The Educational Intelligent Economy by Jules Tavis D.;Salajan Florin D.;

Author:Jules, Tavis D.;Salajan, Florin D.;
Language: eng
Format: epub
Publisher: Emerald Publishing Limited
Published: 2019-11-01T00:00:00+00:00

METHODS, TRENDS, AND CREATING DATA ANALYSTS

While the use of Big Data is burgeoning in government, a glaring issue is that the capacity to engage in such advanced data analytics for policy development has not kept pace in the field of adult and workforce learning. Over the past 30 years – roughly the same period that saw an explosion of quantitative research in government – research regarding adult learning and workforce development has more consistently conducted small-scale qualitative studies (Boeren, 2018; Fejes & Nylander, 2015) and published in the form of critique and social commentary. Correspondingly, however, public adult and workforce education programing has increasingly been mandated to increase data use, evidence-based initiatives, and systematic evaluation since the late 1990s (Roumell et al., 2018). Regrettably, this particular trend in research has contributed to the siloing of the field’s research from implementation science and data-driven disciplines (Roessger, 2018). This, in turn, has unwittingly stunted the growth of adult and workforce learning programs in research-based universities and positioned many stakeholders and experts in the field as antagonists and skeptics of data-driven decision-making and evidence-based practice (Cranton, 2000; Cunningham, 2000; Roessger, 2018; Wilson, 1993). Further, it has created an insular phenomenon where the field’s preferences and ideological views have been reinforced and rewarded internally, but resulted in a body of scholarship that is mostly unresponsive to external trends and policy demands, and often viewed as outdated and lacking relevance (Daley, Martin, & Roessger, 2018; Roessger, 2017).

This siloing effect is suboptimal for adult and workforce education policy for several reasons. First, it minimizes possibilities of interdisciplinary and team-based research, and limits stakeholders and scholars in terms of collaboration, their ability to solicit funding for research in adult learning sciences, and ultimately limits their public impact. Secondly, it limits the field’s capacity to engage in practices critical for building an educational intelligent economy based on interconnected systems of experts, industry, and governance structures. When adult and workforce learning professionals are unable to converse in the research language of data analytics – rendering them effectively innumerate in a public policy sense – they struggle to get involved in research that occurs beyond their silo, and lack the language and data-driven decision-making skills necessary to justify and promote critical policy and adult learning initiatives. Second, this shift has failed to provide those trained in adult and workforce learning programs with the skills most valued by contemporary industries, the same industries working to shape adult and workforce learning policy at the national level. For instance, a recent survey by the National Association of Colleges and Employers (2017) found that 67.5% of companies seek analytic/quantitative skills in their new hires, yet these skills are rarely a strong focus in adult and workforce learning graduate programs. When the field fails to provide these skills to the professionals who should become critical stakeholders and policy advocates, industries end up looking elsewhere. Finally, this shortage in advanced data analytics competencies means that important policy is increasingly fashioned without the input of adult learning professionals.

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