TECHNOLOGY ENABLED TEACHING AND LEARNING PROCESSES:
A STUDY OF PERCEPTION AND USAGE
Kavita Pathak Professor of Marketing,
Jaipuria Institute of Management,Lucknow
IntroductionTechnology and its perception among faculty members
Internet-enabled self-learning is fast developing to be the high-growth area in higher education (Elaine Allen & Seaman, 2011). Introducing digital technology to teaching and learning processes has posed multi-pronged challenges for the teaching fraternity. Inadequacy of technology proficiency, and failing to engage the highly tech-savvy students in a physical class based teacher centric learning ambience is creating a divide between the teachers and students. The institutions are investing in necessary technology ambience, however, the end user ease (student usage) of technology in teaching and learning; majorly depends on the how efficiently do the teachers incorporate technology in their teaching and learning processes. An important question institutions would need to answer is why bring technology to the teaching and learning process? Also in what ways does it enable the process? Absorbing, processing and analyzing of content is difficult all at the same time. A lot of value in class room is lost due to restrictive ambience; structured delivery, lack of playfulness in curriculum and mainly due to the teachers steering the course of events during a class in a well-planned manner. This is necessary to deliver courses on schedule, but is it time to make content secondary and to make the teacher take the role of facilitator and navigator; while enabling students to learn more independently and interdependently.
The increasing role of technology in class room and student engagement is necessitated both for reasons of effectiveness as well as managing capacity. An
additional dimension is that of cost economy. The new age learners operate and learn in an ecosystem where man to man interactions are facilitated in a man to machine ambience. The proficiency in technology usage is on the rise and a substantial period of the day is spent by these learners online. The dependence on teachers as sole source of information is on the decline. Therefore technology adoption by institutions as well as individual faculty members is not an option anymore; it is increasingly becoming a lifeline for those empty benches in the class room. The ecosystem of new age learner had advanced much beyond the preparedness of institutions of education. Advancements in communication technology and social media, present both a challenge as well as an opportunity to them. It is entirely up to the institutions to leverage the technology at hand in provider greater access of education to all and improving the cost efficiency to the institution (Salmi, 2000; Tremblay, Lalancette&Roseveare, 2012)
The obvious advantage of technology usage is increased access to a wider cohort in diverse geographical and time locations. Expanding presence and reaching out to existing students beyond the class room; while engaging with potential and new students outside the classroom is gaining significance. These advantages bring with them some challenges. For the faculty members this implies significant changes and maneuvering in their teaching and learning approach; both at post graduate as well as under-graduate level. The structure of the classroom, average duration of classes, customized to highly standardized courses often delivered online, flipping the classes and using the class time in seminars,
ARTICLE INFO
Article History Received 4th May 2017
Received in revised form 4th May 2017
Accepted on 29th May 2017
Corresponding author.
and close interactions as well as spending time on individual student projects are some of the significant changes on their way in(Schuetze & Slowey, 2002).
‘As a result of applying ICTs in university administration, a dynamic new shift occurred in higher education. Large and complex institutions could be created (e.g. the UK Open University with 200,000 students) and function in a highly efficient and user-friendly manner’ (Balasubramanian, Clarke-Okah, Daniel, Farreira, Kanwar, Kwan, Mallet, Umar & West, UNESCO Report, 2009:26). Online learning is helping plug this gap (Swan, Garrison & Richardson, 2009; Anderson, Boyle &Raine, 2012). The cost economy generated through technology enabled learning ambience are an additional reason for the institutions to invest in technology. In recent years a surge in technology enabled teaching and learning solutions, has placed technology adoption on top of the agenda in academic institutions. While the applications are developing; driving up adoption and usage of such solutions is emerging as a major area of concern. The hitherto unstructured, opaque world of academics is gradually turning transparent and evidence based. While some readily embrace change; others are unstructured in adoption due to various extraneous and intrinsic factors. Technology and its perceptions among faculty members have been explored at various levels. Studies in the past have attributed the rates of technology adoption to the nature of innovation itself i.e. its complexity or relative advantage as an example (Rogers, 1983; Sappery & Relf, 2010). Also it is found that perceptions formed by faculty members influence the adoption rate (John, 2015) as well the effectiveness of such interventions (Johnson, Miesenwski, Kuhlemeyer, Isaac, &Kzykowski, 2012).
Lag in adoption of technology among faculty members has a snow balling impact among students; is the instructor is not able to support technology enabled pedagogy, the student is most likely not to learn in a technology setting.
2. Lit Review and hypothesis development Literature on technology adoption in teaching and learning is presented below under three broad thematic categories: first, perceived utility of technology and its usage intensity; second, user satisfaction with technology and its usage intensity; and third, user characteristics and their impact on usage.
