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Large stocks of soil carbon (C) and nitrogen (N) in northern permafrost soils are vulnerable to remobilization under climate change. However, there are large uncertainties in present‐day greenhouse gas (GHG) budgets. We compare bottom‐up (data‐driven upscaling and process‐based models) and top‐down (atmospheric inversion models) budgets of carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O) as well as lateral fluxes of C and N across the region over 2000–2020. Bottom‐up approaches estimate higher land‐to‐atmosphere fluxes for all GHGs. Both bottom‐up and top‐down approaches show a sink of CO₂in natural ecosystems (bottom‐up: −29 (−709, 455), top‐down: −587 (−862, −312) Tg CO₂‐C yr⁻¹) and sources of CH₄(bottom‐up: 38 (22, 53), top‐down: 15 (11, 18) Tg CH₄‐C yr⁻¹) and N₂O (bottom‐up: 0.7 (0.1, 1.3), top‐down: 0.09 (−0.19, 0.37) Tg N₂O‐N yr⁻¹). The combined global warming potential of all three gases (GWP‐100) cannot be distinguished from neutral. Over shorter timescales (GWP‐20), the region is a net GHG source because CH₄dominates the total forcing. The net CO₂sink in Boreal forests and wetlands is largely offset by fires and inland water CO₂emissions as well as CH₄emissions from wetlands and inland waters, with a smaller contribution from N₂O emissions. Priorities for future research include the representation of inland waters in process‐based models and the compilation of process‐model ensembles for CH₄and N₂O. Discrepancies between bottom‐up and top‐down methods call for analyses of how prior flux ensembles impact inversion budgets, more and well‐distributed in situ GHG measurements and improved resolution in upscaling techniques.

 

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. 

Integrating visual representations in an interactive learning activity effectively scaffolds performance and learning. However, it is unclear whether and how sustaining or interleaving visual scaffolding helps learners solve problems efficiently and learn from problem solving. We conducted a classroom study with 63 middle-school students in which we tested whether sustaining or interleaving a particular form of visual scaffolding, called anticipatory diagrammatic self-explanation in an Intelligent Tutoring System, helps students’ learning and performance in the domain of early algebra. Sustaining visual scaffolding during problem solving helped students solve problems efficiently with no negative effects on learning. However, in-depth log data analyses suggest that interleaving visual scaffolding allowed students to practice important skills that may help them in later phases of algebra learning. This paper extends scientific understanding that sustaining visual scaffold does not over-scaffold student learning in the early phase of skill acquisition in algebra. 

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.