ROMANIAN JOURNAL OF

EXPERIMENTAL APPLIED PSYCHOLOGY

VOL. 6, ISSUE 1 – www.rjeap.ro

SELF-DIRECTED LEARNING AND ACADEMIC

ADJUSTMENT AT ROMANIAN STUDENTS

ANA-MARIA CAZAN a, MARIA MAGDALENA STAN b

a University Transilvania of Brasov, Department of Psychology and Training in

Education b University of Pitești, Department of Educational Sciences

Abstract

Self-directed learning has become one of the primary aims of education in the

last few decades and the theory of self-directed learning (SDL) has been

increasingly applied in the context of higher education. Facilitating the

development of SDL skills will contribute to the learners’ success in their future

careers, will enable them to engage in lifelong learning. The aim of this study was

to analyse the relationships between academic adjustment, self-directed learning

and learning engagement. We used the following scales: the Academic Adjustment

Questionnaire, the Utrecht Work Engagement Scale and the Self-Rating Scale of

Self-Directed Learning. The results showed that all the scales had good

psychometric properties. The Pearson correlation coefficients between the

dimensions included in the study were significant. The investigation of age and

gender differences was not possible, given the high homogeny of the sample. The

results were consistent with previous results, showing that self-directed learning

and learning engagement could efficiently predict academic adjustment at the

university level. The ability of a student to become a self-directed learner implies

the development of their metacognitive skills, the ability to monitor and evaluate

their own learning strategies, the ability to manage their interpersonal

relationship, a self-directed learner being a successful student.

Cuvinte cheie: învățare autodirijată, adaptare academică, implicare în învățare.

Keywords: self-directed learning, academic achievement, learning engagememt.

1. INTRODUCTION

Academic adjustment represents one of the permanent challenges of

university pedagogy. The researchers in the field develop studies intended to

*Corresponding author: Ana-Maria Cazan Email address: ana.cazan@unitbv.ro

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identify the weight of the determining factors so that the students’ opportunities to

adjust to the university environment and implicitly to academic learning should

enhance. Academic adjustment represents an integrating construct, being fairly

difficult to define (Clinciu & Cazan, 2013).

Sax & al. (2000) defines academic adjustment as successfully understanding

what professors expect academically from students, the development of effective

study skills, adjusting to the academic demands of college and not feeling

intimidated by professors. Thus, adjustment is seen as “a dynamic and interactive

process that takes place between the person and the environment and is directed

towards an achievement of a fit between the two” (Anderson, 1994; Ramsay,

Barker, & Jones, 1999).

Three categories of academic adjustment are mentioned in the field research:

academic adjustment which refers to the students’ positive attitudes related to their

academic activities and objectives, as well as to the positive evaluations of their

efforts and the quality of their academic environments; social adjustment which

refers to the extent in which students are involved in social activities and groups

and to the existence of interpersonal relationships and personal- emotional

adjustment defined as students' psychological and physical well-being (Baker &

Siryk, 1984).

The negative effects of academic non-adjustment of students are associated

with anxiety, depression, stress, vulnerability, anger, moodiness, mental illness

(Clinciu, 2012). The specific of academic adjustment derived from the blending of

relational adjustment mechanisms with the pedagogic adjustment mechanisms

(Negovan, 2006), academic adjustment is described as the fit which students

achieve with the academic context of the college environment.

Academic adjustment is considered to be the expression of the positive

reaction of students to the formative pressure of academic requirements. With a

view to identify the predictive factors of academic adjustment, Clinciu & Cazan

(2014) propose an evaluation instrument, Academic Adjustment Questionnaire-

AAQ, which has two scales: neuroticism which offers a specific expression to the

emotional adjustment reaction and procrastination which offers information about

the efficiency of academic adjustment.

Researches on the predictors of academic adjustment were focused upon the

personality variables but also upon academic performance and test scores (Zea,

Jarama, & Trotta-Bianchi, 1995), which correlates with performance in college and

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to steady attendance. Baker & Siryk (1989) underline the fact that the most

important components of academic adjustment included motivation to learn, taking

action to meet academic demands, clear goal-settings and general satisfaction with

the academic environment.

