Technological Tools Impact on Learning in Online Professional Development Courses
Concept Paper
Submitted to Northcentral University
Graduate Faculty of the School of Education
in Partial Fulfillment of the
Requirements for the Degree of
DOCTOR OF EDUCATION
by
Stephen W. Watts
Prescott Valley, Arizona
August 2012
Table of Contents
Introduction ......................................................................................................................... 1
Statement of the Problem .................................................................................... 2
Purpose of the Study ........................................................................................... 3
Research Questions ............................................................................................. 4
Hypotheses .......................................................................................................... 5
Brief Review of the Literature ............................................................................................ 6
Adult Learning Theories ..................................................................................... 6
Adult E-Learning ................................................................................................ 9
Measuring Adult Learning ................................................................................ 13
E-Learning Factors ........................................................................................... 14
Summary ........................................................................................................... 16
Research Method .............................................................................................................. 16
Research Design ............................................................................................... 16
Data Collection and Analysis ........................................................................... 22
Operational Definition of Variables ................................................................. 24
Measurement ..................................................................................................... 26
Summary ........................................................................................................... 26
References ......................................................................................................................... 28
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1
Introduction
In the past decade technological advances in information and communication
technology (ICT) have caused drastic changes in the way many people communicate,
socialize, work, and receive training or education. A decade ago, institutions of higher
education and professional development were beginning to explore the new terrain of
what has become e-learning, but few were successful in delivering quality education
using this relatively new media (Broadbent, 2002). Now, there are many colleges,
universities, and professional development firms that have embraced e-learning in their
instructional portfolio.
The benefits to learners are abundant with e-learning. Specific benefits include;
improving learning efficiency (Cabrera-Lozoya, Cerdan, Cano, Garcia-Sanchez, & Lujan,
2012; Chen & Lien, 2011; Huang, Lin, & Huang, 2012), affecting the way learners
behave (Bhuasiri, Xaymoungkhoun, Zo, Rho, & Ciganek, 2011; Haythornthwaite, Bruce,
Andrews, Kazmer, Montague, & Preston, 2007), enhancing communication (Abrami,
Bernard, Bures, Borokhovski, & Tamim, 2010; Alshare, Freeze, Lane, & Wen, 2011),
convenience (Anderson, 2008; Desai, Hart, & Richards, 2008), saving of time (Lam &
Bordia, 2008; Pastore, 2012), and improved learning ability (Donavant, 2009; Ismail,
Gunasegaran, & Idrus, 2010). More learners are attending online classes due to these
benefits despite factors such as?? lower learner satisfaction with the delivery of such
courses (McGlone, 2011). The dilemma of e-learning is that not all students experience
these benefits as the incidence of dropout or failure in online courses is much larger than
for traditional classes; as much as 32% for online as opposed to 4% for traditional classes
(Al-Fahad, 2010; Pigliapoco & Bogliolo, 2008).
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The high rate of dissatisfaction with online courses has led to studies focusing on
the causes of satisfaction and dissatisfaction with online learning (Gunawardena, LinderVanBerschot, LaPointe, & Rao, 2010; Martinez-Caro, 2009). Despite dissatisfaction
with online learning, studies show that students in online courses learn better than
students in traditional/face-to-face courses??, as indicated by grades or acknowledging
perceived learning, as they participate more (Huang et al., 2012; Martinez-Caro, 2009;
Watkins, 2005; Zemke & Zemke, 1995), and as their personal satisfaction with the course
increases (Chen & Lien, 2011; Kozub, 2010; Martinez-Caro, 2009). Student satisfaction
with online courses decreases as learner-to-learner interaction, teacher-to-learner
interaction (Martinez-Caro, 2009), and the amount of reflection allowed within the course
decreases (McGlone, 2011; Watkins, 2005). The succeeding study is proposed within
this context of student satisfaction and dissatisfaction with online learning.
Statement of the Problem
The largest factor of dissatisfaction in adult online learning is the lack of face-toface interaction between the learner and the facilitator or other learners (Alshare et al.,
2011; Boling, Hough, Krinsky, Saleem, & Stevens, 2011; Donavant, 2009; Pigliapoco &
Bogliolo, 2008). Dissatisfaction culminates in higher dropout rates (Al-Fahad. 2010;
Pigliapoco & Bogliolo, 2008), decreased motivation to learn (Omar, Kalulu, &
Belmasrour, 2011; Park & Choi, 2009), less participation, and consequently, less learning
(Jackson, Jones, & Rodriguez, 2010; Martinez‐Caro, 2009; Shea, Fredericksen, &
Pickett, 2006; Zemke & Zemke, 1995). A relationship has been demonstrated between
online participation and learning performance (Huang et al., 2012; Martinez‐Caro, 2009;
Pelz, 2010; Ruey, 2010), as well as between learning performance and student
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satisfaction in online courses (Ali & Ahmad, 2011; Chen & Lien, 2011; Ferguson &
DeFelice, 2010; Kozub, 2010; Martinez‐Caro, 2009). However, there is little empirical
research regarding online adult professional development or appropriate techniques for
teaching and engaging non-traditional learners (Donavant, 2009), or on appropriate
modes of interaction in learning management systems (So & Bonk, 2010). The specific
problem is that a lack of understanding regarding techniques or methods for improving
interactions in adult online professional development courses will continue to have higher
than necessary failure, dropout, and dissatisfaction rates. Knowledge gained will enlarge
the currently small knowledge base regarding online professional development training
(Chen & Lien, 2011; Donavant, 2009), will contribute a better understanding of
facilitating engaging online instruction (Bradley, 2009; Huang et al., 2012; Watkins,
2005), and assist in identifying the proper level and types of media for use in the online
classroom (Fletcher, Tobias, & Wisher, 2007; Martinez‐Caro, 2009).
