Trends in Workplace Training
Learning About Learning
Amy DuVernet and Tom Whelan
Training Industry, Inc.
As we’ve discussed in previous columns, the overlaps between corporate learning and development (L&D) and I-O are as common as they are obscured. That’s why this column ostensibly exists, to (hopefully) help I-Os gain some context for conversations with our kinfolk in L&D. To that end, in the next two installments of Learning About Learning, we’re focusing on the trends in training from both L&D and I-O perspectives.
In doing so, we’ll highlight areas that overlap and those that are misaligned in terms of focus. Sometimes, the “hottest new thing” in L&D is an approach to learning that I-Os have been researching for over a decade. Other times, the latest and greatest fashions in L&D don’t even show up on the I-O radar. But we’re stating the obvious here; the scientist–practitioner gap in all its forms isn’t exactly breaking news. Two of the most salient points of departure are the frame through which the endeavor of training is interpreted and the level of analysis of greatest interest. I-Os are interested primarily in learner reactions and behavior, and how people’s attitudes and personal characteristics relate to elements of the design and delivery of training across organizational contexts. The objective is to understand what makes the interaction between learners and training content most effective, with the implication that such insights can be incorporated into applied contexts to drive organizational effectiveness as part of a larger system of recruitment, selection, succession planning, and so on. On the other hand, those in corporate L&D are mostly interested in the organizational results of training initiatives and what contributes to return on investment and lower training costs. The objective here is to understand what drives outcomes at the organizational level through delivering a “best-in-class” training experience (or at least attempting to) that incorporates the latest market intelligence insights and learning tools. So, the end goal is similar on both sides of the fence, but the path taken to understanding and improving training can manifest very differently. Accordingly, the pattern of trends between I-O and L&D reflect these differences in perspective, but as we will hopefully illustrate over the course of this two-part column, there are many similarities and much to inform both sides about the cumulative training landscape. (Yes, we grossly oversimplified the dichotomy above, we know that exceptions abound.) In this column, we’ll begin by exploring trends in training from the corporate L&D perspective.
What’s Trending in the Corporate L&D World?
To get a feel for the trends impacting L&D practitioners, we reviewed the 2017 trend predictions of numerous L&D professionals and organizations that have some of the largest footprints in the world of corporate L&D. We also conducted a text analysis of four major L&D conference programs: ATD’s 2017 International Conference and Expo (ICE), CLO’s 2016 Symposium, The eLearning Guild’s 2017 DevLearn, and Training Industry’s 2017 Conference and Expo (TICE). Table 1 (click here to view) presents a summary of both. In reviewing these trends, four dominant themes emerged:
- the need to customize training content and experiences to meet individual learners’ needs;
- an emphasis on accommodating changes in learners’ expectations;
- new technologies driving innovations; and,
- the use of multiple modalities to deliver training.
These trends make it clear that the landscape of learning is changing and that these changes are primarily driven by innovations in technology and the expectations with which workers approach learning. We’ll highlight some of these trends below, providing information about relevant research and the role that I-Os can take in understanding their impact on learner and business outcomes.
Accommodating Changes in Learner Expectations
Today’s workers are exposed to information from a multitude of sources. Answers to their questions are available at the tips of their fingers via Google and other search engines. Learners are empowered to solve their own problems and fill their own knowledge and skills gaps using these tools. Moreover, their interactions with social media sites and other digital platforms have led them to expect engaging and entertaining experiences.
Thus, they bring with them expectations, preferences, and habits that impact the way they learn (or, at least, the way they think they learn). To accommodate these expectations, training departments are moving toward more demand-based, participant-driven learning initiatives that allow learners to access content on demand at the time of their need. They’re also incorporating delivery mechanisms designed to capture learners’ attention such as microlearning, gamification, and storytelling into their training programs and portfolios to facilitate learning in spite of the many other distracting factors vying for learners’ attention.
Microlearning breaks content into small learning objects, allowing learners to easily hone in on what’s most relevant as they need it (Harward, 2015). Providing content in smaller, easily consumed chunks seems intuitively beneficial, based on the well-worn concept of spaced versus massed practice (e.g., Garrett, 1940). But although proponents extol the value of microlearning for providing just-in-time training (Patten, 2016), we know of no published research demonstrating its positive impact in comparison to other, more traditional approaches (e.g., longer programs that deliver multiple learning objects at a time). Although we located one empirical study investigating the optimal size of microlearning objects (i.e., smaller is better; Matthews, Hin, & Choo, 2014), research is certainly needed to identify the necessary conditions for achieving positive learning outcomes while better explicating the impact of both content type and the delivery modality.