The theory of self-directed learning (SDL) has been increasingly applied in

the context of higher education. Self-directed learning has been often used as a

factor which correlates strongly with students ‘performance and even to their

academic success. Self-directed learning implies independent learning, self-

planned learning, autonomous learning and self-education. Knowles (1975) defines

self-directed learning as a process in which individuals take the initiative without

the help of others to determine their learning needs, set themselves their learning

objectives, discover human and material learning resources, select and implement

suitable learning strategies and assess the results of their efforts to learn.

In character with the constructivist theory of leaning, the student constructs

his own understanding of a subject through engaged activities, rather than passively

accepting information presented to them. The individual direct implication in the

learning act represents a factor of academic adjustment.

Self-direction is the basis of all learning; be it formal or informal, the

effectiveness of learning is relative to an individual's motivation. All individuals

are capable of self-directed learning but the degree of development varies due to

their individual differences (Williamson, 2007). Long (1989) emphasizes the role

of the pupil’s characteristics within the SDL process – asserting that those

characteristics are the most significant indicators of whether the individual will

engage with the learning structures. The differentiating variables at the individual

level include knowledge, attitudes, values, motivations, cognitions, and affective

characteristics (Kasworm, 1992; Oddi, 1987). Among the psychological variables

of the students, the personality traits such as emotional balance, independence,

super-ego, sensitivity and scrupulosity correlate positively with SDL (de Bruin,

2007; Lounsbury, Levy, Park, Gibson, & Smith, 2009).

The measurement of the students’ learning potential related to the SDL theory

proved to be necessary for the optimization of the learning process. Williamson

(2007) develops the scale the self-rating of self-directed learning – SRSSDL which

proposes to measure the level of self-directedness in one's learning process. Thus,

the scale measures: awareness - relating to learners' understanding of the factors

contributing to becoming self-directed learners; learning strategies- explaining the

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various strategies self-directed learners should adopt in order to become self-

directed in their learning processes; learning activities - specifying the requisite

learning activities learners should actively engage in order to become self-directed

in their learning processes, evaluation - revealing learners' specific attributes in

order to help monitor their learning activities and interpersonal skills- relating to

learners' skills in inter-personal relationships, which are pre-requisite to their

becoming self-directed learners. Based on the above-mentioned instrument, the

students can be evaluated over their behaviour in SDL learning, strong and weak

points can be identified and appropriate strategies for the furtherance of their SDL

skills can be selected.

2. OBJECTIVES

The aim of this study was to analyse the relationships between academic

adjustment, self-directed learning and learning engagement.

Our main objectives were:

to analyse the psychometric properties of the scales

to determine whether self-directed learning and learning engagement

could efficiently predict academic adjustment at the university level.

3. METHOD

3.1. PARTICIPANTS/SUBJECTS

The participants were 100 students, preparing to become primary school

teachers, with a mean age of 27 years, from the Faculty of Education Sciences of

University of Pitești.

3.2. INSTRUMENTS

We used the following scales:

1) The Academic Adjustment Questionnaire (AAQ - Clinciu & Cazan, 2014)

is an extension for university level of the School adjustment questionnaire (Clinciu,

2014). It is a self-report instrument scored with 0 and 1 and it assesses the students’

adjustment to academic learning process: Neuroticism (14 items, Alfa

Cronbach,.79) and Procrastination (10 items, Alfa Cronbach,.74). The Alfa

Cronbach coefficient for the entire scale is.84. The Academic Adjustment

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Questionnaire included two additional questions measured on a three point Likert

scale, ranging from not at all to very much: How much maladjustment did you feel

at high school? and How stressed do you feel now, in college?

2) The Utrecht Work Engagement Scale (UWES) assesses learning

engagement (Schaufeli & Bakker, 2003) through three dimensions: Vigour (6

items, Alfa Cronbach,.79), Dedication (5 items, Alfa Cronbach,.88) and Absorption

(6 items, Alfa Cronbach,.86). The Alfa Cronbach coefficient for the entire scale

is.91. The dimensions of engagement were defined as follows: vigor represents the

energy, the willingness and the persistence no matter the difficulties; dedication

refers to the significance, the enthusiasm, the inspiration and the pride in one’s

work; absorption is characterized as being fully determined and focused on one’s

work, (Schaufeli et al., 2002).