Perceived Utility and Satisfaction with usage experience:
Potential usage of information technology are broad based and pervasive, including academic planning, research for class preparation, facilitating pre-recorded/in-class recording of lecture content for accessing as a later date (Franciszkowicz, 2008, Copley, 2007). Video-based additional instruction. Learning in virtual menu driven self-learning environment as well learning in technology enabled in-class ambience is found to be more engaging. Literature suggest that faculty members as key stakeholders in technology implementations have little or no role in the decisions related to technology implementations (Smith, Ling & Hill, 2006; Laurillard, 2008). Yet faculty members and their buying in of enterprise’s technology interventions is critical. Unlike machine based environments, in a class based interactive setting, understanding technology implementations and the factors that drive their usage are critical in achieving the outcome desired from them (John, 2015). Studies in the past suggest that technology is often perceived by faculty members as a solution looking for a problem (Laurillard, 2008:8). Low levels of faculty proficiency in developing and implementing technology based teaching and learning interventions; and their perception of the infrastructure as supportive/ non-supportive work as barriersin diffusion of technology solutions (Butler & Sellbom, 2002; John, 2015). Time taken to learn a new technology solution, and the infrastructure support available emerged as a barrier to adoption in a study conducted at a US university (Butler & Sellbom, 2002). As per Rogers (1983) an innovation’s perceived utility is best captured in its five characteristics i.e. extent to which potential users perceive an innovation to be significantly different from already existing solutions (relative advantage); the degree of continuity the users perceive is usage (compatibility); the perceived complexity involved in using the innovation (complexity); the extent to which the outcome of usage can be demonstrably obvious to others (observability) and the degree to which the user finds it easy to play with the innovative solution (trialability). Technology in higher education teaching-learning rests on same principles, i.e. so long as an academic feels reassured by the scope of an innovation, and finds it supportive of her current pedagogical approach; the adoption and consequent usage will be swift (Moore & Benbasat, 1991).
major dichotomy in the role of a teacher created by technology is separation of the knowledge resource owned by faculty members and medium through which it is delivered. Following the principle of ‘inseparability’ as an essential service characteristics teachers and teaching have traditionally not been viewed as separate in time, space and delivery. With the advent of technology and interactive solutions; an important question is has the paradigm changed? Overall, it is established that technology solutions a re drive n by pe d ag ogy (L a ud rilla rd , 2 0 07 ; Clark,1983; Sappery& Relf, 2010). ‘Without academic teacher’s passion for, and enjoyment of what they are doing, there can be no excellence in teaching and learning’ (Sappery & Relf, 2010: 2). Studies in the past have reported perceived barriers to adoption such as equipment failure, time needed for learning new technology, and the time it takes to learn new technology as strong barriers to technology adoption and usage in teaching and learning (Johnson et al, 2012). In view of the twin arguments that pedagogy drives t echn ology adoption; and that desired outcomes from technology implementation are possible only when the usage of such interventions is driven by positive faculty perception of technology and its scope of adding value to the curriculum. Therefore it is proposed that:
HI: Perceived utility and the satisfaction with usage experience are positively related.
Usage intensity and user satisfaction:
The Technology Adoption Model (Davis, 1989) and the innovation diffusion of high-tech innovations (Moore, 1991) are advancements on the innovation diffusion curve proposed by Rogers (1983). It is well established that unlike low or no technology embedded innovation, the technology intensive innovations, depend on the adopters perception of relative ease in usage, and evidence of superior performance for faster adoption. As per the innovation diffusion theory late adopting customers of a technology based innovation are characteristically different from early adopters (R ogers 19 83, Moore, 199 1).B rea kdow ns a nd malf unctions not only disrupt usag e but also contribute to user dissatisfaction, which in turn impacts the usage intensity. Studies in the past have also reported a negative relationship between technology integration barriers and technology adoption, as the barriers decrease, technology adoption increases (Cherry, 2013). Adoption of an
in nova tion is at trib ute d to five innovation characteristics i.e. relative advantage, complexity, compatibility, observability and trialability (Rogers, 1983; Moore & Benbasat, 1991). In higher education particularly, the debate on the role of technology extends to the capacity of technology as a standalone solution to drive learning; and it has been argued that technology is in effect little more than the ‘grocery delivery truck’ where faculty is in the driving seat; suggesting that medium cannot bring effectiveness to teaching and learning, unless steered by the ‘academic teachers’ passion for, and enjoyment of what they are doing’ (Sapper & Relf, 2010). There is an increasing pressu re on a cade mics to adopt tech nology interventions to their teaching and learning processes. To an institution it offer twin obvious advantages of offering blended learning solutions to tech-savvy gen next students thus creating improved time and place convenience to facilitate their learning process. Also, an ICT enabled learning ambience offers various cost advantages such as marketing pre-recorded teaching content as packaged product to remote and overseas location based students thus expanding their franchise to a larger catchment and across geographies (Johnson et al, 2012). Secondly it also offer them a chance to replace an ongoing cost of faculty salaries with prepackaged content (variable expense). A major issue associated with technology usage is the perceived reliability of a solution (Butler & Sellbom, 2002). Usage intensity is embedded in the extent to which the technology to be adopted is viewed as uncomplicated. Also it has been found that as the technology available for teaching increases, teacher’s level of technology adoption increases (Cherry, 2014). The significance of creating satisfying technology solutions, play a vital role in adoption by faculty members. Technology per se, has limited power to drive effective usage, unless coupled with professional development of wise designers, educators and learners. User satisfaction and adequacy of comprehending the benefits of technology interventions, thus play an important role in driving up the usage of technology; there it is proposed that:
H2: User satisfaction and intensity of technology usage are positively related.