Training organizations are also incorporating various engagement mechanisms, such as gamification and storytelling, to create a rich user experience that’s captivating and mimics their interactions with other popular platforms. Gamification applies gaming mechanics to training contexts in order to motivate learner interaction (DuVernet & Popp, 2014). Unlike microlearning, gamification has received a sizable and growing attention within the research literature. Research has demonstrated the positive impact of gamification on learner reactions (e.g., Taylor, 2014) and motivation (e.g., Landers, Bauer, & Callan, 2015), and continues to investigate the circumstances under which these positive outcomes are realized (e.g., Landers & Armstrong, 2015). Interested readers are directed to an in-press issue of Computers in Human Behavior focused solely on gamification.
Storytelling paints a vivid picture, enhancing learners’ connections with the trained material, with the goal of increasing their retention of training knowledge and skills (Harward, 2015). The objective is to illustrate applications or recount past successes or failures through business-relevant narratives, thereby grounding an abstract concept in a concrete example. Although anecdotal evidence of its effectiveness abounds (e.g., Beigi, 2014), research in this area has generally focused on storytelling within organizations (e.g., Boje, 1991) rather than as a specific training technique. Research is needed to understand best practices for integrating this technique into trained content; I-O can add to this area by facilitating this effort.
The shift in learner expectations has also led organizations toward strategies for customizing content to meet individual learner’s needs. Training organizations are adopting content curation techniques to sift through the sea of available content and provide direction in terms of the training opportunities that best address the types of skills and knowledge learners need to perform on the job and they are doing this in a more personalized way. Here we can bring our expertise in work analysis; training professionals have been playing the role of content curator for a while, but how well is that content matched to the demands of the role and the knowledge and skills of the learner? We have the opportunity to assist our L&D counterparts in this effort.
Adaptive learning uses algorithms to personalize content based on individual learner responses, so that each individual receives only the content he or she needs in order to acquire necessary skills and knowledge and is not required to consume content that doesn't address those needs or that he or she already possesses (Harward, 2016). Although some empirical work has examined the technical links inherent in adaptive learning (e.g., Hou & Fidopiastis, 2017; Pérez-Sánchez, Fontenla-Romero, Guijarro-Berdiñas, 2016), there are a great deal of unanswered questions about the impact of adaptive learning and best practices for developing and implementing the technique. I-Os have the opportunity to study this trend through our unique lens to consider the interplay between learner perceptions, motivational mechanisms, and diverse learning outcomes (e.g., time savings, job satisfaction, self-efficacy).
Another trend toward customization comes in the form of learning libraries, which training organizations are using to offer courses across a broad spectrum of topics. Using these diverse offerings, learning practitioners can chose to design targeted curriculums based on the specific knowledge and skills desired or place the learner in the driver’s seat in terms of choosing courses to consume (Harward, 2016). Here work is needed to provide guidance on curation, including contextual variables to consider (e.g., job complexity, organizational culture) and the impact of individual characteristics on consumption patterns. The dynamics of learner interactions with these learning libraries are ripe for investigation and could provide insights about the learners themselves. For example, Xerox’s learning platform tracks content consumed by learners and uses this information to identify high potential employees (Hearns-Smith, 2014); the extent to which this information is an accurate gauge of future outcomes is a worthy avenue for future research. Although learning departments have already begun implementing initiatives to provide learners with options for consuming training, it is yet to be seen whether and who will choose to pursue these options and if the provision of such latitude leads to greater learning outcomes.
Advances in Technology
Many of the aforementioned trends are also driven by advances in technology that equip L&D professionals with enhanced features and tools. For example, machine learning and automation hold the promise of enabling training organizations to personalize user experiences with less manual input into appropriate learning paths. Both have received quite a bit of attention in the popular press recently (e.g., Magnacca, 2017; Smith, 2016) but are still in their infancy in terms of empirical guidance on their most appropriate use in training scenarios. Here our expertise in statistical methodologies gives I-Os an edge in studying and fine tuning the application of these technologies. As Landers (2017) explained, many of the concepts and techniques utilized in machine learning have direct parallels to those of our statistical methods.
Virtual and augmented reality have also garnered a great deal of buzz as methods of immersing the learner in training content (e.g., Duqette, 2016). Unlike some of the other trends, a body of research has already been conducted and generally points to positive outcomes (e.g., Cohen-Hatton & Honey, 2015; Ke, Lee, & Xu, 2016; Mitchell et al., 2011). However, the costs associated with utilizing virtual and augmented reality techniques in training have been largely prohibitive since their inception. As these tools become more accessible, our role should be to translate the research already conducted to ensure these techniques are matched to appropriate training purposes (e.g., not implemented simply for the sake of making training “cool”) and incorporated in ways that feel natural and enhance learning.