3) The Self-Rating Scale of Self-Directed Learning (SRSSDL – Williamson,

2007) measures the level of self-directedness in one’s learning process. The 60

items are categorized under five broad areas of self-directed learning, each area

comprising 12 items: Awareness (it explores learners' understanding of the factors

contributing to becoming self-directed learners, Alfa Cronbach,.80), Learning

strategies (it measures the various self-directed learning strategies, Alfa

Cronbach,.81), Learning activities (it measures the requisite learning activities that

learners should actively engage in, to become self-directed learners, Alfa

Cronbach,.81), Evaluation (it measures learners' specific attributes for monitoring

learning activities, Alfa Cronbach,.86), and Interpersonal skills (it measures

learners' skills in inter-personal relationships, Alfa Cronbach,.87).

4. RESULTS

The results showed that learning engagement is significantly correlated with

academic maladjustment, the only scale which was not associated with academic

adjustment being absorption. The results revealed that the feeling of being fully

determined and focused on one’s work is negatively correlated with procrastination

and independent of academic neuroticism. As expected, the highest correlations

were obtained for the associations with the procrastination scores, as the students

fully engaged in their studies have a low tendency to postpone academic tasks

(Table 1).

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Table 1. Pearson correlation coefficients between learning engagement and academic adjustment

AAQ Neuroticism AAQ Procrastination AAQ Total

UWES Vigor -.234* -.371** -.337**

UWES Dedication -.224* -.415** -.351** UWES Absorption -.050 -.268** -.162

UWES Engagement total -.192 -.407** -.326**

*. p <.05 level (2-tailed), **. p <.01 level (2-tailed), N = 100

All the self-directed learning dimensions negatively correlated with academic

maladjustment. The highest correlation was obtained between interpersonal skills

and academic neuroticism, showing that the low level of skills in inter-personal

relationships could be an efficient predictor of neuroticism as indicator of academic

stress (Clinciu, 2014).

Table 2. Pearson correlation coefficients between self-directed learning and academic adjustment

CIA Neuroticism CIA Procrastination CIA Total

SDL Awareness -.276** -.288** -.325**

SDL Learning strategies -.221* -.286** -.287**

SDL Learning activities -.174 -.220* -.224* SDL Evaluation -.194 -.275** -.264**

SDL Interpersonal skills -.370** -.263** -.377**

SDL Self-directed learning total -.273** -.311** -.335**

* p <.05 level (2-tailed), **. p <.01 level (2-tailed), N = 100

The significant correlations between the self-directed learning dimensions and

academic adjustment (Table 2) proved that SDL is a key component of academic

adjustment (Avdal, 2013).

As expected, SDL correlated positively with learning engagement (Table 3).

SDL involves active engagement, goal-directed behaviours, learning tasks being

initiated by the learner. A high level of engagement is an indicator of self-directed

learning (Evensen, Salisbury-Glennon, & Glenn, 2001). Learning engagement

implies a high interaction with the learning content and an active experimentation

of various learning strategies, which support the development of self-directed

learning skills.

Table 3. Pearson correlation coefficients between self-directed learning and learning engagement

UWES Vigour UWES Dedication UWES Absorption UWES total

SDL Awareness .283** .257* .341** .348**

SDL Learning strategies .184 .189 .181 .216* SDL Learning activities .293** .254* .374** .365**

SDL Evaluation .290** .260* .439** .394** SDL Interpersonal skills .362** .313** .343** .399**

SDL Self-directed learning total .327** .283** .373** .389**

* p <.05 level (2-tailed), **p <.01 level (2-tailed), N = 100

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Having as starting point the obtained correlations, we tested whether self-

directed learning and learning engagement could efficiently predict academic

adjustment at the university level. We used the multiple linear hierarchical

regression technique. In order to avoid the multicoliniarity, the overall scores for

the three instruments were included in the analysis (Table 4).

We tested three models having as dependent variable the overall score for the

academic maladjustment. The first model included a single predictor, the learning

engagement, the model being statically significant. At the second step, we added

the self-directed learning dimension which led to a better prediction. Finally, at the

last step, we added two predictors (the two additional items of AQQ): the self-

assessment for the high-school maladaptation and the perceived level of stress

(university stress). The Spearman correlation coefficients of the two variables with

the academic maladjustment were low albeit statistically significant (.21 for the

high-school maladaptation and.29 for the perceived stress at the university, p

<.001,). The four variables explained 27% of the academic maladjustment, all the

predictors being significant excepting the high-school maladaptation (Table 4). We

can conclude that the third model is the most relevant one.

