Interactive, text message-based advising programs have become an increasingly common strategy to support college access and success for underrepresented student populations. Despite the proliferation of these programs, we know relatively little about how students engage in these text-based advising opportunities and whether that relates to stronger student outcomes – factors that could help explain why we’ve seen relatively mixed evidence about their efficacy to date. In this paper, we use data from a large-scale, two-way text advising experiment focused on improving college completion to explore variation in student engagement using nuanced interaction metrics and automated text analysis techniques (i.e., natural language processing). We then explore whether student engagement patterns are associated with key outcomes including persistence, GPA, credit accumulation, and degree completion. Our results reveal substantial variation in engagement measures across students, indicating the importance of analyzing engagement as a multi-dimensional construct. We moreover find that many of these nuanced engagement measures have strong correlations with student outcomes, even after controlling for student baseline characteristics and academic performance. Especially as virtual advising interventions proliferate across higher education institutions, we show the value of applying a more codified, comprehensive lens for examining student engagement in these programs and chart a path to potentially improving the efficacy of these programs in the future.
Text as data, college persistence, data science, natural language process, nudge, higher education, field experiment
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