Purpose of the Study
The purpose of this quasi-experimental nonequivalent groups study is to
investigate whether the addition of a visual element (webcam) can foster increased
learner participation, increased learner satisfaction, and increased perceived learning in
an online adult professional development learning environment. At least ten instructors
will teach two separate live virtual classes (LVC) for a US-based technology company.
One class each will be a control class and one will utilize the webcam to promote
additional interaction for the student-instructor relationships, and attempt to mitigate the
lack of face-to-face interaction noted as the primary source of dissatisfaction for online
students. The students of these LVC, who can sign in from any location worldwide, will
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be surveyed after each class to ascertain their satisfaction, engagement, and perceived
learning with the class as measured by sections of the Learner Satisfaction and Transferof-learning Questionnaire (LSTQ) developed by Gunawardena et al. (2010). A Wilcoxon
rank-sum test will be conducted to determine whether the use of the visual element
changes the means of measures of learner participation, satisfaction, or perceived
learning in the experimental classes versus the control classes.
Research Questions
The research questions identified for this study are included to evaluate the
relationships between the independent variable - the introduction and use of a visual input
(webcam) in an adult online professional development learning environment, and the
dependent variables of learner satisfaction, learner engagement, and perceived learning.
Associated with the problem and purpose statements the following research questions
will be addressed.
Q1. How does satisfaction of adult learners, as measured by the Learner
satisfaction subsection of the LSTQ (Gunawardena et al., 2010), vary, if at all, in an
online LVC environment between learners who continuously see the instructor through
visual technology (webcam) and learners who see the instructor through a webcam only
at the beginning of each day?
Q2. How does engagement or participation of adult learners, as measured by the
Learner-learner interaction and Learner-instructor interaction subsections of the LSTQ
(Gunawardena et al., 2010), vary, if at all, in an online LVC environment between
learners who continuously see the instructor through visual technology (webcam) and
learners who see the instructor through a webcam only at the beginning of each day?
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Q3. How does perceived learning of adult learners, as measured by the Ability to
transfer subsection of the LSTQ (Gunawardena et al., 2010), vary, if at all, in an online
LVC environment between learners who continuously see the instructor through visual
technology (webcam) and learners who see the instructor through a webcam only at the
beginning of each day?
Hypotheses
H10.
Measures of learner satisfaction are statistically equivalent when the visual
(webcam) element is used continuously as opposed to when it is minimally used in online
LVC instruction of adult technical professional development courses ( μLVC = μwc ).
H1a.
Measures of learner satisfaction are statistically different when the visual
(webcam) element is used continuously as opposed to when it is minimally used in online
LVC instruction of adult technical professional development courses ( μLVC ≠ μwc ).
H20.
Measures of learner participation are statistically equivalent when the
visual (webcam) element is used continuously as opposed to when it is minimally used in
online LVC instruction of adult technical professional development courses (μLVC = μwc).
H2a.
Measures of learner participation are statistically different when the visual
(webcam) element is used continuously as opposed to when it is minimally used in online
LVC instruction of adult technical professional development courses ( μLVC ≠ μwc ).
H30.
Measures of learner perceived learning are statistically equivalent when
the visual (webcam) element is used continuously as opposed to when it is minimally
used in online LVC instruction of adult technical professional development courses (μLVC
= μwc).
H3a.
Measures of learner perceived learning are statistically different when the
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6
visual (webcam) element is used continuously as opposed to when it is minimally used in
online LVC instruction of adult technical professional development courses (μLVC ≠ μwc).
Brief Review of the Literature
The rapid advances of technology over the past decade have led to a dramatic
shift in the demographics of post-secondary students, as about 40% are over the age of
25, and a majority of these more mature learners are increasingly choosing e-learning to
pursue higher education (Ke & Xie, 2009) and professional development (Gunawardena
et al., 2010). Adults, or nontraditional students, learn differently than do traditional
students, or younger adults entering post-secondary education straight from secondary
education (Bye, Pushkar, & Conway, 2007; Ke & Xie, 2009; Kenner & Weinerman,
2011; Zemke & Zemke, 1995). Historically, these differences have been ignored in
higher education, and in online courses, where the same pedagogies and curriculum face
both the traditional and non-traditional learner (Ke & Xie, 2009). There has also been
little research outside of higher education regarding how mature adults learn best in a
virtual classroom (Chen & Lien, 2011; Donavant, 2009). In this section various adult
learning theories will be expounded to create a foundation from which to address
research findings on the optimal ways that adults learn online, along with characteristics
that detract from online learning. With a grasp of the characteristics that enhance adult
learning, various means of measuring learning will be identified and expanded upon,
which will help identify factors contributing to learning.