Multiple Modalities for Training Delivery
Finally, advances in technology have also made the choice of delivery modality more complex. Just in the past 10 years, the options for computer-mediated training have exploded, and cross-platform compatibility (e.g., phones, tablets, personal computers) has increasingly become a necessity rather than a “nice to have.” The use of multiple and blended delivery modes, such as mobile, video, and simulation-based training, allows learners to consume information via the platform they prefer. The ability to deliver training content through an assortment of technologies has provided organizations with a vehicle for delivering training as learners need it, regardless of their location, and provided mechanisms for reinforcing content learned in more formal arenas (e.g., instructor led training classrooms; Harward, 2016). Although research has investigated the match of different modalities and combinations of modalities with the knowledge and skills trained (e.g., Arthur, Bennet, Edens, & Bell, 2003), work is needed to understand the optimal number and combination of these commonly used delivery modalities. This is particularly true given the majority of organizations report that they’re already using between three and six modalities to deliver training (Training Industry, 2016).
I-O researchers and practitioners have the opportunity to apply our characteristic rigor and methodology to investigate (or continue the research on) whether or not these trends live up to their hype. Hopefully this installment of our column has helped to shed light on the ways that we can do this by working with L&D professionals to realize the promises of popular trends. If you’d like to discuss these or other trends further, we hope you’ll join us to continue our discussion of training trends at the 2017 SIOP conference in Orlando, where Amy and Tara Behrend will be hosting a community of interest discussion on L&D trends on Thursday at 1:30 p.m. Stay tuned for our next column, which will discuss trends in training research from the I-O perspective; taken together, we hope these two columns will provide a pulse on what’s happening in the world of training and an understanding of how we can better bridge both the practitioner–academic disconnect and the gap between I-Os and L&D professionals.
Arthur, W., Jr., Bennett, W., Jr., Edens, P. S., & Bell, S. T. (2003). Effectiveness of training in organizations: A meta-analysis of design and evaluation features. Journal of Applied Psychology, 88, 234–245. http://dx.doi.org/10.1037/0021-9010.88.2.234
Beigi, M. (2014). Using fictional stories to facilitate training and development. Human Resource Development International, 17, 491-496.
Boller, S. (2017a, January 10). Seven 2017 learning trends: Novel or norm? [Blog post]. Retrieved from http://www.bottomlineperformance.com/seven-2017-learning-trends-novel-or-norm/
Boller, S. (2017b, January 10). Tired of trends? Five 2017 training realities. [Blog post]. Retrieved from http://www.bottomlineperformance.com/tired-of-trends-five-2017-training-realities/
Boje, D. M. (1991). The storytelling organization: A study of story performance in an office-supply firm. Administrative Science Quarterly, 36(1), 106–126.
Cohen-Hatton, Sabrina R., & Honey, R. C. (2015). Goal-oriented training affects decision-making processes in virtual and simulated fire and rescue environments. Journal of Experimental Psychology: Applied, 21(4). http://dx.doi.org/10.1037/xap0000061
Drewitt, T. (2017). 7 learning technology trends for 2017. Inside Learning Technologies & Skills, 61, 121-122. Retrieved from http://viewer.zmags.com/publication/c492d1b9#/c492d1b9/1
Duqette, D. (2017, January 25). Forecasting success: Learning trends for 2017. [Blog post]. Retrieved from https://www.gpstrategies.com/blog/forecasting-success-learning-trends-2017
DuVernet, A., & Popp, E. (2014). Gamification of workplace practices. The Industrial Organizational Psychologist, 52(1), 39-44.
Engle, S. 6, February 2017). Workplace predictions for 2017. [Blog post]. Retrieved from https://trainingmag.com/workplace-predictions-2017
Garrett, H. E. (1940). Variability in learning under massed and spaced practice. Journal of Experimental Psychology, 26(6), 547-567.
Harward, D. (2016, November/December). Key trends for 2017: Innovation in educational technology. Training Industry Magazine, 25-28. Retrieved from http://www.nxtbook.com/nxtbooks/trainingindustry/tiq_20171112/#/24.
Harward, D. (2015). Key trends for 2015: Transforming the training the function. Training Industry Magazine, Winter, 28-32. Retrieved from http://www.nxtbook.com/nxtbooks/trainingindustry/tiq_2015winter/#/28
Hearns-Smith, K. (2014, May). Delivering capability, value & growth through leadership development programs. Presented at the annual Training Industry Conference and Expo of Training Industry, Inc. Raleigh, NC.
