This paper must directly address the applications and implications of a law or regulation discussed in this course(Topic: Security and Privacy of Health Information) to the conduct of your duties as an Information Technology professional and contain all of the following elements;
implications of a law or regulation discussed in this course:
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a title page.
an introduction of the content of the paper.
a brief review of the recent literature related to your selected law or regulation.
a brief analysis of the applications of that law or regulation.
a conclusion that summarizes the content of your paper and discusses future research opportunities related to your topic.
a reference page(s).
To complete this assignment, upload a Microsoft Word document ( or x) that contains your complete paper. Remember that your paper, including your list of sources, must be in APA format, and you MUST cite your references in the body of the paper using APA in-text citation format. A source is any paper or article that you will reference in your paper. If you need more information on APA format (for references list AND in-text citations), visit this reference: https://owl.english.purdue.edu/owl/resource/560/01/
This assignment must be YOUR OWN WORK! This is an individual assignment. Plagiarism detected in your work will be addressed as discussed in the plagiarism section of the syllabus.
Here are a few details about the overall research paper Please look at the attached rubric for details on how the paper will be graded.
Your paper must include both a Title page and a Reference page.
Your paper should NOT include an abstract.
Your paper must include a minimum of 4 peer-reviewed resources (articles or papers)
Cited sources must directly support your paper (i.e. not incidental references)
Your paper must be at least 800 words in length (but NOT longer than 1000 words; Scholarly writing should be efficient and precise. Be clear in the information that you are conveying and with the evidence used to support it. Here is a good resource to help with writing concisely:https://academicguides.waldenu.edu/writingcenter/scholarlyvoice/writingconcisely)
Title and reference pages are NOT included in calculating the paper length.
If you are not sure how to identify peer-reviewed papers or articles, please visit the following resources:
http://diy.library.oregonstate.edu/using-google-scholar-find-peer-reviewed-articles
http://libguides.gwu.edu/education/peer-reviewed-articles
Self-Efficacy in M-Learning
Jason Hutcheson
Running head:
3
Capella University
Table of Contents
Literature Review
5
Self-Efficacy Theory
5
Theoretical Foundations.
5
Intentional Development of Self-Efficacy.
7
Self-Efficacy in Learning
9
Role of Self-Efficacy in Andragogy.
9
Relationship between Self-Efficacy and Academic Achievement.
10
Integration of Self-Efficacy in Learning Design.
12
Self-Efficacy in Technology Acceptance
14
Technology Acceptance Modeling.
14
Mobile Technology Acceptance.
16
Methodology and Approach
16
Methodology and Rationale
17
Research Methodology Analysis.
17
Methodology Selection Rationale.
18
Population and Sample
19
Sample Recruitment Strategy
19
Instrument
19
Conclusion
20
Abstract
Technology has become engrained into daily life. The most prominent technology today is mobile technology. Through mobile “smart” phones, tablets, and laptops, the modern population is connected through mobile technology; everywhere, all of the time. However, many of the benefits of mobile technology have not translated into the educational environment. This represents a problem for both the education and the information technology industries. In order to effectively address this problem, researchers need to understand the challenges of integrating mobile technology in the course room and determine the drivers influencing the acceptance of mobile technology. Existing literature has indicated a relationship between self-efficacy and the acceptance of mobile technology in the course room. However, the degree of correlation between learner self-efficacy and the acceptance of mobile technology has not yet been determined. This paper analyzes the existing literature concerning the role of self-efficacy in mobile learning (m-learning) and presents the foundation for research concerning the relationship between self-efficacy and mobile technology acceptance.
Self-Efficacy in M-Learning
Existing literature has identified value in the integration of mobile technology in the course room with respect to the promotion of collaboration (Fuegen, 2012; Liljestrom, Enkenberg, & Pollanen, 2013; Pegrum, Oakley, & Faulkner, 2013; Shree Ram & Selvaraj, 2012). Still, mobile technology for education remains underutilized. Existing literature extensively discusses the challenges associated with transitioning to an m-learning enabled environment (Cheon, Lee, Crooks, & Song, 2012; Eteokleous & Ktoridou, 2009; Ktoridou, Gregoriou, & Eteokleous, 2007; Male & Pattinson, 2011; Rossing, 2012). Chief among the challenges for transitioning to m-learning is the acceptance of mobile technology in learning, which lends to the importance of identifying and classifying key determinates for mobile technology acceptance.