Adult Learning Theories
There are dozens of learning theories that provide a rich foundation for
understanding the complexity of learning and teaching (Minter, 2011). These theories
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often have common characteristics, have strengths and shortcomings, and have their
supporters and detractors. Many of these theories do not differentiate between teaching
adults and teaching children, or are not applicable to adult learners (Minter, 2011). When
working with the adult learner the underlying premise of these theories is adults learn in a
different way; therefore teachers of adults need to use different instructional methods
(Minter, 2011; Zemke & Zemke, 1995). For this reason many authors differentiate the
term andragogy to identify the methods of teaching adult learners, and pedagogy to
identify the methods of teaching children (Commonwealth of Learning, 2000; Karge,
Phillips, Dodson, & McCabe, 2011).
Andragogy, “the art and science of helping adults learn” (Blanchard, Hinchey, &
Bennett, 2011, p. 2; Cercone, 2008, p. 137), is a foundational theory that has many
supporters. The term was originally coined by Alexander Kapp in 1833 and
philosophically flows from Plato’s theory regarding education (Abela, 2009). Malcolm
Knowles was the leading proponent of andragogy in the U.S. and developed a number of
tenets describing the adult learner, and these have been expanded by various authors.
Although originally touted as a complete explanation of how adults learn, Knowles later
acknowledged “pedagogy and andragogy probably represent the ends of a spectrum that
ranges from teacher-directed to student-directed learning. Both approaches, he and
others now suggest, are appropriate with children and adults, depending on the situation”
(Zemke & Zemke, 1995, para. 12). The main principles of andragogy include:

Adult learners are independent and will not necessarily learn what they are
told but need to understand why they need to learn something and the benefits
it will bring (Baskas, 2011a; Fidishun, 2011; Kenner & Weinerman, 2011;
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Strang, 2009).

Adult learners become more self-directed and need to have control over their
learning (Blanchard et al., 2011; Guilbaud & Jerome-D’Emilia, 2008;
McGlone, 2011).

Adult learners have a varied and rich experience base, as well as different
learning styles and motivators. Adult learners want to be acknowledged for
and have their experiences used in learning (Abela, 2009; Blanchard et al.,
2011; Fidishun, 2011; Kenner & Weinerman, 2011).

Adult learners are more motivated to learn when a challenge enters their life;
encouraging them to discover how to handle it better (Baskas, 2011a;
Donavant, 2009; Zemke & Zemke, 1995).

Adult learners are interested in learning how to solve problems, perform tasks,
or improve their life (Cercone, 2008; Chyung & Vachon, 2005; Kenner &
Weinerman, 2011).

Adult learners become more intrinsically motivated, focusing on aspirations
than extrinsically motivated (Abela, 2009; Donavant, 2009; Minter, 2011).

Adult learners expect a student-centered approach to learning in an
environment of mutual respect between teacher and student, and between
students (Karge et al., 2011; Kenner & Weinerman, 2011; McGlone, 2011;
Minter, 2011).
There are numerous arguments, discussions, principles propounded regarding
adult learning theory and there is still no single unified model, theory, or set of principles
all subscribe to (Baskas, 2011b; Merriam, Caffarella, & Baumgartner, 2007; Zemke &
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Zemke, 1995). The principles of andragogy are accepted by most educators of adults as
foundational even though it is acknowledged several factors important to the teaching of
adults are not included or emphasized (Abela, 2009; Blanchard et al., 2011; Donavant,
2009; Strang, 2009).
Another popular theory in the literature professes adults have certain preferred
learning styles, and this predilection dictates certain behaviors; among these behaviors
are an inclination for receiving instruction in certain ways (Buch & Bartley, 2002;
Kozub, 2010), which acts as a predictor of performance (Huang, Lin, & Huang, 2012;
Kozub, 2010). Whereas the research results regarding learning styles is mixed (Cercone,
2008; Kirschner, Sweller, & Clark, 2006), it does underscore adults learn differently.
Adult E-Learning
The literature consistently identifies six characteristics contributing to optimal elearning for adults (Cercone, 2008). These six characteristics include (a) a strong
student-instructor relationship and facilitation by the instructor (Boling et al., 2011;
Chyung & Vachon, 2005; Jackson et al., 2010; Simonson, Schlosser, & Hanson, 1999),
(b) student-student interaction and collaboration (Abrami et al., 2010; McGlone, 2011;
Pelz, 2010; Sinclair, 2009; Yang & Cornelious, 2005), (c) reflection by the learner to tie
new learning to existing experience (Ali & Ahmad, 2011; Cacciamani, Cesareni, Martini,
Ferrini, & Fujita, 2012; Cercone, 2008; Ruey, 2010), (d) development of a sense of
community among participants (Andrews & Haythornthwaite, 2007; Sharples, Taylor, &
Vavoula, 2007; Tallent-Runnels, Thomas, Lan, Cooper, Ahern, Shaw, & Liu, 2006), (e)
immediate real world application of learning (Baskas, 2011b; Blanchard et al., 2011; Ke
& Xie, 2009; Segrave & Holt, 2003; Zemke & Zemke, 1995), and (f) student motivation
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(Abrami et al., 2010; Baskas, 2011b; Kenner & Weinerman, 2011; Omar et al., 2011).
