The Impact of Mobile Phone Self-efficacy and Computer Anxiety on Distance Learners’ Attitude to Online Courses
Chapter One
OBJECTIVES OF THE STUDY
The aims include:
- Determining with some level of accuracy the effect of computer anxiety to students
- Finding ways to help with the issue of computer anxiety on students
- Improving mobile phone self-efficacy in students as regards to the online learning.
The objectives include:
- Examining in detail the field of computer education
- Looking at previous work done in the field of compute anxiety and self efficacy
- Gathering responses of people to determine their level of anxiety and their level of self-efficacy.
CHAPTER TWO
LITERATURE REVIEW
CONCEPTUAL DISCOURSE
Use of technology sometimes has unpleasant side effects, which can include strong, negative emotional states that arise not only during interaction but even before, when the thought of getting to interact with the computer begins. Frustration, confusion, anger, anxiety, and similar emotional states can affect not only the interaction itself, but also productivity, learning, social relationships, and overall well-being. There are variety of related definitions explaining what anxiety is:
Leso and Peck (1992) define computer anxiety “as a sense of being fearful or apprehensive when using or considering the utilization of a computer.” Evidently, factors like the context in which an individual was first introduced to the computer (Brosnan, 1998a, 1998b; Rosen & Weil, 1995), past failure and successes with hardware or software, and the current tasks being attempted, including the use of a new computer applications (Saadé & Otrakji, 2007), are all determinants of the state and type of anxiety the individual is experiencing. These researchers have attempted to predict those that will experience computer anxiety by identifying factors that correlate with its occurrence. Frequently, such factors as selfefficacy and attitudes towards computer usage are posited as influencing the computer anxiety (Ayersman & Reed, 1995; Igbaria & Chakrabarti, 1990; Reed, Ayersman, & Liu, 1996). There are three sorts of anxieties: trait, state, and concept-specific.
Trait anxiety is defined as a general pervasive anxiety that is experienced by a person over the entire range of life experience. People who exhibit trait anxiety are chronically anxious and constantly under tension regardless of their situation. This anxiety is frequently used as a construct for personality, learning theory, and psychopathology. Trait anxiety defines a personality characteristic and may be inherited (Howard & Smith, 1986).
State anxiety is experienced as anxiety that fluctuates over time and arises to a responsive situation. State anxiety is related to a person’s learning background. A person may have experienced some anxiety in a situation and that anxiety is transferred to a similar situation.
Concept-specific anxiety is a transitory-neurotic sort of anxiety. Concept-specific anxiety is that the range between the trait and state anxieties. it’s an anxiety that’s related to a selected situation. Therefore, computer anxiety may be a concept-specific anxiety because it’s a sense that’s related to a person’s interaction with computers (Oetting, 1983). Howard and Smith (1986) further define computer anxiety “as the tendency of an individual to experience A level of uneasiness over his or her impending use of a computer.”
In information systems study, anxiety has been viewed as a personality variable that influences system use (Agarwal & Karahanna, 2000). variety of IS studies are according to the view that the connection between anxiety and behavior is mediated by the private beliefs (Schlenker & Leary, 1982) and anxiety is incorporated as an antecedent to the beliefs of usefulness and simple use (e.g., Igbaria, 1993; Venkatesh & Davis, 2000). it’s interesting to notice that classical view of hysteria is that it mediates the connection between beliefs and behavior (Spielberger, 1972). Thus, anxiety are often viewed as a results of the beliefs a private has, instead of as an antecedent to them. for instance , a private who features a belief that s/he are going to be embarrassed by delivering a speech has speech anxiety (commonly called stage fright); as a results of the anxiety, s/he refuses to offer speeches. the assumption results in the fear (i.e., anxiety), which results in the behavior (i.e., avoidance). Following an equivalent line of reasoning, one could presume that a student (in some cases) who features a belief that s/he will experience technological problems while doing a web test has computer anxiety; as a results of the anxiety, s/he are going to be paranoid about computer problems while doing the web test. the assumption results in fear, which results in the behavior of paranoia, thereby causing the scholar to be less focused on doing the test (leading to reduced performance).
Igbaria and Parasuraman (1989) apply these theories and define computer anxiety “as the tendency of people to be uneasy, apprehensive, or fearful about current or future use of computers”. variety of studies have provided evidence supporting an immediate relationship between computer anxiety and computer use (Brosnan, 1999; S. L. Chau, Chen, & Wong, 1999; Howard & Mendelow, 1991; Igbaria, Parasuraman, & Baroudi, 1996). the pc anxiety research clearly shows that a highly computer anxious individual are going to be at a big disadvantage compared to his/her peers. One example of such an environment is an e-learning offered by many higher learning institutions.
Anxiety & Perceived Ease of Use
Prior research has shown that past experience is a determinant of behavior (Ajzen & Fishbein, 1980). In general, TAM identifies the relationships between PEU, PU, attitude (ATT), and behavioral intentions (BI) towards a target system (Davis et al., 1989). In the context of the present elearning study, perceived ease of use (PEU) refers to the degree to which the student expects the LMS to be free from cognitive effort (Davis et al., 1989). Enhanced course performance implies that the student can obtain a better grade by using the LMS without any usage difficulties (Igbara & Tan, 1997, Saadé & Bahli, 2005). Students’ perception of enhanced performance affects attitudes. In other words, students that perceive the system to be easy to use, develop better attitudes towards the LMS as reported by previous studies (Adams, Nelson, & Todd, 1992; Pedersen & Nysveen, 2003; Saadé & Kira, 2007). Specifically we make the following hypothesis (H1) related to ANX and PEU, shown in Figure 1.
