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Purpose: With the changing perspective in modern education systems, success means more than grades and includes emotional, social, cognitive, and academic development. The aim of this study was to investigate the role of personal factors (academic selfefficacy, organization and attention to study, time utilization, classroom communication, stress and emotional components, student involvement with college life) in predicting student success. Method: Three hundred and seventeen college students participated in the study, and a demographic information form and the College Learning Effectiveness Inventory (CLEI) were used. A correlational research design was utilized for data analysis. Findings: The results indicate that personal variables significantly predicted student success, ΔR² =.16, ΔF (6, 310) =10.16, p<.05, and that 16% of the total variance was explained with the model. Among the personal variables of effective learning, stress and time pressure and classroom communication were found to be significant predictors of success. Implications for Research and Practice: The findings indicate that students who communicate better and feel more stressed in the classroom reached a higher level of achievement in college learning. The results suggest that activities that increase student communication in the class should be given priority in the classroom environment. In addition, instructors and university counselors should pay attention to the positive relationship between stress and academic success, as a balanced level of stress should not always be feared during studies. For further research, the CLEI should be used with college students in all grades rather than preparatory students to investigate college students’ profiles about personal factors.
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Eurasian Journal of Educational Research 69 (2017) 93-112
Eurasian Journal of Educational Research
www.ejer.com.tr
Personal Factors Predicting College Student Success
Gokcen AYDIN1
A R T I C L E I N F O
A B S T R A C T
Article History:
Purpose: With the changing perspective in modern
education systems, success means more than grades
and includes emotional, social, cognitive, and
academic development. The aim of this study was to
investigate the role of personal factors (academic self-
efficacy, organization and attention to study, time
utilization, classroom communication, stress and
emotional components, student involvement with
college life) in predicting student success. Method:
Three hundred and seventeen college students
participated in the study, and a demographic
information form and the College Learning
Effectiveness Inventory (CLEI) were used.
Received: 07 May 2015
Received in revised form: 18 March 2017
Accepted: 12 April 2017
DOI: http://dx.doi.org/10.14689/ejer.2017.69.6
Keywords
effective learning
college learning
student success
personal variables
A correlational research design was utilized for data analysis. Findings: The results indicate
that personal variables significantly predicted student success, ΔR² = .16, ΔF (6, 310) =10.16, p<
.05, and that 16% of the total variance was explained with the model. Among the personal
variables of effective learning, stress and time pressure and classroom communication were
found to be significant predictors of success. Implications for Research and Practice: The
findings indicate that students who communicate better and feel more stressed in the classroom
reached a higher level of achievement in college learning. The results suggest that activities that
increase student communication in the class should be given priority in the classroom
environment. In addition, instructors and university counselors should pay attention to the
positive relationship between stress and academic success, as a balanced level of stress should
not always be feared during studies. For further research, the CLEI should be used with college
students in all grades rather than preparatory students to investigate college students’ profiles
about personal factors.
© 2017 Ani Publishing Ltd. All rights reserved
1 Corresponding Author: Gokcen AYDIN, Center for Advancing Learning and Teaching, Middle East
Technical University, Turkey, gokcenaydn@gmail.com
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94
Introduction
Higher education is important in today’s world by means of its contribution to well-
qualified graduates, personal development, and economic, scientific, and
technological advancements (Nguyen, 2011). The relevance of higher education
underlines the issue of student success or college achievement, which is a complex
term to define due to multiple personal and institutional factors affecting college
success (Mills, Heyworth, Rosenwax & Carr, 2009; Toutkoushian & Smart, 2001).
College success is defined by Kim, Newton, Downey and Benton (2010) as
“acceptable grade averages, retention toward a degree and attainment of productive
life skills” (p. 112). In recent years, the perspective of education systems has
undergone a change from “How should we teach students?” to “How should we
help students learn?” to develop and maintain the academic achievement of
students. It means that student success is related to student and faculty responsibility
as well as a campus-wide responsibility (Hunter, 2006).
Studies in recent years have indicated that success means more than grades.
Thus, success has been defined in several ways, including whole student
development and having many dimensions beyond cognitive and academic factors
(Hunter, 2006). According to Pritchard and Wilson (2003), emotional and social
factors are crucial in relation to student success, and there is a strong impact of
psychological variables on students’ academic achievement through students’
adjustment to college. Pike and Kuh (2005) emphasized the value of students’
behaviors, attitudes, expectations, and their engagement into college life to measure
student success. However, Finn and Rock (1997) argued that success means
graduating from the institution on time with good grades. In this regard, it is entirely
crucial to notice that, even though the emphasis is placed on attaining high grades to
signify success, recent years have introduced the idea of considering faculty-student
face-to-face interaction as a supplier of an increase in academic success (Crisp, Baker,
Griffin, Lunsford & Pifer, 2017).
A vast number of studies have been conducted to examine student success. Kuh
(2006) proposed that student success is formed by pre-college experiences (student
academic background and readiness for college); student engagement (studying
skills, involvement in social life and campus environment); and post-college
outcomes (grades and job-related issues). Expectancy-value theory attaches
importance to motivation as being a crucial component of academic achievement.
According to this theory, motivation is the direct source of expectations for achieving
success (Wigfield, 1994). As the level of expectation increases regarding academic
tasks, students’ motivation increases as well, and they, intentionally and willingly,
commit themselves to achieve the desired goals. Similar to this sense, Tinto’s
academic and social integration model underlines the prevalence of engagement in
the new college environment. The more students commit themselves to college, the
more they display retention and achievement (Tinto & Pusser, 2006). Furthermore,
achievement goal theory is another theory of student success in which defining goals
is emphasized because of a leading level of higher achievement (Canfield &
Zastavker, 2010).