Table 4. The multiple linear hierarchical regression technique for the prediction of academic adjustment

Model Unstandardized

Coefficients

Standardized

Coefficients

t

B SE B β

1 R Square =.098

R Square Change =.098

F Change (1,88) = 9.570**

(Constant) 16.991 2.330 7.291**

UWES Engagement -.105 .034 -.313 -3.094**

2 R Square =.156

R Square Change =.058

F Change (1,87) = 6,016*

(Constant) 26.695 4.559 5.855**

UWES Engagement -.073 .036 -.217 -2.044*

Self-directed learning total -.050 .020 -.260 -2.453**

3 R Square =,270

R Square Change =.113 F Change (1,85) = 6,60 **

(Constant) 22.624 4.448 5.086**

UWES Engagement -.065 .034 -.192 -1.918* Self-directed learning total -.058 .019 -.300 -2.986**

High school maladjustment .548 .320 .159 1.710

University stress 1.563 .502 .291 3.116*

* p <.05 level (2-tailed), **p <.01 level (2-tailed), N = 100; Dependent Variable: AAQ_total

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5. CONCLUSIONS

The Pearson correlation coefficients between the dimensions included in the

study were significant. The results confirmed previous research, showing that

procrastination and task engagement are negatively correlated (Tice & Bratlavsky,

2000) due to the procrastinators’ tendency towards low concentration.

Procrastinators show a lower ability to maintain study behaviour, their

concentration is often impaired and they tend to drift from one learning task to

another (Chu & Choi, 2005; Schouwenburg, Lay, Pychyl, & Ferrari, 2004). On the

other hand, self-directed learning and academic neuroticism were negatively

correlated, although previous results revealed that the only personality trait which

was not associated with self-directed learning was emotional stability (Cazan &

Schiopca, 2014). Thus, despite the high correlation reported by the AAQ’s author

(Clinciu, 2014), Neuroticism as a NEO PI-R super-factor and academic

neuroticism are distinct aspects, covering different dimensions.

The results were consistent with previous results, showing that self-directed

learning and learning engagement could efficiently predict academic adjustment at

the university level. The students’ ability to become self-directed learners implies

the development of their metacognitive skills, the ability to monitor and evaluate

their own learning strategies, the ability to manage their interpersonal relationship

(Williamson, 2007). A self-directed learner is a successful student, self-directed

learning predicting academic performance and more generally, academic

adjustment. In the context of the investigated sample, the results are very

important. Self-directed learning is a key competence for a future teacher. The

teacher’s task, as a self-directed learner, is to help the students (the other learners)

to develop their ability to define all aspects of learning, to monitor and to control,

their learning process, to choose the most efficient learning strategies (Fabela-

Cárdenas, 2012).

The present study contributes to the theory and research of self-directed

learning, demonstrating the complexity of the relationship between SDL and

academic adjustment. Despite the promising results, several limitations should be

mentioned. A major limitation is the convenience sample included in the research

and its relative homogeny, all the participants having the same background and

educational level. Thus, gender and age differences were not investigated. Another

limitation is the use of a correlational design which cannot explain the development

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of self-directed learning. Future research could include a longitudinal design in

order to demonstrate that self-directed learning skills are not innately present in

students and that curricular activities could support the development of self-

directed learning skills.

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REZUMAT

Învățarea autodirijată a devenit unu dintre scopurile principale ale educației

contemporane. Obiectivul acestei cercetări este să analizeze relațiile dintre

dimensiunile învățării autodirijate, implicarea în învățare și inadaptarea

academică. Au fost folosite următoarele instrumente: Chestionarul de Inadaptare

Academică, Scala Implicării în învățare Utrecht și Scala Învățării Autodirijate.

Rezultatele au arătat că instrumentele folosite au bune calități psihometrice.

Totodată, între dimensiunile instrumentelor menționate au fost obținuți coeficienți

de corelație Pearson semnificativi statistic. Rezultatele obținute sunt concordante

cu cele raportate în literatura de specialitate, demonstrând că învățarea

autodirijată și implicarea în învățare prezic eficient adaptarea academică. Gradul

ridicat de omogenitate al eșantionului și alegerea unui eșantion de conveniență

sunt unele dintre limitele acestei cercetări. Din perspectiva celui care învață,

autodirijarea implică dezvoltarea abilităților metacognitive, abilitatea de a

planifica,monitoriza și evalua propria învățare, de a alege strategiile eficiente, de

a le ajusta atunci când situația impune acest lucru,învățarea autodirijată fiind o

componentă a succesului academic.