Students' time and study environment management: A structural model

Şenol Şen

Department of Secondary School Science and Mathematics Education, Hacettepe University,

Ankara, Turkey, schenolschen@hacettepe.edu.tr

Ayhan Yılmaz

Department of Secondary School Science and Mathematics Education, Hacettepe University,

Ankara, Turkey, ayhany@hacettepe.edu.tr

Students' Time and Study Environment Management: A Structural Model

Abstract

This study aims to analyse the relationship of education faculty students’ self-efficacy for

learning and performance, control of learning beliefs, metacognitive self-regulation, and

effort regulation with time and study environment management. Besides, this study

investigates the direct and indirect effects of metacognitive self-regulation on time and study

environment management. Totally 506 education faculty students participated in this study.

Data were collected through the Motivated Strategies for Learning Questionnaire (MSLQ).

The results of the study show that a positive and a significant correlation exists between the

variables of control of learning beliefs and metacognitive self-regulation; self-efficacy for

learning, performance and metacognitive self-regulation; metacognitive self-regulation and

time and study environment management; time and study environment management and

effort regulation; metacognitive self-regulation and effort regulation. In addition to the direct

effect of metacognitive self-regulation on time and study environment management, it has

also an indirect effect through self-regulation.

Key words: Control of learning beliefs, effort regulation, metacognitive self-regulation, self-

efficacy for learning and performance, time and study environment management

1. Introduction

Individual differences of the students are the characteristics that should be taken into

consideration in the process of learning-teaching process due to the fact that students’

preferences about learning-teaching approaches and their reactions towards teaching

implications vary according to these individual differences. These individual characteristics

could be classified under the categories of cognitive, affective, social and physiological traits.

Many factors which may be considered to be based on individual differences such as

intelligence level, motivational differences, perceptual preferences and psychological factors

impact human learning process (Kuzgun & Deryakulu, 2004). When literature is considered,

it could be seen that there are lots of studies in which affective variables such as belief,

attitude, motivation, self-efficacy, goal orientation, control of learning beliefs and

epistemological beliefs are analysed (Buehl, & Alexander, 2005; Cavallo, Rozman,

Blickenstaff, & Walker, 2003; Chan, 2007; Chen & Pajares, 2010; Deci, Vallerand, Pelletier,

& Ryan, 1991; Paulsen & Feldman, 1999, 2005; Sungur & Tekkaya, 2006; Şen, Yılmaz, &

Yurdugül, 2014). However, more attention is paid to cognitive variables unlike affective

variables.

According to Bloom, the contributing factor for the success of students is due to

cognitive variables (50%), affective variables (25%) and quality of instruction (25%)

(Mitchell & Simpson, 1982). The main goal of today’s information society is to train

individuals who have the capacity of setting learning goals and of having self-regulated skills

in order to enhance their own learning and performance. Self-regulated learning is an active

and constructive process where students are directed by the contextual factors around their

goals and environment, and also it is a process where they set their own learning goals,

regulate their cognition, motivation and behaviour (Pintrich, 2000a). Self-regulated learning

model which is suggested by Pintrich is important as it reflects social cognitive point of view

and it includes motivational processes. This is because of the fact that if the students are not

motivated to use cognitive and metacognitive skills, these skills have no importance (Pintrich

& DeGroot, 1990).

When the current studies are analysed, it could be seen that there are so many

researches about the nature, origin and development of self-regulated learning. (Boekaerts,

1993; Boekaerts & Cascallar, 2006; Borkowski, 1992; Pintrich, 2000b; Zimmerman, 2000b).

Considering the conveyed studies, it could be pointed out that there is a relationship between

student motivation and use of learning strategies (Elliot, McGregor, & Gable, 1999; Pintrich,

1999; Pintrich & De Groot, 1990, Schiefele, 1991; Şen, Yılmaz, & Yurdugül, 2014). There is

an assumption that the higher motivated students use more learning strategies (Pintrich & De

Groot, 1990). According to this assumption, motivational components of self-regulation

predict learners’ learning strategies. Moreover, higher motivated students are expected to be

more strategic.

