This section reviews various studies which have shown the inter-relationship amid the impact of
faculty perception inputs pertinent to online learning. The most significant powerhouse for
change in our lives is information technology (Alshurideh and Alkurdi, 2012; AL-Syaidh et al.,
2015; Hajir et al., 2015; Shannak et al., 2012). Today many students want to learn online and in
turn, get degrees from reputable global Universities and Colleges but still cannot go anywhere as
they live isolated areas without proper communication systems (Tarhini et al., 2014; Darawsheh
et al., 2016). Consequently, many researchers encourage learning courses under the online
learning system as it saves time and energy of those students staying at any far off distant regions
from the universities or colleges they have enrolled (Hubackova and Golkova, 2014; Alenezi et
2.2. Online education in technical colleges
Technical colleges have a unique role to play in American culture. They are two-year colleges
providing education concentrated in specific areas and train students towards working within that
particular field (Gordon, 2014). In the United States, several studies have been conducted and
analyzed concerning the growth of online teaching in higher learning (Watson, Pape, Murin,
Gemin, & Vashaw, 2014). The state of Georgia has 22 technical colleges offering a diverse range
of online courses. It is important to assess the factors that affect the acceptance of e-learning as a
way of attracting the students into the institution. According to Tarhini, Hone and Liu, (2014) the
effects of individual differences on e-leaning includes perceived usefulness (PU), subjective
norms (SN), perceived ease of use (PEOU), and Quality of Work Life (QWL) positively affect
students’ behavioural intention (BI).
Many technology-rich classrooms for teachers have been developed countrywide with the hope
of attaining increased student achievement and restructured classrooms through technological
advancements (Elliott, Rhoades, Jackson, & Mandernach, 2015). Professional development
among teachers has continued to be supported. In 2012, The Recognizing Educational Success
and the Professional Excellence and Collaborative Teaching program (RESPECT) offered 5
billion dollars in grants to support programs that included incorporation of technology and
professional development for teachers (Schleicher, A. 2016). Other federal funding initiatives
have also come forward and supported the provision of professional development for teachers
and faculty to change the teacher practice and hence improve the student achievement (Steinert,
Mann, Anderson, Barnett, Centeno, Naismith & Ward, 2016). Unfortunately, the faculty
continues to struggle when incorporating technology into their teaching because online
instructors get skills on how to use technology instead of the impact of technology on teaching
and learning (Tarhini, Hone & Liu, 2014).
Online instructors need more opportunities to learn and work, which is possible through
professional development, which should be continuous and ongoing with embedded
opportunities for the online instructors to acquire expert learning (Elliott, Rhoades, Jackson, &
Mandernach, 2015). Professional development for online instructors must be linked directly to
the work they do in their classrooms. Technology on its own is not transformative, and therefore
professional development for online instructors is significant in improving the quality of learning
in classrooms. Technology can only be efficient and effective in improving the quality of
learning if the instructor believes in it and is willing to use it. Therefore, there is a need for
continuous or ongoing professional development plans for online instructors (Jonassen,
Howland, Marra, & Crismond, 2008).
2.3 Demand for high-quality online education
In today's education system, distance education has formed an integral part of the mission and
vision of most technical colleges (Betts & Heaston, 2014). In the U.S., student enrollment
numbers of those taking at least one online learning course have increased to over 7 million
based on the 2017 Distance Education Enrollment Report (Allen & Seaman, 2017). Therefore,
online learning is one of the critical components required for the long-term strategies and success
of technical colleges (Allen & Seaman, 2016). A study by Allen and Seaman (2016) indicated
that within the next five years, most of the students in higher learning institutions would be
taking at least one online course. Therefore, higher learning institutions must provide online
education that is of high quality to meet the student population needs as well as effectively
engage them throughout the learning process (Betts & Heaston, 2014). As the online education
system continues to expand, the focus aims at the need to promote the faculty knowledge and
skills, which will then lead to student's success in the online learning environment (Entwistle &
Ramsden, 2015). Success in online learning is important because it is the motivating factor that
will make the people to decide whether to use online learning processes or the conventional
learning processes (Lane, 2019).