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Research has focused on student success by taking various perspectives into
consideration. Kim et al. (2010) proposed that factors affecting student success can be
categorized under three types of variables. The first one is previous success in high
school (academic) (Wolfe & Johnson, 1995). The second category is constructed by
demographics or socio-economic status that are found to influence student
achievement and are difficult to change by the students themselves. For example,
regarding gender difference in college success, although some of the studies stated
that there was no significant difference (Campbell & Fuqua, 2008; Peterson, 2009),
others indicated that female students were more successful than male students in the
first year of college (Adams, Marsh, Irons, & Carlson, 2010; Mills et al., 2009). The
third one is formed by the factors such as perceptions, actions, attitudes, and values
that individuals can control and change (Forsyth & Schlenker, 1977). Personal factors
are due to many kinds of individual differences. Every individual has the capacity to
influence, enhance, or shape his/her own life. Therefore, for the sake of success,
personal variables, also known as “psychosocial factors,” have taken the attention of
researchers. According to Newton, Kim, Wilcox, and Beemer (2008), these factors
include attitudes, motivation, usage of campus resources, study approach, etc.
(Newton et al., 2008, p. 4). Even though there are several types of personal factors,
according to Newton et al. (2008), academic self-efficacy and confidence, strategic
organization and study approach, time utilization, stress and emotional components,
student involvement with college life and motivation are among the most
outstanding personal factors that affect college student success. In this sense, Newton
et al. (2008) developed College Learning Effectiveness Inventory, consisting of these
six subscales to measure personal variables in college learning. Considering their
emphasis on naming these personal variables as the most powerful influences over
success, this study was designed to place importance into personal factors and study
them separately.
To begin, academic self-efficacy refers to the competency level of students in
achieving academic responsibilities, such as tests or homework (Schunk, 1991).
Investigating academic self-efficacy is not only a concern for college learning, but
also for high school education (Peguero & Shaffer, 2015). A vast number of studies
have indicated a positive relationship between academic self-efficacy and college
grades (Bong, 2001; Linnenbrink & Pintrich, 2002). As another variable of personal
factors, the significance of study habits and attitudes has been underlined to increase
college students’ success (Crede & Kuncel, 2008). In addition to time management
efforts, the ability to organize tasks and concentrate on their studies also affects
students’ success (Pauk & Owens, 2011). The literature has also advocated the view
that the ability to organize time and responsibilities can increase students’ level of
success. However, when students do not have organizational skills, they tend to view
themselves as failures.
Time management, as another personal variable that leads to success, is defined
as “the ability to effectively organize your time and responsibilities in order to get
most out of your day” (Combs, 2007, p. 74). Combs (2007) also mentioned that time
management during the college years creates a difference between students who are
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successful in achieving their goals and those who regret being unaware of the critical
aspect of managing time. Balduf (2009) also emphasized that a lack of time
management leads to underachievement. Furthermore, colleges should be regarded
not only as places to provide education, but also as places in which students can
develop their social lives. Research has indicated that college students who become a
member of social activities engage in the college life and experience the advantages
of being social around campus (Reason, Terenzini & Domingo, 2006). Along with
activities inside the classroom, evidence shows that attending extracurricular
activities forms another part of the personal factors that increase the satisfaction and
engagement of students, influencing student success (Pascarella & Terenzini, 2005).
Apart from other personal variables, student achievement is also based on
students’ satisfaction level regarding the college. Satisfaction is explained by means
of various parts of campus life, such as satisfaction about faculty, program quality,
college activities and environment, and overall satisfaction about life (Klein, Kuh,
Chun, Hamilton & Shavelson, 2005; Pascarella & Terenzini, 2005). Literature has
supported the view that students who are emotionally satisfied with the college
attend courses at a higher rate and achieve more academic success (Pascarella &
Terenzini, 2005). Moreover, Decker, Dona, and Christenson (2007) argued that good
relationships between students and faculty members exhibit a greater influence on
emotional functioning than academic achievement. Accordingly, learning and
teaching happens through open communication (Nurzali & Khairu’l, 2009; Wall,
2007). The increase in sharing and learning happens more frequently by means of
communication between student-student and student-teacher. As a crucial factor
influencing achievement, student-faculty relationships display a fundamental effect
if the faculty is attentive to students’ achievements (Komarraju, Musulkin &
Bhattacharya, 2010). Similarly, student-student relationships exert a prominent
influence over success. Rubin, Graham, and Mignerey (1990) mentioned that
students who feel relaxed when communicating demonstrate a higher level of
achievement in terms of GPAs at the end of their college education, as openness in
communication creates good relationships, which lead to increased academic
success. Therefore, classroom communication is viewed as another personal variable
that affects student success.
The present study is expected to make a valuable contribution to the literature by
investigating the association between personal variables and academic success.
Firstly, the study was conducted with a different sample than suggested by Newton
et al. (2008), that is, students who were at the beginning of their college education. In
addition, the results of the study might contribute to previous studies indicating
significant personal factors as well as other affective variables of effective learning
(Aydin, 2012) and priorities of prevention facilities, since beginning students would
provide valuable information for the development and improvement of services
offered by psychological counseling centers. Moreover, an adapted inventory
measuring several personal factors can be a practical inventory tool for psychological
counselors at university counseling centers while discovering the personal variables
that might affect academic success. Furthermore, students might use the inventory
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by themselves to gain insight about their strengths and weaknesses. Additionally, the
study might be crucial for psychological counselors working with low-achieving
first-year students because personal variables, which students are able to exert some
control to change by themselves, can be used to increase the academic success of
students. Finally, there is a lack of literature concerning first-year college students’
success in the Turkish population. Even though there are studies about
undergraduate students’ success, the literature lacks information about the personal
factors that affect academic success of students during their first year of college.