Pintrich, Smith, Garcia, and McKeachie (1991) suggest that self-regulated students

manage and regulate their time and studying environment. The students who manage their

time and study environment management (resource management strategies) can make

schedules, manage time of planning and studying and try to find out how they can make the

efficient use of time in order to reach their goals. Apart from regulating studying time, these

students also define realistic goals by using studying time in an efficient way. As for

management of studying environment, it means the regulations that the students make for

classroom studies. Ideally, the studying environment should be neat and quiet; there should

be no distracting visual and auditory factors. The studies conducted by Credé and Phillips

(2011) and Fallon (2006) indicate that there is a significant relationship between time and

study environment and effort regulation.

Self-regulation includes effort regulation (resource management strategies). Self-

regulated students have a tendency to maintain their attention and effort when they face

uninteresting tasks and distractions. As for effort management, it shows the individuals’

determination to reach goals despite of difficulties related with self-management and

environment. Effort management not only reflects determination to reach goals but only it

affects use of learning strategies. For this reason, effort management is vital for academic

success. Control of learning beliefs (expectancy component) is the self-perception of students

about obtaining positive results in the end of their efforts. Here, learners consider their

success or failures without attributing them to external factors. Provided that they believe

they create a difference with their efforts in the learning process, they are expected to study

more strategically and efficiently. Sungur (2007) indicates that individuals with higher

motivational beliefs (intrinsic goal orientation, task value, control of learning beliefs, and

self-efficacy for learning and performance) have higher strategy use and effort management.

Studies in the literature indicate that there exists a significant and positive relationship

between control of learning beliefs and self-efficacy for learning and performance (Araz &

Sungur, 2007; Sungur, 2007) and metacognitive learning strategies (Johnson, 2013; Sungur,

2007). Sungur and Tekkaya (2006) show that variables of control of learning beliefs and

self-efficacy have significantly meaningful correlation with metacognitive self-regulation,

time and study environment and effort regulation. The study conveyed by Johnson (2013)

presents positive correlation between the variables of effort regulation and time and study

environment managements; self-efficacy for learning and performance and metacognitive

self-regulation

Self-efficacy for learning and performance (expectancy component) includes the

students’ expectancy of success and self-efficacy. Expectancy for success is rather

performance based expectations and is related with task performance. As for expectancy for

self-efficacy, it is self-appraisal of an ability to perform a task. Self-efficacy implies not only

judgements about a task accomplishment but also confidence to perform that task. Lots of

the studies in literature highlight that motivational beliefs have an important impact on

students’ metacognitive strategy use (Al-Ansari, 2005; Coutinho, 2007; Dembo & Eaton,

2000; Kanfer & Ackerman, 1989; Neber & Schommer-Aikins, 2002; Pintrich & De Groot,

1990; Shu-Shen, 2002; Sungur & Şenler, 2009; Tung-hsien, 2004; Valle et al. 2003). The

studies on self-efficacy point out that self-efficacy has an important role in students’

metacognition (Kanfer & Ackerman, 1989; Sungur, 2007). The students who are highly self-

efficient use metacognitive strategies much more than the ones who have lower self-efficacy

(Bouffard-Bouchard, Parent, & Larivee, 1993; Kanfer & Ackerman, 1989). Pajares (2002)

points out that high level self-efficacy is due to highly use of cognitive and metacognitive

strategies. Sungur (2007) claims there is a positive relationship between the students’ beliefs

regarding self-efficacy and their learning goals’ metacognitive strategy use. Similarly,

Greene, Miller, Crowson, Duke and Akey (2004) state that self-efficacy and learning goals

predict strategy use in a significant way.

Metacognitive self-regulation (cognitive and metacognitive strategies) is another

variable that is used in this study. Metacognition is related with the awareness, knowledge,

and control of cognition. There are three general metacognitive self-regulatory activities such

as planning, monitoring, and regulating. Planning activities such as goal setting and task

analysis help to activate prior knowledge which is beneficial for understanding the subject.

