![An Exploration of Self-regulated Learning
Among Health Science Students
Salvador Bondoc1 and Christine Fitzgerald2
1Department of Occupational Therapy, 2Department of Biomedical Sciences
Quinnipiac University, Hamden, CT
Data Analysis / Results
Literature Background
MethodsResearch Objectives
Table 2. Two-year comparison of MSLQ Scores
• To describe health science students’ self-
regulation and how they may change over time.
• To determine whether self-regulated learning
(SRL) among health science students is linked
to academic achievement and if so, which
among many SRL constructs determine such
achievement.
Outside of health professions education, the
application of various concepts of self-regulated
learning has been receiving attention for they have
been linked to academic achievement (Rubam,
McCoach, McGuire & Reise, 2003; Hsieh, Sullivan &
Guerra, 2007; Schloemer & Brennan, 2006).
Development of Self-Regulation
As SRL is complex and context-mediated (Schunk &
Zimmermann, 1994; Green and Azevedo, 2007), how
a student’s development of self-regulated learning
from the time they enter college until they acquire
professional competencies and graduate, require
considerable attention. Zimmerman (1998)
suggested that the development of SRL is a cyclical
and iterative process. However, such development
may differ across disciplines (Vanderstoep, Pintrich &
Fagerline, 1996) or the type of course work (Duncan
& McKeachie, 2005). Furthermore, profiles in self-
regulated learning vary among college students
(Barnard-Brak, Yan and Paton, 2010). How students
with unique SRL profiles develop over time and how
these differences in development may account for
academic achievement also require further
exploration.
Theoretical framework
Self-regulated learning (SRL) is originally rooted in
social cognitive theory (Bandura, 1986). Self-
regulation is thought of as a natural process of self-
monitoring and adjusting one’s behaviors based on
internal and external factors (Bandura, 1986;
Zimmermann, 1990). In the field of education, the
concepts of SRL continue to emerge and have been
infused with various concepts related to learning
including knowledge drawn from information
processing and constructivist perspectives (Schunk &
Zimmermann, 1998; Paris & Paris, 2001).
• Design: longitudinal cohort design.
• Participants: Health science majors entering in their
freshman year. Average age is 18.3 yrs, predominantly
Caucasian (83%).
• Setting: Comprehensive master’s/4-year private
university.
• Measure: Motivational Strategies for Learning
Questionnaire ([MSLQ]; Pintrich, Smith, Garcia &
McKeachie, 1991/1993), a self-report instrument with
two broad domains: motivation and learning strategies.
The MSLQ contains 81 items divided into 15 subscales,
clustered into 3 (4) components. See table below.
Demographic,Variables, N=138,
Freshman,Cumulative,GPA,(/4Bpt,scale),
Mean%Total%(Range)%%
A"average"GPA"=">than"3.3"(%)"
B"average"GPA"="2.67"to"3.33"(%)"
C"average"GPA"="2.0"to"2.66"(%),
Incomplete%data%
,
%
3.22%(2.1983.99)%
58#(42.0%)#
53#(38.4%)#
14#(10.1%)#
13%(9.4%)%
Major,
Health%Science%Studies%
Occupational%Therapy%
,
%
79%(57.2%)%
59%(42.8%)%
%
Gender,(%),
Male%%
Female,
%
15%(10.9%)%
122%(88.4%)%
%
Employment,Status,
Employed%
Not%Employed%
,
%
27%(19.6%)%
111%(80.4%)%
!
! FA!‘12! SP!’13! Change!
A.#Value#Components# 5.74% 5.71%
%
'0.03%
B.#Affective/Expectancy#Components# 5.32%
%
5.37% 0.05%
C.#Cognitive/Metacognitive#Strategies#
#
4.99% 5.00% 0.01%
D.##Resource#Management#Strategies##
#
5.30% 5.20% &0.10%
TOTAL!MSLQ! 5.14) 5.14) &0.01)
!
Table 1. Characteristics of Participants
Fig 1. Effort Regulation Fig 2. Self-Efficacy of Learning
Fig 3. Test Anxiety Fig 4. Metacognitive Strategies
Differences in the total and subscale scores of the MSLQ by major
were not statistically significant. However, there is a statistically
significant interaction between MSLQ scores and GPA (F[1, 119] =
5.028, p =.027).
Further analysis showed students who had a C average GPA have a
significantly lower total and select subscale MSLQ scores than those
who had either an A or B average GPA. The difference in MSLQ scores
between A average GPA and B average GPA students is not statistically
significant. Figures 1 through 4 highlight the differences among
students by average GPA in four different subscales: effort regulation
test anxiety, self-efficacy of learning, and metacognitive strategies.