Therefore, the present study may offer different perspectives for further research
because the role of personal factors is investigated separately by means of a newly
adapted instrument measuring effectiveness of college learning. Although there was
a previous study investigating personal and affective variables of success in college
students (Aydin, 2012), it is necessary to analyze the predictive role of personal
variables separately to get a clear picture over the concept of effective learning in
college. Considering all the related information regarding the impact of personal
factors, the present study aimed at answering the following research question: What
is the role of personal factors (academic self-efficacy, organization and attention to
study, stress and time pressure, involvement in college activities, emotional
satisfaction and classroom communication) in predicting students’ academic success?
Method
Research Design
In relation with the purpose of this study, a correlation research design was
utilized. The personal variables affecting student success were independent
variables, and each personal variable was a subscale of the college learning
effectiveness inventory. The dependent variable (students’ academic success) was
gathered via English proficiency exam scores.
Research Sample
A demographic data form and Turkish version of the College Learning
Effectiveness Inventory (CLEI) were administered to language preparatory students
of a state university in Turkey. The convenient sampling method was used, and the
participants were volunteer students from the pre-intermediate and intermediate
English levels at the preparatory school. A total of 317 students participated in the
study (50.8% female, 49.2% male). The mean age of the participants was 19.55 with a
standard deviation of 2.28, and their ages ranged between 18 and 28. Among the
participants, 174 (54.9%) of respondents were pre-intermediate level students and
143 (45.1%) of participants were intermediate level students. The demographic
information is indicated in Table 1.
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Table 1
Demographic Information of the Participants
Variables
n
%
Gender
Female
Male
161
156
50.8
49.2
English Proficiency Level
Pre-Intermediate
Intermediate
174
143
54.9
45.1
The language preparatory school, from where participants of the study were
selected, aims to provide basic language skills for students whose level of English is
below proficiency level during the first year of university. The department functions
as a language preparatory school, offering English courses through two semesters.
The courses are based on reading, writing, listening, and speaking skills to prepare
students for the English medium of instruction in undergraduate study. Students’
academic success was obtained via English Proficiency Exam scores given to students
at all levels at the end of the year. The exam includes tests consisting of standard
grammar and vocabulary, reading comprehension, and listening and writing
sections.
Research Instruments and Procedures
Two instruments were used for data collection: The demographic information
form was designed by the researcher, including gender, age, and English proficiency
level at preparatory school; and the College Learning Effectiveness Inventory (CLEI)
developed and revised by Newton et al. (2008) to measure personal factors
influencing college student success. The inventory consists of 51 items on a 5-point
Likert scale (“1. Never” to “5. Always”) and six subscales: Academic Self-efficacy,
Organization and Attention to Study, Stress and Time Pressure, Involvement in
College Activity, Emotional Satisfaction, and Class Communication. Sample items
from the scale are “30. I make study goals and keep up with them, and “36. I feel
there are so many things to get done each week that I am stressed.” For the scales,
high scores indicate expectations to be successful in achieving goals; effective
organizational planning; managing pressures, such as procrastination; engaging in
activities; encouragement; and active communication with friends and faculty. The
Cronbach Alpha levels of scales were .87 (ASE), .81 (OAS), .77 (STP), .81 (ICA), .72
(ES), and .68 (CC) (Newton et al., 2008). A summed score can be calculated for each
subscale representing a different personal variable. Similarly, the Cronbach alpha
values of the scale with the Iranian sample were between .68 and .79 (Saeed, 2014).
The inventory was adapted to Turkish by Aydin (2012) with a Confirmatory
Factor Analysis (CFA) to examine how well the model fit the Turkish population.
CFA results confirmed the six-factor structure of the Turkish version of the College
Learning Effectiveness Inventory and indicated a significant chi-square value χ²
(1051) =1957.84, p<.05 with a mediocre fit. However, Items 12 (I am discouraged
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with how I am treated by my instructors.”) and 41 (My friends have good study
habits.”) were found as insignificant and omitted in the data analysis. In the end, the
scale consisted of 49 items in the Turkish adaptation. For internal consistency,
Cronbach’s alpha level of the total scale was found as .88, and the reliability of the
subscales for the Turkish adaptation were .75 (ASE), .79 (OAS), .68 (STP), .73 (ICA),
.62 (ES), and .61 (CC). Although the internal consistency value was low for some
subscales, the researcher preferred to use this inventory because it exhibited a high
quality with different subscales measuring the most prominent factors of personal
variables, and there were no learning effectiveness inventories that measured all
these psychosocial factors together in Turkish.
Academic achievement scores were gathered via The English Proficiency Exam
(EPE) scores administered by the language preparatory school. The data for this
study were gathered from preparatory students via the demographic information
form and the Turkish version of the College Learning Effectiveness Inventory with
an explanation of the current study. Prior to data collection, permission was granted
from the Human Subjects Ethics Committee. The scales were administered in
approximately 15 minutes via paper-pencil format during class hours with the
permission of instructors. The data were collected in two weeks, and the English
Proficiency Exam results of each student were collected from the administration at
the end of the semester.
Data Analysis
In the study, descriptive statistics (means, standard deviations, and correlation
coefficients among variables) and a Multiple Regression Analysis were utilized to
analyze the data. Prior to running the analysis, a missing value analysis was
conducted and all the necessary assumptions of multiple regression analysis
(normality, multicollinearity, homoscedasticity, independence of residuals, and
outliers) were checked (Tabachnick & Fidell, 2007).
The normality assumption of the residuals was checked through the histogram
and normal probability plot of residuals. According to Tabachnick and Fidell (2007),
the distribution of the histogram should not be too peaked or too flat. The histogram
(Figure 1) of residuals showed an approximately normal distribution. Moreover, the
normal P-P plots (Figure 2) indicated no serious deviation from the straight line.
