Social And Emotional Development Add Up

Michael Ben-Avie, Senior Director Of Learning Assessment And Evaluationat Quinnipiac University

In both academic literature and practice, the trajectories of students are increasingly being assessed through the use of predictive modeling, with real consequences for student achievement and which students receive university support. However, predictive modeling software is only as good as the model that feeds it and too often in both literature and practice, models overindex on immutable characteristics of students. Immutable characteristics are unalterable or are not amenable to change after enrollment in college. They are easy to measure in contrast to students’ levels of social and emotional development, for example. Predictive models that focus on immutable characteristics risk prematurely abandoning or writing off students who, with the appropriate intervention, could continue their development and succeed academically. A better approach to the use of predictive modeling in this context is to instead focus on students’ malleable characteristics that have been shown to be antecedents to college success.

Students’ malleable characteristics are amenable to change after college enrollment. For example, in previous research, we found that students who develop key social and emotional development competencies, which includes the nature and quality of relationships that students have with themselves (e.g., self-regulation) and others, were much more likely to have higher academic achievements in such subjects as math and science. Colleges may impact students’ level of social and emotional development.

In higher education, the focus of research tends to be on static points in time (e.g., withdrawal), which misses the antecidents to shifts in students’ trajectories. The focus on malleable student characeristics anticipates the question of what to do with the data. A focus on immutable precollege characteristics limits possible actions that the university may take. A score on the precollege math placement exam is only descriptive: it describes excellent math students, but not how they got that way. To turn data into actionable information, it is necessary to investigate effective strategies for working with students who are low performing in math until they perform at a level that exceeds expectations. Effective strategies strengthen students’ emotional resourefulness and help them to cope with self-limiting beliefs, for example. What’s important to include in a predictive model is the level of students’ improvement over time (and the antecedents to their improvement) and not their static math placement scores prior to attending college.

In preparing to write our book, How Social and Emotional Development Add Up: Getting Results in Math and Science Education, we realized that if we could articulate how social and emotional development are related to math and science learning, then we would have something truly innovative and powerful to say. We knew this had not been done, and at the time did not pause to wonder why not. At first glance, the premise of this book may seem like a paradox: The way to improve math and science learning is to widen, not narrow, our focus, to include social and emotional development as necessary components of student learning. Social problem-solving skills are related to cognitive problem-solving skills. For example, we found in one of our studies that the strength of the relationship between students’ social knowledge of themselves and others and their achievement in mathematics was very strong. The relationship found in that study between social competence and achievement in math suggests a convergence in problem-solving skills— both in tackling tough social situations and succeeding in them and in tackling and solving cognitive problems in math. This convergence is not usually included in predictive models of college success. Far more attention is paid to the impact of students’ past development, unchangeable characteristics, and precollege learning on performance in higher education in typical predictive models of student outcomes.

The implication of having a focus of this nature is that it is insufficient to purchase the “right” technological program or software to engage in predictive modeling: An explanatory framework is necessary to determine which metrics to include in the model, how to measure “hard-to-measure” aspects of students’ learning and development, and how to turn data into usable information. The explanatory framework that we use in our predictive modeling emerges from the developmental sciences (the developmental sciences include the study of developmental psychology, cognitive science, and neuroscience). Since development is about change, the developmental sciences focus on intentional veers in students’ trajectories that could not have been predicted based solely on their precollege characteristics. In other words, there is more to the story of retention, academic achievement, and graduation than just unchangeable precollege experiences, demographic characteristics, and SAT test scores.

To empirically evaluate the importance of malleable characteristics, our Quinnipiac University Office of Academic Innovation & Effectiveness conducts longitudinal cohort studies that follow students from orientation through graduation from the university, or subsequent enrollment in other colleges and universities. Predictive models that are informed by the longitudinal cohort studies take into consideration students’ social and emotional development. At the college level, social and emotional development encompasses:

• The ability to develop and maintain healthy relationships (work well on teams; value the individual contributions of team members; establish rapport with others from different backgrounds, active citizenship/participation on campus; negotiate challenging relationships and implement effective resolutions to these challenges).

• Emotional resourcefulness (adaptability, resiliency, the ability to productively struggle, cope, persevere, respectfully respond to feedback, celebrate successes, stay engaged even when confused). Also, seeking help, overcoming hesitancy to connect with faculty and staff, and handling performance anxiety and other cognitive distortions that impede learning.

• Engagement with communities (sustained engagement in intentional, education-based communities).

• Responsible participation in public life (emerging competency in assessing, deciding, and acting in ways that are good for themselves and others in preparation to demonstrate responsible participation in public life).

As part of the longitudinal cohort studies, performance-based assessments of students’ levels of social and emotional development were administered to college students. This approach stands in contrast to conventional surveys and other self-report measures that are almost exclusively used to measure social and emotional development. In one of our research studies, students’ papers were scored by nine faculty using the Social and Emotional Intelligence Rubric developed by our university’s Office of Academic Innovation & Effectiveness. In total, 889 papers were scored with 374 (42%) scored by two different faculty members to determine the strength of the inter-rater reliability. The faculty members were consistently able to discern specific and distinct levels of student performance on a 4-point rating scale. Students’ social and emotional development predicted their academic achievement, as measured by cumulative GPA and performance-based assessments of learning.

It takes far more than just purchasing a technological solution to build predictive models that will inform action: Knowing which factors to include in the predictive models, beyond the “easy to measure” immutable factors that cannot be changed after college enrollment, is also essential.

Cognitive