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An Investigation of the Causal Relationship Between Academic Motivation and Community College Student Success.

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An Investigation of the Causal Relationship Between Academic Motivation and Community College Student Success

LijuanZhai, Director, Institutional Research, Fresno City CollegeMary Ann Valentino, Psychology Faculty, Fresno City CollegeChuck Kralowec, Institutional Research Coordinator, District Grant Office, State Center Community College District

2014 RP Group Conference (April 10-11)

introduction
Introduction
  • Psychology faculty investigating possible variables that influence success rates in general psychology class, which historically has success rates that are lower than the college and the division (social sciences). 
  • Anecdotally, instructors often cite motivation or lack thereof as a reason for student success or failure. 
introduction1
Introduction
  • We decided to find and use an instrument to investigate the correlation between success and motivation with our general psychology students. 
  • If motivation, and specifically certain types of motivation, is associated with higher rates of success, would it be possible to design interventions, at the department- and/or at the college-level to increase motivation? 
introduction2
Introduction
  • Motivation is defined as “a process that arouses, maintains, and guides behavior toward a goal” (Cacioppo & Freberg, 2013)

Cacioppo, J.T. & Freberg, L.A. (2013). Discovering Psychology: The Science of Mind. Canada: Wadsworth, Cengage Learning.

introduction3
Introduction
  • Formal education is an essential prerequisite of professional progress and it is vital to identify the psychological factors which determine academic achievement.
  • Recent research conducted in this field emphasizes the importance of difference motivational constructs.
introduction4
Introduction
  • Motivation is related to student learning and performance (Vallerand, Pelletier, Blais, Brière, Senécal, & Vallières, 1992).
  • In their recent article, Wagner & Szamoskozi (2012) conducted a meta-analysis on the effects of academic motivation and achievement training programs. They concluded that academic motivation has a great impact on academic performance (Wagner & Szamoskozi, 2012). It contributes to the prediction of school achievement above and beyond intelligence.
academic motivation
Academic Motivation
  • Academic motivation has been defined in various ways:
  • “a student’s willingness, need, desire and compulsion to participate in, and be successful in the learning process” (Moenikia & Babelan, 2010, p. 1538).
academic motivation1
Academic Motivation
  • Several conceptual perspectives have been proposed in order to better understand academic motivation, including intrinsic motivation, extrinsic motivation, and amotivation (Vallerand, et al.,1992). These three types of motivations are defined as follows:
  • Intrinsic Motivation (IM) refers to the fact of doing an activity for itself, and the pleasure and satisfaction derived from participation (Deci, 1975; Deci & Ryan, 1985, as cited in Vallerand, et al.,1992)
  • Extrinsic Motivation (EM) pertains to a wide variety of behaviors which are engaged in as a means to an end and not for their own sake (Deci, 1975, cited in Vallerand, et al.,1992).
academic motivation2
Academic Motivation
  • Amotivation is neither intrinsic nor extrinsic. When amotivated individuals experience feelings of incompetence and expectancies of uncontrollability, they perceive their behaviors as caused by forces out of their own control. Eventually, they may stop participating in academic activities (Vallerand, et al.,1992).
survey instrument
Survey Instrument
  • To measure student motivation, Academic Motivation Scale (AMS) was used. AMS was developed and validated by a group of scholars in Canada (Vallerand, et al.,1992). The survey instrument was widely used and validated by many researchers.
  • Academic Motivation Scale (AMS) was developed based on the self-determination theory (SDT) proposed by Deci and Ryan (1985).
  • Deci and Ryan basically identify “several distinct types of motivation” (Ryan & Deci, 2000, p. 69). These types of motivation root in the perceived locus of causality, which can be internal, external or impersonal (see figure 1):
survey instrument1
Survey Instrument

AMS contains 28 survey questions and addresses the following seven aspects of academic motivation:

