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ISSN: 0146-3373 (Print) 1746-4102 (Online) Journal homepage: http://www.tandfonline.com/loi/rcqu20
College Students’ Psychological Needs and
Intrinsic Motivation to Learn: An Examination of
Zachary W. Goldman, Alan K. Goodboy & Keith Weber
To cite this article: Zachary W. Goldman, Alan K. Goodboy & Keith Weber (2017)
College Students’ Psychological Needs and Intrinsic Motivation to Learn: An Examination
of Self-Determination Theory, Communication Quarterly, 65:2, 167-191, DOI:
To link to this article: http://dx.doi.org/10.1080/01463373.2016.1215338
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College Students’Psychological Needs
and Intrinsic Motivation to Learn: An
Examination of Self-Determination
Zachary W. Goldman, Alan K. Goodboy, & Keith Weber
Over the last several decades, instructional communication scholars have studied and
measured student motivation as an important learning outcome. Unfortunately, this
research has lacked theoretical guidance and has treated student motivation as a construct
that varies only in quantity, ignoring existing theory that suggests student motivation is best
understood as a construct that differs in quality (i.e., intrinsic motivation). To create two
new measures that incorporate theoretical explanations of student motivation, three studies
(N = 1,067) were undertaken using self-determination theory (SDT) to operationalize
students’intrinsic motivation as a product of basic psychological need satisfaction. In the
first two studies, the Student Psychological Needs Scale and the Intrinsic Motivation to Learn
Scale were developed and validated. In the third study, parallel mediation analyses sup-
ported SDT’s prediction that the fulfillment of students’psychological needs (i.e., autonomy,
competence, relatedness) would mediate the relationship between personalized education
practices and intrinsic motivation to learn.
Keywords: Instructional Communication; Intrinsic Motivation; Personalized Education;
Self-Determination Theory; Student Motivation
Zachary W. Goldman (Ph.D., West Virginia University, 2015) is an Assistant Professor in the Department of
Communication and Rhetorical Studies at Illinois College. Alan K. Goodboy (Ph.D., West Virginia University,
2007) is an Associate Professor in the Department of Communication Studies at West Virginia University. Keith
Weber (Ed.D., West Virginia University, 1998) is a Professor in the Communication Studies Department at
Chapman University. Correspondence: Zachary W. Goldman, Department of Communication and Rhetorical
Studies, Illinois College, Jacksonville, IL. E-mail: email@example.com
Vol. 65, No. 2, 2017, pp. 167–191
ISSN 0146-3373 print/1746-4102 online © 2017 Eastern Communication Association
Most college educators have been fortunate enough to work with students who are
genuinely excited about coming to class and eager to learn the course material. On the
other hand, most educators have also worked with students who are apathetic about
learning and generally uninterested in the events that transpire in the classroom; these
students view learning as a “a chore rather than a joy—an activity to be avoided rather
than sought out”(Ryan & Deci, 2009, p. 171). Contrary to early evidence, the
distinction between these two types of students is not general intelligence, which
accounts for less than 25% of students’overall achievement (Kuncel, Hezlett, & Ones,
2004). Rather, students’attitudes, communication behaviors, and success are best
understood as products of their own intrinsic motivation to learn (Reeve, 2002).
Intrinsic motivation has been described as “one of the most important psychological
concepts in education”(Vallerand et al., 1992, p. 1004). When individuals are intrinsically
motivated, they “engage in activities that interest them and, in so doing, help them to learn,
develop, and expand their capacities”(Ryan & Deci, 2000b, p. 16). At the college level,
intrinsically motivated students find academic activities worthwhile and meaningful; thus,
they actively seek out the intended benefits of assignments, assessments, and other forms of
coursework (Brophy, 1983). It is unsurprising, then, that a considerable amount of inter-
disciplinary research has been devoted toward understanding and promoting the conditions
that foster students’intrinsic motivation in the classroom (Black & Deci, 2000; Deci,
Koestner, & Ryan, 2001; Pintrich, 2004). Student motivation has also been a topic of interest
in the instructional communication literature; however, this research has been characterized
as largely atheoretical (see Nussbaum, 1992) and has grown disjointed in many ways from
the pioneering research originally conducted on the topic in educational psychology (Bro-
phy, 1983). Moreover, due to limited instruments created in the discipline, instructional
communication scholars have struggled with operationalization concerns that likely hinder
the interdisciplinary appeal of this research (c.f., Rodriguez, Plax, & Kearney, 1996). There-
fore, the purpose of this investigation was to (a) identify methodological weaknesses
associated with student motivation research in the communication literature and incorpo-
rate self-determination theory (Deci & Ryan, 1985) to help address such shortcomings; (b)
operationalize students’intrinsic motivation to learn as a product of their psychological
needs in the classroom by creating and validating two original self-report instruments; and
(c) evaluate the applicability of self-determination theory as a valid explanation for the
relationships between instructional communication practices and the fulfillment of college
students’psychological needs and their intrinsic motivation to learn.
Student Motivation in the Communication Literature
Regarded as one of the field’s traditional learning outcomes (Goodboy & Myers, 2008),
student motivation has been historically examined in the instructional communication
literature as an outcome of instructors’communication behaviors (McCroskey, Rich-
mond, & McCroskey, 2002). As Christophel (1990) noted, this focus is rooted in the idea
that instructors are “active agents within the educational environment, capable of stimu-
lating the development of student motivation toward learning”(p. 324). Scholars have
168 Z. W. Goldman et al.
investigated student motivation as both a state- and a trait-like variable. State motivation
is a situational construct that refers to the effort put toward a particular task or content
area at a given point in time (Christophel, 1990). Trait motivation is a relatively stable
construct that refers to the overall drive students have toward studying and learning in
general (Richmond, 1990). Communication researchers often favor state motivation
because of its strong associations with effective teaching behaviors such as nonverbal
immediacy (Kerssen-Griep & Witt, 2012), clarity (Comadena, Hunt, & Simonds, 2007),
affinity seeking (Frymier, 1994), confirmation (Goodboy & Myers, 2008), and humor
(Wanzer & Frymier, 1999). Although these findings have yielded important pedagogical
implications, this research has been limited by three methodological concerns that should
be addressed in order to better align communication scholarship with modern theories of
motivation (c.f., Ryan, 2012).
First, instructional communication researchers have not created instruments con-
sistent with the historical conceptualization of student motivation to learn. Previous
studies have relied almost exclusively on Christophel’s(1990) 12-item self-report scale
to operationalize state motivation in the classroom (Beatty, 1994). With essentially no
alterative measures in the field, Christophel’s instrument has been labeled by some as
the “gold standard in the communication discipline”for investigating student motiva-
tion (Brooks & Young, 2011, p. 56); the overreliance on this bipolar adjective measure
(as well as the scale’s shortened five-item version; see Richmond, 1990) is problematic
because it focuses mostly on short-term attention and affective learning (e.g., with
items such as “Not Aroused/Aroused”), rather than intrinsic motivation to learn (c.f.,
Brophy, 1983). Rodriguez et al. (1996) argued that an “examination of the items
employed to assess these two constructs [state motivation, affective learning] indicates
that they measure highly similar affective states”(p. 298). Previous research has
validated this concern of measurement isomorphism between state motivation and
affective learning as the two variables often correlate at 0.80 or higher (Goodboy,
2011). That said, researchers continue to use Christophel’s(1990) and Richmond’s
(1990) versions of the state motivation instrument in survey research, arguably
because suitable alternatives have not been created in the field.
Second, instructional communication researchers have not yet made a concerted effort
to differentiate the origins of student motivation; specifically, scholars have often over-
looked the distinction between intrinsic and extrinsic motivation (Ryan & Deci, 2009).
With few exceptions (e.g., Bolkan, Goodboy, & Griffin, 2011; Kerssen-Griep, Hess, &
Trees, 2003), communication researchers treat student motivation as a construct that
varies only in quantity (i.e., low to high), rather than quality. This is concerning because
decades of research in educational psychology (see Lin, McKeachie, & Kim, 2001;
Pintrich, 2004) have shown that multiple types of student motivation exist. Notably,
scholars have identified three major forms of motivation: intrinsic, extrinsic, and amoti-
vation (Vallerand et al., 1992). Intrinsic motivation refers to individuals’tendency to
“engage in activities that interest them and, in so doing, help them to learn, develop, and
expand their capacities”(Ryan & Deci, 2000b, p. 16); extrinsic motivation refers to the
“performance of an activity in order to attain some separable outcome”(Ryan & Deci,
2000a,p.71);amotivation refers to “having no intentions for behavior and not really
Communication Quarterly 169
knowing why one is doing it”(Gagne & Deci, 2005, p. 336). Instructional communication
scholars often ignore these differences and continue to treat state motivation as a variable
that differs in quantity alone.
