This document provides an overview of a proposed mixed-methods study examining how students' self-efficacy beliefs, outcome expectations, and goal setting influence satisfaction in online learning environments. The study will survey graduate students enrolled in an online course to collect quantitative data on these factors and conduct interviews to gain qualitative insights. Interviews will use a phenomenological approach to understand students' experiences from their perspectives. Quantitative data will be analyzed using statistical methods while qualitative data will undergo thematic analysis to identify themes in students' perceptions. The results aim to provide a deeper understanding of the relationship between motivational beliefs, expectancies, goals, and satisfaction in online higher education.

1. An analysis of motivational beliefs, expectancies and goals and their impact on learners’
satisfaction in online learning environments in higher education
GREV 721 Qualitative Research Method
Emtinan Alqurashi
March 2015

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Introduction
Online courses differ from traditional courses in the way students are required to be
confident in performing technology-based activities. Students with low level of
confidence in online learning might not engage in learning activities, which lead to
dissatisfaction in online learning environments (Kuo, et. al., 2013). Moreover, students
with low level of expectations may lead to decreasing level of learning satisfaction in
online learning (Hawkins, 2010). Similarly, goals that students set for themselves can
predict students’ satisfaction in online learning (Locke, and Latham, 2006). This study
proposes to examine self-efficacy beliefs, expectancies and goals, and how they influence
students’ satisfaction in online learning environments in higher education. Thus, a
number of questions are addressed in this research as follows:
1. How do students perceive their self-efficacy in online learning environments in
relation to their satisfaction?
2. How do students’ outcome expectation of the online course relate to their
satisfaction?
3. How do students’ goals in online learning relate to their satisfaction?
The research questions in this study were influenced by the literature after investigating
about issues associated with online learning and how to measure them. This research will
help to have and develop deeper understanding of the problem. It aims to analyze how
online learners’ self efficacy, outcome expectancies and goal setting can influence
students’ satisfaction in online learning environments in higher education.

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Literature Review
Self-efficacy refers to “beliefs in one’s capabilities to organize and execute the courses of
action required to produce given attainments” (Bandura, 1997, P. 3). That is, the level of
confidence that one’s have to perform a particular task, activity, action or challenge.
Bandura (1994) defines self-efficacy as someone’s beliefs “about their capabilities to
produce designated levels of performance that exercise influence over events that affect
their lives” (p.71). So efficacy beliefs determine how people might feel, think, be
motivated and accordingly how they act and behave.
Efficacy beliefs can influence individuals to become committed to achieve their desired
outcomes successfully. Several studies (Bandura & Schunk 1981; Relich et al., 1986
Schunk, 1984a) have found that “a strong sense of self efficacy fosters a high level of
motivation, academic accomplishments and development of intrinsic interest in academic
subject matter” (cited in Bandura, 1997 p. 174).
Self-efficacy in online learning
Research on self-efficacy started before online learning has occurred. Hodges (2008) has
stated, “The bulk of research done on self efficacy was conducted between the late 1970s
and the early 1990s, prior to the birth of internet-based online learning” (p. 8). It is found
that learners’ efficacy beliefs are directly related to their academic performance. One of
the important factors in learners’ perception of self-efficacy is their prior performance.
Several studies (Bouffard-Bouchard, 1989; Schunk, 1982, 1983, 1984; Zimmerman,
Bandura & Martinez-Pons, 1992) have found that there is a number of factors that form

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efficacy beliefs such as “prior performance, self perceptions of ability, effort expended,
task difficulty, and the amount of assistance received” (cited in Hodges, 2008, p. 8).
Therefore, Hodges (2008) have suggested that instructors should focus on learners’
perception on their ability as well as evaluating their actual ability.
An early study (Zvacek, 1991) found that when designers create instruction for distance
learning, they usually focus on the questions: “what do the students need to know? what
instructional strategies would be most appropriate? on what criteria will the students be
evaluated?”, but the affective domain is missing from the list of the question. A reason
for the lack of the affective domain can be the difficulty to conceptualize and evaluate the
affective behaviors (Hodges, 2008, p. 11). Bandura (2002) has argued that if learners
doubt their efficacy beliefs in managing technological tools, they will quickly be
overwhelmed by the informational overload. In the other hand, technological tools in
online learning environments can be useful if learners possess self-efficacy for regulating
their own learning, which leads to positive self-efficacy for using online learning.
Most of the researches on self-efficacy in online learning environments were conducted
in higher education, as that is not the case with researches on self-efficacy in traditional
learning environments (Hodges, 2008). In Bandura’s article (2002), some studies (Ellen,
1988; Hill et al., 1987; Jorde-Bloom & Ford, 1988) found that people with low computer
self efficacy learn little from computer-based learning and resist adopting new
technologies, where McDonald & Siegall, (1992) found that people with high learning
self efficacy perform better, are more satisfied with their performance and they are
committed to change and develop (Bandura, 2002).