Research demonstrates that as these characteristics are included and emphasized in online
learning the performance of adult learners increases, as does their participation, and
satisfaction.
Success in distance education has many factors, but key to learning for the student
is development of the student-instructor relationship (Simonson et al., 1999) and the
instructor’s level of interaction with the learner (Jackson et al., 2010; Martinez-Caro,
2011). Chyung and Vachon (2005) identified that four of the seven most significant
factors contributing to a learner’s satisfaction were directly related to an instructor’s skills
or their interaction with the student. The supportive and nurturing relationship of learner
and instructor increases learner satisfaction with online courses (Ali & Ahmad, 2011;
Jackson et al., 2010; Shea et al., 2006), improves motivation (Al-Fahad, 2010; Omar et
al., 2011; Park & Choi, 2009; Pigliapoco & Bogliolo, 2008), and optimized learning
outcomes (Abrami et al., 2010; Boling et al., 2011; Jackson et al., 2010; Pelz, 2010).
Regarding the second critical success factor in e-learning, Boling et al. (2011)
argued today’s technology requires a shift from a teacher-centered to a student-centered
paradigm, which relegates the instructor to the role of mentor, guide, coach, or facilitator
(Blanchard et al., 2011; Cabrera-Lozoya et al., 2012; Oncu & Cakir, 2011). One of the
most important factors in successfully facilitating online is projecting teaching presence
(Archambault, Wetzel, Fouger, & Williams, 2010; Bradley, 2009; Pelz, 2010); the ability
to connect with students (Ke, 2010), and encourage them and provide the necessary
scaffolding to promote learning and self-reliance in the learner (Anderson, 2008;
Cacciamani et al., 2012; Cercone, 2008; Tallent-Runnels et al., 2006) while staying in the
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background as much as possible (Hoic-Bozic, Mornar, & Boticki, 2009; Ke, 2010).
When transitioning from the traditional classroom to online, mastering facilitation can be
a great challenge for the instructor (Allen, Crosky, McAlpine, Hoffman, & Munroe,
2009; Jackson et al., 2010) and can be the key to success or failure (Lombardi, 2007). As
teaching presence increases, so does student satisfaction (Donovant, 2009; Ferguson &
DeFelice, 2010; Gunawardena et al., 2010), engagement (Ke & Hoadley, 2009),
motivation (Diaz & Entonado, 2009), and accomplishments (Ally, 2008) as students
actively participate in learning (Yang & Cornelious, 2005).
Another key element to successful learning is self-reflection by the learner, which
engenders deep learning (Cercone, 2008; Ke & Xie, 2009), high-quality learning (Ke,
2010; Ruey, 2010), meta-learning (Baskas, 2011a; Bradley, 2009), and metacognitive
expertise (Cacciamani et al., 2012). Reflection also allows learners to examine their
biases (Baskas, 2011b), other perspectives (Sinclair, 2009) so they can internalize (Ally,
2008; Strang, 2009), contextualize (Bradley, 2009; Fidishun, 2011), and transform
experience and knowledge into learning (Buch & Bartley, 2002; Chan Mow, 2008), while
boosting motivation (Abela, 2009; Baskas, 2011a), and promoting higher order learning
(Taran, 2006). Studies demonstrate reflection is a key online design dimension (Ali &
Ahmad, 2011; Ke, 2010; Yang & Cornelius, 2005) and students seem to prefer e-learning
because of their ability to reflect before engaging in discussions (Andrews &
Haythornthwaite, 2007; Ke & Hoadley, 2009; Martinez-Caro, 2011; Sinclair, 2009).
A sense of community is vital for successful online learning (Andrews &
Haythornthwaite, 2007; Boling et al., 2011; Tallent-Runnels et al., 2006). It is the role of
the instructor to lead community-building activities (Ally, 2008; Muirhead, 2004) and his
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or her example is key to the establishment of a sense of community (Ally, 2008; Ambrose
& Ogilvie, 2010) through accurate and timely feedback (Desai et al., 2008; TallentRunnels et al., 2006), encouragement of participation and interaction (Boling et al., 2011;
Cornelius, Gordon, & Ackland, 2011; Yang & Cornelius, 2005), nurturing caring and
healthy relationships (Abrami et al., 2010; Caine, 2010), and modeling effective and open
communication (Desai et al., 2008). When students feel a sense of belonging to a
community and care for other members of the group significant benefits have been noted.