This paper analyzes the existing literature concerning self-efficacy in order to assess the role of self-efficacy in m-learning. The paper begins by analyzing the theoretical foundations of self-efficacy and how self-efficacy can be developed. This is followed by an analysis of the role of self-efficacy in learning, especially concerning andragogy and how self-efficacy is engaged in support of learning design. Then the paper evaluates the role of self-efficacy in technology acceptance for both general technology and mobile technology. The paper concludes in the analysis and selection of a research methodology, sampling strategy, and instrument to address the research question.
Literature Review
Self-Efficacy Theory
Theoretical Foundations. The theoretical foundations of self-efficacy are rooted in Bandura’s (1986) Social Cognitive Theory. Social Cognitive Theory seeks to define human behavior through the personal interaction of individuals with their environment: defining the concept of self-efficacy and the relationship of self-efficacy toward task engagement. Self-efficacy addresses an individual’s belief that he or she can accomplish what he or she set out to accomplish (Bandura, 1986). Through this definition, self-efficacy presents as a strong influence toward task engagement. Through self-efficacy, individuals analyze and determine their perceived ability to accomplish a task against the perceived difficulty of the task. This implies that individuals with low domain self-efficacy are unlikely to engage in tasks which are perceived to be moderately or highly difficult. The uncertainty that drives low self-efficacy can be confused with a lack of self-confidence.
Although closely related, self-efficacy and self-confidence are two very distinct concepts. Self-efficacy is distinguished from self-confidence primarily through self-efficacy’s domain specific relevance and specific regard toward defined tasks (Bandura, 1986). Where self-confidence presents as a general concept, self-efficacy relates to specific task engagement. In consideration of this distinction, an individual may have varying degrees of self-efficacy in reference to several different, but similar, tasks. Therefore, efficacy in learning mathematics is distinctly different from efficacy in learning history. This domain specific nature of self-efficacy enables specific engagement toward cognitive development.
Self-efficacy theory establishes the role of self-efficacy within cognitive development. Bandura (1993) asserts that self-efficacy strongly influences cognitive development through cognitive, motivational, affective, and selection processes. This influential effect of self-efficacy on cognition and affection enforces self-efficacy’s influence on task engagement though the influence of intellectual and emotional responses. Similarly, the effect of self-efficacy on motivational processes implies influences toward task sustainment. Additionally, in consideration of self-efficacy’s effect on selection processes, self-efficacy presents an influential role in task selection: affecting selection between simple or difficult tasks. However, the role of self-efficacy before and after initial task engagement are distinctly different.
The relationship between self-efficacy and performance presents in a cyclic nature. While initial performance is only moderately influenced by self-efficacy, subsequent performances are strongly influenced by self-efficacy (Bandura, 1993). This cyclic relationship between self-efficacy and performance indicates that repeated failures will negatively impact task self-efficacy and subsequently influence decisions to further engage in failed tasks. However, likewise, this relationship indicates that repeated success will positively impact task self-efficacy and subsequently encourage repeated task engagement. This relationship supports development learning approaches which engage in increasingly difficult tasks in order to promote task self-efficacy.
In addition to internal factors, self-efficacy is also influenced through external interactions. Tan (2012) determined that self-efficacy is strongly influenced by perceptions of individual performance compared to the performance of both peers and mentors. As individuals engage in new tasks, their perceptions of success are derived in comparison to the performance of others. Therefore, performance which consistently aligns with peers and matures towards the performance levels of mentors positively influences self-efficacy. Consequently, in understanding the role of self-efficacy in cognitive development, how can educators engage the intentional development of self-efficacy toward the enhancement of learning activities?
Intentional Development of Self-Efficacy. One method of self-efficacy development lies is goal definition. According to Artino (2012), self-efficacy is enhanced through the establishment of clear and specific goals. With this in mind, educational practices, such as definition of learning objectives and provision of grading rubrics, work to build self-efficacy. However, Clinkenbeard (2012) expands on this concept of goal definition: asserting that self-efficacy is promoted through student involvement in the definition of goals. Therefore, the definition of goals alone is not sufficient to actively develop self-efficacy. Self-efficacy development is best served when students are engaged to help establish learning goals. This concept of self-efficacy development aligns with Knowles (1970) concept of adult learning which asserts that adults seek learning which is practically relevant within their lives.
