Emerging Trends in Higher Education

A key trend that is emerging in higher education is the use of blended learning designs in the educational delivery platform. This short term trend, likely to impact higher education in the next one to two years, is emerging as a direct result of student demand for immediate access to educational information (Johnson, Adams Becker, Cummins, Estrada, Freeman, & Hall, 2016). Blended learning designs involve integrating and coupling of the facets of online and face-to-face learning which make each successful. Benefits of this approach include independent and self-directed learning, increased channels of communications, significantly improved collaboration amongst students, and the ability for faculty to track student progress and success and tailor learning to specific student needs (Smyth, Houghton, Cooney, & Casey, 2012).

A key technology that is emerging in higher education is affective computing. This is a long term technology implementation, likely four to five years from widespread use. Affective computing is “the idea that humans can program machines to recognize, interpret, process, and simulate the range of human emotions” (Johnson et al., 2016). Affective computing enables platforms to detect and recognize emotional information with passive sensing without interpreting received input (Garcia-Molina, Tsoneva, & Nijholt, 2013). The concept involves extracting meaningful patterns from data using machine learning techniques to process the received modalities. Key pieces of the technology include emotional speech, facial affect detection, body gestures, physiological monitoring, and visual aesthetics (Tao & Tan, 2005). A key potential learning application of this technology is adaptive presentation of material by a computer instructor based upon the modalities of the engaged learner (Asteriadis, Tzouveli, Karpouzis, & Kollias, 2009).

Two key forces impacting this trend and technology are technological and social forces. For the trend of blended learning programs, the technological factors are facilitating factors. Blended learning has demonstrated an effective learning platform since the early 2000s (Singh & Reed, 2001). The technology to build effective technical platforms for blended learning has existed for just as long (Graham, 2006). Recent developments in technology have only increased the benefits and capabilities of blended learning (Wankel, & Blessinger, 2013). Technological factors are also facilitating factors for the technology of affective computing. The need for affective computing technology systems was demonstrated as early as the mid-1990s (Picard, 1995), while the development of technical platforms and systems capable of interfacing with human emotions almost immediately followed (Elliott, Rickel, & Lester, 1997). Recent advancements have demonstrated the capability of affective computing to actively engage in effective human-computer interaction (Su, Lin, Wang, & Huang, 2016). These advancements make technological forces a facilitating force for affective computing.

For the trend of blended learning programs, social factors are also a facilitating force. Recent use of blended learning environments has shown these environments effective from both the student and faculty perspective – both players of the educational systems embraced social interactions facilitated in the online and face-to-face environments of blended learning (Garner & Rouse, 2016). This social acceptance demonstrates the facilitating nature of these forces. However, within affective computing, social factors are potentially reducing factors. There has been a mixed social reaction and impact of the effectiveness of affective computing on a variety of variables, including age and gender (Rukavina, Gruss, Hoffmann, Tan, Walter, & Traue, 2016). While affective computing can recognize and react to emotions, it presently lacks the capability to understand, process, and manage social norms. This inability to grasp the need to have both emotional and social aspects of human-computer interaction may hinder the capability of affective computing to integrate effectively into higher education, resulting in a reducing factor.

What exactly does this trend and technology have to do with secure user behavior? Rather than speaking to the technical controls, which many security professionals and academics alike are familiar with, this trend and technology speaks to administrative controls, which are often overlooked. Although commonly neglected, a key piece of any information assurance and information security program is the implementation of an effective user training, education, and awareness program. The trend of blended learning speaks to the need to tailor and respond to the demands and requirements of users for how their education is delivered. It is not enough to simply conduct classroom training or deploy an online training module. Rather, trends clearly show a blended approach to be most effective at facilitating meaningful interaction (and education) of users. To deploy an effective user training, education, and awareness program, we must deploy a suitable platform which will be capable of facilitating effective education. Clearly, blended learning is emerging as a solution to this problem. The technology of affective computing also demonstrates great promise for the use of blended programs. A common problem of online learning programs is understanding and responding to the emotional needs of the learners. In a classroom, this can easily be gauged with synchronous interaction between instructors and learners, but in an online portion this is often lost. Rather than accept this limitation, affective computing is emerging as a solution. For the online portions of blended learning environments, affective computing could non-invasively engage with users taking the online portion of the blended user training, education, and awareness program and provide many of the benefits of traditional classroom-based education which cannot be deployed due to cost of logistics issues. Both this technology and trend are poised to shape and change the ways users learn, and the time is ripe to consider their use for the way effective user training, education, and awareness programs are deployed to users.

References

Asteriadis, S., Tzouveli, P., Karpouzis, K., & Kollias, S. (2009). Estimation of behavioral user state based on eye gaze and head pose—application in an e-learning environment. Multimedia Tools and Applications41(3), 469-493.

Elliott, C., Rickel, J., & Lester, J. (1997, August). Integrating affective computing into animated tutoring agents. In Proceedings of the IJCAI Workshop on Animated Interface Agents: Making Them Intelligent (Vol. 113, p. 121).

Garcia-Molina, G., Tsoneva, T., & Nijholt, A. (2013). Emotional brain–computer interfaces. International journal of autonomous and adaptive communications systems6(1), 9-25.

Garner, R., & Rouse, E. (2016). Social presence–connecting pre-service teachers as learners using a blended learning model. Student Success7(1), 25-36.

Graham, C. R. (2006). Blended learning systems. CJ Bonk & CR Graham, The handbook of blended learning: Global perspectives, local designs. Pfeiffer.

Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., and Hall, C. (2016). NMC Horizon Report: 2016 Higher Education Edition. Austin, Texas: The New Media Consortium.

Picard, R. W. (1995). Affective computing.

Rukavina, S., Gruss, S., Hoffmann, H., Tan, J. W., Walter, S., & Traue, H. C. (2016). Affective Computing and the Impact of Gender and Age. PloS one11(3), e0150584.

Singh, H., & Reed, C. (2001). A white paper: Achieving success with blended learning. Centra software1.

Smyth, S., Houghton, C., Cooney, A., & Casey, D. (2012). Students’ experiences of blended learning across a range of postgraduate programmes. Nurse education today32(4), 464-468.

Su, S. H., Lin, H. C. K., Wang, C. H., & Huang, Z. C. (2016). Multi-Modal Affective Computing Technology Design the Interaction between Computers and Human of Intelligent Tutoring Systems. International Journal of Online Pedagogy and Course Design (IJOPCD)6(1), 13-28.

Tao, J., & Tan, T. (2005). Affective computing: A review. In Affective computing and intelligent interaction (pp. 981-995). Springer Berlin Heidelberg.

Wankel, C., & Blessinger, P. (2013). Increasing student engagement and retention in e-learning environments: Web 2.0 and blended learning technologies (Vol. 6). Emerald Group Publishing.

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