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100
Figure 1 Figure 2
The variance inflation factor (VIF) and tolerance values were checked for the
multicollinearity assumption testing high correlations among the independent
variables. According to Field (2005), VIF values should be less than 10, and the values
of tolerance should be more than .20. Tolerance and VIF value requirements were
satisfied, as tolerance changed from .42 to .83 and VIF values from 1.21 to 2.63. Then,
scatter plots were checked for the assumption of homoscedasticity, and there seemed
to be no violation of assumption as a result of any pattern of scores indicated by
randomly spread scatterplots. Moreover, the independence of residuals assumption
was checked via the Durbin-Watson value, which should be between 1 and 3
(Tabachnick & Fidell, 2007). It was found as 1.58; therefore, the independence of
residuals assumption was satisfied in the current study. Finally, Cook’s distances,
Leverage, and Mahalanobis distance were checked for assumption relevancy; none of
them showed any violation, as Cook’s distance was observed as .03; leverage values
were within the standards, which should be between 0 and 1; and Mahalanobis
distance indicated no outliers, as all the values were lower than critical 2 (Field,
2005).
Results
In line with the aim of the study, which was to investigate the role of personal
factors in predicting student success, the findings are presented in two sections: the
relevant descriptive statistics (means and standard deviations), and the findings of
the Multiple Linear Regression Analysis [the effect size (adjusted R2) of the overall
regression model, the associated significance test value (p), and the individual
contribution of each predictor (β)]. Descriptive statistics of model showed that the
mean of the English Proficiency Exam is 62.88 with a standard deviation of 10.51.
Among personal variables, the mean of academic self-efficacy is 57.41 with a
standard deviation of 6.47; organization and attention to study M=25.27, SD=5.76;
stress and time press M=18.78, SD=4.23; involvement in college activities M=27.52,
SD=5.53; emotional satisfaction M=21.89, SD=3.58; and classroom communication
M=21.32, SD=3.45. The highest mean was academic self-efficacy and the lowest was
stress and time press. Then, Pearson’s correlation coefficients between independent
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101
variables were analyzed and the level of correlations was suitable for the required
limits as indicated in Table 2.
Table 2
Means and Standard Deviations of Subscales and Correlations Between Variables
Variables
M
SD
1
2
3
4
5
6
7
Proficiency
62.88
10.51
Academic Self-Efficacy
57.41
6.47
.21
1.00
.63
.46
.30
.63
.41
Organization and
Attention to Study
25.27
5.76
.13
.63
1.00
.47
.08
.55
.29
Stress and Time Press
18.78
4.23
.36
.46
.49
1.00
.05
.46
.41
Involvement in College
Activities
27.52
5.53
.12
.30
.08
.05
1.00
.32
.28
Emotional Satisfaction
21.89
3.58
.26
.63
.55
.46
.32
1.00
.40
Classroom
Communication
21.32
3.45
.28
.41
.27
.41
.28
.40
1.00
In terms of correlations, all independent variables were positively correlated with
academic success. While the highest correlations were related to stress and time press
(r = .36, p<.05) and classroom communication (r = .28, p<.05 ), correlations for
involvement in college activities (r = .12, p<.05) and organization and attention to
study (r = .13, p<.05) dimensions were rather low.
Finally, a Multiple Linear Regression Analysis was conducted to examine the
relationship between personal factors (academic self-efficacy (ASE), organization and
attention to study (OAS), stress and time press (STP), involvement in college
activities (ICA), emotional satisfaction (ES) and class communication (CC)) and
students’ academic success. The results of the analysis are summarized in Table 3.
Table 3
Results of the Multiple Regression Predicting Academic Success
Predictors
B
SE
T
P
ΔR ²
ΔF
Model
.16*
10.16
Academic Self-Efficacy
.02
.13
.01
.13
.09
Organization and Attention to
Study
-.21
.13
-.11
-1.59
.11
Stress and Time Press
.75
.16
.30
4.70*
.00
Involvement in College Activities
.08
.11
.04
.76
.45
Emotional Satisfaction
.31
.21
.11
1.45
.15
Class Communication
.39
.19
.13
2.08*
.04
Note. Dependent Variable = English Proficiency Exam. *p<.05
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The regression model was significant as shown in Table 2. Overall, 16% of the
variance of the scores can be accounted by the personal factor variables in predicting
student success. Results of the multiple regression analysis indicated that personal
variables significantly predicted student success, ΔR² = .16, ΔF (6, 310) =10.16, p< .05.
The stress and time press (β = .30, p< .05) and class communication (β= .13, p< .05)
appeared as significant predictors of student success. However, academic self-
efficacy (β= .01, ns), organization and attention to study (β =-.11, ns), involvement in
college activities (β= .04, ns), and emotional satisfaction (β= .11, ns) were not found to
be significant predictors in the model.
Discussion and Conclusion
The purpose of the current study was to examine the role of personal variables in
predicting students’ academic success. The results showed that 16% of the variance
in the criterion variable was explained by the model through personal variables.
Furthermore, stress and time pressure and classroom communication appeared as
significant predictors of success. Contrary to expectations, academic self-efficacy,
organization and attention to study, involvement with college activities, and
emotional satisfaction were not found as significant predictors.
The variance explained by the model showed that while the model was
significant in terms of predicting academic success of students, the explained
variance was not good enough. Consequently, the results should be discussed with
caution. First, there might be some problems related to the inventory. The Cronbach
Alpha of the Turkish version was similar to the original scale, and they were rather
low. Similar to the current study, Leung, Ng, and Chan (2011) found the reliability of
the subscales to be a little bit lower, as .71 (ASE), .40 (OAS), .43 (STP), .73 (ICA), .45
(ES), and .43 (CC) with college students. This finding may indicate that the inventory
might have some problems within itself, or there could be some cultural aspects that
cannot be reflected in adaptation studies into other languages.