Third, instructional communication scholars have often failed to use theory to
explain how classroom interactions influence student motivation. Instructional commu-
nication research has historically been criticized for lacking theory (Waldeck, Kearney,
&Plax,2001), but perhaps nowhere is this criticism more evident than within the
student motivation literature. Few attempts have been made to explain the mechanisms
behind the communication–motivation relationship, leaving scholars to speculate as to
how instructors encourage or discourage student motivation through classroom inter-
actions. One theory that fills this void, as well addresses the previous two criticisms of
communication research, is self-determination theory (SDT; Deci & Ryan, 1985).
Self-Determination Theory: Psychological Needs and Intrinsic Motivation
SDT assumes that individuals possess “an active tendency toward psychosocial growth
and integration,”which drives them to “seek challenges, to discover new perspectives,
and to actively internalize and transform cultural practices”(Ryan & Deci, 2002, p. 3).
SDT asserts that people are naturally motivated to self-improve; yet, this drive can be
supported or discouraged by one’s social environment (Deci & Ryan, 1985). Specifi-
cally, SDT predicts that intrinsic motivation depends on three basic psychological
needs: the need for autonomy, the need for competence, and the need for relatedness
(Deci & Ryan, 1985). Autonomy refers to being the perceived source of one’s own
actions. Individuals feel autonomous when they internalize their behavior as an
expression of their own freewill (Ryan & Deci, 2002). Competence refers to feeling
effective in one’s ongoing interactions in a social environment. Individuals experience
competence when they encounter challenging opportunities that allow them to
express their true capacities (Deci & Ryan, 1985). Relatedness refers to perceiving a
connection with others (Ryan & Deci, 2009). Individuals experience relatedness when
they develop a sense of belongingness with their peers, community members, or with
others whom they respect (e.g., Beachboard, Beachboard, Li, & Adkison, 2011; Moller,
Deci, & Elliot, 2010; Niemiec & Ryan, 2009).
SDT has been particularly useful for understanding college students’intrinsic
motivation to learn (e.g., Black & Deci, 2000; Deci, Vallerand, Pelletier, & Ryan,
1991; Niemiec & Ryan, 2009). Reeve (2002) noted that two conclusions can be
made from this extensive body of research. First, intrinsically motivated students
flourish across academic settings, especially in comparison to extrinsically motivated
and amotivated students. Students who are intrinsically motivated experience greater
academic achievement (Miserandino, 1996) and have higher retention rates through-
out college (Vallerand, Fortier, & Guay, 1997) than extrinsically motivated students.
Second, instructors play an important role in promoting students’intrinsic motivation
by helping to fulfill their psychological needs in the classroom. Multiple studies (e.g.,
Bolkan & Goodboy, 2015; Reeve & Jang, 2006) have shown that instructors who
170 Z. W. Goldman et al.
support students’autonomy, competence, and relatedness through their behaviors are
more likely to increase their intrinsic motivation to learn.
Overall, SDT provides a unique understanding of how motivation varies in both
quantity and quality, an aspect that is often overlooked in communication research.
As Deci and Ryan (2002) noted, “mainstream theories of human motivation…con-
tinue to use a relative mechanistic meta-theory to view motivation as a unitary
phenomenon—something that varies in amount but not kind”(p. 433). By adopting
SDT into the instructional communication literature, researchers could substantially
improve students’learning experiences by examining the role that communication
plays in facilitating intrinsic motivation to learn in the classroom. Put differently,
“communication researchers [must] begin to embrace self-determination theory in
order to understand how instructors meet students’basic needs and how the fulfill-
ment of these needs facilitates students’behaviors and, ultimately, learning”(Bolkan &
Goodboy, 2015, p. 60).
One way in which scholars have begun to incorporate SDT into the communication
literature is by adapting generic scales created in psychology to fit their investigations
(Bolkan & Goodboy, 2015; Bolkan et al., 2011; Kerssen-Griep et al., 2003). This process
of adapting proxy scales has helped introduce SDT into the field of instructional
communication, but it also comes with additional problems. Many of the scales (see
Broeck, Vansteenkiste, Witte, Soenens, & Lens, 2010; Reeve & Sickenius, 1994;Ryan&
Connell, 1989) require significant modifications before they can be applied to the
context of the classroom, and these alterations must be made in a consistent fashion
to preserve content validity; such modifications unnecessarily burden communication
researchers. Moreover, scales that do measure needs and motivation in the educational
context also require their own modifications to meet the unique characteristics that
define the contemporary college learning environment. For instance, Vallerand et al.’s
(1992) Academic Motivation Scale (AMS) was designed to assess students’overall
motivation for attending college, rather than students’motivation to learn material in
a specific course or classroom. Additionally, scales such as the Academic Self-Regulation
Questionnaire (ASRQ; Ryan & Connell, 1989), which was created to measure SDT
among grade-school students, requires alterations to the language of items as students of
this age have fundamentally different ways of perceiving, experiencing, and satisfying
their psychological needs. If done incorrectly, the modification of such scales threatens
the conceptual and structural integrity of the instruments as well as their intended
constructs; thus, many psychometricians endorse the creation of items that organically
assess their intended context (see DeVellis, 2017).
Therefore, the overall intention of this investigation was to utilize SDT to improve
the study and measurement of students’motivation to learn in the communication
literature. In line with this goal, we sought to create new measures to assess college
students’psychological needs and their intrinsic motivation to learn in the classroom,
Communication Quarterly 171
rather than relying on the few measures that have been created in the field of
communication (i.e., Christophel, 1990; Richmond, 1990) or adapting measures
created in other contexts. Moreover, our research sought to test SDT as a valuable
theory for future instructional communication research by examining the extent to
which psychological needs mediate the relationship(s) between effective teaching
practices and students’intrinsic motivation to learn. To meet these intentions, three
studies were conducted.
Study One: Scale Development
The purpose of the first study was to generate a preliminary item pool and explore the
factor structures for two original instruments. Following standard psychometric
procedures (Clark & Watson, 1995; DeVellis, 2017), study one included two phases
of the scale development process: item-generation and item-reduction. Two recom-
mendations guided the item-generation process. First, we utilized Haynes, Richard,
and Kubany’s(1995) suggestion that content for the scale items should be derived
from multiple sources, including (a) previous literature and theoretical frameworks,
(b) preexisting scales and related instruments, and (c) deductive reasoning on the
behalf of the researchers. Second, we followed the recommendation of DeVellis
(2017), who suggested that scale developers create at least three times the items
expected in the measure in order to utilize the best-preforming items in the scale’s
The first portion of the item pool was created by reviewing the basic tenets of SDT
(e.g., Ryan & Deci, 2002) as well as dozens of SDT-related investigations that have
been conducted within the educational context (e.g., Black & Deci, 2000; Niemiec &
Ryan, 2009; Vansteenkiste, Lens, & Deci, 2006). An additional portion of the items
were developed by reviewing and modifying preexisting SDT instruments that were
originally designed to assess needs and motivation in contexts such as sports, general
activities, workplaces, and K–12 education (Broeck et al., 2010; Guay, Vallerand, &
Blanchard, 2000; Reeve & Sickenius, 1994). Finally, original items were also created by
the authors after a thorough review of the instructional communication literature.
Clark and Watson (1995) noted that “the initial pool should be broader…than
one’s own theoretical view of the target construct and should include content that
ultimately will be shown to be tangential or even unrelated to the core construct”(p.
311). Thus, an extensive item pool was created so that the best-performing items
could be retained. Specifically, 120 total items were created to measure students’
psychological needs (90 items) and intrinsic motivation to learn (30 items) using a
seven-point Likert scale ranging from strongly disagree (1) to strongly agree (7). These
items were pretested on a group of students (n= 25) who reviewed the wording and
directions for readability. Based on this feedback, minor grammatical changes were
172 Z. W. Goldman et al.
made to several items and the revised item pool was then distributed to a larger
sample for completion.