It is apparent that defining the essential elements that constitute a high-quality online learning
environment can be very difficult. Barnard-Brak, Paton, & Lan, (2010) notes the fact that
regulation is important when it comes to improving the online learning environment. However,
Richardson, Koehler, Besser, Caskurlu, Lim, & Mueller (2015) described seven categories of
quality online instruction. The study identified course structure, course development, evaluation,
and assessment of the course, teaching, and learning of the course, institutional support, student
support, and faculty support. The study conducted by Bernard, Borokhovski, Schmid, Tamim, &
Abrami (2014) which affirmed the enduring viability of the previously highlighted quality
indicators supported the above information. However, two additional categories were added, and
these were social and student engagement and technology support (Jaggars & Xu, 2016). These
two categories had relevance in measuring and quantifying the quality of online education
programs. Buzzetto-More (2015) notes that the proliferation of the user generated content as in
the social media as well as the other media sharing websites such as YouTube. Through the user
generated content the learners are able access the content that has been shared by various
teachers and the social media. Salehudin, Khairuddin, & Razalek, (2013) found out that the use
of the online tutorials played an important role in improving the performance of the students that
take part in the online learning.
2.2 Advantages of Online Learning
According to preachers Callen et al. (2010) and Garrison (2011), there are many
advantages of online learning technologies, which include the fact that they are less expensive to
deliver, affordable, and saved time and the fact that they are flexible-anytime anywhere. It,
therefore, means that online learning enables the students to access the material from anywhere
at any time, access to global resources, and articles that meet students' level of knowledge and
interest. The preachers recognize that online learning enables self-pacing for slow or quick
learners, reduces stress, and increases satisfaction and retention. Online learning allows more
effective interaction between the learners and their instructors through the use of emails,
discussion boards, and chat rooms. Learners can track their progress. They can also learn
through a variety of activities that apply to many different learning styles that learners have, and
It helps the learners develop knowledge of using the latest technologies and the internet. Finally,
online learning can improve the quality of teaching and learning as it supports face-to-face
teaching approaches. According to Kyei-Blankson, Ntuli, & Donnelly (2016) online learning
promotes relationships between the students and the tutors just the way the conventional learning
environment does. It there means that in addition to getting various skills in connection to what
they are learning, they will also establish and improve the relation among the learners and the
tutors that take part in the teaching process.
2.3 Disadvantages of Online learning
Whereas online learning curses have numerous advantages, it is equally important to note
that there are disadvantages as well. The demerits include: includes little or no in-person contact
with the faculty members, feelings of isolation, a steep learning curve about how to navigate
within the system, and problems with the technology. The other problem is that it limits the need
for the student to be actively involved in learning and increasing lead-time required for feedback
regarding assignments (Holmes and Gardner, 2006; Masa’deh et al., 2013; Kanaan et al., 2013;
Tarhini et al., 2013b). There are also different aspects of online learning, especially in
developing countries. They include: providing the required funds to purchase new technology,
lack of adequate online strategies, training for staff members, and, most importantly, the student
and faculty resistance to use the online learning systems (Wagner 2008).
2.4 Faculty Perception of online learning adoption
Regardless of the enormous growth of online learning in education and its perceived
benefits, the efficiency of such tools will not be fully utilized if the users inclined not to accept
and use the system. It has become imperative for practitioners and policymakers to understand
the factors affecting the user acceptance of the online learning system to enhance the learning
experience (Tarhini et al., 2014a). However, recent studies have shown that online learning
adoption is not merely a technological solution, but also a process of many different factors
(Schepers and Wetzels, 2007; Tarhini et al., 2014b; 2015). The elements may be an individual
(Liaw and Huang, 2011), organizational such as facilitating conditions (Sun and Zhang, 2015) in
addition to behavioral and cultural factors (Masoumi, 2010).
Fischer et al. (2015) studied how proceedings of scientific conferences can be used for
trend studies in the field of online learning. Fischer et al. (2015) made an essential contribution
to the diffusion of digital media in higher education. The researcher found that the detailed
analysis of the frequency distribution over the seven years reflects the intensity of scientific
discussion towards online learning trends, and conclusions about the didactical or technical
potentials of innovations can be introduced and impact students' achievement. According to
Moravec et al. (2015), the study, which was attended by nearly 2000 students and faculty
members, compares the results of questions from the areas of law where the tools were provided
in a pilot version with the results questions, where the online tool was not provided. The
researchers found that online tools have affected the students' results (Moore, Dickson-Deane &
However, the understanding of the online learning tool may hurt students who will
depend on given materials was disproved. By using the Cohen's model and based on data
collected from 15 documents from relevant research studies conducted on the effect of ICT based
online learning on academic achievement during 2010-2012, Mothibi (2015) examined the
relationship between online learning and students' academic performance in higher education.