(Moore, 1991; Easingwood & Harrington, 2002). An element of user skepticism intervenes, the adoption process. The fear, uncertainty and doubt perceived by target users’ act as a barrier for smooth diffusion of technology interventions (Mohr&Shooshtari, 2003). As per Rogers’ classification of adopter categories the late majority and laggards consist mostly of traditionalists, who prefer status-quo due to low resources (Rogers, 1983). In an advancement Moore (1991) defines mainstream market for technology products as consisting of technology averse users; who due to disinterest, associated risk or lack of proficiency avoid adopting new technology innovations. Research in the past has established that ‘firms with a higher share of younger employees are more likely to adopt new technologies and the older the workforce is, the less likely is the adoption of new technologies’ (Meyer, 2008:1). It has also been found that male faculty members with less usage of ICT, perceive more barriers in adoption than females (Senaidi, Lin & Poirot, 2009). The literature on the so-called age-biased technological change using firm-level data finds that technological progress negatively impacts the share of older workers or older low-skilled workers (Behaghel & Greenan 2007, in Meyer, 2008:3).
3. Methodology
The objective of the study is to understand how user’s perception of technology and satisfaction with the technology interventionsshape their usage of digital technology enabled teaching and learning processes. The study is based on the data collected from faculty members of a private B-school in India, across four locations. Since the study focused upon understanding the faculty member’s perception of, satisfaction with and usage of digital technology; andsince the interventions were already underway; a quantitative research focus was deemed appropriate. Therefore a survey approach was adopted. Studies in the past have relied upon survey as a robust method of understanding the teaching and learning phenomenon in case of student based research as it is less time consuming, convenient and relatively easy to administer.Web-based surveys offer advantages such as direct transfer of data to a database, better control, and less scope for transcription related or linguistic errors (Andrews, Nonnenecke&Preece, 2003).
A survey instrument was developed with the help of items generated through review of extant literature and depth interviews with 5 faculty members. An expert review of the survey instrument was undertaken by 3 academicians who were familiar with the technology
interventions made. The instrument was suitably refined on the basis of expert review. All one hundred members of faculty were sent out invitations to participate the survey, explaining the purpose of the study and assuring that the information shared will be kept confidential. The link to the survey was made available at the dashboard of learning management system of the institute, and the link was live for four days. This duration for the collection window in an online survey is considered as adequate for meaningful collection of data (Fricker&Shonlau, 2002). Responses were received 65 faculty members in the window of four days for which the survey link was live. Thus the survey had a 65% response rate. In comparison to previous researches on faculty perceptions of technology enabled teaching and learning, this is a fairly robust rate of response.
4. Measures
The measures for the study were developed with the help of literature review and depth interviews conducted during the instrument development phase. Three focal variables in the study whose measures were developed are Usage Intensity, Perceived Utility, and User Satisfaction. In addition three control variables i.e. age, technology proficiency and were also used.
Perceived Utility:perceived utility of teaching and learning technology as a measure has its roots in the seminal work of Rogers (1983); where he defines ‘relative advantage’ i.e. the extent to which an innovation is perceived to be better than its predecessors. The perceived utility was captured through 10 items generated through literature review, and existing technology interventions. The items were measured using a five point scale anchored 1 (strongly agree) and 5 (strongly disagree). The Cronbach’s alpha for the construct was 0.685, which is very close to the prescribed cut-off of 0.70; the measure is thus reliable.