A second method of self-efficacy development is associated with goal difficulty. Artino (2012) asserts that self-efficacy is enhanced through the encouragement of challenging goals. This indicates that the more challenging the goal, the better then influence on self-efficacy. However, if a goal is too challenging, failure to meet that goal can actually damage self-efficacy. Clinkenbeard (2012) provides clarification that tasks should be established with an optimal difficulty. Under this premise, goals are defined which will challenge students, but are not so difficult as to impose likely failures. This concept of goal development reinforces the engagement of increasingly difficult tasks in support of self-efficacy development.
Another method of self-efficacy development promotes quality communication between student and teacher. Artino (2012) presents that the provision of honest feedback is productive to the development of self-efficacy toward learning. Again, however, Clinkenbeard (2012) expands on this concept: asserting that feedback needs to be presented in a positive manner. However, the two independent assessments of feedback are not mutually exclusive. Synthesized, these assessments assert the delivery of feedback which is both honest and positively presented.
A fourth method of self-efficacy development involves the engagement of group activities. Artino (2012) and Clinkenbeard (2012) both agree that self-efficacy in learning is enhanced through the engagement of managed group activities. Through these group activities, Artino asserts, “teachers can use other students as models to demonstrate how to successfully complete a learning task” (p. 83). By working in groups, learners are able to vicariously experience task completion and can experience in positive peer pressure to engage in tasks themselves.
Self-Efficacy in Learning
Role of Self-Efficacy in Andragogy. The role of self-efficacy in andragogy is directly related to the self-directed nature of andragogical learning. According to Knowles (1970), adult learning asserts the maturation of learner engagement toward self-directedness. In his seminal work on andragogy, Knowles describes the distinctions between the effective learning approaches of adults and children. However, Knowles asserts that andragogy should not be considered as the antithesis of pedagogy, and that the selection of andragogical and pedagogical instructional methods should relate to student topical maturity rather than age (p. 59). As students mature in their understanding of a topic, engagement of self-directed learning activities become more appropriate. However, self-directed learning requires persistence to persevere through difficult tasks without external motivation.
Self-efficacy and self-directed learning intersect in the engagement of difficult tasks. Gao, Lee, Xiang, and Kosma (2011) concluded that self-efficacy directly influences engagement in vigorous activity and persistence. Therefore, as self-efficacy is enhanced, individual engagement in vigorous activity and persistence are also increased. In the role of self-directed learning, this indicates that development of self-efficacy indirectly enables engagement in self-directed learning. Furthermore, as e-learning primarily engages self-directed learning approaches, development of self-efficacy in support of e-learning is highly supported. However, self-efficacy does not address all elements of e-learning.
E-learning has received large degrees of criticism for heightened susceptibility to plagiarism and issues of academic honesty. However, the influence of self-efficacy does not extend into concerns of academic honesty. Ananou (2014) determined that, although students perceived cyber-plagiarism as a significant concern, student self-efficacy is not related to self-reported cyber-plagiarism. In consideration of these findings, concern can be raised regarding the balance in developing self-efficacy and reducing the likelihood of cyber-plagiarism. While self-efficacy may not reduce cyber-plagiarism, it apparently doesn’t support to counter cyber-plagiarism either: indicating that cyber-plagiarism does not derive from concerns of non-performance. However, additional research is required to fully investigate this phenomenon as the reliability of self-reported plagiarism is questionable considering that the participants have no incentive to self-incriminate. Regardless, the relationship between self-efficacy and andragogy is well established and presents strongly in correlation to academic achievement.
Relationship between Self-Efficacy and Academic Achievement. The role of self-efficacy in the improvement of academic achievement is built on the effects of social cognition on the learning process. Higher educational programs that are grounded in a basis of social cognitive theory demonstrate improved success in academics (Dinther, Dochy, & Segers, 2011). As social cognitive functions define human behavior (Bandura, 1986), programs which seek to develop specific academic behavior are enabled through the influence of individual social cognitive constructs. In consideration of this research, engagement of social cognitive development activities support the improvement of academic achievement. These social cognitive functions, as defined by Bandura (1986), include identification, vicarious learning, and self-efficacy. Furthermore, additional research further supports the relationship between self-efficacy and academic achievements.