The researcher found that ICT had a statistically significant favorable influence on online
learning based on students' academic achievements. The results also indicated that ICT had a
significant beneficial impact on students' educational overall academic achievements
This paper critically reviewed the literature related to online learning systems and
identified some of the most influential factors used in the field of information systems research.
More specifically, this paper had an insight into the origins, characteristics as well as the
limitations, weaknesses, and strengths of web-based learning systems, which has affected the
adoption and acceptance by faculty. Student variables, such as behaviors and attitudes, cultural
backgrounds, and other demographic characteristics, are important variables that influence
student learning, especially in a collaborative online learning environment. Understanding these
variables is now helpful for instructors (faculty) to design meaningful educational activities to
promote student knowledge construction and make learning more efficient and appealing. In
particular, this research helps to understand better the perception of the faculty towards online
learning, which can help policymakers, educators, and experts to understand what the students
expect from the learning management systems. The results can help the management achieve the
most efficient deployment of such order and also helps them improve their strategic decision
making about technology in the future. They can decide on the best approach that fits both the
faculties and their students before implementing any new technology.
2.5 Technology Acceptance Model
For some time now, educationists have made several attempts to include techniques in the
education market place. Make 1988 indicated that pressure has always been mounted by the
electronics industry on the education system to embrace and make use of technologies not only
in teaching and learning but to support other services within the school system. However, a
significant setback to the addition of technology in education is the prediction of its acceptance
and use, particularly for academic purposes.
The reluctance of the user (teachers and students) to accept and utilize technology is a significant
factor which obstructs the ample use of such educational technology in institutions (Davis 1989).
This unwillingness to embrace and use technology is known as resistance to technology.
According to Makau 1988, the educational system resisted slide rules, biro pens, an electronic
calculator, and also computers.
It is often quite challenging to achieve success in programs that proposes to include technology
during learning (Park, 2009). So for a long while, industry players have depended on instruments
that have been accepted and validated by researchers in the electronic and training industry.
However, these instruments are supported by theoretical structures called technology acceptance.
2.6 Frameworks for Technology Acceptance
According to Shroff et al., there are various frameworks used to measure the determinants of
technology acceptance and adoption. Examples include the Theory of Reasoned Action by Aizen
and Fishbein, Diffusion of Innovation Model by Rogers, and the Technology Acceptance Model
(TAM). Amongst these frameworks, the Technology Acceptance Model (TAM) has been the
most widely used and most influential to predict the utilization of the different technologies for
learning (Kurnia 2005).
Current literature states that TAM is a highly cited model. Fred Davis initially proposed the
framework in 1985 at the Massachusetts Institute of Technology as a doctoral thesis. Chuttur
argues that the universal use of TAM is because the model is grounded in practical effectiveness
and theoretical assumptions (Chuttur 2009). Ever since the model had been proposed in 1985, it
has been revised to include relationships and variables obtained from the theory of reasoned
2.7 Role of TAM in Online Learning
Kandire 2014 indicated that the purpose of TAM in online learning in recent years, students and
institutions have fully agreed that Information Communication Technologies (ICT) can help
improve their performance in a much better way. However, due to the conveniences and
affordance gotten by online learning, consumers now prefer technologies that are mobile to fixed
technologies. This resulted in the increased acceptance and adoption of online learning and the
use of mobile technologies across the different aspects of human endeavors, including education
Consequently, using mobile technologies for learning is now a regular practice and expectation
amongst learners (Rueckert et al.). Rueckert claimed that learners, especially those living in
remote and rural areas, now demonstrates enthusiasm towards the use of mobile technologies as
tools for communication and entertainment. As of result of these developments, researchers in
the area of educational technology seeks to determine if online learning would fit perfectly into
TAM since the model has been adopted in numerous fields of communication technology.
Allen, I. E., & Seaman, J. (2016). Online Report Card: Tracking Online Education in the United
States. Babson Survey Research Group.
Alvarez, I., Espasa, A., & Guasch, T. (2012). The value of feedback in improving collaborative
writing assignments in an online learning environment. Studies in Higher Education,
Arkorful, V., & Abaidoo, N. (2015). The role of e-learning, advantages and disadvantages of its
adoption in higher education. International Journal of Instructional Technology and
Distance Learning, 12(1), 29-42.
