M7 with originality as a mediator: see Note to Fig. 1.

Fig. 3. Model M8 with flexibility as a mediator: see Note to Fig. 1.

Discussion

The models distinguished between the shared and partial groups. In the shared models, psychometric intelligence and psychometric (verbal) creativity variables taken together did not mediate the individual traits or academic achievement. In the partial models, fluid intelligence and fluency did not act as mediators either. The partial models involving crystallized intelligence, originality, and flexibility taken separately were suitable and mediational. The mediator effects varied in the composition and combination of individual traits.

In some cases, the partial models supported the claim of individual-intellectual integrations. Several of the students’ individual traits, psychometric intelligence, and psychometric (verbal) creativity served as predictors of academic achievement. The data support the hypothesis that psychometric intelligence and psychometric (verbal) creativity selectively serve as mediators between individual traits and academic achievement.

Such individual multilevel traits as excitation (nervous system), activity (temperament), belonged self, open-mindedness, and agreeableness (personality) represented the exogenous variables. Other individual traits were not included in mediator models as exogenous variables at a significant level. Crystallized intelligence, originality, and flexibility served mediators in the models, but fluid intelligence and fluency were not significant mediators. Thus, one can assume that some personality traits, varieties of psychometric intelligence, and indicators of psychometric (verbal) creativity yield integrations that are specific and rely on variables, their composition and structure.

The finding above supports the hypothesis that mediation models selectively differ in individual multilevel traits, psychometric intelligence, and psychometric (verbal) creativity. They exhibit not one, but several methods of mediation. Therefore, the basis for their integration can be dynamic.

Conclusion

The mediation models included individual traits in their various combinations. The model with crystallized intellect as a mediator included multilevel individual traits: excitation (nervous system), activity (temperament), open-mindedness, and belonged self (personality); the model with originality as a mediator included open-mindedness (personality); the model with flexibility as a mediator included agreeableness and open-mindedness (personality). This may mean that the properties of individuality can be included in different mediation models by changing their compositions and structures. Within the mediator models, individuality reveals the ability, to varying degrees, to replace some traits with others. This means that generally,individual traits present a dynamic structure when they enter mediator models.

The results have practical importance. They have made it possible to identify among the properties of individuality, psychometric intelligence, and psychometric (verbal) creativity the factors that make the most significant contributions to the students’ academic achievement.

Limitations and perspectives of the study

Psychometric creativity was studied in only one aspect — verbal creativity, so we cannot extend the results to other types of creativity.

The study involved predominantly girls, which is consistent with the sex ratio in the humanities but does not allow the results to be extrapolated to the wider population. In this regard, gender alignment and the study of gender specificity, may become a promising area of research.

In the future, it is necessary to consider individual-intellectual integrations not only among representatives of the humanities but also to study those in technical, natural science, military, and other areas of training.

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Information About the Authors

Leonid Y. Dorfman, Doctor of Psychology, Professor, Professor, Head of the Department of Humanities, Perm State Institute of Culture, Perm, Russia, ORCID: https://orcid.org/0000-0001-8494-5674, e-mail: dorfman07@yandex.ru

Alexey Y. Kalugin, PhD in Psychology, Associate Professor, Head of the Department of Theoretical and Applied Psychology, Perm State Humanitarian Pedagogical University, Perm, Russia, ORCID: https://orcid.org/0000-0002-3633-2926, e-mail: kaluginau@yandex.ru

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