As education evolves with society, self-efficacy development becomes increasingly beneficial to goals of academic achievement. Tella, Tella, and Adeniyi (2011) concluded self-efficacy to have a direct influence on academic achievement: indicating that this influence is stronger in the context of self-directed learning. This research confirms the suggestion that self-efficacy positively influences academic achievement, and justifies recent efforts to integrate self-efficacy development in the educational environment. Furthermore, as educational programs continue to migrate toward online and mobile learning platforms, this andragogical role of self-efficacy becomes increasingly important.
The effect of self-efficacy on academic achievement operates in conjunction with other variables. Cordova, Sinatra, Jones, Taasoobshirazi, and Lombardi (2014) classified students into three categories, demonstrating varying degrees of self-efficacy in combination with prior knowledge and interest. The results of their research indicate a highly complex relationship between self-efficacy and academic achievement. While students with low self-efficacy, prior knowledge, and interest correlated directly with lower academic achievement, students with higher self-efficacy, prior knowledge, and interest were divided between low and high academic achievement (p. 172). These results indicate that the influence of self-efficacy on academic achievement is affected by other factors. Although self-efficacy may be a good predictor of academic achievement, other factors, including prior knowledge and interest, have either a mediating or moderating effect on this relationship. This integrated relationship of various social cognitive constructs with academic achievement becomes clearer through analysis of the relationships between constructs.
Integrated relationships between constructs, or covariance, can distort the perceived relationship between self-efficacy and performance. Hong, Pei-Yu, Shih, Lin, and Hong (2012) identified a negative correlation between self-efficacy and anxiety. This relationship between self-efficacy and anxiety indicates that the direct influence of self-efficacy may be weaker than the study perceives in consideration of the mediating effect that self-efficacy could have on the relationship between anxiety and performance. Although the research of Hong et al. does not address this mediation effect, hierarchical regression analysis could be employed to better understand the distinct relationships present. Although this research gap is not the focus of this study, it presents an opportunity for future research which should be explored further. Regardless, the effect of self-efficacy on academic achievement is still largely supported in existing research and justifies investigation regarding how self-efficacy can be integrated into learning design.
Integration of Self-Efficacy in Learning Design. The active development of social cognitive attributes demonstrates a positive enhancement in learning. Adams (2014) determined that active development of collective trust in students directly influenced academic achievement. This research demonstrates the indirect influence of active cognitive development on learning. Therefore, the active development of core learning capabilities enables learning beyond the standard distribution of information and knowledge. By developing learning capability, students become more adept and efficacious in the learning process and are better equipped to engage in learning across multiple disciplines. One method of active self-efficacy development is presented through supervised mastery experiences.
In alignment with the cyclic relationship between performance and self-efficacy, student teaching experience presents positive influences on self-efficacy in pre-service teachers. Al-Awidi and Alghazo’s (2012) evaluation of pre-service teaching experience identified that engaging in student teaching enhanced both self-efficacy and future performance. This research clarifies the relationship between self-efficacy and performance, and demonstrates the effect of active self-efficacy development on performance. Furthermore, this research implies that the engagement of practical application instructional techniques advances self-efficacy and subsequently advances learning. However, the non-experimental nature of this research precludes the experiences of those student teaching participants whom did not continue into the role of pre-service teachers.
Experimental research presents a more holistic insight into the relationship between practical application and self-efficacy development. Through experimental research, Chen and Usher (2013) evaluated the effect of mastery experiences on self-efficacy development through the analysis of self-efficacy both before and after participation in mastery experiences. They concluded that mastery experiences provide a powerful source for self-efficacy development (Chen & Usher, 2013). Mastery experiences provide opportunities for students to work through problems in a supervised environment: eliminating feeling of inadequacy, producing successful performances, and building self-efficacy. Interestingly, although mastery experiences produce consistent results across multiple student bases, some students presented a heightened development of self-efficacy.
Not all students benefit from active self-efficacy development equally. Exposure to multiple sources of self-efficacy development enhances self-efficacy development in some students. Highly adaptive students draw from multiple sources of efficacy development simultaneously (Chen & Usher, 2013). Therefore, to effectively engage self-efficacy development in learning, educators need to 1) provide multiple sources of self-efficacy development simultaneously, and 2) maintain awareness of how students respond to varied activities: identifying students which are less adaptive and adapting learning activities to accommodate student needs. While supervised practical application is a powerful efficacy building tool, unsupervised practical application, especially in group settings, may actually be harmful to self-efficacy.