The benefits to students are they (a) bond earlier and better than in traditional classrooms
(Pelz, 2010), (b) engage in more reflective thinking (Bradley, 2009), (c) better understand
the material (Bradley, 2009), (d) are more motivated (Abrami et al., 2010; Boling et al.,
2011; Karge et al., 2011) and satisfied (Pigliapoco & Bogliolo, 2008), (e) persist with
their studies (Pigliapoco & Bogliolo, 2008), and (f) learn more (Boling et al., 2011; Fahy,
2008; Moisey & Hughes, 2008; Pigliapoco & Bogliolo, 2008).
An additional factor to successful online courses is addressing real-world
applications. According to andragogy, students are more interested in immediate
problem-centered approaches to learning, so learning can improve their work, family, or
personal life (Abela, 2009; Blanchard et al., 2011; Kenner & Weinerman, 2011). By
encouraging students to bring their experience and problems into the classroom, learners
are able to construct deeper and more robust knowledge, while expanding their abilities
to handle actual problems (Allen et al., 2009; Ruey, 2010). This application of real-world
learning is a motivator (Fidishun, 2011) and enriches learning.
The final factor mentioned regularly in the literature regarding optimal online
learning is the need for students to be motivated. Motivation has been demonstrated to
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significantly increase in students because of good student-instructor relationships (AlFahad, 2010; Chickering & Gamson, 1987; Lam & Bordia, 2008), strong teaching
presence (Diaz & Entonado, 2009), having a sense of community (Abrami et al., 2010;
Boling et al., 2011; Karge et al., 2011), participating or collaborating in learning (Omar et
al., 2011; Park & Choi, 2009; Pigliapoco & Bogliolo, 2008), being encouraged to reflect
on new learning (Abela, 2009; Baskas, 2011b), having material clearly presented
(Abrami et al., 2010; Ali & Ahmad, 2011; Alshare et al., 2011), and working through
real-world problems (Fidishun, 2011). Though student motivation is assumed to be a
major factor of adult learning, Kiliç-Cakmak (2010) identified that “little or no attention
[has been] paid to presentation methods that influence” (p. 195) motivation.
As each of these factors is present in an online course, it is important to be able to
verify their effects on students. Verification comes in the form of measurement.
Measurement of adult learning takes many forms, which will be discussed next.
Measuring Adult Learning
Learning is a subjective and deeply personal experience. Correlations have been
made between measures of perceived learning and other factors to make it possible to
determine how well an instructor has performed even in situations where students do not
necessarily receive grades. The most common methods of determining the amount of
learning that has taken place in a class are through measuring performance or satisfaction
(Martinez-Caro, 2009). Recently, structural equation modeling (SEM) has been used to
estimate “causal relationships using a combination of statistical data and qualitative
causal assumptions” (p. 576) to identify how various factors affecting learning work
together (Martinez-Caro, 2009; Strang, 2009).
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Performance is usually measured in terms of quantitative assessments. Teachers
attempt to determine the level and amount of learning through tests, quizzes, and papers,
and generally apportion grades in accordance with some rubric identifying how well they
believe a student has learned specific material. Performance as measured by grades is
highly dependent on several factors independent of learning, e.g., writing skills, class
participation, prior knowledge, or grading inconsistencies (Martinez-Caro, 2009). In
professional development courses grades are not usually given, so learning of the student
generally comes from self-report data of how much knowledge or skill the learner
believes he or she acquired (Donavant, 2009; Gunawardena et al., 2010; Martinez-Caro,
2009).
Another means of determining learning in adults is through their satisfaction with
a course. Martinez-Caro’s (2009) SEM analysis demonstrated there is a strong positive
correlation between a student’s perceived learning and his or her satisfaction, r = .73.
Several other authors have used learner satisfaction as the appropriate measure of
effectiveness of learning in online courses (Gunawardena et al., 2010; Kozub, 2010;
McGlone, 2011). This precedent means that under proper circumstances evaluation of a
student’s satisfaction can be an effective means of determining the effectiveness of the
instruction and of the learning.
E-Learning Factors
Numerous studies have been conducted to determine which factors affect
effective e-learning. As learners participate more in an online class their satisfaction
increases and they learn more (Huang et al., 2012; Gunawardena et al., 2010; Watkins,
2005). Multiple studies have found one of the most important factors regarding
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performance and satisfaction is the amount of interaction between the student and the
instructor (Gunawardena et al, 2010; Zemke & Zemke, 1995). One study found
“interaction is key to effective e-learning, with teacher-student interaction the strongest
predictor of learning in e-learning” (Martinez-Caro, 2009, p. 578). As mentioned
previously, the interactions and collaboration between learners weigh heavily on the
satisfaction of the student (Cercone, 2008; Ke & Xie, 2009; Martinez‐Caro, 2009;
McGlone, 2011; Sinclair, 2009; Zemke & Zemke, 1995), with one study showing it is
“the highest predictor of transfer of learning” (Gunawardena et al, 2010, p. 223). The
convenience and flexibility of the online experience rank highly in factors adding to
learner satisfaction (Donavant, 2009; Ismail et al., 2010).