As for monitoring activities, they include self-testing, questioning and self-monitoring during

reading. These activities help learners to comprehend the material and combine the existing

knowledge with new knowledge. Regulating means individual’s adjustment of the cognitive

activities. It is assumed by the researchers that regulating activities can enhance the learners’

performance by helping them control and improve the learning behaviours (Pintrich, 1999;

Pintrich & De Groot, 1990; Pintrich et al., 1991). In the study conveyed by Sungur (2007), it

is indicated that highly motivated students, despite of various difficulties, make more effort to

learn and use several learning strategies. At the same time, other studies in the literature show

that self-efficacy has an impact on self-regulating learning process and self-management

behaviours such as self-observation, self-judgment and self-reaction (Dembo, 2000; Pintrich

& Schunk, 2002; Schunk, 1990, 1994, 2001). Likewise, it is stated that there is a high

Figure 1. The proposed model

CLB

SELP

MSR

ER

TSEM

H1

H2

H3

H4

H5

H6

H7

correlation between metacognitive self-regulation and self-efficacy (Fallon, 2006; Wu, 2006).

The starting point of this study is the cognitive, metacognitive and motivational

characteristics that self-regulated students are expected to have such as effort regulation,

control of learning beliefs, metacognitive self-regulation, self- for learning and performance,

time and study environment management. For this reason, this study aims to analyse the

correlation between the variables of effort regulation, control of learning beliefs,

metacognitive self-regulation, self-efficacy for learning and performance and variables of

time and study environment management through using a path model. In this study, the

correlation between the students’ time and studying environment management is analysed.

During these analyses direct and indirect effects of variables are observed and it is

investigated whether they have any mediating effects or not. The proposed structure of the

model is summarised schematically in Figure 1. Consequently, the hypotheses of the study

are as follows:

H1: Students’ control of learning beliefs (CLB) will be a positive predictor of metacognitive

self-regulation (MSR).

H2: Students’ control of learning beliefs (CLB) will be a positive predictor of effort

regulation (ER).

H3: Students’ self-efficacy for learning and performance (SELP) will be a positive predictor

of metacognitive self-regulation (MSR).

H4: Students’ self-efficacy for learning and performance (SELP) will be a positive predictor

of effort regulation (ER).

H5: Students’ metacognitive self-regulation (MSR) will be a positive predictor of effort

regulation (ER).

H6: Students’ metacognitive self-regulation (MSR) will be a positive predictor of time and

study environment management (TSEM).

H7: Students’ effort regulation (ER) will be a positive predictor of time and study

environment management (TSEM).

H8: The relationship between time and study environment management (TSEM) and

metacognitive self-regulation (MSR) will be mediated by effort regulation (ER).

2. Methodology

2.1. Participants

In this study, totally 506 students (345 females and 161 males) who studied in the

departments of chemistry, biology, physics and science education participated. Participation

by the students was voluntary. The mean age of students was 20, 27 (SD=.85). The majority

of students came from a middle class background.

2.2. Instrumentation

Motivated Strategies for Learning Questionnaire (MSLQ). MSLQ is a self-reported

questionnaire developed by Pintrich et al. (1991). The MSLQ is composed of two main

sections; namely motivation and learning strategies. The motivation section includes 31 items

and 6 subscales, while the learning strategies section includes 50 items and 9 subscales.

Students rate themselves on a 7-point Likert scale from ‘‘not at all true of me’’ to ‘‘very true

of me’’ concerning motivation and learning strategy use. The MSLQ was translated and

adapted into Turkish by Büyüköztürk, Akgün, Özkahveci, and Demirel (2004). The subscales

of questionnaire are modular and can be used either fully or selected subscales for the

purpose of the study. In the present study, effort regulation (ER), control of learning beliefs

(CLB), metacognitive self-regulation (MSR), self-efficacy for learning and performance

(SELP), time and study environment management (TSEM) subscales were used.