Baran, E. (2018). Professional Development for Online and Mobile Learning: Promoting
Teachers’ Pedagogical Inquiry. Second Handbook of Information Technology in Primary
and Secondary Education, 463-478.
Baran, E., & Correia, A. P. (2014). A professional development framework for online teaching.
TechTrends, 58(5), 95-101.
Baran, E., Correia, A.P. & Thompson, A. (2011). Paths to Exemplary Online Teaching: A Look
at Teacher Roles, Competencies and Exemplary Online Teaching. In T. Bastiaens & M.
Ebner (Eds.), Proceedings of ED-MEDIA 2011–World Conference on Educational
Multimedia, Hypermedia & Telecommunications (pp. 2853-2860). Lisbon, Portugal:
Association for the Advancement of Computing in Education (AACE). Retrieved
November 08, 2018 from https://www.learntechlib.org/primary/p/38268/.
Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies & academic achievement
in online higher education learning environments: A systematic review. The Internet and
Higher Education, 27, 1-13.
Bayar, A. (2014). The components of effective professional development activities in terms of
teachers’ perspective. 6, 319–327. doi: 10.15345/iojes.2014.02.006
Barnard-Brak, L., Paton, V. O., & Lan, W. Y. (2010). Profiles in self-regulated learning in the
online learning environment. The International Review of Research in Open and
Distributed Learning, 11(1), 61-80.
Buzzetto-More, N. (2015). Student attitudes towards the integration of YouTube in online,
hybrid, and web-assisted courses: An examination of the impact of course Modality on
Perception. Journal of Online Learning and Teaching, 11(1), 55.
Carril, P. C. M., Sanmamed, M. G., & Sellés, N. H. (2013). Pedagogical roles and competencies
of university teachers practicing in the e-learning environment. The International Review
of Research in Open and Distributed Learning, 14(3), 462-487.
Cavanaugh, J. K., & Jacquemin, S. J. (2015). A large sample comparison of grade based student
learning outcomes in online vs. face-to-face courses. Online Learning, 19(2), n2.
Chen, L. (2010). Web-based learning programs: Use by learners with various cognitive styles.
Computers & Education, 54(4), 1028-1035. doi:10.1016/j.compedu.2009.10.008
Comas-Quinn, A. (2011). Learning to teach online or learning to become an online teacher: An
exploration of teachers' experiences in a blended learning course. ReCALL, 23(3), p.218-
Costagliola, G., Ferrucci, F., Polese, G., & Scanniello, G. (2005). Visual language-based system
for designing and presenting e-learning courses. International Journal Of Distance
Education Technologies, 1(1).
Deming, D. J., Goldin, C., Katz, L. F., & Yuchtman, N. (2015). Can online learning bend the
higher education cost curve?. American Economic Review, 105(5), 496-501.
Desimone, L. M. (2009). Improving impact studies of teachers’ professional development:
Toward better conceptualizations and measures. Educational Researcher, 38(3), 181–
Elliott, M., Rhoades, N., Jackson, C. M., & Mandernach, B. J. (2015). Professional
Development: Designing Initiatives to Meet the Needs of Online Faculty. Journal of
Educators Online, 12(1), n1.
Gregory, J., & Salmon, G. (2013). Professional development for online university
teaching. Distance Education, 34(3), 256-270.
Gordon, H. R. D. (2014). The history and growth of career and technical education in America
(3rd ed.). Long Grove, IL: Waveland Press.
Guasch, T., Alvarez, I., & Espasa, A. (2010). University teacher competencies in a virtual
teaching/learning environment: Analysis of a teacher training experience. Teaching and
Teacher Education, 26(2), 199-206.
Jonassen, D. H., Howland, J., Marra, R., & Crismond, D. (2008). Meaningful learning with
technology. Upper Saddle River, NJ: Pearson.
Kennedy, M. M. (2016). How does professional development improve teaching?. Review of
Educational Research, 86(4), 945-980.
Kolowich, S. (2013). Faculty Backlash Grows Against Online Partnerships. Chronicle of
Higher Education, 59(35), A3-A4.
Kuo, Y. C., Walker, A. E., Schroder, K. E., & Belland, B. R. (2014). Interaction, Internet self
efficacy, and self-regulated learning as predictors of student satisfaction in online
education courses. The Internet and Higher Education, 20, 35-50.