Chyung and Vachon (2005) identified factors for dissatisfaction with e-learning,
and recognized that satisfaction is not the inverse of dissatisfaction, and vice versa. Their
study showed because students are not satisfied does not automatically mean they are
dissatisfied; conversely just because students are not dissatisfied does not mean they are
satisfied. For this reason they suggested factors contributing to both should be identified,
and those tending toward satisfaction should be maintained or added, and those causing
dissatisfaction should be eliminated or reduced. In this vein, the number one perceived
disadvantage of e-learning by new students is the lack of face-to-face interaction between
the student and instructor (Donavant, 2009). While the most significant factors
contributing to learner dissatisfaction in e-learning courses is a perceived lack in (a) the
instructors participation level, (b) the instructors feedback and responsiveness, and (c) the
instructor’s giving of clear directions and setting of expectations (Chyung & Vachon,
2005).
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16
Summary
Outside of higher education there is little research in the area of adult education,
and even within higher education very little research has sought to distinguish the
characteristics, traits, and proclivities of the non-traditional student. Andragogy provides
seven assumptions pertaining to the adult learner, but fails to mention or expand on
several factors important to learning. The most common factors mentioned in the
literature for successful adult online learning are (a) the need for a rich and engaging
student-instructor relationship and facilitation by the instructor, (b) collaboration between
students, (c) reflection by the student to meld new knowledge with past knowledge and
experience, (d) building a sense of community between the participants of an online
class, (e) the application of knowledge to immediate, real-world problems, and (f) the
need to enhance student motivation.
To determine if a treatment is successful it is necessary to accurately measure
whether learning has occurred. Because learning is an internal process and its effects are
not usually visible to the outward observer, several elements have been researched to
determine how well they measure actual learning. Self-reported satisfaction of the
learner has been demonstrated to correlate closely with other factors representative of
adult learning. Many satisfiers have been verified through research such as participation,
interaction between student and instructor, and between students, and convenience, but
the main dissatisfier in online classes remains the lack of face-to-face interaction between
learner and instructor.
Research Method
Research Design
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This study will use a quantitative, nonequivalent control group, quasiexperimental design. Classes at Oracle USA are scheduled at the discretion of
management and based upon various factors, such as popularity of the class or instructor?
and instructor availability. Classes can be conducted or cancelled and students
rescheduled depending on the number of enrollments in each class. Whether a student
purchases a technological course that is conducted in a traditional face-to-face
environment or in a LVC may not be completely at the discretion of the learner,
providing institution, or presenting instructor. Without randomization there can be no
true experimental design; without an experiment it is more difficult to determine causeand-effect. I chose a quasi-experimental design because it is not possible to randomly
place learners into separate control and test groups since learners purchase the
appropriate class for their professional development needs and such other motivators
personal to each student. Even without randomization, a sufficient sample size and
control group allows for statistical manipulation to roughly approximate randomization
(Edgington, 1966; Wright, 2006).
A nonequivalent control group with post-test only quasi-experimental design has
been chosen for this study due to the factors above. Because of these environmental
conditions this design provides a strong basis from which to conduct the research and
mitigates some of the weaknesses normally inherent in this design. The strengths,
advantages, and necessity of this design for this study will be discussed in terms of four
pairs of possibilities in design, (a) quasi-experimental versus experimental, (b) random
assignment versus nonrandom assignment, (c) post-test only versus pretest-posttest, and
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(d) between-subjects versus within-subjects.
Quasi-experiment versus experiment. Experiments have two specific
characteristics that enhance internal validity. Strong internal validity allows the
researcher to pronounce that the dependent variables of an experiment are caused by the
independent variables (Greenhoot, 2003). The two characteristics of an experiment are
direct manipulation of one or more independent variables and the control and
experimental groups are probabilistically equivalent and can therefore be compared. A
quasi-experiment consists of the first characteristic but lacks the second.
The control group will experience an online technology class with minimal use of
a visual element, while the experimental group will experience the same class with
continuous use of a visual element. The proposed study is not an experiment in the truest
sense because subjects already exist in groups and will not be randomized. The
consequences of nonrandomization will be discussed next.
Random versus non-random assignment. Random assignment of subjects to
control and experimental groups increases internal validity because of the principle of
probabilistic equivalence. Without random assignment a greater possibility exists that
two groups are not equivalent. Without groups being equivalent it becomes more difficult
to compare them, weakening internal validity.
In the proposed study there is only one online class for each course during any
given week. A student will be assigned to a class based on the timeframe that he or she is
available to attend. Each class is likely to have students that are different in many
characteristics from students in a preceding or succeeding class of the same course. In
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the proposed design each instructor will teach the same course twice with one class being
randomly chosen as the control and one as the experiment. Combining the results of
multiple instructors, courses, and technologies of the control and experimental groups
mitigates selection bias through random assignment of the test condition to multiple
intact groups (de Anda, 2007; Yu & Ohlund, 2010).
Since random assignment of subjects to groups is not possible, a true
experimental design is not possible. The usual assumption regarding quasi-experimental
existing groups is that they are members of a “group because of something they chose or
did” (Jackson, 2012, p. 348). In the proposed study, what constitutes membership in one
group versus another is a matter of time availability. Each prospective subject comes
from the same adult population with a desire to learn a technology or set of technological
skills. Each student is different in many characteristics from every other student. There
is little reason, however, to expect that any group of these students is different in the
characteristics of import to this study than any other group of these students simply
because of their availability for training, and a case will be made that although the
researcher is unable to randomly assign subjects to groups, the process of enrollment
effectively performs a similar function.