  • Intrinsic motivation - to know (questions # 2, 9, 16, 23)Intrinsic motivation to know is defined as the fact of performing an activity for pleasure and the satisfaction that one experiences while learning, exploring, or trying to understand something new (Vallerand, et al.,1992)..
survey instrument2
Survey Instrument
  • Intrinsic motivation - toward accomplishment (questions # 6, 13, 20, 27)Intrinsic motivation toward accomplishments is defined as the fact of engaging in an activity for the pleasure and satisfaction experienced when one attempts to accomplish or create something (Vallerand, et al.,1992).
  • Intrinsic motivation - to experience stimulation (questions # 4, 11, 18, 25)Intrinsic motivation to experience stimulation is operative when someone engages in an activity in order to experience stimulating sensation (e.g., sensory pleasure, aesthetic experiences, as well as fund and excitement) derived from one’s engagement in the activity (Vallerand, et al.,1992).
survey instrument3
Survey Instrument
  • Extrinsic motivation – identified (questions # 3, 10, 17, 24)To the extent that the behavior becomes valued and judged important for the individual, and especially that it is perceived as chosen by oneself, then the internalization of extrinsic motives become regulated through identification. The individual might say: ‘I study the night before exams because it is something important for me.”
  • Extrinsic motivation – introjected (questions # 7, 14, 21, 28)Introjected means the individual begins to internalize the reasons for his or her actions. This form of internalization, while internal to the person, is not truly self-determined since it is limited to the internalization of past external contingencies. Thus the student might say: “I study the night before exams because that’s what good students are supposed to do.” (Vallerand, et al.,1992).
survey instrument4
Survey Instrument
  • Extrinsic motivation - external regulation (questions # 1, 8, 15, 22)External regulation refers to the behavior regulated through external means such as rewards and constraints (Vallerand, et al.,1992). For instance, a student might say: “I study the night before exams because my parents force me to.”
  • Amotivation(questions # 5, 12, 19, 26)Amotivation is either intrinsic nor extrinsic motivation. Individuals are amotivated when they do not perceive contingencies between outcomes and their own actions. When amotivated individuals experience feelings of incompetence and expectancies of uncontrollability, they perceive their behaviors as caused by forces out of their own control.
survey instrument5
Survey Instrument
  • AMS has been widely used to measure motivation in student populations and, based upon results, various methods have been posited to improve motivation in students.
  • This instrument has been applied to elementary, high school, and undergraduate college students, in a number of different languages.
  • Areview of the literature has not uncovered previous research in which the AMS has been applied to community colleges.
  • A motivation study of community college students would provide useful information to community college educators, allowing them to understand the type and level of motivation of our students and to develop motivation interventions.
research objective
Research Objective
  • The objectives of the current research are to understand student academic motivation in a community college and to investigate casual relationship between academic motivation and academic performance.
method definitions
Method Definitions
  • GPA = Grade Point Average
    • Includes letter grades only, no pass/fail
    • Includes unit credit calculations
  • SR% = Success Rate
    • No unit credit calculations
    • A, B, C, or Pass = 1
    • D, F, or Fail = 0
  • “Latent” refers to unobserved variables that remove error variance from their observed variables and can serve as error-free IV’s in path models, as in the current study
method
Method
  • Model 1 & 2
    • Missing item replacement using regression
    • Multivariate outliers removed
    • Seven factor oblique used to predict GPA
  • Model 1
    • Cumulative, career GPA as of Fall ’13
  • Model 2
    • Independent observations of term 1, mid-career, and last term success rate
before analysis model 1
Before Analysis, Model 1
  • Prior to analysis, subjects with missing questions from subscales 1 (n=27), 2 (n=54), 3 (n=51), or 7 (n=39) were dropped because independent t-tests showed that their scores differed significantly on other average subscale scores than subjects without missing items from those subscales. 103 subjects with missing items from one of the four subscales and were dropped, and 8 subjects without cumulative GPAs as of fall 2013 were dropped.
  • Regressions were performed to determine new scale scores for any missing items from predicting the average score on the missing subscale from the average scores on all the other subscales. Each new score was rounded to the nearest whole number. Afterwards, 680 subjects had no missing items, 12 had items missing on the first subscale, 24 had items missing from the 2nd, 12 had items missing from the 4th, 5 from the 5th, 5 from the 6th, and 2 were missing items from both the 2nd and 5th scales and were removed. All missing items were replaced with regression-obtained scores. 8 students with missing valid GPAs were also removed. Only 22 students had missing items replaced with regression-obtained answers.
  • 53 cases where identified through Mahalanobis distance as multivariate outliers with p < .001 and were subsequently removed.
  • The final sample size for the study is N = 640after removing 164 participants.
slide21