Participants and Procedures
Participants were 450 undergraduate students (215 males, 235 females) from a large
Mid-Atlantic university. The age of the participants ranged from 18 to 38 years
(M= 19.25, SD = 1.83). The majority of participants were freshmen (n=242,
53.8%), followed by sophomores (n= 102, 22.7%), seniors (n= 55, 12.2%), and juniors
(n= 51, 11.3%). Participants reported having an overall grade point average of 3.26
(SD = 0.49), they represented 34 majors from across the university, and they primarily
self-identified as Caucasian (n= 386, 86%). Participants were solicited from numerous
communication and sociology classes to complete an anonymous self-report survey.
They were instructed to reference the class they had before the data collection to answer
all questions contained in the survey (Plax, Kearney, Richmond, & McCroskey, 1986).
Two exploratory factor analyses (EFAs) with principal axis factoring and promax
rotation were used to examine the factor structures of the newly developed scales. A
large sample (n= 450) was recruited to ensure that each of the items were represented
by at least five participants (Hatcher, 1994). Bartlett’s test of sphericity (χ
 = 26,874.25, p< 0.001) and Kaiser-Meyer-Olkin’s test of sampling adequacy
(0.95) suggested the initial item pool and sample size met the necessary assumptions
for EFA. To be retained for analyses, each factor was required to: (a) have an
eigenvalue greater than one, (b) account for more than 5% of the overall variance,
(c) contain at least two or more items, and (d) have interpretability/face validity
(McCroskey & Young, 1979). Individual items were required to have a primary
loading greater than 0.60 and a secondary loading less than 0.40 to be retained
(Hatcher, 1994). Items that cross-loaded or failed to meet the aforementioned criteria
were deleted and the EFAs were recalculated until all remaining items met the
requirements (DeVellis, 2017).
The final iteration of the Student Psychological Needs Scale (SPNS) produced a four-factor
solution that contained 24 of the original 90 items and accounted for 61.29% of the overall
variance. The first factor, competence, consisted of eight items (e.g., “I can accomplish the
most difficult assignments given in this class”) and accounted for 34.38% of the variance. The
second factor, autonomy, consisted of eight items (e.g., “The way this class is structured
allows me to learn in my own unique way”) and accounted for 11.74% of the variance. The
third factor, relatedness with classmates, consisted of four items (e.g., “I share several
common interests with my fellow classmates”) and accounted for 9.65% of the variance.
The fourth factor, relatedness with instructor, consisted of four items (e.g., “I can relate to my
Communication Quarterly 173
instructor as a person”)andaccountedfor5.52%ofthevariance. Cronbach alpha reliability
coefficients for the factors included: 0.94 (competence), 0.88 (autonomy), 0.86 (relatedness
with classmates), and 0.81 (relatedness with instructor). Items and factor loadings for the
SPNS can be found in Table 1.
The final iteration of the Intrinsic Motivation to Learn Scale (IMLS) produced a
unidimensional solution that contained 10 of the original 30 items and accounted for
64.42% of the overall variance. Example items included “Learning new concepts in
this class is fulfilling to me”and “Learning new things in this class makes me feel like I
am growing as a person.”The Cronbach alpha reliability coefficient for this scale was
0.94. Factor loadings and descriptive statistics for each of the 10 retained items can be
found in Table 2.
Study Two: Scale Validation and Student Learning Outcomes
Kline (2011) argued that researchers should replicate newly uncovered factor structures if a
measure “is ever to represent anything beyond a mere statistical exercise”(p.94).Thus,the
second study was conducted to (a) confirm the factor structures using an independent
sample and (b) provide concurrent and discriminant validity for both of the newly developed
scales. Validity refers to the degree to which an instrument assesses what it was intended to
measure (Wolf, 1978). Specifically, concurrent validity refers to the extent to which a measure
is empirically related to a similar construct in a way that is both theoretically meaningful and
interpretable (Campbell & Fiske, 1959). In this study, both the SPNS and the IMLS were
expected to correlate positively with intrinsic goal orientation (i.e., students’desire to find
schoolwork meaningful and rewarding), affective learning (i.e., students’attitudes toward
their learning experiences and their course instructor), and perceived cognitive learning in
the classroom (i.e., students’success at comprehending and retaining knowledge). Moreover,
the SPNS and the IMLS were thought to correlate negatively with extrinsic goal orientation
(i.e., students’trait-like desire to engage in class-related behaviors exclusively for the
acquirement of tangible rewards or outcomes).
It should also be noted that newly developed measures may be invalidated if they
correlate too highly with instruments from which they were intended to differ
(Campbell & Fiske, 1959). In other words, “although strong correlations may indicate
construct similarity, different measures must have divergent factor structures if they
are indeed measuring similar but distinct constructs”(Mazer, 2012, p. 115). This
concern reflects discriminant validity, or the degree to which a measure can be
empirically distinguished from related but discrete variables. In this study, the Student
Motivation Scale (Christophel, 1990; Richmond, 1990), which assesses state-like feel-
ings of arousal and interest, was hypothesized to be similar, but distinct from college
students’intrinsic motivation to learn. Put differently, while the two scales were
thought to share a strong positive correlation, previous instructional communication
research and SDT suggest they should be distinguishable operationalizations.
174 Z. W. Goldman et al.
Table 1 EFA Factor Loadings for Student Psychological Needs Scale (SPNS)
1. In this class, I have the freedom to learn in my
0.24 0.56 0.04 0.25 (4.61, 1.74)
2. I complete assignments in this class in the way
I want to do them.
0.13 0.70 0.09 0.05 (4.22, 1.66)
3. The way this class is structured allows me to
learn in my own unique way.
0.17 0.58 0.12 0.20 (4.00, 1.73)
4. I have the freedom to complete course
assignments in my own way.
0.12 0.77 0.15 0.09 (4.28, 1.70)
5. I dictate how I will complete the assignments
in this course.
0.09 0.62 0.03 0.11 (4.27, 1.58)
6. I have the opportunity to decide for myself
how I will learn in this class.
0.17 0.68 0.12 0.16 (4.63, 1.57)
7. I have the freedom to succeed however I want
to in this class.
0.27 0.62 0.13 0.21 (4.89, 1.58)
8. I am free to complete classroom assignments
the way I want to do them.
0.05 0.64 0.17 0.12 (4.38, 1.66)
9. I am competent in this class. 0.62 0.18 0.05 0.16 (5.25, 1.44)
10. When it comes to class assignments, I do not
know what I am doing.
0.68 0.14 0.05 0.20 (5.53, 1.52)
11. I can accomplish the most difficult
assignments given in this class.
0.72 0.09 0.05 0.09 (5.14, 1.62)
12. I am not confident in my abilities to perform
well in this class.
0.55 0.07 0.07 0.26 (5.07, 1.89)
13. I can accomplish anything that is assigned to
me in this class.
0.74 0.21 0.09 0.08 (5.49, 1.49)
14. I do not feel competent when I am working
on coursework for this class.
0.67 0.21 0.09 0.24 (4.99, 1.66)
15. I do well in this class compared to other
0.58 0.11 0.08 0.01 (4.63, 1.36)
16. I do not know what I’m doing in this class.
0.73 0.12 0.04 0.23 (5.52, 1.67)
Relatedness with Classmates
17. I am close to several of my classmates. 0.03 0.11 0.67 0.12 (3.77, 2.05)
18. I can relate to several of my classmates in this
0.09 0.11 0.81 0.17 (4.74, 1.58)
19. I share several common interests with my
0.13 0.22 0.82 0.17 (4.76, 1.54)
Communication Quarterly 175
Participants and Procedures
Participants for the second study were 348 undergraduate students (153 males, 195
females) from two large Mid-Atlantic universities. Students ranged in age from 18 to
40 years (M= 21.00, SD = 2.16) and were predominately juniors (n= 143, 41.1%),
followed by seniors (n= 128, 36.8%), sophomores (n= 55, SD = 15.8%), and freshmen
(n= 22, 6.3%). The majority of students identified as Caucasian (n= 286, 82.2%) and
they were asked to complete a self-report survey in reference to their previous class. In
addition to demographics and the two scales created in study one (i.e., SPNS, IMLS),
participants completed the following: the Intrinsic Goal Orientation Subscale (Pin-
trich, Smith, García, & McKeachie, 1993), the Extrinsic Goal Orientation Subscale
(Pintrich et al., 1993), the 12-item version (Christophel, 1990) and the five-item
version (Richmond, 1990) of the Student Motivation Scale, the Affective Learning
Scale (McCroskey, Richmond, Plax, & Kearney, 1985), the Cognitive Learning Mea-
sure (Frisby & Martin, 2010), and the Revised Cognitive Learning Indicators Scale
(Frymier & Houser, 1999). Means, standard deviations, and Cronbach reliability
coefficients for each of these scales can be found in Table 3.