Post-test only versus pretest-posttest. In a post-test only design, the difference
in mean scores between the control and experimental group are assumed to be because of
the effect of the independent variable. This assumption can be problematic for a
nonequivalent control group design because there is no way to determine whether the
groups are statistically equivalent in relevant characteristics. By adding a pretest to the
design it is possible to compare between the groups and also within the groups for
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differences. A pretest also makes it possible to statistically adjust measures to obviate
initial differences.
In the proposed study a pretest has no meaning. The instrument used to measure
student satisfaction, engagement, and perceived learning cannot be utilized in the context
of no learning. An assumption can be made that prior to a class a student will neither be
satisfied or dissatisfied, that he or she is not yet engaged, and has yet to perceive any
learning from the class. In the real-world context of the proposed study the post-test
design is the better choice.
Between-subjects versus within-subjects. The proposed study utilizes a
between-subjects, or independent groups, design. Differences in the means of
measurements of study constructs can be ascribed to manipulation of the independent
variable. There are two designs that can improve the statistical power of the betweensubjects design; matched-subjects and within-subjects. Neither of these options is viable
in the proposed study. A matched-subjects design measures subjects regarding
characteristics believed to impact the variables in the study, and then subjects are placed
in control and experimental groups as matched pairs. In the within-subjects design there
is only one group and participants are measured with regards to both the control and
experimental conditions at different times. The matched-subjects design is not feasible
regarding the proposed study for the same reasons that randomization is not feasible; the
researcher has no control over which students appear in which groups. The withinsubjects design is not feasible regarding the proposed study for the same reason that a
pretest will not be administered; the carryover effect of one survey may impact
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subsequent surveys, while also creating a demand characteristic in which astute students
may guess at the purpose of the study and change their behavior accordingly.
Ethical considerations and protections. In this study informed consent will be
in writing, and will be the first screen that students will see when they enter the survey.
Included in the statement, written at an eighth grade comprehension level, will be the
following elements; (a) an explanation of the research being conducted, (b) associated
risks, (c) what the study is designed to determine, (d) a statement regarding
confidentiality, (e) researcher contact information, and (f) a statement regarding
voluntary participation and non-consequential withdrawal (“Belmont”, 1979). Since the
informed consent will be entered online, acceptance will consist of the participant typing
their full name and clicking on a link to enter the study. No possibility will exist for
collecting data unknowingly from participants since informed consent will be presented
to each participant before collection of data.
The nature of the questions in my research is such that negative comments are
reflective of the instructor rather than the participant. Regardless, consent and identifying
information will be retained separately from responses to survey questions. The data will
be stored separately from identifying information, and will only be presented in the
aggregate. By this means confidentiality of participants will be preserved. Although data
will be collected regarding responses to student satisfaction, engagement, and perceived
learning in relation to specific classes, control and experiment data will be aggregated
separately across instructors and presented so that no specific instructor can be identified
in the findings. Though the instructors are not subjects of the study, they are also not the
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focus of the study, and their anonymity will be maintained in data storage, and in the
findings of the study.
My research consists of a single survey in which few identifiers will be collected
and will be conducted after a regularly scheduled and delivered professional development
class. A survey allows the collection information regarding the feelings and views of
participants about their classroom experience, and therefore consists of no more than
minimal risk (“Belmont”, 1979). The proposed research should be exempt from
continuing review because (a) it will be conducted in an educational setting and involves
evaluating educational practices, (b) includes responses to survey procedures that if
disclosed would not put participant’s at risk legally, financially, socially, physically, or
psychologically, and (c) will not involve minors (“Belmont”, 1979).
Data Collection and Analysis
Data will be collected from the classes of ten (minimum) instructors who teach
various technologies. Each instructor will teach two instances of two different online
classes of five consecutive days or less duration. These classes will be paired, such that
one instance of the class will be taught according to that instructor’s normal delivery (the
control) and one instance will be taught in the normal style with the addition of a webcam
transmitting the instructor’s image to the class during interactive periods of the class (the
experiment). Whether the control class or experimental class will be taught first will be
randomized. Each student will be encouraged at the end of the class to fill out the
Learner Satisfaction and Transfer-of-learning Questionnaire (LSTQ) developed and
validated by Gunawardena et al. (2010), in addition to the regular course evaluation.
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Incomplete or surveys that have the same value for all sixteen questions will be
discarded.