CHAPTER THREE
METHODOLOGY OF THE STUDY
TYPE OF RESEARCH.
This study makes use of the descriptive survey method. This design was selected for the study to elicit a reasonable response rate from the participants. This will help the researcher in gathering sufficient data to be coded and quantitatively analyzed. Relationships were measured to determine the degree of association between the variables of interest (Howell, 2007; McMillan & Schumacher, 2006; Osuala, 2005).
The nature of the research involve a mixture of both the quantitative and the qualitative research.
Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions. This type of research can be used to establish generalizable facts about a topic which in this case is a study of the impact of mobile phone self-efficacy and computer anxiety on the attitude of distance learners on online courses.
Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.
Qualitative research on the other hand is expressed in words. It is used to understand concepts, thoughts or experiences. This type of research would enable us gather in-depth insights on the topic of the effect of Computer Anxiety and Mobile phone self-efficacy on the online learning process.
Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews which have been conducted in the previous chapter that explore concepts and theories.
SOURCES OF DATA
PRIMARY SOURCES
The primary sources come from the survey questions that have been answered by law enforcement agents, journalists and some members of the general public.
SECONDARY SOURCES
The secondary sources of the research was gotten from the examination of scholarly journal articles.
METHOD(S) OF DATA COLLECTION.
The method for collecting the research data is the use of the Questionnaire survey method. The respondents are going to answer questions based on the aforementioned research questions in order to test the hypothesis.
CHAPTER FOUR
DATA PRESENTATION AND ANALYSIS
INFORMATION AND ANALYSIS OF THE SAMPLE.
In this this chapter, results of data were discussed and interpreted. The number of questionnaires that were administered was 120. A total of 74 questionnaires were properly filled and returned. This represented an overall successful response rate of 62%. According to Mugenda and Mugenda (2003), as cited in Murithi, Tiberious, Mwania and Mwinzi (2016), a response rate of more than 50% is adequate for analysis. In essence, return rates of 50% are acceptable to analyze and publish.
Demographic Characteristics
A total number of seventy-four (74) copies of the questionnaire were properly filled by stakeholders in education located in Osun State. Responses were provided for all the question items in the questionnaire, thus making them useful for the analysis. The demographic characteristics of the respondents are presented in below.
CHAPTER FIVE
SUMMARY CONCLUSION AND RECOMMENDATIONS
SUMMARY OF RESEARCH FINDINGS.
From the results obtained from the survey, it showed that majority of stakeholders have an optimistic and favourable view to the use of mobile phones and computers as a means of instruction in the Education sector. It also gathered that in majority of the stakeholders’ opinion, they are not satisfied with the extent to which ICT is used in their instruction and lectures.
STRATEGIES TO IMPROVE THE SUBJECT MATTER (PLUS SAFE TEST AND RISK ANALYSIS).
In order to improve the subject matter, there is a need to broaden the scope of study in order to gauge with more accuracy the opinion of the public and the stakeholders involved in the use of the e-learning methods of giving and receiving instructions.
CONCLUSION
This study examined the impacts of mobile phone self-efficacy and computer anxiety on online course instruction in select institutions in Osun State, Nigeria. Three hypotheses were tested.
- Computers are used often in receiving educational instruction
- There is a level of anxiety when using the computer for educational purposes
- Mobile phone self-efficacy is useful in combating computer anxiety.
The findings show that
- There is an average use of computers as a means of instruction in the educational sector.
- A significant minority of people have at least a level of anxiety when using computers as a primary means and method of education.
- There is about a 76 percent confidence rate in each respondents self efficacy in the use of mobile phones and computers in taking online courses.
RECOMMENDATIONS AND (DETAILED) IMPLEMENTATION OUTLINE.
At the end of this study, the following are recommended:
- More training in form of orientation should be given to students who intend to interact with computing devices to receive instruction.
- There should be a basic level of troubleshooting knowledge
- There should be more injection of facilities to the educational sector so as not to make the concept of e-learning and online education a strange idea.
SUGGESTIONS FOR FURTHER STUDIES.
For further studies, I would recommend a larger scope of the number of respondents and across states to reflect a more accurate representation of opinions regarding the issue of Computer Anxiety and the respective mobile phone self-efficacy solutions.
REFERENCES
- Adams, D. A., Nelson, R. R., & Todd, A. P. (1992). Perceived usefulness, ease of use, and usage of information technology: a replication. MIS Quarterly 16(2), 227-247.
- Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.
- Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly 24(4), 665-694.
- Ayersman, D. J., & Reed, W. M. (1995). Effects of learning styles, programming, and gender on computer anxiety. Journal of Research on Computing in Education, 28(2), 148-161.
- Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215.
- Bandura, A. (1978). Reflections on self-efficacy. Advances in Behavioral Research and Therapy, 1(4), 237-269.
- Bandura A. (1982) Self-efficacy mechanism in human agency. Journal of American Psychology, 37, 122-147.
- Bandura A. (1986a) The explanatory and predictive scope of self-efficacy theory, Journal of Society and Clinical Psychology, 4, 359-373.
- Bandura, A. (1986b). Social foundations of thought and action. Englewood Cliffs, New Jersey: Prentice-Hall.