The Intrinsic Goal Orientation subscale is a four-item measure that assesses students’
general orientation toward learning tasks. Responses are solicited using a five-point
Likert-scale ranging from completely disagree (1) to completely agree (5). Previous
alpha coefficients for the scale have ranged from .67 to .80 (Pintrich et al., 1993;
Table 1 (Continued)
20. I have a lot in common with several of my
peers in this class.
0.09 0.19 0.82 0.14 (4.62, 1.53)
Relatedness with Instructor
21. I cannot relate to my instructor.
0.27 0.20 0.27 0.74 (4.85, 1.80)
22. My instructor does not care about me as a
0.31 0.23 0.11 0.55 (5.79, 1.47)
23. I feel distant from my instructor in this class.
0.27 0.17 0.28 0.62 (4.45, 1.85)
24. I can relate to my instructor as a person. 0.23 0.29 0.22 0.65 (4.69, 1.70)
Eigenvalue 8.25 2.82 2.32 1.33
% of Variance 34.38 11.74 9.65 5.52
Note. Principal Axis Factoring with Promax Rotation. Response format ranging from (1) strongly disagree to (7)
176 Z. W. Goldman et al.
The Extrinsic Goal Orientation subscale is a four-item measure taken from the
MSLQ to assess students’external orientation toward learning. Responses are solicited
using a five-point Likert-scale ranging from completely disagree (1) to completely agree
(5). Previous reliability coefficients for the scale have ranged from 0.62 to 0.80
(Pintrich et al., 1993; Weber, 2003).
The Student State Motivation Scale has been used as a 12-item or five-item
assessment of students’attitude, effort, and energy toward a particular class.
Responses are solicited using 7-point bipolar adjectives. Previous reliability coeffi-
cients ranging from 0.92 to 0.95 have been discovered for the scale (Christophel, 1990;
Goodboy & Myers, 2008; Myers & Rocca, 2001).
The Affective Leaning Scale is a 12-item measure that assesses participants’affect
for the course content, course instructor, and behaviors recommended in the course.
Responses are solicited using three 7-point bipolar adjective subscales. Previous
reliability coefficients ranging from 0.95 to 0.96 have been discovered for the scale
(Ellis, 2000,2004; Goodboy & Myers, 2008).
The Cognitive Learning Measure is a 10-item scale that assesses perceived recall,
knowledge, and application of material. Responses are solicited using a five-point
Likert-scale ranging from strongly disagree (1) to strongly agree (5). Previous reliability
coefficients ranging from 0.83 to 0.88 have been discovered for the scale (Frisby,
Mansson, & Kaufmann, 2014; Frisby & Martin, 2010).
The Revised Cognitive Learning Indicators Scale is a seven-item measure that
assesses behaviors associated with cognitive learning. Responses are solicited on a
five-point Likert-scale ranging from never (0) to very often (4). Previous reliability
Table 2 EFA Factor Loadings for Intrinsic Motivation Scale
Items Loading (M, SD)
1. Learning new concepts in this class is fulfilling to me. 0.78 (4.90, 1.59)
2. Developing my understanding of the content is rewarding to me. 0.68 (5.07, 1.48)
3. Learning new things in this class makes me feel better about myself. 0.75 (4.98, 1.56)
4. I find learning new things in this class to be unfulfilling.
0.65 (4.83, 1.73)
5. Understanding new concepts in this class is enjoyable to me. 0.81 (4.76, 1.57)
6. It is personally satisfying for me to learn new concepts in this class. 0.88 (4.80, 1.60)
7. I get a sense of fulfillment when I learn new things in this class. 0.85 (4.88, 1.57)
8. I do not enjoy trying to comprehend new ideas in this class.
0.72 (4.94, 1.64)
9. Learning new things in this class makes me feel like I am growing as a
0.76 (4.82, 1.72)
10. I desire to learn new things in this class because it gives me a sense of
0.87 (4.72, 1.63)
% of Variance 64.42
Note. Response format ranging from (1) strongly disagree to (7) strongly agree.
Communication Quarterly 177
Table 3 Study 2—Means, Standard Deviations, Cronbach’s Alphas, and Correlation Matrix
Variables M SD α1 2 3 4 5 6 7 8 9 10 11
1. Intrinsic Motivation Scale 51.17 12.60 0.95 –
2. Autonomy 36.46 10.05 0.90 0.45
3. Competence 44.02 8.45 0.87 0.51
4. Relatedness with Instructor 20.50 5.68 0.87 0.55
5. Relatedness with Classmates 17.17 6.52 0.93 0.29
6. Intrinsic Goal Orientation 13.16 3.18 0.79 0.65
7. Extrinsic Goal Orientation 15.20 3.83 0.86 0.05 0.08 0.01 0.05 0.01 0.11* –
8. State Motivation (12 item)
56.28 14.97 0.94 0.75
9. State Motivation (5 item)
24.02 7.11 0.92 0.74
10. Affective Learning 67.55 14.39 0.94 0.60
11. Cognitive Learning 37.58 6.37 0.83 0.71
12. Learning Indicators 15.91 6.52 0.88 0.68
Note.*p< 0.05, ^p< 0.01,
Measured with Christophel’s 12-item scale.
Measured with Richmond’s(1990) 5-item scale. Means are from overall composite scores.
178 Z. W. Goldman et al.
coefficients for the scale have ranged from 0.83 to 0.88 (Frymier & Houser, 1999;
Goodboy & Bolkan, 2009).
The data in Study 2 was analyzed over three stages. First, the previously uncovered
factor structure for the new scales was evaluated using a confirmatory factor analysis
(CFA) with maximum likelihood estimation. A five-factor measurement model was
examined to assess the model fit for both the SPNS and the IMLS. This measurement
model was chosen because it is essential for assessing quantitative instruments and
because it can serve as a “test of construct validity”by testing the fit of “manifest
indicators and, by implication, the adequacy of the proposed latent variables”(James,
Mulaik, & Brett, 1982, p. 112). Models are considered to be good fitting when they
obtain: (a) non-significant chi-square values (χ
), (b) a root mean square error of
approximation (RMSEA) value less than 0.10 (although values less than 0.05 are ideal;
Kline, 2011), comparative fit index (CFI) value greater or equal to 0.95, and a
standardized root mean square residual (SRMR) less than 0.08 (see Hu & Bentler,
1999; Kline, 2011).
Second, two alternative CFAs were computed to offer discriminant validity for the
IMLS. Specifically, these CFAs were used to distinguish the new measure of intrinsic
motivation from Christophel’s(1990) state motivation scale. In the first model, all 22
items from both scales were examined as one latent variable (i.e., treating student
motivation as a unidimensional construct). In the second model, a two dimensional
solution was tested with both scales loading on their own latent construct and indices
from each model were compared to determine the best model fit.
Third, to establish concurrent validity, Pearson correlations were calculated to
examine the relationships between students’psychological needs, intrinsic motivation
to learn, and their affective and cognitive learning. The scales were also correlated
with intrinsic and extrinsic goal orientation to highlight the relationships (or lack
thereof) that exist between the new SDT measures and students’overall orientation
toward completing their schoolwork in general.