According to the results, classroom communication, including asking questions
in a relaxed classroom environment, offering new ideas, and sustaining good
relations with peers and faculty members are positively correlated with academic
achievement among the personal variables. This finding was in line with the
literature stating that students who had better communication skills found it easy to
express themselves and behaved in a more relaxing manner, which led to higher
achievement (Reason et al., 2006; Rubin et al., 1990). In addition, the study indicated
surprising results for the second personal predictor, which was stress and time
pressure. Although the literature stated the adverse effect of stress over student
academic success (Alzaeem, Sulaiman & Wasif Gillani, 2010; Bland, Melton, Welle &
Bigham, 2012), the findings of the present study showed that stress was a significant
positive predictor of students’ academic success. It means a higher level of stress
results in higher achievement. This finding was partially consistent with the
literature underlying the benefit of an appropriate rate of stress for motivation and
performance (Cahir & Moris, 1991) and perceived stress resulting in academic
success (Jepson & Forrest, 2006). According to Heikkila, Niemivirta, Nieminen and
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Lonka (2011), stress can help people survive in critical situations and should not
always be considered as negative. Furthermore, the study provided an interesting
result to the literature: when stress is balanced and kept at a tolerable level, it might
bring success, or when students experience time pressure, they may learn more
effectively. Overall, it can be inferred that an appropriate level of stress might be
necessary for higher achievement.
The findings of the study did not reveal the other personal variables, such as
academic self-efficacy, organization and attention to study, involvement in college
activities, and emotional satisfaction, to be significant predictors of academic success
in the model, contrary to the literature (Komarraju et al., 2010; Kuh, Kinzie, Schuh, &
Whitt, 2005). The study provided surprising results in terms of the insignificant
relationship between academic self-efficacy and academic success. Whether students
had the belief that they could succeed or not did not correlate to their academic
success. Although the positive influence of self-efficacy on academic achievement
was underlined in various research studies (Landis, Altman & Cavin, 2007;
Linnenbrink & Pintrich, 2002; Margolis, 2005; Zimmerman, 2000), this study
provided adverse results compared to previous research. The reason may be because
preparatory students participated in the current study; that is, their academic self-
efficacy should be considered from the view of language learning self-efficacy and,
specifically, their English self-concept should have been studied, as well. As the
students were freshmen who recently started college life, they might not have
finished the adjustment process. Furthermore, it was found that academic self-
efficacy can be influenced by other variables (Peguero & Shaffer, 2015). Additionally,
the findings of the present study indicated that students’ academic success was not
correlated with organization and attention to study. This finding was very surprising
because the literature pointed out underachievement as a result of inadequate
knowledge of how to study (Balduf, 2009), and that students should pay attention to
their studies and responsibilities, concentrate better while studying, and be
organized in order to be successful (Pauk & Owens, 2011).
The current study did not provide significant results in terms of the relationship
between involvement in college activities and academic success. As another indicator
of personal variables, attending college activities was not associated with students’
academic success. In the literature, it is highly possible to find a great amount of
research supporting that, as the proportion of participation in college activities
increased, college students became more successful (Kuh et al., 2005; Pascarella &
Terenzini, 2005; Reason et al., 2006). Similar to the findings of the current study,
Aitken (1982) found involvement in extracurricular activities as an insignificant
predictor of academic success. It is crucial to mention that Aitken (1982) highlighted
that the impact of involvement in college activities could be seen in the second year;
that is, the first year might not reflect student involvement. This result might be valid
for the current study, as well. As the preparatory school building is not close to the
center of the university, where most of the extracurricular activities take place, the
language preparatory school students might not have been informed about the
possibilities and activities around campus, or how to participate in the activities. In
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addition, they may also be struggling with other variables, such as loneliness,
homesickness, etc.
The personal variable of emotional satisfaction covers the interest of faculty in
students’ academic success, enjoying the courses and university, instructors’
behaviors, and feeling satisfied about future career plans. Unlike the literature
(Pritchard & Wilson, 2003; Pascarella & Terenzini, 2005), emotional satisfaction was
not found as a significant predictor of academic success in the present study.
Literature has stated that when students feel cared for by faculty, they believe in their
capacity to achieve more and increase academic self-efficacy (Komarraju et al., 2010).
Hence, according to Decker et al. (2007), this belief should be considered as a better
indicator of emotional satisfaction than academic success. The reason of the
insignificance of emotional satisfaction as a predictor of success might be
understandable considering Umbach and Wawrzynski’s (2005) suggestion.
According to the researchers, students might be prone to ask support not from
faculty, but from other sources like friends or family.
Finally, as a newly adapted instrument was utilized within the study, the
findings related to the instrument should also be discussed. It was found that
reliability results were lower than the original scale, but similar to psychometric
properties of the scale adapted into other languages (e.g., Saeed, 2014). One reason
might be the nature of the sample that participated in the study. As preparatory
school students are the newcomers at the university, they might have not acquired
effective learning strategies at college yet, due to the lack of experience and having
another major concern, namely learning a foreign language, which requires other
competences such as reading, listening, speaking, etc. That is, the scale might not be
valid for this group of participants, or there may be some problems related to the
Turkish version of the scale. In addition, another reason may be some concerns
related to item reflection in the original scale, because the findings of the study were
similar to the study that was conducted with the same type of sample, i.e.,
preparatory school students (Aydin, 2012). The College Learning Effectiveness
Inventory was adapted and used for the first time in that study, and although there
were other significant affective variables in the study, among personal variables,
communication and stress were found as the significant predictors, similar to this
study. Due to attaining nearly the same results in both studies, it can be
recommended that the instrument should be used with university students in other
grades rather than preparatory students in order to attain a clearer picture about
personal variables of success regarding concerns for item reflection.