Intrinsic

To Know

Item 2

Measurement

Model (CFA)

Item 9

Item 16

Item 23

Intrinsic

Accomplishment

Item 6

Item 13

Item 20

Item 27

Intrinsic

Stimulation

Item 4

Item 11

Item 18

Item 25

Item 3

Extrinsic

Identified

Item 10

Item 17

Item 24

Item 7

Extrinsic

Introjected

Item 14

Item 21

Item 28

Item 1

Extrinsic

Regulation

Item 8

Item 15

Item 22

Item 5

Amotivation

Item 12

Item 19

Item 26

slide22

Intrinsic

To Know

Item 2

Structural

Model (SEM)

Item 9

Item 16

Item 23

Intrinsic

Accomplishment

Item 6

Item 13

Item 20

Item 27

Intrinsic

Stimulation

Item 4

Item 11

Item 18

Item 25

Item 3

Extrinsic

Identified

Cum GPA as of fa2013

Item 10

Item 17

Item 24

Item 7

Extrinsic

Introjected

Item 14

Item 21

Item 28

Item 1

Extrinsic

Regulation

Item 8

Item 15

Item 22

Item 5

Amotivation

Item 12

Item 19

Item 26

slide24

Item 2

1.00

Intrinsic

To Know (#1)

Item 9

.86

Item 16

.79

-.938

(.338)

Item 23

.86

.784

(.099)

.87

1.00

Item 6

Intrinsic

Accomplishment (#2)

.84

Item 13

Cum GPA as of fa2013

.69

.86

Item 20

2.102

(.847)

.89

Item 27

.90

Item 7

1.00

Extrinsic

Introjected (#5)

Item 14

.82

Item 21

.85

-1.526

(.591)

Item 28

.91

before analysis model 2
Before Analysis, Model 2
  • The same students that were removed for missing data in model 1 were also absent for model 2.
  • 57 cases where identified through Mahalanobis distance with p < .001 as multivariate outliers on the motivation scale and were removed.
  • 8 cases did not have complete academic records and were removed. 71 more were removed because they only had two or fewer terms in their academic careers, and three were needed to detect a linear slope.
  • The final sample size for Model 2 is N = 566 after removing 229 participants.
model 2 definitions
Model 2 Definitions
  • “Latent Intercept”
    • Y = A + b1X1
    • As in regression, serves as the starting point for an equation that would explain academic success…
    • …But latent intercept removes error in academic performance caused by sick days, family problems, etc.
    • Latent intercept could be conceived of as actual (as opposed to simply observed) academic performance
  • “Latent Trajectory”
    • Y = A + b1X1
    • Serves as beta term in regression…
    • …but removes error between time periods
    • And could be conceived of as actual academic trajectory
slide28

Unconditional Means Model

Note. All semester success rate observations are independent.

Latent Intercept

1

1

1

1st Semester SR%

Mid Career SR%

Last Sem SR%

.712

.769

.700

slide29

Unconditional Slope Model

-.012

Latent Intercept

(.788)

Latent (Linear) Trajectory

(-.041)

0

1

2

1

1

1

1st Semester SR%

Mid Career SR%

Last SemSR%

.591

.471

.658

Intercepts of time periods were set to 0

slide31

Conditional Slope Structural Model

To Know

Accomplishment

Stimulation

Identified

Introjected

Regulation

Amotivation

Latent Intercept

Latent Linear Trajectory

0

1

2

1

1

1

1st Semester SR%

Mid Career SR%

Last SemSR%

slide32

Conditional Slope Structural Model

Note. Intercepts of SR% in parentheses.