The first CFA (see Figure 1) revealed the five-factor measurement model fit the data
reasonably well, χ
(517) = 1413.18, p< 0.001, RMSEA = 0.071 [90% CI = 0.066 to
0.075], CFI = 0.95, SRMR = 0.06. All of the individual items loaded significantly
(p< 0.05) on their respective factors with standardized loadings ranging from 0.43 to
0.93. The Cronbach’s alphas for each of the subscales were as follows: 0.87 (Compe-
tence), 0.90 (Autonomy), 0.93 (Relatedness with Classmates), 0.87 (Relatedness with
Instructor), and 0.95 (Intrinsic Motivation to Learn). All five latent variables signifi-
cantly co-varied with each other with standardized coefficients ranging from 0.13 to
Communication Quarterly 179
The 22-item unidimensional CFA that combined the IMLS and Christophel’s
(1990) state motivation scale yielded poor model fit, χ
(209) = 1647.41, p< 0.001,
RMSEA = 0.141 [90% CI = 0.135 to 0.147], CFI = 0.79, SRMR = 0.08. The follow-up
CFA that examined Christophel’s scale and the IMLS as separate, yet related, latent
constructs yielded acceptable model fit, χ
(208) = 814.54, p< 0.001, RMSEA = 0.092
[90% CI = 0.085 to 0.098], CFI = 0.91, SRMR = 0.05. A chi-square difference test
revealed that the two-factor solution yielded a significantly better (p< 0.001) fit than
the unidimensional 22-item model (χ
 = 832.87), thus offering discriminant
validity for the scale. Lastly, a follow-up CFA conducted only on Christophel’s 12-
Figure 1. Five-factor measurement model with the SPNS and the IMLS.Note. Fit statistics: χ
(517) = 1,413.18,
p< 0.001, RMSEA = 0.071 [90% CI = .066 to .075], CFI = 0.95, SRMR = 0.06. All paths are significant and
displayed with standardized values.
180 Z. W. Goldman et al.
item scale indicated a poor model fit, χ
(54) = 474.94, p< 0.001, RMSEA = 0.150
CI = 0.138 to 0.162]
, CFI = 0.87, SRMR = 0.06.
Results of the Pearson correlation matrix (see Table 3) demonstrated that positive
correlations existed between the SPNS, the IMLS, and students’(a) intrinsic goal
orientation toward schoolwork, (b) affective learning, and (c) perceived cognitive
learning (i.e., both the cognitive learning measure and learning indicators). However,
neither the SPNS nor the IMLS were related (p> 0.05) to students’extrinsic goal
orientation toward schoolwork.
Study Three: Theory Testing and Instructional Communication
A third study was conducted to integrate SDT into the instructional communication
literature by replicating the theory’s core assumption (i.e., the fulfillment of psycholo-
gical needs mediate the effect of external stimuli on intrinsic motivation). We hypothe-
sized that students’psychological need fulfillment would be met when their education is
personalized by their instructor. Waldeck (2006) described personalized education as a
multifaceted pedagogical approach that instructors utilize to address the individual
learning needs of students. Goodboy, Myers, and Bolkan (2012) observed that perso-
nalized education “creates student perceptions of individualized attention from instruc-
tors in an effort to meet students’needs”(p. 94). Waldeck (2006) acknowledged that
personalized education helps to ensure that researchers and teachers are “delivering on
what has become a promise valued and relied upon by our students and their families”
(p. 351). According to Waldeck (2007), personalized education fulfills students’needs
across three dimensions: instructor accessibility (i.e., being physically and socially
available for students), course-related practices (i.e., attempting to personalize the course
through its design and management), and instructor interpersonal competence (i.e.,
communicating in a way that is friendly, approachable, dynamic, and warm). Because
these constructs theoretically align with students’needs of autonomy, competence, and
relatedness, we hypothesized that each of the three dimensions of personalized educa-
tion would be related positively to the fulfillment of students’psychological needs and
their intrinsic motivation to learn. More specifically, as SDT predicts, we hypothesized
that perceptions of personalized education would indirectly influence intrinsic motiva-
tion to learn through its unique effects on students’psychological needs of autonomy,
competence, and relatedness (with their classmates and their instructor).
Participants and Procedures
Participants were 269 undergraduate students (139 males, 130 females) who were
enrolled in communication courses at a large Mid-Atlantic university. Participants
ranged in age from 18 to 34 years (M= 20.39, SD = 1.96) and were primarily
Caucasian (n= 230, 85.5%). Participants represented 32 majors, including commu-
nication studies (n= 37, 13.8%), exercise physiology (n= 24, 8.9%), and business
Communication Quarterly 181
management (n= 19, 7.1%). Participants were asked to complete a survey by report-
ing on their previous course and they completed demographic questions along with
the SPNS, IMLS, and the Personalized Education Scale (Waldeck, 2007).
The Student Psychological Needs Scale is a 24-item measure (created from Study 1)
that measures the fulfillment of students’psychological needs: autonomy, competence,
relatedness with instructor, and relatedness with classmates. Responses were solicited
on a 7-point Likert scale ranging from strongly disagree (1) to strongly agree (7). In the
current study, Cronbach alphas ranging from 0.85 to 0.92 were obtained for each of
subscales: 0.90 (Autonomy), 0.88 (Competence), 0.85 (Relatedness with Instructor),
and 0.92 (Relatedness with Classmates).
The Intrinsic Motivation to Learn Scale is a 10-item measure (created from Study 1)
that assesses students’intrinsic motivation to learn course material. Responses are
solicited using a 7-point Likert scale ranging from strongly disagree (1) to strongly
agree (7). In this study, a Cronbach reliability coefficient of 0.95 (M= 52.33,
SD = 12.90) was obtained for the measure.
The Personalized Education Scale is a 25-item instrument that assesses three
dimensions of teaching behaviors: instructor accessibility (e.g., “The instructor has
an adequate number of office hours to provide extra help for students”), course-
related practices (e.g., “The instructor changes the syllabus based on student sugges-
tions”), and interpersonal competence (e.g., “The instructor is a dynamic commu-
nicator”). Responses are solicited on a 5-point Likert scale ranging from not at all (1)
to very often (5). Previous reliability coefficients ranging from 0.83 to 0.92 have been
reported for the subscales (Goodboy et al., 2012; Waldeck, 2007). Cronbach alphas in
this study were: 0.87 (M= 25.23, SD = 7.53) for accessibility, 0.88 (M= 23.49,
SD = 8.46) for course practices, and 0.85 (M= 25.55, SD = 6.48) for interpersonal
To test the hypotheses, three parallel multiple mediation models were calculated using
ordinary least squares path analysis. These models were estimated using PROCESS
(Hayes, 2013) to determine whether the effects of personalized education (i.e.,
instructor accessibility, course-related practices, instructor interpersonal competence)
on students’intrinsic motivation to learn were mediated through the fulfillment of
students’psychological needs. Parallel multiple mediation models were selected
because SDT predicts the causal mechanisms that promote intrinsic motivation
occur through the fulfillment of psychological needs, which all operate as simulta-
neous mediators in tandem (c.f., Trepanier, Fernet, & Austin, 2013). Using a parallel
mediation model (over simple mediation models) allows for multiple mediators to be
correlated with each other to determine the unique indirect effects in the presence of
each other and allows for pairwise comparisons between the strength of mediated
182 Z. W. Goldman et al.
effects (Hayes, 2013). Indirect effects were calculated using 50,000 bootstrap samples
and 95% bias-corrected confidence intervals.
The first parallel mediation model (see Figure 2) revealed that instructor accessibility
indirectly influenced students’intrinsic motivation to learn through its unique effects
on students’psychological needs (controlling for each other) with a total indirect
effect of 0.447 and a 95% bootstrapped CI ranging from 0.295 to 0.612. Indirect
effects, confidence intervals, and completely standardized indirect effects were as
follows: autonomy (ab = 0.072, 95% CI = –0.005 to 0.154, ab
= 0.042), competence
(ab = 0.098, 95% CI = 0.027 to 0.191, ab
= 0.057), instructor relatedness (ab = 0.220,
95% CI = 0.104 to 0.361, ab
= 0.128), and class relatedness (ab = 0.056, 95%
CI = 0.014 to 0.115, ab
= 0.033). Bootstrapped confidence intervals were entirely
above zero for competence, instructor relatedness, and class relatedness, suggesting
parallel mediation from these needs, with no evidence of a direct effect for instructor
accessibility on intrinsic motivation (c’= 0.085, p= 0.36). Pairwise comparisons
indicated that instructor relatedness was a stronger mediator than class relatedness
(indirect effect contrast = 0.164, 95% CI = 0.028 to 0.319).
