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

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

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set in (rich) detail". In other words, thematic analysis is usually 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 process1.Familiarizing yourself

with your dataTranscribing 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

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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

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 he\she 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

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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). 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

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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).

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

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(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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