Kyei-Blankson, L., Ntuli, E., & Donnelly, H. (2016). Establishing the importance of interaction
and presence to student learning in online environments. World Journal of Educational
Lane, A. (2019). The impact of technology on the teaching and assessment of ‘systems’
diagrams in two online environmental management modules. Open Learning: The
Journal of Open, Distance and e-Learning, 34(1), 61-77.
Meyer, K. A., & Murrell, V. S. (2014). A National Study of Training Content and Activities for
Faculty Development for Online Teaching. Journal of Asynchronous Learning Networks,
McCutcheon, K., Lohan, M., Traynor, M., & Martin, D. (2015). A systematic review evaluating
the impact of online or blended learning vs. face‐to‐face learning of clinical skills in
undergraduate nurse education. Journal of advanced nursing, 71(2), 255-270.
Moore, J. L., Dickson-Deane, C., & Galyen, K. (2011). e-Learning, online learning, and distance
learning environments: Are they the same?. The Internet and Higher Education, 14(2),
Murray, M., Pérez, J., Geist, D., Hedrick, A., & Steinbach, T. (2012). Student interaction with
online course content: Build it and they might come. Journal of Information Technology
Education, 11, 125-140.
Petrides, L. & Nodine, T. (2005). Online developmental education: Who’s ready?
Community College Journal, 76(2).
Richardson, J. C., Besser, E., Koehler, A., Lim, J., & Strait, M. (2016). Instructors’ perceptions
of instructor presence in online learning environments. The International Review of
Research in Open and Distributed Learning, 17(4).
Riel, J., Lawless, K. A., & Brown, S. W. (2017). Defining and designing responsive online
professional development (ROPD): A framework to support curriculum implementation.
In T. Kidd & L. R. Morris (Eds.), Encyclopedia of instructional systems and
Salehudin, N., Khairuddin, N. N., & Razalek, R. (2013). Online tutorial (i-Learn) usage and
students’ performance: an empirical evidence in Malaysian University/Norzaidi Mohd
Daud, Nasyitah Salehudin, Naura Nadira Khairuddin, Roslinda Razalek and Nuur
Fadhlika Mohd Sani. International Journal of Undergraduates Studies, 2(3), 40-47.
Salmon, G., Gregory, J., Lokuge, D.K, Ross, B., (2015). Experimental online development for
educators: The example of the Carpe Diem MOOC. British Journal of Educational
Technology, 46(3), 543-556.
Salmon, G., Pechenkina, E., Chase, A., & Ross, B. (2017). Designing Massive Open Online
Courses to take account of participant motivations and expectations. British Journal of
Educational Technology, 48(6), 1284-1294. doi:10.1111/bjet.12497
Stickler, U., & Hampel, R. (2007). Designing online tutor training for language courses: A case
study. Open Learning, 22(1), 75-85.
Supovitz, J. A. (2001). Translating teaching practice into improved student performance. In S.
Fuhrman (Ed.), From the capitol to the classroom: Standards-based reform in the states
(pp. 81-98). Chicago, IL: University of Chicago Press.
Tallent-Runnels, M. K., Thomas, J. A., Lan, W. Y., Cooper, S., Ahern, T. C., Shaw, S. M., &
Liu, X. (2006). Teaching Courses Online: A Review of the Research. Review of
Educational Research, 76(1), 93–135. https://doi.org/10.3102/00346543076001093
Tarhini, A., Hone, K., & Liu, X. (2014). The effects of individual differences on e-learning
users’ behaviour in developing countries: A structural equation model. Computers in
Human Behavior, 41, 153-163.
Technical College System of Georgia(TCSG). (2017). End of quarter/semester and end of year
reports (Annual AY 2017 Enrollment Report #ER21). Retrieved from
Technical College System of Georgia, Knowledge Management System
Watson, J., Pape, L., Murin, A., Gemin, B., & Vashaw, L. (2014). Keeping pace with K-12
digital learning: An annual review of policy and practice. Evergreen Education Group.
Yurtseven Avci, Z. & O'Dwyer, L. (2016). Effective Technology Professional Development: A
Systematic Review. In G. Chamblee & L. Langub (Eds.), Proceedings of Society for
Information Technology & Teacher Education International Conference (pp. 2455-2460).
Savannah, GA, United States: Association for the Advancement of Computing in
Education (AACE). Retrieved November 07, 2018