The results of this study can provide some information regarding personal factors
related to the academic success of preparatory students. As language preparatory
school takes place during the first year of college, possible personal factors can be
investigated as the starting point. It is expected that the findings may offer valuable
information to language preparatory school administrators, instructors, and
university counseling centers who provide psychological help to students. The
positive influence of classroom communication over students’ academic success
might suggest that, when students have a good relationship with each other, feel
relaxed while asking questions, and contribute with different ideas to the topic, they
Gokcen AYDIN / Eurasian Journal of Educational Research 69 (2017) 93-112
105
are more likely to attain academic success. Instructors at preparatory school can use
this information to create a classroom or outside environment in which students can
work and study together with their friends and provide a place to work
collaboratively for activities such as group projects, role plays, or other performance
studies. In addition, this finding can shed a light for faculty not to use direct
instruction methods, as students learn better when they interact among themselves.
University counseling centers can view the invaluable finding of stress and time
pressure as a predictor of student success into consideration while preparing
activities. The impact of stress can be included in student seminars about stress and
academic studies so that students do not put so much pressure on themselves when
they feel stressful about their courses or academic studies. Contrary to the literature,
the positive relationship between stress and academic success contributes to the
literature in a way that perceived stress can influence academic success in a positive
way, as well. Instructors at preparatory school may employ this finding, that a
balanced level of stress is not always something to fear during studies. Not only the
instructors, but also faculty at universities can be informed about the possible
positive effect of stress.
In conclusion, in the present study, a newly adapted instrument into Turkish, the
CLEI, was used to measure the personal factors of effective learning. The reliability
results of the scale demonstrated that the results could have been influenced by the
grade level of the participants. Contrary to the hypothesis in the beginning, collecting
information about personal variables that influence effective learning might not
reflect accurate results when the participants have not yet adapted to university life.
Therefore, for further research, the inventory should be used with other grade levels
(such as juniors and seniors) in different departments as well as at different
universities to explore information about effective learning, as undergraduate
students may provide more valuable information related to their experiences at
university more than preparatory students who are freshmen and have less
experience both academically and socially. As the very similar study indicated nearly
the same results, there should be extra studies regarding this inventory. The CLEI
can be used to reveal students’ profiles about personal factors (organization and
attention to study, stress and time press, classroom communication, etc.) that impact
their achievements at colleges since it includes a wide range of psycho-social factors.
The current study had also some limitations. The first one was related to
generalizability of the results because only the language preparatory students
enrolled in a state university participated in this study. Therefore, the results cannot
be generalized to the entire population of college students. Although the sample was
chosen on purpose that the students were freshmen at the college, enrollment in the
language learning preparatory school caused other variables to be taken into
consideration. Moreover, the self-reporting nature of the study is another limitation,
as the students may have not provided accurate responses to the items. Finally,
although the inventory was composed of different psycho-social variables, the low
reliability of subscales might have indicated possible problems related to the
inventory. Therefore, the inventory should be used by taking all these aspects into
consideration.
Gokcen AYDIN / Eurasian Journal of Educational Research 69 (2017) 93-112
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Üniversite Öğrencilerinin Başarısını Yordayan Kişisel Faktörler
Atıf:
Aydın, G. (2017). Personal factors predicting college student success. Eurasian Journal
of Educational Research, 69, 93-112. http://dx.doi.org/10.14689/ejer.2017.69.6
Özet
Problem Durumu: Günümüzde yükseköğrenim derecesine sahip olmak yalnızca
seçkin mezunlar açısından değil aynı zamanda ekonomi, bilim, teknoloji ve kişisel
gelişim için önemlidir. Yükseköğretim kurumları, en önemli misyonlarından biri
olarak öğrenci başarısının altını çizmektedir. Farklı teoriler başarı söz konusu
olduğunda akademik ve sosyal uyum, beklenti ve motivasyon, hedefleri belirleme
gibi noktalara vurgu yapmaktadır. Öğrenci başarı pek çok farklı biçimde
tanımlanmasına rağmen, modern eğitim sistemindeki değişen bakış açısıyla birlikte,
başarının notlardan çok daha öte bir anlam ifade ettiği görülmektedir. Bu da,
öğrencinin bütün olarak gelişiminin, bilişsel ve akademik boyutlarının yanı sıra
duygusal ve sosyal boyutlarının da olduğuna işaret etmektedir. Yani öğrenci başarısı
üniversite öncesi deneyimler ve üniversite yaşamına katılım gibi pek çok unsurla
şekillenmektedir. İlgili alan yazın, diğer etkenlerin yanı sıra, akademik öz-yeterlilik,
çalışmaya dikkatini verme ve organize olma, sınıf iletişimi, stres ve zaman baskısı,
duygusal etkenler ve öğrencilerin üniversite yaşamına katılımları gibi öğrencilerin
kendi kontrolleri altında olan kişisel değişkenlerin öğrenci başarısını etkileyen en
önemli faktörler olduğuna işaret etmektedir. Kişisel faktörler bireysel farklılıklardan
oluşmaktadır ve her birey kendi yaşamını geliştirme ya da değiştirme kapasitesine
sahiptir. Bu nedenle, başarı söz konusu olduğunda “psiko-sosyal faktörler” olarak da
adlandırılan kişisel faktörler araştırmacıların odak noktası olmaktadır. İlgili alan
yazında etkili öğrenme üzerindeki kişisel faktörleri belirlemede kullanılabilecek
Türkçe bir ölçme aracının olmaması sebebiyle geliştirilen kapsamlı ölçme araçlarının
uyarlanarak yeni örneklemde kullanılması ve ayrıca etkili öğrenmeyi üzerindeki
faktörlerin belirlenerek bu kapsamda yapılabilecek önleyici rehberlik hizmetleri
geliştirilmesi açısından bu çalışma önem taşımaktadır.