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Outcome expectancies
People motivate themselves and anticipate their actions by expecting that a particular
action will result to a specific outcome (Bandura, 1997). The concept of outcome
expectations is derived from the expectancy-value theory. It focuses on the idea that
people’s behavior is related to their expectations of a particular outcome as a result of a
certain performance, and also related to how people value those outcomes (Schunk,
1991). Several researchers (Ajzen & Fishbein, 1980; Atkinson, 1964; Rotter, 1982;
Vroom 1964) believe that the expectancy-value theory was designed “to account for this
form of incentive motivation” (cited in Bandura, 1997, p. 125). The expectancy-value
theory states that people with high outcome expectancy of certain action result to specific
outcomes, which leads to high level of motivation to perform successfully. Several
studies (Feather, 1982; Mitchelle, 1974; Schwab, Olian-Gottlieb, & Heneman 1979)
found that outcome expectations can predict performance motivation (Bandura, 1997).
People act based on what they believe about what they can do as well as what they
believe about the effects of their actions. People’s motivation of outcomes expectancy is
formed by their beliefs of their personal capabilities (Bandura, 1997). Some studies (Beck
& Lund 1981; Betz & Hackett, 1986; Dzewaltowski et al., 1990; Wheeler, 1983) found
that there are many activities that might guarantee valued outcomes, but they are not
persuaded by learners who have doubts that they can do anything to succeed (Bandura,
1997). An example of that is when a student believes that a medical degree would bring
highly valued social status but he/she would not try to enroll because they doubt their
abilities to take heavy scientific courses. Efficacy beliefs are usually related to outcome

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expectation; however, it is likely sometimes for a student with high efficacy beliefs to a
particular task to have a negative outcome expectation. A simple high/low efficacy in
comparison to a high/low outcome expectation would provide insight into behavior and
affect (Pintrich & Schunk, 2002).
Goal setting
Goal setting is a theory of motivation in which it provides an explanation behind the
reasons of why some people perform better on tasks than others. The goal setting theory
defines the term goal as the aim for an action (Locke & Latham, 2013). Bandura has
divided goal setting into four types: specific, challenging, short-term, and realistic goals.
According to Bandura (1977), “when individuals commit themselves to explicit goals,
perceived negative discrepancies between what they do and what they seek to achieve
create dissatisfactions that serve as motivational inducements for change” (p. 161).
Locke & Latham (2013) have stated that there are two main findings from almost 400
studies involving close to 40,000 participants in eight different countries which led to the
development of the 1990 theory of goal setting. First, they found a linear relationship
between the goal difficulty level and performance. In 1967, Locke found that the
participants with the highest goals had a 250% higher performance than the ones with the
easiest goals. Second, people who set themselves specific difficult goals perform better
than people who have no goals at all or vague goals like “do your best”. Locke reported
that 51 out of 53 studies showed the benefit of setting specific difficult goals (Locke &
Latham, 2013).

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Student’s satisfaction with online learning
There are many factors that affect student’s satisfaction, and evaluating those factors is
important in online learning environments (Kuo, et. al., 2013). Self-efficacy is considered
as a major factor to predict student’s satisfaction in online learning environments (Shen et
al., 2013). Tow studies conducted by Kuo et al., (2014) & Puzziferro, (2008), have found
that there was a positive correlation between online self-efficacy and students’
satisfaction but it was not a significant predictor of it. However, Lim (2001) found that
computer self-efficacy was a significant predictor of student’s satisfaction and their
willingness to take other online courses in the future.
Hawkins (2010) suggested that if learner’s expectations in specific domains decrease,
their level of learning satisfaction decrease as well. There is still a need to determine the
effect of leaner’s outcome expectations on their satisfaction in online learning
environments.
Shen et al., (2013) have developed a new scale to measure online learning self-efficacy.
Their results suggested five factors of online learning self-efficacy as follow: (a) self-
efficacy to complete an online course, (b) self-efficacy to interact socially with
classmates, (c) self-efficacy to handle tools in a Course Management System (CMS), (d)
self-efficacy to interact with instructors in an online course, and (e) self-efficacy to
interact with classmates for academic purposes. The findings of the study showed that
self-efficacy to complete an online course had a significant relation with learning
satisfaction. Students’ self-assessment about their confident with their capabilities in
completing an online course was found to be more important and critical than any other