The preliminary results of the study have implications on design of
academic supports for at-risk students especially ways that would
encourage the development of effort regulation, decreased test anxiety,
improved self-efficacy of learning, and the use of metacognitive
strategies.](https://image.slidesharecdn.com/anexplorationofselfregulatedlearningamonghealthsciencestudentsbondocfitzgerald-160111171223/85/an-exploration-of-self-regulated-learning-among-health-science-students-1-320.jpg)
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An exploration of self regulated learning among health science students
- 1. An Exploration of Self-regulated Learning Among Health Science Students Salvador Bondoc1 and Christine Fitzgerald2 1Department of Occupational Therapy, 2Department of Biomedical Sciences Quinnipiac University, Hamden, CT Data Analysis / Results Literature Background MethodsResearch Objectives Table 2. Two-year comparison of MSLQ Scores • To describe health science students’ self- regulation and how they may change over time. • To determine whether self-regulated learning (SRL) among health science students is linked to academic achievement and if so, which among many SRL constructs determine such achievement. Outside of health professions education, the application of various concepts of self-regulated learning has been receiving attention for they have been linked to academic achievement (Rubam, McCoach, McGuire & Reise, 2003; Hsieh, Sullivan & Guerra, 2007; Schloemer & Brennan, 2006). Development of Self-Regulation As SRL is complex and context-mediated (Schunk & Zimmermann, 1994; Green and Azevedo, 2007), how a student’s development of self-regulated learning from the time they enter college until they acquire professional competencies and graduate, require considerable attention. Zimmerman (1998) suggested that the development of SRL is a cyclical and iterative process. However, such development may differ across disciplines (Vanderstoep, Pintrich & Fagerline, 1996) or the type of course work (Duncan & McKeachie, 2005). Furthermore, profiles in self- regulated learning vary among college students (Barnard-Brak, Yan and Paton, 2010). How students with unique SRL profiles develop over time and how these differences in development may account for academic achievement also require further exploration. Theoretical framework Self-regulated learning (SRL) is originally rooted in social cognitive theory (Bandura, 1986). Self- regulation is thought of as a natural process of self- monitoring and adjusting one’s behaviors based on internal and external factors (Bandura, 1986; Zimmermann, 1990). In the field of education, the concepts of SRL continue to emerge and have been infused with various concepts related to learning including knowledge drawn from information processing and constructivist perspectives (Schunk & Zimmermann, 1998; Paris & Paris, 2001). • Design: longitudinal cohort design. • Participants: Health science majors entering in their freshman year. Average age is 18.3 yrs, predominantly Caucasian (83%). • Setting: Comprehensive master’s/4-year private university. • Measure: Motivational Strategies for Learning Questionnaire ([MSLQ]; Pintrich, Smith, Garcia & McKeachie, 1991/1993), a self-report instrument with two broad domains: motivation and learning strategies. The MSLQ contains 81 items divided into 15 subscales, clustered into 3 (4) components. See table below. Demographic,Variables, N=138, Freshman,Cumulative,GPA,(/4Bpt,scale), Mean%Total%(Range)%% A"average"GPA"=">than"3.3"(%)" B"average"GPA"="2.67"to"3.33"(%)" C"average"GPA"="2.0"to"2.66"(%), Incomplete%data% , % 3.22%(2.1983.99)% 58#(42.0%)# 53#(38.4%)# 14#(10.1%)# 13%(9.4%)% Major, Health%Science%Studies% Occupational%Therapy% , % 79%(57.2%)% 59%(42.8%)% % Gender,(%), Male%% Female, % 15%(10.9%)% 122%(88.4%)% % Employment,Status, Employed% Not%Employed% , % 27%(19.6%)% 111%(80.4%)% ! ! FA!‘12! SP!’13! Change! A.#Value#Components# 5.74% 5.71% % '0.03% B.#Affective/Expectancy#Components# 5.32% % 5.37% 0.05% C.#Cognitive/Metacognitive#Strategies# # 4.99% 5.00% 0.01% D.##Resource#Management#Strategies## # 5.30% 5.20% &0.10% TOTAL!MSLQ! 5.14) 5.14) &0.01) ! Table 1. Characteristics of Participants Fig 1. Effort Regulation Fig 2. Self-Efficacy of Learning Fig 3. Test Anxiety Fig 4. Metacognitive Strategies Differences in the total and subscale scores of the MSLQ by major were not statistically significant. However, there is a statistically significant interaction between MSLQ scores and GPA (F[1, 119] = 5.028, p =.027). Further analysis showed students who had a C average GPA have a significantly lower total and select subscale MSLQ scores than those who had either an A or B average GPA. The difference in MSLQ scores between A average GPA and B average GPA students is not statistically significant. Figures 1 through 4 highlight the differences among students by average GPA in four different subscales: effort regulation test anxiety, self-efficacy of learning, and metacognitive strategies. The preliminary results of the study have implications on design of academic supports for at-risk students especially ways that would encourage the development of effort regulation, decreased test anxiety, improved self-efficacy of learning, and the use of metacognitive strategies.