This design will use two groups of approximately equal size, one representing
each attribute of the independent variable. It is not known whether the distribution of
scores from the LSTQ will be normal, so the Wilcoxon rank-sum test will be used to
determine whether the experimental sample has significantly different values than the
control sample. According to the hypotheses, it is not known whether use of the webcam
will increase or decrease subscale scores on satisfaction, engagement, or perceived
learning, so a two-tailed test will be used. It is expected that the scores from each
subscale will be leptokurtic, with negative skew; therefore the parent distribution of
Laplace is selected. As the standard deviation of the data is unknown, the expected effect
size will be set to d = 0.3; slightly larger than a small effect size, but not a medium effect
size. Traditional values of α = 0.05 and β = 0.2 are generally acceptable for most
research in the social sciences (Cohen, 1992) and have been selected in this case. Using
G*Power 3.1 for an a priori analysis based on the preceding factors, a minimum sample
size of N = 234 is required to have an optimal chance of rejecting the null hypothesis, if it
is false (Faul, Erdfelder, Lang, & Buchneer, 2007).
Professional development courses offered by Oracle USA are designed for adult
functional and technical students who are usually sent by commercial or governmental
customers. These students have generally been chosen for training because they are
either new hires or have changed positions and require a practical knowledge of how to
create and implement a new technology or skill set to perform their job functions. As the
vast majority of students in Oracle courses have a bachelor’s degree the level of content
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is on a par with undergraduate coursework. The purpose of the proposed research is to
determine whether a visual element can foster increased learner participation,
satisfaction, and perceived learning in an online adult professional development learning
environment. The selection of learners in multiple professional development courses
utilizes subjects from that environment, making any results found generalizable to the
extended population of adult online professional development students. There are no
other distinguishing characteristics of interest to the proposed study, so neither age,
gender, culture, nor ethnicity are considered regarding the sample.
Operational Definition of Variables
The independent variable for this study is whether the visual element (webcam) of
the instructor is continuously transmitting to adult learners in a LVC as in the
experimental classes, or not as in the control classes. All instructors engaged in the
research are to conduct and facilitate their classes as they normally would with the sole
exception of the independent variable. Measures of three dependent variables will be
collected; these dependent variables are learner satisfaction, learner participation, and
perceived learning.
Independent variable - visual element. The webcam in this research allows the
transmitting of limited facial expressions and body language of the instructor to the
student. It is a nominal variable as minimal or no visual transmissions will occur in the
control classes. In the experimental classes there will be continuous visual transmissions
during lecture or participation cycles between instructor and students. The visual element
variable has two attributes; full use of webcam (1) and minimal use of webcam (0).
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Dependent variable - learner satisfaction. Learner satisfaction has been chosen
as a dependent variable. Studies indicate that as student satisfaction increases so does
participation (Gunarwardena et al., 2010) and learning outcomes (Gunawardena et al.,
2010; Kozub, 2010; Martinez-Caro, 2009; McGlone, 2011). Learner satisfaction is a
construct that will be derived from the Learner satisfaction subscale of the LSTQ;
consisting of five 5-point Likert scale questions. The learner satisfaction construct is an
ordinal variable varying from strongly agree = 5 to strongly disagree = 1.
Dependent variable - learner engagement. Learner engagement has been
chosen as a dependent variable. Studies indicate that as students are interactive with the
instructor, other students, and the content they learn more effectively (Abrami et al.,
2010; Bradley, 2009). Learner engagement is a construct that will be derived from both
the learner-learner interaction and learner-instructor interaction subscales of the LSTQ
and consists of six 5-point Likert scale questions. The learner engagement construct is an
ordinal variable varying from strongly agree = 5 to strongly disagree = 1.
Dependent variable - learner perceived learning. The objective of adult
professional development is to enhance the knowledge and skills of adult workers so that
they are more productive and effective in their working environment. Generally, students
in adult professional development courses do not participate in evaluated activities or
receive grades. Multiple studies have identified that a student’s self-perception of
learning is as valuable an indicator of learning as any external measure (Gunawardena et
al., 2010; Kenner & Weinerman, 2011). Perceived learning is a construct that will be
derived from the ability to transfer subscale of the LSTQ and consists of five 5-point
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Likert scale questions. The perceived learning construct is an ordinal variable varying
from strongly agree = 5 to strongly disagree = 1.
Measurement
Collection of data for this research will be done at the culmination of each of the
LVC through an online survey. The 16 questions, each using a 5-point Likert scale, from
the LSTQ will be presented in no particular order to each student. The LSTQ has been
previously validated from similar research regarding student satisfaction and transfer of
learning. The learner satisfaction subscale of the LSTQ has a Cronbach alpha of .83
making it extremely reliable. The reliability of the learner-learner interaction subscale of
the LSTQ has a Cronbach alpha of .69 for good reliability and the learner-instructor
interaction subscale of the LSTQ has fair reliability with a Cronbach alpha of .52. The
ability to transfer subscale of the LSTQ has fair to good reliability with a Cronbach alpha
of .62.
Summary
This research study proposes a quantitative, nonequivalent control group, quasiexperimental study to investigate whether the addition of a visual element (webcam)
fosters increased learner participation, satisfaction, and perceived learning in an online
adult professional development learning environment. Each participating instructor will
teach two separate LVC. One class each will be a control class, with minimal use of a
webcam per company LVC policy, and the other will have full use of the webcam to
promote additional interaction. An online survey with questions from four subscales of
the LSTQ will be administered at the end of each class; the quantitative data garnered and
evaluated, and finally compared using a Wilcoxon rank-sum test.
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