Araştırmanın Amacı: Bu çalışmanın amacı, üniversitede etkili öğrenme üzerindeki
kişisel faktörlerin (akademik öz-yeterlilik, çalışmaya dikkatini verme ve organize
olma, sınıf iletişimi, stres ve zaman baskısı, duygusal etkenler ve öğrencilerin
üniversite yaşamına katılımları) öğrenci başarısını yordamadaki rolünü
araştırmaktır.
Araştırmanın Yöntemi: Bu çalışmada, ilişkisel tarama modeli kullanılmıştır.
Çalışmanın örneklemini Türkiye’de bir devlet üniversitesinde öğrenim gören 317
İngilizce Hazırlık Okulu öğrencisi (161 kadın, 156 erkek) oluşturmaktadır. Veri
toplama aracı olarak demografik bilgi formu ve Üniversitede Etkili Öğrenme
Envanteri kullanılmıştır. Demografik bilgi formu, yaş, cinsiyet, dil seviyesi gibi
bilgilerden oluşmaktadır. Üniversitede Etkili Öğrenme Envanterinin Türkçe formu
Gokcen AYDIN / Eurasian Journal of Educational Research 69 (2017) 93-112
111
5’li dereceleme ölçeği (1. Asla, 5. Her zaman) üzerinde 49 madde ve 6 alt boyuttan
oluşmaktadır. Bu alt boyutlar, akademik öz-yeterlilik, çalışmaya dikkatini verme ve
organize olma, stres ve zaman baskısı, öğrencilerin üniversite yaşamına katılımları,
duygusal etkenler ve sınıf iletişimidir. Ölçekte sorulan sorulara verilen yüksek
puanlar başarıyla ilgili beklentinin ve hedeflerin olduğunu, etkili planlama
yapılabildiğini, erteleme gibi akademik baskılarla baş edilebildiğini, kampüsteki
etkinliklere katılımını ve öğretim elemanı ve öğrencilerle iyi bir iletişimi
göstermektedir. Ölçeğin alt boyutlarının güvenirlik katsayıları .87 ila .68 arasındadır.
Araştırmanın Bulguları: Çalışmanın amacına uygun olarak yapılan Çoklu Regresyon
Analizi sonuçları kişisel değişkenlerin öğrenci başarısını anlamlı şekilde yordadığını
göstermektedir, ΔR² = .16, ΔF (6, 310) =10.16, p<.05. Sonuçlar, bu modelin toplam
varyansın % 16’sını açıkladığını göstermiştir. Stres ve zaman baskısı (β = .30, p< .05)
ve sınıf iletişimi (β= .13, p< .05) öğrenci başarısını anlamlı şekilde yordarken,
akademik öz-yeterlilik, çalışmaya dikkatini verme ve organize olma, duygusal
doyum ve öğrencilerin üniversite yaşamına katılımları başarıyı anlamlı ölçüde
yordamamıştır. Üniversitede etkili öğrenme üzerindeki kişisel faktörler arasında
stres ve zaman baskısı ile sınıf iletişiminin yer aldığı görülmektedir ve iki değişkenin
de başarıyla pozitif yönde ilişkili olduğu bulunmuştur.
Araştırmanın Sonuçları ve Önerileri: Bu çalışmaya göre, sınıfla iletişim, stres ve zaman
baskısı öğrenci başarısını yordayan değişkenlerdir. Diğer bir deyişle, sınıf içi iletişimi
iyi olan ve çalışmaları sırasında stres ve zaman baskısı hisseden öğrencilerin
üniversitede etkili öğrenmede daha başarılı olduğu bulunmuştur. Ancak akademik
öz-yeterlilik, çalışmaya dikkatini verme ve organize olma, öğrencilerin üniversite
yaşamına katılımları başarıyı yordamamaktadır. Seçilen örneklemin üniversitenin
hazırlık okulunda öğrenim gören, yani üniversitede henüz ilk yılını geçiren
öğrencilerden oluşması, bu durumun kaynağı olarak düşünülebilir. Yani öğrencilerin
henüz üniversite yaşamına alışamadıklarından, bu yeni yaşama etkin bir şekilde
katılamadıkları ve üniversitede nasıl çalışacaklarına dair ders çalışma becerilerini
henüz geliştirememiş oldukları düşünülebilir. Öğretim elemanları bu sonuç ışığında,
derslerinde öğrencilerin sınıf içi iletişimi geliştirebilecekleri rol oynama, takım
çalışmaları gibi etkinliklere yer verebilirler. Araştırmanın sonucu ayrıca, düz anlatım
tekniği kullanan öğretim elemanlarına kullandıkları bu yöntemin öğrenmede etkili
olmayabileceği yönünde bir bilgi verebilir. Ayrıca öğretim elemanlarına ve üniversite
psikolojik danışmanlarına, stres ve akademik başarı arasındaki pozitif ilişkiye dikkat
etmeleri önerilebilir çünkü bazen dengeli bir stres, çalışmalar sırasında her zaman
korkulacak bir durum olarak karşımıza çıkmayabilir. Öğrencilere yönelik hazırlanan
akademik başarı ve stres hakkındaki seminerlerde ilgili bu sonuçlardan bahsedilerek,
öğrencilere kendi üzerlerinde kurdukları baskıyı azaltma yönünde yardımcı
olunabilir. Bunun yanı sıra, Türkçeye uyarlanan Üniversitede Etkili Öğrenme
Envanterinin yapılan güvenirlik hesaplamaları ve bu çalışma sonucunda elde edilen
bulgular, ölçme aracıyla ilgili bazı sorunların olabileceğine ve dikkatle yaklaşılması
gerektiğine vurgu yapmaktadır. Gelecekteki araştırmalar açısından, hazırlık
sınıfından ziyade birinci, ikinci ya da son sınıf öğrencileri seçilerek üniversiteye
uyumunu sağlamış bir örneklem üzerinde çalışılması önerilebilir. Bu doğrultuda,
Gokcen AYDIN / Eurasian Journal of Educational Research 69 (2017) 93-112
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uyarlanan ölçme aracı, üniversitenin ilk yılı ve dil öğrenme odaklı bir grubunu
yansıtan hazırlık öğrencileri dışında farklı sınıf düzeyleri ve farklı üniversitelerde de
kullanılarak ilgili alan yazına katkıda bulunulabilir. Ölçme aracı, öğrencilerin etkili
öğrenmelerini etkileyen kişisel faktörler açısından oldukça zengin değişkenleri bir
arada bulundurması açısından kullanışlı ve yeni bir ölçme aracı olarak düşünülebilir
ancak ölçeğin kendi içinde ya da Türkçesi üzerinde bazı sorunlar olabileceği göz ardı
edilmemelidir. Son olarak, farklı örneklemlerde kullanılarak uygun sonuçlar elde
edilmesi halinde, üniversite psikolojik danışma merkezleri tarafından öğrenci
başarısını etkileyebilecek değişkenleri bulmada kullanılabilecek pratik bir envanter
olarak görülebilir.