Usage intensity:
prescribed cut-off of 0.70; the measure is thus reliable. Satisfaction: satisfaction was measured in this study as a composite index of four different aspects of technology interventions, which included learning management system, video lecture capture technology, use of videos, and use of databases. Satisfaction was measured on a five point scale anchored 1 (very dissatisfied) and 5 (very satisfied).
Control variables:
In addition to the above measures, two control measures i.e. age of the user in years, and experience of teaching in years were also included in the two regression models. Composite scores were used in the analysis for the scale based items.
5. Data Analysis and results
The data was collected through an online survey, as reported in section 3. The response rate for the study was 65%, which is high for a survey of post graduate level academicians. The data was cleaned following the prescribed procedures. The data was reviewed for any missing value. However, all responses were complete in all respects and therefore missing data was not considered an issue in the study. Since the data was collected in a span of four days, non-response bias was assessed with the help of comparing early and late responses. No statistically significant difference was found between responses of early and later responding samples. Also the skewness and kurtosis values were reviewed for all constructs; and the value were found to be within the acceptable thresholds (refer Table 2). Similarly, multi-collinearity was tested by using the collinearity diagnostic test and the VIF for the constructs were much below the acceptable threshold of 10 (within 1.5 in all cases); therefore multi-collinearity is not an issue in this study. The summary sample profile is presented in Table 1. As can be noted the sample consisted of female faculty members predominantly (58.5%) followed by male (41.5%). Also the percentage of faculty members in 35-44 years age bracket was the highest (63.2%); while those in age brackets 25-34, 45-54, and 55+ were 13.5%, 21.5% and 0.015% respectively.
6. Discussion
The findings support the hypothesized relationship between satisfaction with technology usage experience and intensity of usage. Extant literature also lends support to the characteristics of a technology innovation in teaching and learning ambience, and faculty members felt ease in using technology significantly effects their rate of adopting technology enabled teaching and learning solutions. Studies in the past have also established that inadequacy of infrastructure and class-room support services act a barrier to increased usage of technology by faculty members. These findings point towards the need of managing the technology usage experience, adequate training and infrastructure support for innovation technology based teaching and learning interventions to gain quick acceptance. However another interesting finding was that the control variable of experience was not found to have a significant relationship with usage; while age had a significant negative relationship. This strongly point towards the role of age distribution of
perceptions. The findings suggest that the felt satisfaction with technology is based apart from other factors, on its perceived utility. It is found to be a moderately strong relationship. Studies in the past have tested this relationship, and have found that key determinant of swift adoption of technology are its reliability, user’s existing level of knowledge about technology, belief that technology improves or enhances learning among others (Johnson et al, 2012). In another set of findings, Cherry (2004) reports a negative relationship between technology integration barriers and technology adoption. Al-Senaidi, Lin & Poirot (2009) in a study of barriers to adopting technology in teaching and learning find disbelief in ICT benefits as a key barrier to adopting technology. This is a problem area as ‘without academic teacher’s passion for, and enjoyment of what they are doing, there can be no excellence in teaching and learning’ (Seppy & Relf, 2010:2). The wider academic perception discussed earlier that technology is a ‘solution looking for a problem’ (Laurillard, 2008:8); needs to be actively managed through training, infrastructure support and even faculty boot camps (Johnson, et al, 2012) to reduce the anxiety associated with technology adoption.
7. Limitations and conclusion
The study makes a contribution to literature by testing two regression models. These models were developed based on an extensive review of literature, and they also included two control variables. Section 5 reported the findings of data analysis while section 6 presents the implications of findings. The study is pertinent in view of increasing level of technology implementations in teaching and learning; and the findings while lending support to extant literature provide scope for future research. However the study is limited in few respects. First, while the size of the sample was adequate to carry out a meaningful study, it was small from in comparison to other studies in similar contexts. In future studies could test the two models using a larger sample size. Second, the study was conducted among the post graduate teachers in a business school, and thus findings may be limited in their scope of generalization to larger population of higher education institutions. Future researches could test the models in different types of higher education institutions. Third, the study is cross sectional in nature; this however is a common practice in technology implementations related studies in higher education. However a more effective study of the outcomes of technology interventions can be conducted by deploying a longitudinal research design,
particularly when the objective is study of intervention effectiveness. Future studies could focus on carrying out a longitudinal research or using experimental design.
