Prompting students to self-explain during problem solving has proven to be an effective instructional strategy across many domains. However, despite being called “domain general”, very little work has been done in areas outside of math and science. In this dissertation, I investigate whether the self-explanation effect holds when applied in an inherently different type of domain, second language grammar learning. Through a series of in vivo experiments, I tested the effects of using prompted self-explanation to help adult English language learners acquire the English article system (e.g., teaching students the difference between “I saw a dog” versus “I was the dog”). In the pilot study, I explored different modalities of self-explanation (free-form versus menu-based), and in Study 1, I looked at transfer effects between practice and self-explanation. In the studies that followed, I added an additional deep processing manipulation (Study 2: analogical comparisons) and a strategy designed to increase the rate of practice and information processing (Study 3: worked example study). Finally, in Study 4, I built and evaluated an adaptive self-explanation tutor that prompted students to self-explain only when estimates of prior knowledge were low. Across all studies, results show that self-explanation is an effective instructional strategy in that it leads to significant pre- to post-test learning gains, but it is inefficient compared to tutored practice. In addition to learning gains, I compared learning process data and found that both self-explanation and practice lead to similar patterns of learning and there was no evidence in support of individual differences. This work makes contributions to learning sciences, second language acquisition (SLA), and tutoring system communities. It contributes to learning sciences by demonstrating boundary conditions of the self-explanation effect and cautioning against broad generalizations for instructional strategies, suggesting instead that strategies should be aligned to target knowledge. This work contributes to second language acquisition theory by demonstrating the effectiveness of computer-based tutoring systems for second language grammar learning and providing data that supports the benefits of explicit instruction. Furthermore, this work demonstrates the relative effectiveness of a broad spectrum of explicit learning conditions. Finally, this work makes contributions to tutoring systems research by demonstrating a process for data-driven and experiment-driven tutor design that has lead to significant learning gains and consistent adoption in real classrooms.

A key challenge of the learning sciences is moving research results into practice. Educators on the front lines perceive little value in the outputs of education research and demand more “usable knowledge”. This work explores the potential instead of usable artifacts to translate knowledge into practice, adding scientists as stakeholders in an interaction design process. The contributions are two effective systems, the scientific and contextual principles in their design, and a research model for scientific research through interaction design. College student study practices are the domain chosen for the development of these methods. Iterative ethnographic fieldwork identified two systems that would be likely to advance both learning in practice and knowledge for applying the employed theories in general. Nudge was designed to improve students’ study time management by regularly emailing students with explicit recommended study activities. It reconceptualizes the syllabus into an interactive guide that fits into modern students' attention streams. Examplify was designed to improve how students learn from worked example problems by modularizing them into steps and scaffolding their metacognitive behaviors though problem-solving and self-explanation prompts. It combines these techniques in a way that is exceedingly easy to author, using existing answer keys and students' self-evaluations. Nudge and Examplify were evaluated experimentally over a full semester of a lecture-based introductory chemistry course. Nudge messages increased students’ sense of achievement and interacted with students’ existing time management skills to improve exam grades for poorer students. Among students who could choose whether to receive them, 80% did. Students with access to Examplify had higher exam scores (d=0.26), especially on delayed measures of learning (d=0.40). A key design decision in Examplify was not clearly resolvable by existing theory and so was tested experimentally by comparing two variants, one without prompts to solve the steps. The variant without problem solving was less effective (d=0.77) and less used, while usage rates of the variant with problem solving increased over time. These results support the use of the design methods employed and provide specific empirical recommendations for future designs of these and similar systems for implementing theory in practice.