Opportunities for supervised practical application provide an immensely valuable resource in the development of self-efficacy and the promotion of task engagement. Discrepancies in early performance, especially in persons with low levels of cognitive self-worth, can negatively impact self-efficacy (Wang, Fu, & Rice, 2012). However, discrepancies are not restricted to failed task execution and can include lower degrees of success in comparison to peers or other self-established success criteria (p. 97). As people judge personal performance in comparison to peers, students that fall behind are likely to experience negative self-efficacy even in the engagement of practical application exercises. Therefore, it is properly managed self-efficacy development which has demonstrated positive results in the application of learning.
Self-Efficacy in Technology Acceptance
Technology Acceptance Modeling. With the increasing use of technology to enable and enhance education activities, it is important to understand the role of self-efficacy in the use of technology enabled learning, or e-learning. In their 2010 study regarding the role of enjoyment, computer anxiety, computer self-efficacy, and internet experience toward intent to engage in e-learning, Alenezi, Karim, Malek, and Veloo determined that computer self-efficacy had significant influence on student intention to engage in e-learning (p. 32). This research provides an important link between self-efficacy and the acceptance of technology in the learning environment, indicating mobile self-efficacy as likely to influence the use of mobile technology.
Self-efficacy indirectly influences technology acceptance through the influence of perceived ease of use. While computer self-efficacy is not a direct determinate of technology acceptance, it does influence perceived ease of use. Similarly, computer anxiety and attitudes toward using technology also influence perceived ease of use (Venkatesh et al., 2003; Celik & Yesilyurt, 2013). In fact, Celik and Yesilyurt (2013) determined that self-efficacy and anxiety significantly influence teacher attitudes toward computer supported education. Through these indirect relationships, technology developers, organizational leaders, and educators can improve technology acceptance through programs which build user groups’ self-efficacy and reduce the anxiety and negative stereotypes of computer use. Understanding these intertwining relationships is necessary in designing and marketing new technologies. Furthermore, these relationships do not represent unidirectional influence. As self-efficacy influences technology acceptance, technology engagement further builds self-efficacy.
Not only does self-efficacy influence technology acceptance, but technology engagement reflectively influences self-efficacy. In a study conducted by Shank and Cotton (2014), technology enabled learning demonstrated direct influences on multiple domains of self-efficacy; technological, mathematics/science, academic, and general. Therefore, the successful engagement of technology produces improved efficacy in the learner’s ability to subsequently engage that same technology in the future. This aligns with Bandura’s (1993) presentation of the cyclic nature between performance and self-efficacy, and future supports the concept of presenting mastery experiences with increasing difficulty. Therefore, engagement of simple, unrelated tasks may be necessary while integrating technology into the classroom in order to build technology self-efficacy to the point necessary to recognize the full educational benefit of the technology.
Mobile Technology Acceptance. Despite the findings of early technology acceptance research, research specific to mobile technology acceptance has determined direct relationships with predictors which have been defined as indirect by the TAM. For example, research conducted by Park, Nam, and Cha (2012) specifically evaluates mobile technology acceptance in relation to previously identified indirect influences of technology acceptance. The study determined attitude toward mobile learning as the primary direct construct in predicting the acceptance of mobile technology in an educational environment (p. 602). Furthermore, Irby and Strong’s (2013) research, concerning mobile technology acceptance among agriculture students, determined self-efficacy as a direct determinate of mobile technology acceptance (p. 84). The assertion of attitude and self-efficacy as direct determinates of mobile technology acceptance run contrary to the assessment of Venkatesh et al. (2003) of both attitude and self-efficacy as indirect determinates, and implies a deviation in acceptance relationships concerning mobile technology.
Methodology and Approach
This research will use a quantitative methodology with a non-experimental approach. The quantitative methodology provides the opportunity to investigate the phenomenon from an objective perspective, adding credibility to Bandura’s (1986) self-cognitive theory (Creswell, 2009). With a multiple regression research design, the research will evaluate the relationship between mobile self-efficacy and mobile technology acceptance, clarifying the existence and strength of the relationship (Creswell, 2009).
The existing literature concerning technology acceptance maintains strong support for quantitative research. In their seminal works on technology acceptance, both Davis (1989) and Venkatesh et al. (2003) engage quantitative research toward the development and refinement of survey instruments designed to evaluate technology acceptance constructs. Furthermore, research has engaged these surveys in combination with various statistical techniques to study and validate technology acceptance theory (Eteokleous & Ktoridou, 2009; Alenezi, Karim, Malek, & Veloo, 2010; Ismail, Bokhare, Azizan, & Azman, 2013; Irby & Strong, 2013). The continued engagement of the academic community in the quantitative study of technology acceptance demonstrates an implied acceptance of the propriety in using quantitative research methodologies to evaluate this topic. However, not every quantitative methodology aligns with every research question related to technology acceptance.