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self-efficacy factors in explaining learning satisfaction with online learning. Hodges
(2008) believed that “research on self-efficacy in online environments is in its infancy”
(p. 10), therefore, more research is needed in the area.
Methodology
Overview
This study proposes to examine self-efficacy beliefs, expectancies and goals, and how
they influence students’ satisfaction in online learning environments in higher education.
This is a mixed methods study; quantitative data will be collected though web-based
survey and qualitative data will be collected though in-depth interviews to have a deeper
understanding of the problem. Phenomenological method is chosen for this research to
collect qualitative data. It concerns with the study of experiences from individual’s
perspectives and it “aims at gaining a deeper understanding of the nature or meaning of
our everyday experiences” (Van, 1997, p. 9).
Participants and setting
Participants of this study will be graduate students from the school of education who are
enrolled in a fully online course at Duquesne University. The setting of this study will
take place in face-to-face environment and in online environment. The web-based survey
will sent to participant via email. Some of those participants will be individually
interviewed, if agreed, face-to-face or by phone.
The participants will be informed that their participation is voluntarily, they are under no
obligation to participate in this study and they are free to withdraw their consent to

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participate at any time. They will be also informed that their name will never appear on
any survey or research instruments. No identity will be made in the data analysis. All
materials will be stored in a locked file in the researcher's home. Their response(s) will
only appear in statistical data summaries. All materials will be destroyed at the
completion of the research.
The participants will have the option to type their email addresses if they wish to share
the summary of the results with them at no cost. The emails of the students will not be
linked to survey responses, thus confidentiality is protected. Rather, all data will be
reported in aggregate and confidentiality will be protected. Email addresses will be
discarded at the conclusion of this study.
Procedure
This is a mixed method study that aims to collect quantitative and qualitative data for a
deeper understanding of the problem. In order to collect quantitative data, an online
survey will be sent to the participants after getting Duquesne’s IRB approval. For
qualitative data collection, a structured in-depth interview will be conducted in order to
collect deeper understanding of the participants’ beliefs and experiences.
The study of lived experience is one of the main focuses of phenomenology. In other
words, it investigates the way people experience the world. Phenomenology “attempts to
gain insightful descriptions of the way we experience the world pre-reflectively, without
taxonomizing, classifying, or abstracting it” (Van, 1997, p. 9). Van (1997, p. 30) has
introduced six methodological themes or research activities for conducting a
phenomenological research.

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1. turning to a phenomenon which seriously interests us and commits us to the
world;
2. investigating experience as we live it rather than as we conceptualize it;
3. reflecting on the essential themes which characterize the phenomenon;
4. describing the phenomenon through the art of writing and rewriting;
5. maintaining a strong and oriented pedagogical relation to the phenomenon;
6. balancing the research context by considering parts and whole.
A structured online survey will be designed using Google Docs and will be sent to
participants to collect their responses for quantitative data. The survey will include four
sections: students’ efficacy beliefs, outcome expectations, goals, and learning
satisfaction. Then, some of those participants will be invited for a personal interview if
they agree. The format of the interview will be structured, and it will include four sets of
questions: student’s confidence, expectation of the outcomes, goals that students set for
themselves, and online learning satisfaction. With the combination of quantitative and
qualitative data, the researcher will have a deeper understanding of students’ perception
and experiences. The researcher will ask the same questions for each interviewee. The
participants will read and sign the consent form before the beginning of the interview.
Qualitative Data Analyses
Thematic analysis was chosen in order to analyze the responses of the interviews.
According to Braun and Clarke (2006, p. 79), "Thematic analysis is a method for
identifying, analysing and reporting patterns (themes) within data. It minimally organises
and describes your data set in (rich) detail". In other words, thematic analysis is usually