Administering the instrument was taken approximately 20-30 minutes. The reliabilities for

the subscales and sample items are presented in Table 1.

Table 1

Sample Items and Reliabilities for the Subscales

Scale Sample item Cronbach’s alphaControl of Learning Beliefs

It is my own fault if I don't learn the material in this course.

0,52

Self-efficacy for learning and performance

I'm confident I can understand the basic concepts taught in this course.

0,86

Metacognitive self-regulation

When reading for this course, I make up questions to help focus my reading.

0,75

Time and study environment management

I make good use of my study time for this course.

0,61

Effort regulation I work hard to do well in this class even if I don't like what we are doing.

0,41

3. Findings

Mean, standard deviations, and correlations among variables of the study are given in Table

2. It is found out that data are normally distributed when skewness and kurtosis values are

considered. When the correlation values between variables in the study are examined, it could

be seen that there exists a negative and insignificant relationship between control of learning

beliefs and self-efficacy for learning and performance with effort regulation. It is pointed out

that there is a positive and significant relationship between other variables.

Table 2

Descriptive Statistics and Bivariate Correlations among Variables of the Study (n = 506)

Variables 1 2 3 4 51. Control of Learning Beliefs

,459** ,234** -,024 ,215

2. Self-efficacy for learning and performance

,319** -,004 ,278**

3. Metacognitive self-regulation

,269** ,510**

4. Effort regulation

,245**

5. Time and study environment managementMean 21,4960 42,9921 59,8913 17,7213 38,3992SD 3,29371 6,62159 7,62344 3,45142 5,61458Skewness -,671 -,484 -,480 -,146 -,242Kurtosis 1,298 ,412 ,870 ,484 ,233**Correlation is significant at the 0.01 level (2-tailed).

Structural Equation Modelling (SEM) is used in order to control this study’s hypothesis. The

goodness of fit indices are given in Table 3. When the goodness of fit indices that belong to

conceptual model are analysed, it is understood that model does not fit the data very well.

Besides, it is pointed out that paths between control of learning beliefs and effort regulation

(Hypothesis 2 is rejected) and the ones between self-efficacy for learning and performance

self-efficacy for learning and performance and effort regulation are not found to be

meaningful. It is tried to create a new model by taking the suggested modifications after the

analyses are done. The paths which do not exist in the model (non-significant paths) are

excluded from the model and a new additional pathway is added to model (from self-effıcacy

for learning and performance to time and study environment management); therefore, an

alternative model is created instead of theoretical model and this new model is tested. (Fig.

2).

Table 3

The Goodness of Fit Indices

Model χ2 df χ2 /df RMSEA CFI IFI GFI AGFI NFI NNFI

Conceptual Model

14,78 (p=0,000) 2 7,39 0,113 0,97 0,97 0,99 0,91 0,97 0,85

Alternative Model

9,09 (p=0,028) 3 3,03 0,064 0,99 0,99 0,99 0,96 0,98 0,95

The result of the conducted path analysis points out that alternative model has better

goodness of fit indices (Table 3). Fit indices which are of alternative model (χ2 = 9,09

(P=0,028) χ2 /df=3,03 RMSEA=0,064, CFI=0,99, GFI=0,99, AGFI=0,96, NFI=0,98 and

NNFI=0,95) are accepted to meet the criteria of goodness-of-fit indices. Garver and Mentzer

(1999) suggest that values of NNFI, CFI and RMSEA could be taken into consideration for

acceptable fit indices. For this reason, fit indices which are used mostly are; NNFI and CFI

(>0.90 indicating a good fit to data) RMSEA (<0.08 indicating a good fit to data) and another

χ2 statistics which could be used as a value. (χ2/df rate is required to be less than 3) (Hoe,

2008). That is why, in this study, it could be said that model shows fitness to all data as

NNFI, CFI, RMSEA and χ2/df rate have acceptable values. Standardized path coefficients are

(direct, indirect and total effects) are calculated for each variable which is in alternative

model. The results of the analysis are presented in Table 4. Path coefficients of alternative

model are shown in Fig.2.