Practically, the topic of technology acceptance addresses two primary concerns: predicting the acceptance of a technology within a population, and explain the factors which are influencing the acceptance of a technology within a population. Both concerns are associated with analyzing the relationships between variables. Vogt (2007) asserts that, while the terms regression and correlation are often used interchangeably, regression analysis is regularly associated with predictions and correlation analysis is regularly associated with explanations of existing relationships. Therefore, the alignment of research towards a correlation technique, two-tailed t test, or a regression technique, hierarchical regression analysis, is highly dependent upon the research objectives, as either methodology is appropriate for technology acceptance research.
Methodology and Rationale
Research Methodology Analysis. In the analysis of existing relationships, the two-tailed t test provides a quality correlational analysis technique. The two-tailed t test independently analyzes the relationship between defined variables (Vogt, 2007). The strength of this statistical analysis technique is that it directly analyzes the relationship between two variables, and clearly demonstrates the presence, or absence, of a relationship. However, the two-tailed t test does not analyze the strength of the correlation in terms of how much variance is explained by the relationship, or the effects of covariance (Tabachnick & Fidell, 2013). Therefore, while the two-tailed t test is appropriate for determining the presence of relationships, this technique does not quantify the effect of that relationship.
In determining predictors for relationships, hierarchical regression analysis provides a quality regression analysis technique. Hierarchical regression analysis engages a multi-step analysis process to analyze the degree of variance in a defined construct which is explained by multiple other constructs (Tabachnick & Fidell, 2013). The strength of this statistical analysis technique is that it analyzes relationship strength and covariance. However, hierarchical regression analysis engages complex statistical analysis and requires the underlying data sets to align with assumptions of normality, homogeneity, and multicollinearity (Fields, 2013). Therefore, this technique is most readily engaged in the analysis of multiple independent variables in conjunction with one or more dependent variables.
Methodology Selection Rationale. The proposed research question most directly aligns with hierarchical regression analysis, which readily analyzes the effects of covariance (Tabachnick & Fidell, 2013). However, Hoyt, Imel, and Chan (2008) claim that the presence of covariates does not, itself, justify the use of hierarchical regression analysis, and that the use of this technique is designed specifically to address the identification or validation of mediator variables. With this consideration, the alignment of the research topic with hierarchical regression analysis is not merely related to the presence of covariates, but with the emphasis of the research topic to validate the moderating relationship of the covariates. Therefore, hierarchical regression analysis is most capable of analyzing the relationship between self-efficacy and mobile technology acceptance in consideration of the moderating effects of effort expectancy and performance expectancy.
Population and Sample
The population for this research will be undergraduate students. Undergraduate students represent a population of learners which are capable of understanding and representing survey response which will address the constructs of self-efficacy, effort expectancy, performance expectancy, and behavioral intent to use. This research will use the SurveyMonkey Audience service which will provide a sample frame of undergraduate students for participation in the survey. This sampling approach will provide a sample of 384 participants, which is similar to samples used in other recent research regarding mobile technology acceptance (Irby & Strong, 2013), aligns with the sampling design, and is supported through power analysis using the GPower3 software.
Sample Recruitment Strategy
To support the recruitment of research participants, the researcher will coordinate with the survey distribution service regarding timelines, survey distribution requirements, and population restrictions. Then, the researcher will assess and approve the distribution of the survey instrument. The survey service will randomly distribute the survey instrument within the sample frame. Participants will complete the survey via the survey distribution service, and the survey service subsequently provides participant survey responses to the researcher.
Instrument
This research will engage a modification of Venkatesh, Morris, Davis, and Davis’s (2003) survey instrument developed in support of the Unified Theory of Acceptance and Use of Technology (UTAUT). The original instrument has been widely accepted and used in support of technology acceptance research (Pi-Hsia Hung, Gwo-Jen Hwang, I-Hsiang Su, & I-Hua Lin, 2012; Stergiaki, 2013; Alenezi, Karim, Malek, & Veloo, 2010; Eteokleous & Ktoridou, 2009). The specific modification that will be engaged by this study was modified by Irby and Strong (2013) to specifically address the acceptance of mobile technology, and presented acceptable reliability coefficients of; performance expectancy = .92, effort expectancy = .91, behavioral intention = .97, and self-efficacy = .95.
