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Deeper learning (DL) is a promising instructional approach for increasing students' self-efficacy and motivation and addressing inequality in student academic achievement. However, limited empirical research has examined how DL impacts essential outcomes exclusively among adolescents from at-risk populations, such as students living in poverty and those at risk for low academic achievement. The current study sought to determine which measures of DL are the most important for predicting the self-efficacy, motivation, and achievement scores (i.e., math, science, and reading) of students living in poverty and those performing below the 50th percentile. A series of path analysis models were estimated with structural equation modeling (SEM) to test these relationships. These models included school-fixed effects to account for time-invariant unobservable characteristics and assess within-school variations. Students' prior achievement scores were also included to account for additional sorting bias. Model fit was evaluated using multiple indices, including the chi-square (χ2) test statistic, comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean residual (SRMR). These goodness-of-fit indices suggested an excellent fit for each estimated model. Results of path analyses with SEM showed that some DL measures were essential predictors of some student outcomes. Specifically, two of the nine DL measures evaluated, including opportunities for complex problem solving and opportunities to receive feedback, were consistently associated with higher levels of self-efficacy and motivation. However, the empirical evidence did not support direct associations between the DL measures and students' math, reading, and science scores.