CLB

SELP

MSR

ER

TSEM

.11

.14

.46 .27

.27

.43

.13

As seen in Figure 2, it could be easily seen that there is a positive and significant correlation

between control of learning beliefs and metacognitive self-regulation (Hypothesis 1 is

accepted) and that 11 % of the change in self-regulation scores (=variance) is due to control

of learning beliefs scores (Hypothesis 1 is accepted). There exists a positive and significant

correlation between self-efficacy for learning and performance and metacognitive self-

regulation (Hypothesis 3 is accepted). There is a significant and positive relationship between

self-efficacy for learning and performance and metacognitive self-regulation (Hypothesis 3 is

accepted). Also, the self-efficacy for learning and performance scores can explain 27% of the

change in self-regulation scores and 14% of change in study environment management

scores. Moreover, it is stated that metacognitive self-regulation and time and study

environment management are correlated positively and significantly (Hypothesis 6 is

accepted). It is claimed that there is a positive and significant relationship between self-

regulation and effort regulation (Hypothesis 5 is accepted) and that metacognitive self-

regulation scores can explain the 43% of change in time and study environment management

and also 27% of effort regulation scores. Finally, there exists a positive and significant

relationship between time and study environment management and effort regulation

(Hypothesis 7 is accepted); effort regulation explains 13% of change in time and study

Figure 2. Path coefficients in alternative model

environment management scores. Furthermore, covariance coefficient between self-efficacy

for learning and performance and control of learning beliefs is 0,46.

Table 4

Direct, Indirect, and Total Effects in the Alternative Model

Variables

Metacognitive self-regulation Effort regulation

Time and study environment management

Direct Indirect Total Direct Indirect

Total Direct Indirect Total

Self-efficacy for learning and performance

.27 .14 .11 .25

Metacognitive self-regulation

.27 .43 .04 .47

In Table 4, standardized direct, indirect and total effects’ coefficients for variables found in

model are presented. The results of the study indicate that self-efficacy for learning and

performance has a direct impact on time and study environment management and it has also

an indirect impact (0,11) through metacognitive self-regulation. Likewise, metacognitive

self-regulation has a direct impact on time and study environment management and it has also

an indirect impact (0,04) through effort regulation (Hypothesis 8 is accepted).

4. Discussion and conclusion

The results of the study present that there is a significant relationship between

motivational variables (control of learning beliefs and self-efficacy for learning and

performance) and learning strategies (metacognitive self-regulation, time and study

environment management, and effort regulation). This finding supports the results of the

studies found in literature (Pintrich & De Groot, 1990; Yumuşak, Sungur, & Çakıroğlu, 2007;

Zusho, Pintrich, & Coppola, 2003).

When the path analysis is considered, the findings show that there is a significant and

positive relationship between control of learning beliefs and metacognitive self-regulation;

self-efficacy for learning and performance and metacognitive self-regulation; metacognitive

self-regulation and time and study environment management; time and study environment

management and effort regulation; metacognitive self-regulation and effort regulation.

Besides, the results show that metacognitive self-regulation has not only direct effect on time

and study environment but also indirect effect through effort regulation. Nevertheless, there is

not a significant relationship between control of learning beliefs and effort regulation; self-

efficacy for learning and performance and effort regulation.

Path analyses show that there is a significant relationship between control of learning

beliefs and metacognitive self-regulation scores. When the studies in literature are taken into

consideration, it could be seen that similar results have been obtained. (Johnson, 2013;

Sungur, 2007; Sungur & Tekkaya, 2006). The study conducted by Sungur (2007) shows that

highly motivated students make more effort to learn in spite of difficulties that they

experience. Therefore, motivational beliefs can explain why some students are successful

whereas some others are not in the process of learning. Highly motivated students can use the

learning strategies which facilitate learning and coding processes in a more effective way. If

students can be successful in using learning strategies, their academic success will be

enhanced at the same time.