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used to identify and analyze the content and the meaning of patterns (i.e. themes) in the
data collected (Braun and Clarke, 2006). Researchers can identify the abstract themes
before, during or after the data analysis (Ryan and Bernard, 2000). Thematic analysis is
widely used in the qualitative method and it helps to identify the students’ perceptions,
thoughts and opinion. The analysis of this study is based on the six phases provided by
Braun and Clarke (2006, p. 87):
Phase Description of the process
1. Familiarizing yourself
with your data
Transcribing data (if necessary), reading and re-reading
the data, noting down initial ideas.
2. Generating initial codes Coding interesting features of the data in a systematic
fashion across the entire data set, collating data relevant
to each code.
3. Searching for themes Collating codes into potential themes, gathering all data
relevant to each potential theme.
4. Reviewing themes Checking if the themes work in relation to the coded
extracts (Level 1) and the entire data set (Level 2),
generating a thematic ‘map’ of the analysis.
5. Defining and naming
themes
Ongoing analysis to refine the specifics of each theme,
and the overall story the analysis tells, generating clear
definitions and names for each theme.
6. Producing the report The final opportunity for analysis. Selection of vivid,
compelling extract examples, final analysis of selected
extracts, relating back of the analysis to the research
question and literature, producing a scholarly report of
the analysis.
Table 1 Phases of Thematic Analysis

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Phase 1: Familiarization with the Data.
The process of the analysis starts by collecting all the interview responses that have been
received. Then the researcher becomes familiar with the depth of the content and
identifies the common ideas by reading it several times. Braun & Clarke, (2006, p. 87)
have mentioned that “It is ideal to read through the entire data set at least once before you
begin your coding, as ideas and identification of possible patterns will be shaped as you
read through”.
Phase 2: Generation of Initial Codes
This phase starts when the researcher finishes reading, become familiarized with the data,
and have an idea about the interesting points heshe may find in the data. This phase
includes generating the initial codes from the data, looking for some similarities between
those codes, and then refocusing on the analysis in order to identify themes. At this
stage, “It may be helpful to use visual representations to help you sort the different codes
into themes” (Braun and Clarke, 2006, p. 89).
Phase 3: Searching for Themes
This phase begins when the researcher finishes collecting and coding all data, and listing
all the different codes that will be identified through the data. This phase involves sorting
all of those different codes into themes. It is when “you start thinking about the
relationship between codes, between themes, and between different levels of themes (e.g.
main overarching themes and sub-themes within them)” (Braun and Clarke, 2006, p. 89).

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As a result of this phase, a list of candidate themes will be identified along with sub-
themes as well, where all the coded data will be categorized into groups.
Phase 4: Reviewing the Themes
In this phase, the researcher has to re-evaluate all the candidate themes that have been
chosen in the previous phase. He/she might find that some of the candidate themes can be
combined together, while others can be divided into different themes. The researcher
might notice that some candidate themes are not really themes if they have no enough
data to support them or the data is too diverse. Braun and Clarke (2006) have mentioned
that this phase involve two levels of reviewing the themes. The first level involves
reviewing the phase when data has been coded. This means that “you need to read all the
collated extracts for each theme, and consider whether they appear to form a coherent
pattern” (Braun and Clarke, 2006, p. 91). The second level is similar to the first level but
in relation to all data set. It is important in this level not just to consider the validity of the
individual themes to the data set, but also if the candidate thematic map reflects the
meaning accurately in the whole data set (Braun and Clarke, 2006).
Phase 5: Defining and Naming Themes
After having a satisfactory thematic map of my data, this phase begins. It involves
defining and refining the themes I will present for my data. This means identifying what
each theme involves or the story of each theme tells, and determining what aspects of the
data each theme capture. It is important by the end of this phase to have clear defined
data for each theme, and concise manes that gives the reader an idea of what the theme, is
about (Braun and Clarke, 2006).

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Phase 6: Producing the Report
Writing up the report is the final phase of the analysis after having a set of fully worked-
out themes. The writing up task of the thematic analysis presents the complicated story of
the data in a way that convince the reader of the validity of the analysis. Braun and
Clarke (2006, p. 93) have stated “it is important that the analysis provides a concise,
coherent, logical, non-repetitive and interesting account of the story the data tell within
and across themes. Your write-up must provide sufficient evidence of the themes within
the data (i.e. enough data extracts to demonstrate the prevalence of the theme)”.
Implementation of the study
Participants must have access to the Internet through computers or smart devices in order
to fill out the online survey. Participants who agree to be interviewed will have to arrange
a date/time to meet on-campus or via phone. The time frame needed for the data
collection is one month from the beginning of the semester. Some of the anticipated
constraints and potential obstacles of this study is the limited number of participation in
the interviews. No generalizations can be made if there were a limited number of
participants. A pilot study is recommended to test the approximate number of
participation in the first month of the course, collecting data in more than one semester is
preferred to get a larger number of participation.

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