Models of Education and Workplace Success

The research presented thus far overwhelmingly underscores the fact that a multitude of characteristics are related to success in both school and the workplace. Of course, some factors are more highly related to success than others, and some may be relatively more important for particular outcomes or at certain points in time. One challenge that remains is determining how to pare down the number of factors to a manageable few for a particular purpose by focusing on those that are important while at the same time being inclusive enough to provide meaningful, personalized feedback to the individual as it relates to his or her level of readiness. We have begun to articulate important constructs from each of the four broad domains as they relate to major education and career transitions (e.g., middle school to high school, high school to college, college to work). Empirical findings and theoretical support have guided the inclusion of specific factors in the model. For example, Figures 12 and 13 present the knowledge and skills that will best equip students as  they transition from high school to college in terms of earning good grades as well as persisting  through graduation (note that college success can be operationalized in a multitude of other ways;  we focus on these two outcomes for illustrative purposes). The proposed models underscore that college success is multidimensional and that some knowledge and skills will be more or less important for specific indicators of college success.

For example, take our model predicting college GPA (Figure 12). From the core academic skills domain, we highlight that knowledge and skills in all three content areas—ELA, mathematics, and science—are important predictors of college GPA. ACT has extensive evidence that scores in ELA, mathematics, and science predict grades in college (e.g., Radunzel & Noble, 2012). Research  also indicates that many of the cross-cutting capabilities are related to performance in college,  in particular studying and learning (Hattie, Biggs, & Purdie, 1996; Liu et al., 2014), thinking skills  (Higgins, Hall, Baumfield, & Moseley, 2005), metacognition (Haller, Child, & Walberg, 1988;  Mevarech & Amrany, 2008; Oladunni, 1998; Schweizer, Wustenberg, & Greiff, 2013), and technology  and information literacy (Huffman & Huffman, 2012; Tien & Fu, 2008; Wentworth & Middleton,  2014). As for the behavioral skills domain, research has shown that persistence, dependability, and self-confidence are positively related to college grades (Robbins et al., 2004; Robbins et al., 2006).  

From the education and career navigation domain, socialization, academic self-efficacy, and goals are important constructs related to college grades (Brady-Amoon & Fuertes, 2011; Brown et al., 2008; Robbins et al., 2004).

When we operationalize education success as graduating college, we find that many of the same  constructs are important predictors, but there are subtle differences, particularly for the behavioral  and navigation domains (Figure 13). From the behavioral domain, research indicates that persistence  remains an important predictor, with research supporting the addition of goal striving, sociability, and optimism into the model (Lounsbury et al., 2004; Taylor, Scepansky, Lounsbury, & Gibson, 2010;  Robbins et al., 2004; Robbins et al., 2006). As for the navigation domain, academic self-efficacy  and goals were again identified as important predictors of education success, specifically college graduation (Baier, 2014; Robbins et al., 2004). Additionally, fit and supports were identified as  important predictors of college graduation (Tracey & Robbins, 2006; Robbins et al., 2004). The high  degree of overlap between the two models highlights that these two outcomes are not independent. Predictors of college success are also often interrelated, and the development of knowledge and skills in all the key college readiness areas will best position students for later success. If students  fail to acquire the academic skills to pass their courses, it’s unlikely that they will persist in college.  On the other hand, if even the highest-performing students are not motivated or interested in their  studies, it is unlikely that they will persist in college through degree completion.

A proposed model of work success was also developed. As is the case for education success, work success can be operationalized in various ways. We chose to focus on job performance here  because there is a stronger empirical foundation to draw from to inform our model development than  for other indicators of work success. As shown in Figure 14, research shows a strong connection between English language arts skills and job performance.

In particular, performance on ELA  measures such as the ACT WorkKeys Reading for Information and Listening for Understanding tests is predictive of subsequent job performance (ACT, 2007). This corroborates other empirical findings showing that oral communication skills have a large impact on performance in many business settings (Crosling & Ward, 2002; Di Salvo & Larsen, 1987; Maes, Weldy, & Icenogle, 1997; Ramsey, & Sohi, 1997). Empirical findings for cross-cutting capabilities for the prediction of job performance support the importance of critical thinking (Heimler, Rosenberg, & Morote, 2012), technology and information literacy (Lira, Ripoll, Peiro, & Zornoza, 2013), decision making (Danner et al., 2012), and collaborative problem solving (DeChurch & Mesmer-Magnus, 2010; DeDreu & Weingart, 2003;  LePine, Piccolo, Jackson, Mathieu, & Saul, 2008). As for important behavioral skills related to job performance, empirical findings support the inclusion of goal striving (Whetzel, McDaniel, Yost, &  Kim, 2010), persistence (Timmerman, 2004; Whetzel et al., 2010), cooperation (Christiansen & Robie, 2011; Judge et al., 2013), and flexibility (Judge et al., 2013) in a model of job performance. From the navigation domain, important predictors of job performance include fit (Kristof-Brown, Zimmerman, & Johnson, 2005), supports (Rhoades & Eisenberger, 2002), and job self-efficacy (Bauer et al., 2007). It is important to note that many of the constructs highlighted in the model of education success also appear in the model of work success; however, there are also differences  between the models. For example, some constructs manifest differently depending on the setting (e.g., academic self-efficacy and job self-efficacy). These findings highlight the importance of considering both the transition and outcome of interest when developing models of success.

As another way of illustrating how the constructs in our education and work readiness framework manifest and/or are defined differently at different transitions/time points, Table 9 shows examples of knowledge and skills from each of the four broad domains included in the ACT holistic model of education and work readiness. Within each domain, the examples highlight knowledge and skills for a particular dimension and grade ranges in K–12. These examples illustrate the rich and more holistic information that can be provided based on a multifaceted and longitudinal understanding of readiness.