The second parallel mediation model (see Figure 3) revealed that instructor course-
related practices indirectly influenced students’intrinsic motivation to learn through
its unique effects on students’psychological needs (controlling for each other), with a
total indirect effect of 0.420 and a 95% bootstrapped CI ranging from 0.276 to 0.578.
Indirect effects, confidence intervals, and completely standardized indirect effects were
as follows: autonomy (ab = 0.064, 95% CI = 0.003 to 0.130, ab
= 0.042), competence
(ab = 0.104, 95% CI = 0.043 to 0.182, ab
= 0.068), instructor relatedness (ab = 0.197,
95% CI = 0.092 to 0.328, ab
= 0.129), class relatedness (ab = 0.055, 95% CI = 0.015 to
b2 = 0.438
Figure 2. Parallel multiple mediation model for personalized education: Instructor accessibility. Note. SDT
model with students’psychological needs simultaneously mediating the association between instructor accessi-
bility and intrinsic motivation to learn. Paths are unstandardized coefficients. Solid paths are significant
Communication Quarterly 183
= 0.036). Bootstrapped confidence intervals were above zero, suggesting
parallel mediation for all four psychological needs, with no evidence of a direct effect
of course-related practices on intrinsic motivation (c’=–0.017, p= 0.83). Pairwise
comparisons between indirect effects indicated that autonomy was a significantly
weaker mediator than instructor relatedness (indirect effect contrast = –0.133, 95%
CI = –0.290 to –0.006] and instructor relatedness was a stronger mediator than class
relatedness (indirect effect = 0.142, 95% CI = 0.026 to 0.281).
The third model (see Figure 4) revealed that instructor interpersonal competence
indirectly influenced students’intrinsic motivation to learn through its unique effects
on students’psychological needs (controlling for each other), with a total indirect
effect of 0.751 and a 95% CI ranging from 0.540 to 0.976. Indirect effects, confidence
intervals, and completely standardized indirect effects were as follows: autonomy
(ab = 0.091, 95% CI = –0.002 to 0.191, ab
= 0.045), competence (ab = 0.247, 95%
CI = 0.136 to 0.387, ab
= 0.124), instructor relatedness (ab = 0.351, 95% CI = 0.154
to 0.562, ab
= 0.176), class relatedness (ab = 0.062, 95% CI = 0.011 to 0.133,
= 0.031). Confidence intervals were above zero for competence, instructor
relatedness, and class relatedness, suggesting parallel mediation, with no evidence of
a direct effect of instructor competence on intrinsic motivation (c’= 0.156, p= 0.22).
Pairwise comparisons indicated that competence was a significantly stronger mediator
than relatedness with classmates (indirect effect contrast = 0.185, 95% CI = 0.048 to
0.642) and instructor relatedness was a stronger mediator than relatedness with
classmates (indirect effect = 0.289, 95% CI = 0.076 to 0.512).
Ryan and Deci (2000a) noted, “Perhaps no single phenomenon reflects the positive
potential of human nature as much as intrinsic motivation”(p. 70). Similar conclusions
Figure 3. Parallel multiple mediation model for personalized education: Instructor course-related practices.
Note. SDT model with students’psychological needs simultaneously mediating the association between course-
related practices and intrinsic motivation to learn. Paths are unstandardized coefficients. Solid paths are
184 Z. W. Goldman et al.
are likely true for students and their potential in the classroom (Deci et al., 1991). Our
research sought to develop and validate measures for understanding college students’
psychological needs and their intrinsic motivation to learn in the classroom in order to
advance the study of motivation in the instructional communication literature. Toward
this goal, SDT was utilized to create and validate two original instruments (i.e., SPNS
and IMLS). Concurrent validity was found as the measures correlated modestly with
each other in addition to students’affective and cognitive learning. Discriminant validity
was also found for the IMLS, as the scale appeared to be a distinct measure of
motivation from the traditionally utilized Student Motivation Scale (Christophel, 1990).
The created measures were also used to evaluate the primary assumption of SDT:
mainly, that elements of the social environment (in this case, the classroom) influence
individuals’intrinsic motivation by either fulfilling or stifling their psychological
needs (Deci & Ryan, 1985). Strong support for the theory was revealed as the results
of parallel mediation path models indicated that the fulfillment of students’psycho-
logical needs mediated the relationships between personalized education practices and
students’intrinsic motivation to learn. In other words, the process by which perso-
nalized education influences students’intrinsic motivation is explained by the fulfill-
ment students’psychological needs in the classroom. Collectively, these results have
important theoretical and practical implications for both researchers and instructors.
For instructional communication researchers, the findings provide an empirically
validated and theoretically supported approach for examining students’intrinsic
motivation in the classroom. Although some scholars have begun to embrace the
importance of SDT in the instructional communication literature (e.g., Bolkan, 2015;
Kerssen-Griep et al., 2003), little is known about the relationship between classroom
communication and college students’self-determined behavior. This gap in the
literature is likely attributable to a lack of classroom-specific SDT instruments
a1= 0 .611
Figure 4. Parallel multiple mediation model for personalized education: Instructor interpersonal competence.
Note. SDT model with students’psychological needs simultaneously mediating the association between instructor
interpersonal competence and intrinsic motivation to learn. Paths are unstandardized coefficients. Solid paths are
Communication Quarterly 185
that, until this study, have caused researchers to use proxies to indirectly assess the
fulfillment of students’needs and their intrinsic motivation to learn (c.f., Bolkan &
Goodboy, 2015). Ideally, researchers can now use these two new scales to expand
their examination of SDT, communication, psychological needs, and intrinsic
For instructors, this study discovered that personalized education can fulfill stu-
dents’psychological needs and encourage their intrinsic motivation to learn. Reeve
(2002) noted that students need opportunities that allow them to demonstrate their
true capacities and challenge their abilities if they are to become self-determined.
Likewise, Waldeck (2007) discovered that personalized education is associated posi-
tively with students’affective and cognitive learning. Instructors demonstrate perso-
nalized education by making themselves available to students outside of class (i.e.,
accessibility), integrating students’interest into their course design (i.e., course-related
practices), and communicating with students in a way that is appropriate and effective
(i.e., interpersonal competence). Instructors should incorporate these techniques to
personalize students’educational experiences as previous SDT research suggests that
“positive motivational outcomes result when students feel that their needs for com-
petence, relatedness, and autonomy are respected in instructional interactions”(Ker-
ssen-Griep et al., 2003, p. 363).
Future Directions and Limitations
There are many ways in which future research can build from this study. Instructional
communication scholars should continue to implement SDT into their examinations
of student motivation because the theory has continuously shown that “positive
classroom outcomes experienced by autonomously [or intrinsically] motivated stu-
dents appear in both the academic and developmental domains”(Reeve, 2002, p. 183).
Similar to Study 2, instructional researchers should continue to examine the relation-
ship(s) between students’psychological needs, intrinsic motivation to learn, and
learning outcomes. Despite its interdisciplinary appeal, SDT has yet to attract main-
stream attention in the communication discipline, even though it is considered to be a
premier theory of human motivation in other disciplines (Ryan, 2012).
We offer two ways in which researchers can further incorporate SDT into this
literature: (a) scholars should begin to recognize the importance of psychological
needs and the mediating role in which they serve between classroom interactions
and students’intrinsic motivation to learn, and (b) researchers should explore the
ways in which instructors satisfy or thwart students’autonomy, competence, and
relatedness needs through classroom communication (c.f., Niemiec & Ryan, 2009).
First, investigating the interceding role of students’psychological needs within the
communication–motivation relationship would advance the theoretical mechanism(s)
underpinning one of instructional communication’s most complex teacher-student
phenomena. Future research should explicitly examine the conditions under which
these mediators maintain, or fail to maintain, their predictive utility of intrinsic
186 Z. W. Goldman et al.
motivation. Such investigations could consider student characteristics, academic
beliefs, or components of the learning environment as potential moderating variables
that limit or extend the explanatory power of SDT in this context.
Second, by uncovering the instructional behaviors that relate to students’intrinsic
motivation to learn, researchers can begin to understand the ways in which instructors
create learning environments that foster autonomous student behavior and promote
high quality learning experiences that transcend the boundaries of the classroom
(Frymier, Shulman, & Houser, 1996).