Conclusion
Where the role of self-efficacy is well defined in support of learning, the role of self-efficacy in the engagement of m-learning is less clear. While self-efficacy has been designated as an indirect determinate for technology in general (Venkatesh, Morris, Davis, & Davis, 2003), specific research regarding mobile technology indicates a relationship between self-efficacy and mobile technology acceptance in the course room (Irby & Strong, 2013; Alenezi, Karim, Malek, & Veloo, 2010; Eteokleous & Ktoridou, 2009; Ismail, Bokhare, Azizan, & Azman, 2013). However, the degree of correlation between learner self-efficacy and the acceptance of mobile technology has not yet been determined. This represents a gap in the existing literature regarding the integration of mobile technology in the educational environment and addresses the recommendation for future research provided by Irby and Strong (2013) to research the effect of self-efficacy on mobile technology acceptance (p. 85).
4
This paper analyzes the existing literature concerning self-efficacy and its role in m-learning. The paper evaluated the theoretical foundations of self-efficacy and methods for the intentional development of self-efficacy. Then the paper assessed the role of self-efficacy in learning and the relationship between self-efficacy and academic achievement. Finally, the paper appraised the role of self-efficacy in technology acceptance and the distinctions in the existing literature regarding mobile technology acceptance. This disconnect in the existing literature regarding the role of self-efficacy in technology and mobile technology acceptance produces the core research problem which will be addressed through the proposed research.
References
Adams, C. M. (2014). Collective student trust a social resource for urban elementary students. Educational Administration Quarterly, 50(1), 135–159. doi:10.1177/0013161X13488596
Al-Awidi, H. M., & Alghazo, I. M. (2012). The effect of student teaching experience on preservice elementary teachers’ self-efficacy beliefs for technology integration in the UAE. Educational Technology Research and Development, 60(5), 923–941.
Alenezi, A. R., Karim, A., Malek, A., & Veloo, A. (2010). An empirical investigation into the role of enjoyment, computer anxiety, computer self-efficacy and internet experience in influencing the students’ intention to use e-learning: A case study from Saudi Arabian governmental universities. Turkish Online Journal of Educational Technology, 9(4), 22–34.
Ananou, T. (2014). Academic Honesty in the Digital Age (Dissertation). Indiana University of Pennsylvania, Pennsylvania.
Artino, A. (2012). Academic self-efficacy: From educational theory to instructional practice. Perspectives on Medical Education, 1(2), 76–85. doi:10:1007/s40037-012-0012-5
Bandura, A. (1986). Social Foundations of Thought and Action: A Socialy Cognitive Theory. Englewood Cliffs, NJ: Prentice-Hall.
Bandura, A. (1993). Perceived self-efficacy in congitive development and functioning. Educational Psychologist, 28(2), 117–148.
Celik, V., & Yesilyurt, E. (2013). Attitudes to technology, perceived computer self-efficacy and computer anxiety as predictors of computer supported education. Computers & Education, 60(1), 148–158. doi:10.1016/j.compedu.2012.06.008
Chen, J., & Usher, E. (2013). Profiles of the sources of science self-efficacy. Learning and Individual Differences, 24, 11–21.
Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054–1064. doi:10.1016/j.compedu.2012.04.015
Clinkenbeard, P. R. (2012). Motivation and gifted students: Implications of theory and research. Psychology in the Schools, 49(7), 622–630. doi:10.1002/pits.21628
Cordova, J., Sinatra, G., Jones, S., Taasoobshirazi, G., & Lombardi, D. (2014). Confidence in prior knowledge, self-efficacy, interest and prior knowledge: Influences on conceptual change. Contemporary Educational Psychology, 39, 164–174.
Creswell, J. (2009). Research Design (3rd ed.). Los Angeles: Sage.
Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Dinther, M., Dochy, F., & Segers, M. (2011). Factors affecting students’ self-efficacy in higher education. Educational Research Review, 6, 95–108.
Eteokleous, N., & Ktoridou, D. (2009). Investigating mobile devices integration in higher education in cyprus: Faculty perspectives. International Journal of Interactive Mobile Technologies, 3(1), 38–48. doi:10.3991/ijim.v3i1.762
Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (Third.). Los Angeles: SAGE Publications Ltd.