However, despite the above limitations, the study makes a significant contribution by establishing that effectiveness of technology usage; and the extent of usage itself need to be driven by active participation of the implementing institution. Particularly when technology is perceived by most academics as a solution chasing the problem. The perceived utility of technology solutions is necessary to drive up its usage and broad based adoption. This issue gains even stronger ground in case of older age faculty members. Therefore technology implementations need to be viewed as ‘grocery trucks’ where the medium should not be assigned more significance than the ‘driver’. The onus of delivering a wholesome teaching and learning experience still rests with the individual faculty members, however, if ably facilitated and supported by the implementing institutions; these academics can potentially adopt new technology based teaching and learning solutions sooner rather than later.
References
Al-Senaidi, S., Lin, L., & Poirot, J. (2009). Barriers to adopting technology for teaching and learning in Oman. Computers & Education, 53(3), 575-590.
Allen, I. E., & Seaman, J. (2011). Going the distance: Online education in the United States, 2011. Sloan Consortium. PO Box 1238, Newburyport, MA 01950.
Anderson, J. Q., Boyles, J. L., & Rainie, L. (2012). The Future Impact of the Internet on Higher Education: Experts Expect More Efficient Collaborative Environments and New Grading Schemes; They Worry about Massive Online Courses, the Shift Away from On-Campus Life. Pew Internet & American Life Project.
Andrews, D., Nonnecke, B., & Preece, J. (2003). Electronic survey methodology: A case study in reaching hard-to-involve Internet users. International Journal of Human-Computer Interaction, 16 (2), 185-210.
Behaghel, L., and N. Greenan (2007). Training and Age-Biased Technical Change. LEA Working Paper 0705. Butler, D. L., & Sellbom, M. (2002). Barriers to adopting technology. Educause Quarterly, 2, 22-28.
Balasubramanian, K., Clarke-Okah, W., Daniel, J., Ferreira, F., Kanwar, A., Kwan, A., Lesperance, J., Mallet, J., Umar A. & West, P. (2009). ICTs for Higher Education, Background paper from the Commonwealth of Learning UNESCO World Conference on Higher Education, Paris. Clark, R. E. (1983). Reconsidering research on learning from media. Review of educational research, 53(4), 445-459.
Copley, J. (2007). Audio and video podcasts of lectures for campus-based students: production and evaluation of student use. Innovations in education and teaching international, 44(4), 387-399.
Davis, F. D., (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, September, 319-339.
Easingwood, C., &Harrington, S. (2002). Launching and re-launching high technology products. Technovation, 22(11), 657-666.
Franciszkowicz, M. (2008). Video-based additional instruction. Journal of the Research Center for Educational technology, 4(2), 5-14.
Fricker, R. D., &Schonlau, M. (2002). Advantages and disadvantages of Internet research surveys: Evidence from the literature. Field methods, 14(4), 347-367.
John, S. P. (2015). The integration of information technology in higher education: A study of faculty's attitude towards IT adoption in the teaching process. Contaduría y Administración, 60, 230-252.
Johnson, T; Wisniewski, M.A.; Kuhlemeyer, G.; Isaacs, G.;&Krzykowski, J. Technology Adoption in Higher Education: Overcoming Anxiety through Faculty Bootcamp, Journal of Asynchronous Learning Networks, 16 (2), 63-72
Laurillard, D. (2008). The teacher as action researcher: Using technology to capture pedagogic form. Studies in Higher education, 33(2), 139-154.
Meyer, J. H. (2008). Threshold concepts within the disciplines. Sense Publishers.
Mohr, J. J., &Shooshtari, N. H. (2003). Introduction to the special issue: marketing of high-technology products and innovations. Journal of Marketing Theory and Practice, 11(3), 1-12.
Moore, G. (1991). Crossing the chasm: How to win mainstream markets for technology products.Harper Business, New York, NY.
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information systems research, 2(3), 192-222.
Rogers, E.M., 1983, Diffusion of Innovations, 3rd edition, The Free Press, New York, NY, USA.
Salmi, J. (2000). Tertiary education in the twenty-first century: challenges and opportunities. In IMHE General Conference, Paris, France (Vol. 11713).
Sappery, J. & Relf, S. (2010). Digital Technology Education and Its Impact on Traditional Academic Riles and Practices. Journal of University Teaching and Learning Practice, 7 (1), 3-17.
Schuetze, H. G., &Slowey, M. (2002). Participation and exclusion: A comparative analysis of non-traditional students and lifelong learners in higher education. Higher education, 44(3-4), 309-327.
Smith, A., Ling, P., & Hill, D. (2006). The Adoption of Multiple Modes of Delivery in Australian Universities. Journal of University Teaching and Learning Practice, 3(2), 67-81.
Swan, K., Garrison, D. R., & Richardson, J. (2009). A constructivist approach to online learning: the Community of Inquiry framework. Information technology and constructivism in higher education: Progressive learning frameworks. Hershey, PA: IGI Global.
