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Although students’ self-regulated learning has been studied extensively, past research has not investigated students’ fine-grained, self regulated choice-making processes during learning with visual representations and strategies to support such processes. We conducted design and experimental studies with 148 students to develop and evaluate an intervention package for supporting students’ self-regulated choice-making in using diagrammatic scaffolding in algebra tutoring software. A classroom experiment showed that students with the intervention learned greater conceptual and procedural knowledge in algebra than students in the control condition whose choices were not supported. Also, students with the intervention chose to use diagrams less frequently overall but showed distinctive use patterns that changed over time, indicating a form of self-regulated diagram use. This study demonstrates the importance of understanding and supporting choice behaviors that change over time during learning, going beyond simply measuring the frequency of choice behaviors and encouraging students to engage in these behaviors more frequently. 

Although students’ self-regulated learning has been studied extensively, past research has not investigated students’ fine-grained, self-regulated choice-making processes during learning with visual representations and strategies to support such processes. We conducted design and experimental studies with 148 students to develop and evaluate an intervention package for supporting students’ self-regulated choice-making in using diagrammatic scaffolding in algebra tutoring software. A classroom experiment showed that students with the intervention learned greater conceptual and procedural knowledge in algebra than students in the control condition whose choices were not supported. Also, students with the intervention chose to use diagrams less frequently overall but showed distinctive use patterns that changed over time, indicating a form of self-regulated diagram use. This study demonstrates the importance of understanding and supporting choice behaviors that change over time during learning, going beyond simply measuring the frequency of choice behaviors and encouraging students to engage in these behaviors more frequently. 

In designing learning technology, it is critical that the technology supports both learning and engagement of students. However, achieving both aspects in a single technology design is challenging. We report on the design and evaluation of Gwynnette, intelligent tutoring software for early algebra. Gwynnette was deliberately designed to enhance students’ algebra learning and engagement, integrating several playful interaction and gamification features such as drag-and-drop interactions, an alien character, and sound effects. A virtual classroom experiment with 60 students showed that the system significantly enhanced both engagement and conceptual learning in early algebra, compared to the older version of the same software. Log data analyses gave insights into how the design might have affected the out-comes. This study demonstrates that a deliberate design of learning technology can help students learn and engage well in an unpopular subject such as algebra, a challenging dual goal in designing learning technologies. 

Learners’ choices as to whether and how to use visual representations during learning are an important yet understudied aspect of self-regulated learning. To gain insight, we developed a choice-based intelligent tutor in which students can choose whether and when to use diagrams to aid their problem solving in algebra. In an exploratory classroom study with 26 students, we investigated how learners choose diagrams and how their choice behaviors relate to learning outcomes. Students who proactively chose to use diagrams achieved higher learning outcomes than those who reactively used diagrams when they made incorrect attempts. This study contributes to understanding of self-regulated use of visual representations during problem solving. 

In designing learning technology, it is critical that the technology supports both learning and engagement of students. However, achieving both aspects in a single technology design is challenging. We report on the design and evaluation of Gwynnette, intelligent tutoring software for early algebra. Gwynnette was deliberately designed to enhance students’ algebra learning and engagement, integrating several playful interaction and gamification features such as dragand- drop interactions, an alien character, and sound effects. A virtual classroom experiment with 60 students showed that the system significantly enhanced both engagement and conceptual learning in early algebra, compared to the older version of the same software. Log data analyses gave insights into how the design might have affected the outcomes. This study demonstrates that a deliberate design of learning technology can help students learn and engage well in an unpopular subject such as algebra, a challenging dual goal in designing learning technologies. 

Learners’ choices as to whether and how to use visual representations during learning are an important yet understudied aspect of self-regulated learning. To gain insight, we developed a choice-based intelligent tutor in which students can choose whether and when to use diagrams to aid their problem solving in algebra. In an exploratory classroom study with 26 students, we investigated how learners choose diagrams and how their choice behaviors relate to learning outcomes. Students who proactively chose to use diagrams achieved higher learning outcomes than those who reactively used diagrams when they made incorrect attempts. This study contributes to understanding of self-regulated use of visual representations during problem solving. 

Although visual representations are generally beneficial for learners, past research also suggests that often only a subset of learners benefits from visual representations. In this work, we designed and evaluated anticipatory diagrammatic self-explanation, a novel form of instructional scaffolding in which visual representations are used to guide learners’ inference generation as they solve algebra problems in an Intelligent Tutoring System. We conducted a classroom experiment with 84 students in grades 5-8 in the US to investigate the effectiveness of anticipatory diagrammatic self-explanation on algebra performance and learning. The results show that anticipatory diagrammatic self-explanation benefits learners on problem-solving performance and the acquisition of formal problem-solving strategies. These effects mostly did not depend on students’ prior knowledge. We analyze and discuss how performance with the visual representation may have influenced the enhanced problem-solving performance. 

Prior research shows that self-explanation promotes understanding by helping learners connect new knowledge with prior knowledge. However, despite ample evidence supporting the effectiveness of self-explanation, an instructional design challenge emerges in how best to scaffold self-explanation. In particular, it is an open challenge to design self-explanation support that simultaneously facilitates performance and learning outcomes. Towards this goal, we designed anticipatory diagrammatic self-explanation, a novel form of self-explanation embedded in an Intelligent Tutoring System (ITS). In our ITS, anticipatory diagrammatic self-explanation scaffolds learners by providing visual representations to help learners predict an upcoming strategic step in algebra problem solving. A classroom experiment with 108 middle-school students found that anticipatory diagrammatic self-explanation helped students learn formal algebraic strategies and significantly improve their problem-solving performance. This study contributes to understanding of how self-explanation can be scaffolded to support learning and performance. 

Prior research shows that self-explanation promotes understanding by helping learners connect new knowledge with prior knowledge. However, despite ample evidence supporting the effectiveness of self-explanation, an instructional design challenge emerges in how best to scaffold self-explanation. In particular, it is an open challenge to design self-explanation support that simultaneously facilitates performance and learning outcomes. Towards this goal, we designed anticipatory diagrammatic self-explanation, a novel form of self-explanation embedded in an Intelligent Tutoring System (ITS). In our ITS, anticipatory diagrammatic self-explanation scaffolds learners by providing visual representations to help learners predict an upcoming strategic step in algebra problem solving. A classroom experiment with 108 middle-school students found that anticipatory diagrammatic self-explanation helped students learn formal algebraic strategies and significantly improve their problem-solving performance. This study contributes to understanding of how self-explanation can be scaffolded to support learning and performance.