*p<.05 †p<.07

To Know*

Accomplishment†

Stimulation

Identified

Introjected†

Regulation

Amotivation

-.15

.29

-.21

-.012

Intercept

(.788)

Linear Trajectory

(-.041)

0

1

2

1

1

1

1st Semester SR%

(0)

Mid Career SR%

(0)

Last SemSR%

(0)

.712

.501

.677

results
Results
  • CFA showed that a slope along with an intercept was more of a valid model than just the intercept
  • This means that there is a latent academic performance and a (slightly negative) latent academic trajectory over the course of one’s academic career
  • Latent trajectory of acad career is assumed to be linear and in a downward direction from first semester, mid career semester, and last recorded semester
  • On top of this, a model of academic motivation predicting intercept and slope provided additional explanation of variance for beginning, mid-career, and end-career performance
  • The same three factors showed at least moderate significant prediction over and above intercept and slope of academic achievement
conclusions
Conclusions
  • “To know”, “Introjected”, and “Toward accomplishment” seem to be the most important three factors of motivation affecting academic performance
  • In both models, “To know” and “Introjected” were negative predictors
  • “Towards accomplishment” was a positive predictor
  • The other four factors did not seem to contribute significantly
implications
Implications

Negative Predictor

Intrinsic motivation - to know (questions # 2, 9, 16, 23)Intrinsic motivation to know is defined as the fact of performing an activity for pleasure and the satisfaction that one experiences while learning, exploring, or trying to understand something new (Vallerand, et al.,1992).

Interested in the topic but not for testing well? Pedagogy change toward more active learning? Educational planning?

implication
Implication

Negative Predictor

Extrinsic motivation – introjected (questions # 7, 14, 21, 28)Introjected means the individual begins to internalize the reasons for his or her actions. This form of internalization, while internal to the person, is not truly self-determined since it is limited to the internalization of past external contingencies. Thus the student might say: “I study the night before exams because that’s what good students are supposed to do.” (Vallerand, et al.,1992).

Engaging in activity/learning but not effective? Educational planning? Introjected ineffective study strategies? Outside pressure?

implication1
Implication

Positive Predictor

Intrinsic motivation - toward accomplishment (questions # 6, 13, 20, 27)Intrinsic motivation toward accomplishments is defined as the fact of engaging in an activity for the pleasure and satisfaction experienced when one attempts to accomplish or create something (Vallerand, et al.,1992).

Engagement? More focus on grades/accomplishment? Educational planning?

implication2
Implication

Motivation enhancement interventions:

  • Achievement motivation training program (AMT)- is to change the achievement motive, an unconscious and recurring preference for emotionally rewarding experiences related to improving one’s performance (Pang, 2010). During these interventions, the participants are taught to think, feel and behave like a person high in need for achievement.
  • Attributional retraining (AR) - a motivational treatment that helps students reframe what they think about success and failure, encouraging them to take responsibility for their academic outcomes (Haynes et al., 2009)
  • Multidimensional motivation and engagement intervention - is the motivation and engagement wheel, in which motivation is characterized in terms of four higher order groups: adaptive cognitions (self-efficacy, mastery orientation, valuing), adaptive behaviors (persistence, planning and task management), maladaptive cognitions (uncertain control, failure avoidance, anxiety) and maladaptive behaviors (self-handicapping and disengagement). (professional development for faculty)
limitations future directions
Limitations & Future Directions
  • Some sample bias: Same sample used in all models, but sample was smaller in LTM.
  • Latent intercepts and slopes are difficult to interpret in general.
  • Model will be re-ran again on completion.
  • Survey could be tested for invariance between completers and non-completers.
  • Replicate the study (post –SEPs)