Another finding of this study is that there is a positive and significant relationship

between self-efficacy for learning and performance and metacognitive self-regulation. This

finding is similar with the other studies in literature. (Dembo, 2000; Fallon, 2006; Johnson,

2013; Pintrich & Schunk, 2002; Schunk, 1990, 1994, 2001; Sungur & Tekkaya, 2006; Wu,

2006). Highly self-efficient students use metacognitive strategies more than students with low

self-efficacy (Bouffard-Bouchard et al., 1993; Kanfer & Ackerman, 1989). The students who

have high self-efficacy and control over learning beliefs can determine learning goals, use

different learning strategies, make more effort to perform any task and try out new strategies

although the strategies they use is insufficient. Also, their effort is long lasting (Hoy, 2004).

Zimmerman (2000a) claims that highly self-efficient students do not give up easily when

faced with hard tasks, they can manage their anxiety, and use self-regulation processes such

as self-monitoring, goal setting and self-evaluation more.

In this study, it is claimed that metacognitive self-regulation scores predict effort

regulation scores. Pintrich and De Groot (1990) claim that self-regulation which involves

metacognitive and effort management strategies is a variable which is the most effective in

predicting the student performance. In the analysis where cognitive strategies and self-

regulation predict academic performance, the researchers state that cognitive strategies and

academic performance is correlated negatively with each other although there is a high

correlation between self-regulation and cognitive strategies. Therefore, the researchers

present that cognitive strategies are not effective without self-regulatory strategies for

academic achievement.

Another finding of this study is that metacognitive self-regulation scores can predict

students’ time and study environment management. Eilam and Aharon (2003) state that high

achievers use self-regulation strategies more than low achievers, and that they are more

effective in planning and time management issues. Effective metacognitive strategy use can

be helpful in regulate and monitor time and effort (Covington, 1985).

In this study, there is a positive and significant relationship between effort regulation

and time and study management. Similarly, in literature, the study conducted by Johnson

(2013) states there is a significant relationship between effort regulation and time and study

environment managements. In the model suggested by Pintrich (2000a), apart from process of

cognition and motivation, self-regulation is highlighted as well. In this context, time and

effort regulation is emphasized. The student who is expected to perform a learning task

monitors himself and takes some precautions in order to use individual effort effectively

through making some planning about how much time and effort he needs to perform that

task. At the end of personal judgements, the learner can make a decision whether to increase,

decrease or give up help and effort. Final judgements are predictive of the performance of

similar tasks (Özbay, 2008).

What is more, another finding is that there is a positive and significant relationship

between students’ self-efficacy and time and study environment management. The study

conducted by Berger and Karabenick (2011) states that students’ self-efficacy can predict use

of deep processing strategies (elaboration, metacognition) and time and study environment

management but it is pointed out that self-efficacy does not predict the use of rehearsal and

organization strategies. Self-regulating students can manage internal and external

environment in order to study within a determined schedule. These students state their

intentions clearly, determine the effort they need and know whom to ask for help (Pintrich,

2004). Credé and Phillips (2011) and Fallon (2006) claim that there is a significant

relationship between time and study environment and effort regulation.

This study apart from other studies found in literature states there is not a significant

relationship between control of learning beliefs and effort regulation; self-efficacy and effort

regulation (Johnson, 2013; Komarraju & Nadler, 2013; Sungur & Tekkaya, 2006). For

instance, study by Komarraju and Nadler (2013) indicates that there is a positive and

significant relationship between self-efficacy and effort management. Besides, it is pointed

out that effort management partially mediates the relationship between self-efficacy and

grade point average (GPA). Johnson (2013) states that there exists a significant and positive

relationship between self-efficacy and effort regulation.

To conclude, metacognitive strategies and effort management have a significant and

important impact on students’ time and working management environment. It is also pointed

out that there is a direct impact of metacognitive learning strategies on time and working

environment management apart from its indirect impact through effort management.

Furthermore, self-efficacy has a direct impact on time and working environment management

as well as its indirect impact through metacognitive impact.

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