Anahtar sözcükler: Etkili öğrenme, öğrenci başarısı, üniversite öğrenimi, kişisel
değişkenler.
... Linear regression models are common applications of predictive analytics in the field of education. The study by [2], for example, is dedicated to predicting students' academic success. For this purpose, authors use a model of multiple linear regression, i.e., the model uses multiple independent variables. ...
... In his study, Aydin [2] also examines, in addition to study factors, emotional, social and cognitive development. He found that students' success is influenced by stress, time constraints and classroom communication. ...
... We interpret the value of coefficient as the increment size effect of Xi on Y while all other variables remain unchanged. Residual sum of squares (RSS) which is used to optimize the model can be defined as (2) where is the predicted value of the dependent variable and is the actual value of the dependent variable. We find the values of coefficients , , … , using the least squares method so that the sum of the squares of the error estimate is minimal [38]. ...
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The paper deals with predicting grade point average (GPA) with supervised machine learning models. Based on the literature review, we divide the factors into three groups—psychological, sociological and study factors. Data from the questionnaire are evaluated using statistical analysis. We use confirmatory data analysis, where we compare the answers of men and women, university students coming from grammar schools versus students coming from secondary vocational schools and students divided according to the average grade. The differences between groups are tested with the Shapiro–Wilk and Mann–Whitney U-test. We identify the factors influencing the GPA through correlation analysis, where we use the Pearson test and the ANOVA. Based on the performed analysis, factors that show a statistically significant dependence with the GPA are identified. Subsequently, we implement supervised machine learning models. We create 10 prediction models using linear regression, decision trees and random forest. The models predict the GPA based on independent variables. Based on the MAPE metric on the five validation sets in cross-validation, the best generalization accuracy is achieved by a random forest model—its average MAPE is 11.13%. Therefore, we recommend the use of a random forest as a starting model for modeling student results.
... K tomuto se přiklání např. i další studie Aydin (2017). Na druhou stranu však autoři jiných studií dokládají výsledky o opaku, tedy že vnímaný stres souvisí s nižší akademickou úspěšností (Talib, Zia-ur-Rehman, 2012;Gustems-Carnicer, Calderón, Calderón-Garrido, 2019). ...
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... Students who have good communication have better learning achievement. Communication skills are one of the determinants of student success in learning activities (Aydin, 2017). In general, more than half of students have online learning readiness in the medium category with an average score of 76.08. ...
... The type of technique used by the teacher in the class will determine whether the students will be active in the class or not. Current views of learning underline that students' motivation is a crucial factor in successful learning and achievement [16,17]. The type of Motivation this study investigated is academic motivation and according to [18] motivation in learning is referred to academic motivation. ...
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The study investigated the effectiveness of Two-Dimensional Animation Technique in enhancing students' Motivation in Quantitative concepts of Economics. The design adopted for the study was Quasi-experimental non-equivalent pre-test, post-test control group design with a sample size of 162 SS II Economics students made up of 80 males and 82 females drawn from four coeducational secondary schools in Enugu metropolis, Enugu State. Purposive sampling was used to select the four coeducational secondary schools while simple random sampling was used to select the four intact classes from each of the four (4) Schools. Students Motivation in Quantitative Economics Learning scale (SMQELS) was used for data collection. The instrument was face validated and tested for reliability. The SMQELS was subjected to factor analysis and 17 items out of the 20 items survived. A reliability coefficient of 0.80 was obtained using Cronbach Alpha. The temporal stability index of the instrument was found to be 0.87 using Pearson Product Moment Correlation. Data collected were analysed using mean and standard deviation to answer the research question while ANCOVA was used in testing the null hypothesis at 0.05 level of significance. The result showed that students taught quantitative concepts of Economics with two-dimensional animation technique had significantly higher post-test mean motivation score than those exposed to conventional method. Thus, 2D animation technique proved to be significantly effective in enhancing students' motivation in quantitative contents of economics. It was recommended among others that two-dimensional animation technique should be used in teaching economics in secondary schools.
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A critical question is whether selection criteria for formal entry into professional courses have the power to prognosticate their later academic performance. This study examined the predictive validity of College Entrance Exam marks (CEEM) and Graduate marks (GM) visa -vis the academic performance during the Post Graduate Program (PGP). It covers a sample of 322 students who entered PGP in Audiology and Speech-Language Pathology (SLP) at a national institute in India between the years 2012-2018.Inter-correlations indicated that CEEM (29 %) are the less correlated and predictive of PGP academic performance than GM (56 %). The lower Predictive Validity (PV) of CEEM is probably attributable to non-cognitive factors involved in their later academic performance. Policy implications and further research are highlighted.
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