Hou, M. & Fidopiastis, C. (2017). A generic framework of intelligent adaptive learning systems: From learning effectiveness to training transfer. Theoretical Issues in Ergonomics Science, 18(2), 167-183. http://dx.doi.org.prox.lib.ncsu.edu/10.1080/1463922X.2016.1166405
Johnson, D. (2016, November 1). A look ahead at L&D 2016/2017: Invisible, contextual & systemic. [Blog post]. Retrieved from http://blog.bersin.com/a-look-ahead-at-ld-2016-2017-invisible-contextual-systemic/
Kapp, K. (2016, February 3). 5 learning tech trends to watch in the next 5 years. [Blog post]. Retrieved from https://www.td.org/Publications/Blogs/Learning-Technologies-Blog/2016/02/5-Learning-Tech-Trends-to-Watch-in-the-Next-5-Years
Ke, F., Lee, S., & Xu, X. (2016). Teaching training in a mixed-reality integrated learning environment. Computers in Human Behavior, 62, 212-220.
Keller, B. (2016, December 15). What L&D trends do we see coming in 2017? [Blog post]. Retrieved from http://fredricksonlearning.com/what-ld-trends-do-we-see-coming-in-2017/
Kelly, R. (18, January 2017). 11 ed tech trends to watch in 2017. Campus Technology. Retrieved from https://campustechnology.com/articles/2017/01/18/11-ed-tech-trends-to-watch-in-2017.aspx
Landers, R. (January 2017). A crash course in machine learning. The Industrial Psychologist. Retrieved from http://www.siop.org/tip/jan17/crash.aspx
Landers, R. N., & Armstrong, M. B. (2015). Enhancing instructional outcomes with gamification: An empirical test of the technology-enhanced training effectiveness model. Computers in Human Behavior. Advanced online publication. http://dx.doi.org/10.1016/j.chb.2015.07.031
Landers, R. N., Bauer, K. N., & Callan, R. C. (2015). Gamification of task performance with leaderboards: A goal setting experiment. Computers in Human Behavior. Advanced online publication. http://dx.doi.org/10.1016/j.chb.2015.08.008
Madhavan, N. (2017, January 9). From learning to marketing, one leader’s worthwhile switch. Retrieved from http://www.clomedia.com/2017/01/09/from-learning-to-marketing/
Magnacca, M. (2017, January 2). Sales learning trends: What to watch for in 2017. Retrieved from http://www.trainingindustry.com/sales-training/articles/sales-learning-trends-what-to-watch-for-in-2017.aspx
Matthews, R., Hin, H. S., & Choo, K. A. (2014). Learning object to enhance introductory programming understanding: Does the size really matter? The Turkish Online Journal of Educational Technology, 13(1), 174-183.
Mitchell, S., Heyden, R., Heyden, N., Schroy, P., Andrew, S., Sadikova, E., & Wiecha, J. (2011). Pilot study of motivational interviewing training in a virtual world. Journal of Medical Internet Research, 13(3), e77.
Patten, B. (2016, April 4). How microlearning improves corporate training. Retrieved from http://www.trainingindustry.com/content-development/articles/how-microlearning-improves-corporate-training.aspx
Pelster, B., Haims, J., Stempel, J., & van der Vyver, B. (2016). Learning. In Global human capital trends 2016 (pp. 57-65). Westlake, TX: Deloitte University Press.
Pérez-Sánchez, B., Fontenla-Romero, O., & Guijarro-Berdiñas, B. (2016). A review of adaptive online learning for artificial neural networks. Artificial Intelligence Review. Advance online publication. http://dx.doi.org.prox.lib.ncsu.edu/10.1007/s10462-016-9526-2
Smith, A. (2016, November 16). Artificial intelligence: The future is now. Retrieved from https://www.shrm.org/resourcesandtools/legal-and-compliance/employment-law/pages/artificial-intelligence-bias.aspx
Taylor, K. (2014, Spring). Tracking trends. Training Industry Magazine, 52–53. Retrieved from http://www.nxtbook.com/ nxtbooks/trainingindustry/ tiq_2014spring/
Taylor, D. H. (2016a). L&D global sentiment survey 2017. Retrieved from http://donaldhtaylor.co.uk/wp-content/uploads/GSS-2017-V6-print-optimized.pdf
Taylor, T. (2016b, November 15). 4 learning and development trends for HR leaders to watch in 2017. Retrieved from http://www.hrdive.com/news/4-learning-and-development-trends-for-hr-leaders-to-watch-in-2017/430471/
Training Industry, Inc. (2016). Aligning sales competencies in L&D. Retrieved from http://www.trainingindustry.com/training-outsourcing/products/research/aligning-sales-competencies.aspx
Weis, C. (2016, October 6). My forecast for 2017. [Blogpost]. Retrieved from https://elearninfo247.com/2016/10/06/my-forecasts-for-2017/
Wentworth, D. (2016, November 30). Top spending trends for training, 2-16-2-17. Retrieved from https://trainingmag.com/top-spending-trends-training-2016-2017