Reeve (2002) noted that college instructors should focus on guiding rather than
controlling behaviors, as previous research has shown that students “achieve highly,
learn conceptually, and stay in school in part because their teachers support their
autonomy”(p. 183). Thus, future research should explore specific teaching beha-
viors that instructors use to communicatively promote autonomous and self-deter-
mined learning environments. Relatedly, future research should also examine the
instructional behaviors and conditions that stifle intrinsic motivation by denying
students of their psychological needs. For instance, future investigations should look
at instructor misbehaviors (Goodboy & Myers, 2015), as they likely thwart students’
psychological needs in the classroom and thus discourage their intrinsic motivation
Of course, this research was not without limitations. First, this study only
examined intrinsic motivation to learn and not the full continuum of self-determi-
nation including students’extrinsic and amotivation. This decision was made to
address the most important shortcoming of the communication/motivation litera-
ture. Nonetheless, researchers should explore additional types of motivation, speci-
fically extrinsic motivation (i.e., external regulation, introjected, identified), as these
motives likely play a significant role in determining students’behavior in the class-
room and ultimately their desire to learn (Reeve, 2002). Similarly, this study
examined just one set of communication behaviors (i.e., personalized education)
in relation to students’self-determination. In the reality of the classroom, instructors
use a combination of behaviors while interacting with students; thus, researchers
should examine teacher behaviors and practices that interact together to enhance
and/or repress students’needs and their motivation to learn. Finally, researchers
should explore whether psychological needs and intrinsic motivation to learn oper-
ate similarly for minority students, non-traditional students, or students at other
types of institutions.
In sum, motivation is imperative for student success in college (Vallerand et al., 1992).
Frymier and colleagues (1996) challenged instructional communication researchers to
investigate how instructors “manage the classroom environment so that students feel
intrinsically motivated to learn and perform high quality work”(p. 181). To an extent, this
study addresses their call by operationalizing students’intrinsic motivation and their
psychological needs in the classroom. With these measures and SDT as a framework for
future studies, instructional communication scholars can more accurately apply theory to
understand the ideal conditions that intrinsically motivate students and help them to
genuinely enjoy learning as a personally fulfilling experience.
Communication Quarterly 187
Beachboard, M. R., Beachboard, J. C., Li, W., & Adkison, S. R. (2011). Cohorts and relatedness: Self-
determination theory as an explanation of how learning communities affect educational
outcomes. Research in Higher Education,52, 853–874. doi:10.1007/s11162-011-9221-8
Beatty, M. J. (1994). Student motivation scale. In R. B. Ruin, P. Palmgreen, & H. E. Sypher (Eds.),
Communication research measures: A sourcebook (pp. 343–346). New York, NY: The Guilford
Black, A. E., & Deci, E. L. (2000). The effects of instructors’autonomy support and students’
autonomous motivation on learning organic chemistry: A self-determination theory perspec-
tive. Science Education,84, 740–756. doi:10.1002/1098-237X200011
Bolkan, S. (2015). Intellectually stimulating students’intrinsic motivation: The mediating influence
of affective learning and student engagement. Communication Research, Ahead of Press,1–12.
Bolkan, S., & Goodboy, A. K. (2015). Exploratory theoretical tests of the instructor humor-student
learning link. Communication Education,64,45–64. doi:10.1080/03634523.2014.978793
Bolkan, S., Goodboy, A. K., & Griffin, D. J. (2011). Teacher leadership and intellectual stimulation:
Improving students’approaches to studying through intrinsic motivation. Communication
Research Reports,28, 337–346. doi:10.1080/08824096.2011.615958
Broeck, A., Vansteenkiste, M., Witte, H., Soenens, B., & Lens, W. (2010). Capturing autonomy,
competence, and relatedness at work: Construction and initial validation of the work-related
basic need satisfaction scale. Journal of Occupational and Organizational Psychology,83, 981–
Brooks, C. F., & Young, S. L. (2011). Are choice-making opportunities needed in the classroom?
Using self-determination theory to consider student motivation and learner empowerment.
International Journal of Teaching and Learning in Higher Education,23,48–59.
Brophy, J. (1983). Conceptualizing student motivation. Educational Psychologist,18, 200–215.
Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait–
multimethod matrix. Psychological Bulletin,56,81–107. doi:10.1037/h0046016
Christophel, D. M. (1990). The relationships among teacher immediacy behaviors, student motiva-
tion, and learning. Communication Education,39, 323–340. doi:10.1080/03634529009378813
Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development.
Psychological Assessment,7, 309–319. doi:10.1037/1040-35126.96.36.1999
Comadena, M. E., Hunt, S. K., & Simonds, C. J. (2007). The effects of teacher clarity, nonverbal
immediacy, and caring on student motivation, affective and cognitive learning. Communica-
tion Research Reports,24, 241–248. doi:10.1080/08824090701446617
Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic rewards and intrinsic motivation in
education: Reconsidered once again. Review of Educational Research,71,1–27. doi:10.3102/
Deci, E. L., & Ryan, R. M. (1985). Self-determination. New York, NY: John Wiley & Sons, Inc.
Deci, E. L., & Ryan, R. M. (2002). Self-determination research: Reflections and future directions. In
E. L. Deci & R. M. Ryan (Eds.), Handbook of self-determination research (pp. 431–442).
Rochester, NY: The University of Rochester Press.
Deci, E. L., Vallerand, R. J., Pelletier, L. G., & Ryan, R. M. (1991). Motivation and education: The
self-determination perspective. Educational Psychologist,26, 325–346. doi:10.1080/
DeVellis, R. F. (2017). Scale development: Theory and applications (4th ed.). Thousand Oaks, CA:
Ellis, K. (2000). Perceived teacher confirmation: The development and validation of an instrument
and two studies of the relationship to cognitive and affective learning. Human Communica-
tion Research,26, 264–291. doi:10.1111/j.1468-2958.2000.tb00758.x
188 Z. W. Goldman et al.
Ellis, K. (2004). The impact of perceived teacher confirmation on receiver apprehension, motivation,
and learning. Communication Education,53,1–20. doi:10.1080/0363452032000135742
Frisby, B. N., Mansson, D. H., & Kaufmann, R. (2014). The cognitive learning measure: A three-
study examination of validity. Communication Methods and Measures,8, 163–176.
Frisby, B. N., & Martin, M. M. (2010). Instructor–student and student–student rapport in the
classroom. Communication Education,59, 146–164. doi:10.1080/03634520903564362
Frymier, A. B. (1994). A model of immediacy in the classroom. Communication Quarterly,42, 133–
Frymier, A. B., & Houser, M. L. (1999). The revised learning indicators scale. Communication
Frymier, A. B., Shulman, G. M., & Houser, M. (1996). The development of a learner empowerment
measure. Communication Education,45, 181–199. doi:10.1080/03634529609379048
Gagné, M., & Deci, E. L. (2005). Self-determination theory and work motivation. Journal of
Organizational Behavior,26, 331–362. doi:10.1002/job.322
Goodboy, A. K. (2011). The development and validation of the instructional dissent scale. Commu-
nication Education,60, 422–440. doi:10.1080/03634523.2011.569894
Goodboy, A. K., & Bolkan, S. (2009). College teacher misbehaviors: Direct and indirect effects on
student communication behavior and traditional learning outcomes. Western Journal of
Communication,73, 204–219. doi:10.1080/10570310902856089
Goodboy, A. K., & Myers, S. A. (2008). The effect of teacher confirmation on student communica-
tion and learning outcomes. Communication Education,57, 153–179. doi:10.1080/
Goodboy, A. K., & Myers, S. A. (2015). Revisiting instructor misbehaviors: A revised typology and
development of a measure. Communication Education,64, 133–153. doi:10.1080/
Goodboy, A. K., Myers, S. A., & Bolkan, S. (2012). Personalized education and student motives for
communicating with instructors: An examination of Chinese and American classrooms.
China Media Research,8,94–100.
Guay, F., Vallerand, R. J., & Blanchard, C. (2000). On the assessment of situational intrinsic and
extrinsic motivation: The situational motivation scale (SIMS). Motivation and Emotion,24,
Hatcher, L. (1994). Step-by-step approach to using the SAS system for factor analysis and structural
equation modeling. Cary, NC: SAS institute.
Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A
regression-based approach. New York, NY: Guilford Press.