Fuegen, S. (2012). The impact of mobile technologies on distance education. TechTrends: Linking Research and Practice to Improve Learning, 56(6), 49–53.
Gao, Z., Lee, A. M., Xiang, P., & Kosma, M. (2011). Effect of learning activity on students’ motivation, physical activity levels and effort/persistence. ICHPER-SD Journal of Research, 6(1), 27–33.
Hong, J.-C., Pei-Yu, C., Shih, H.-F., Lin, P.-S., & Hong, J.-C. (2012). Computer self-efficacy, competitive anxiety and flow state: Escaping from firing online game. Turkish Online Journal of Educational Technology, 11(3), 70–76.
Hoyt, W., Imel, Z., & Chan, F. (2008). Multiple regression and correlation techniques: Recent controversies and best practices. Rehabilitation Psychology, 53(3), 321–339.
Irby, T. L., & Strong, R. (2013). Agricultural education students’ acceptance and self-efficacy of mobile technology in classrooms. NACTA Journal, 57(1), 82–87.
Ismail, I., Bokhare, S. F., Azizan, S. N., & Azman, N. (2013). Teaching via mobile phone: a case study on Malaysian teachers’ technology acceptance and readiness. Journal of Educators Online, 10(1).
Knowles, M. (1970). The Modern Practice of Adult Education: Androgogy vs. Pedagogy. New York, NY: Association Press.
Ktoridou, D., Gregoriou, G., & Eteokleous, N. (2007). Viability of mobile devices integration in higher education: faculty perceptions and perspective. Presented at the 2007 International Conference on Next Generation Mobile Applications, Services and Technologies, IEEE.
Liljestrom, A., Enkenberg, J., & Pollanen, S. (2013). Making learning whole: An instructional approach for mediating the practices of authentic science inquiries. Cultural Studies of Science Education, 8(1), 51–86.
Male, G., & Pattinson, C. (2011). Enhancing the quality of e-learning through mobile technology: A socio-cultural and technology perspective towards quality e-learning applications. Campus-Wide Information Systems, 28(5), 331–344.
Park, S. Y., Nam, M.-W., & Cha, S.-B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592–605.
Pegrum, M., Oakley, G., & Faulkner, R. (2013). Schools going mobile: A study of the adoption of mobile handheld technologies in Western Australian independent schools. Australasian Journal of Educational Technology, 29(1), 66–81.
Pi-Hsia Hung, Gwo-Jen Hwang, I-Hsiang Su, & I-Hua Lin. (2012). A concept-map integrated dynamic assessment system for improving ecology observation competences in mobile learning activities. Turkish Online Journal of Educational Technology, 11(1), 10–19.
Rossing, J. P. (2012). Mobile technology and liberal education. Liberal Education, 98(1), 68–72.
Shank, D. B., & Cotten, S. R. (2014). Does technology empower urban youth? The relationship of technology use to self-efficacy. Computers and Education, 70, 184–193. doi:10.1016/j.compedu.2013.08.018
Shree Ram, B., & Selvaraj, M. (2012). Impact of computer based online entrepreneurship distance education in India. Turkish Online Journal of Distance Education, 13(3), 247–259.
Stergiaki. (2013). Acceptance and usage of extensible business reporting language: an empirical review. Journal of Social Sciences, 9(1), 14–21. doi:10.3844/jssp.2013.14.21
Tabachnick, B., & Fiddell, L. (2013). Using Multivariate Statistics. Upper Saddle River: Pearson Education, Inc.
Tan, P. I. J. (2012). Second career teachers: Perceptions of self-efficacy in the first year of teaching. New Horizons in Education, 60(2), 21–35.
Tella, A., Tella, A., & Adeniyi, S. O. (2011). Locus of control, interest in schooling and self-efficacy as predictors of academic achievement among junior secondary school students in Osun State, Nigeria. New Horizons in Education, 59(1), 25–37.
Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(2), 425–478.
Vogt, P. (2007). Quantitative Research Methods for Professionals. Boston: Pearson Learning Solutions.
Wang, K. T., Fu, C.-C., & Rice, K. G. (2012). Perfectionism in gifted students: Moderating effects of goal orientation and contingent self-worth. School Psychology Quarterly, 27(2), 96–108. doi:10.1037/a0029215