Haynes, S. N., Richard, D., & Kubany, E. S. (1995). Content validity in psychological assessment: A
functional approach to concepts and methods. Psychological Assessment,7, 238–247.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis:
Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisci-
plinary Journal,6,1–55. doi:10.1080/10705519909540118
James, L. R., Mulaik, S. A., & Brett, J. M. (1982). Causal analysis: Assumptions, models, and data.
Beverly Hills, CA: Sage.
Kerssen-Griep, J., Hess, J. A., & Trees, A. R. (2003). Sustaining the desire to learn: Dimensions of
perceived instructional facework related to student involvement and motivation to learn.
Western Journal of Communication,67, 357–381. doi:10.1080/1057031030937477
Kerssen-Griep, J., & Witt, P. L. (2012). Instructional feedback II: How do instructor immediacy cues
and facework tactics interact to predict student motivation and fairness perceptions? Com-
munication Studies,63, 498–517. doi:10.1080/10510974.2011.632660
Communication Quarterly 189
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY:
Kuncel, N. R., Hezlett, S. A., & Ones, D. S. (2004). Academic performance, career potential,
creativity, and job performance: Can one construct predict them all? Journal of Personality
and Social Psychology,86, 148–161. doi:10.1037/0022-35188.8.131.52
Lin, Y. G. McKeachie, W. J., & Kim, Y. C. (2001). College student intrinsic and/or extrinsic
motivation and learning. Learning and individual differences,13, 251–258. doi:10.1016/
Mazer, J. P. (2012). Development and validation of the student interest and engagement scales.
Communication Methods and Measures,6,99–125. doi:10.1080/19312458.2012.679244
McCroskey, J. C., Richmond, V. P., Plax, T. G., & Kearney, P. (1985). Power in the classroom V:
Behavior alteration techniques, communication training and learning. Communication Edu-
cation,34, 214–226. doi:10.1080/03634528509378609
McCroskey, J. C., & Young, T. J. (1979). The use and abuse of factor analysis in communication
research. Human Communication Research,5, 375–382. doi:10.1111/j.1468-2958.1979.
McCroskey, L., Richmond, V., & McCroskey, J. (2002). The scholarship of teaching and learning:
Contributions from the discipline of communication. Communication Education,51, 383–
Miserandino, M. (1996). Children who do well in school: Individual differences in perceived
competence and autonomy in above-average children. Journal of Educational Psychology,
88, 203–214. doi:10.1037/0022-06184.108.40.206
Moller, A. C., Deci, E. L., & Elliot, A. J. (2010). Person-level relatedness and the incremental value of relating.
Personality and Social Psychology Bulletin,36,754–767. doi:10.1177/0146167210371622
Myers,S.A.,&Rocca,K.A.(2001).Perceivedinstructor argumentativeness and verbal aggressiveness in the
college classroom: Effects on student perceptions of climate, apprehension, and state motivation.
Western Journal of Communication,65,113–137. doi:10.1080/10570310109374696
Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, competence, and relatedness in the classroom:
Applying self-determination theory to educational practice. Theory and Research in Educa-
tion,7, 133–144. doi:10.1177/1477878509104318
Nussbaum, J. F. (1992). Effective teacher behaviors. Communication Education,41, 167–180.
Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning
in college students. Educational Psychology Review,16, 385–407. doi:10.1007/s10648-004-
Pintrich, P. R., Smith, D. A., García, T., & McKeachie, W. J. (1993). Reliability and predictive validity
of the motivated strategies for learning questionnaire (MSLQ). Educational and Psychological
Measurement,53, 801–813. doi:10.1177/0013164493053003024
Plax, T. G., Kearney, P., McCroskey, J. C., & Richmond, V. P. (1986). Power in the classroom VI:
Verbal control strategies, nonverbal immediacy and affective learning. Communication Edu-
Reeve, J. (2002). Self-determination theory applied to education settings. In E. L. Deci & R. M. Ryan
(Eds.), Handbook of self-determination research (pp. 183–203). Rochester, NY: University of
Reeve, J., & Jang, H. (2006). What teachers say and do to support students’autonomy during a learning
activity. Journal of Educational Psychology,98,209–218. doi:10.1037/0022-06220.127.116.11
Reeve, J., & Sickenius, B. (1994). Development and validation of a brief measure of the three
psychological needs underlying intrinsic motivation: The AFS scales. Educational and Psy-
chological Measurement,54, 506–515. doi:10.1177/0013164494054002025
Richmond, V. P. (1990). Communication in the classroom: Power and motivation. Communication
Education,39, 181–195. doi:10.1080/03634529009378801
190 Z. W. Goldman et al.
Rodríguez, J. I., Plax, T. G., & Kearney, P. (1996). Clarifying the relationship between teacher
nonverbal immediacy and student cognitive learning: Affective learning as the central causal
mediator. Communication Education,45, 293–305. doi:10.1080/03634529609379059
Ryan, R. M. (2012). The Oxford handbook of human motivation. New York, NY: Oxford University Press.
Ryan, R. M., & Connell, J. P. (1989). Perceived locus of causality and internalization: Examining
reasons for acting in two domains. Journal of Personality and Social Psychology,57, 749–761.
Ryan, R. M., & Deci, E. L. (2000a). Self-determination theory and the facilitation of intrinsic
motivation, social development, and well-being. American Psychologist,55,68–78.
Ryan, R. M., & Deci, E. L. (2000b). When rewards compete with nature: The undermining of
intrinsic motivation and self-regulation. In C. Sansone & J. M. Harackiewicz (Eds.), Intrinsic
and extrinsic motivation: The search for optimal motivation and performance (pp. 14–54). San
Diego, CA: Academic Press.
Ryan, R. M., & Deci, E. L. (2002). An overview of self-determination theory: An organismic-
dialectical perspective. In E. L. Deci & R. M. Ryan (Eds.), Handbook of self-determination
research (pp. 3–36). Rochester, NY: The University of Rochester Press.
Ryan, R. M., & Deci, E. L. (2009). Promoting self-determined school engagement. In K. R. Wentzel &
A. Wigfield (Eds.), Handbook of motivation school (pp. 171–196). New York, NY: Routledge.
Trepanier, S. G., Fernet, C., & Austin, S. (2013). Workplace bullying and psychological health at
work: The mediating role of satisfaction of needs for autonomy, competence, and relatedness.
Work & Stress,27, 123–140. doi:10.1080/02678373.2013.782158
Vallerand, R. J., Fortier, M. S., & Guay, F. (1997). Self-determination and persistence in a real-life
setting: Toward a motivational model of high school dropout. Journal of Personality and
Social Psychology,72, 1161–1176. doi:10.1037/0022-3518.104.22.1681
Vallerand, R. J., Pelletier, L. G., Blais, M. R., Briere, N. M., Senecal, C., & Vallieres, E. F. (1992). The
academic motivation scale: A measure of intrinsic, extrinsic, and amotivation in education.
Educational and Psychological Measurement,52, 1003–1017. doi:10.1177/
Vansteenkiste, M., Lens, W., & Deci, E. L. (2006). Intrinsic versus extrinsic goal contents in self-
determination theory: Another look at the quality of academic motivation. Educational
Waldeck, J. H. (2006). What does “personalized education”mean for faculty, and how should it
serve our students? Communication Education,55, 345–352. doi:10.1080/03634520600748649
Waldeck, J. H. (2007). Answering the question: Student perceptions of personalized education and
the construct’s relationship to learning outcomes. Communication Education,56, 409–432.
Waldeck, J. H., Kearney, P., & Plax, T. G. (2001). Instructional and developmental communication
theory and research in the 1990s: Extending the agenda for the 21st century. In W. B.
Gudykunst (Ed.), Communication Yearbook 24 (pp. 207–230). Thousand Oaks, CA: Sage.
Wanzer, M. B., & Frymier, A. B. (1999). The relationship between student perceptions of instructor
humor and students’reports of learning. Communication Education,48,48–62. doi:10.1080/
Weber, K. (2003). The relationship of interest to internal and external motivation. Communication
Research Reports,20, 376–383. doi:10.1080/08824090309388837
Wolf, M. M. (1978). Social validity: The case for subjective measurement or how applied behavior
analysis is finding its heart. Journal of Applied Behavior Analysis,11, 203–214. doi:10.1901/
Communication Quarterly 191