Motivation: Program visualizations are widely used to support novice programmers, yet students often ignore or resist well-designed visual scaffolds. Research on multiple external representations (MERs) suggests cognitive design principles for coordinating views, but says little about what determines whether learners actually engage with the representations available to them.

As computing education becomes increasingly globalized, many students learn computer science through a second language. Prior work has documented cognitive and performance challenges faced by non-native English-speaking (NNES) students, yet less is known about how language background shapes their motivational experiences and intentions to persist. Drawing on Expectancy-Value Theory, we analyzed matched pre- and post-term survey data from 374 students (198 NNES, 176 NES) in a CS1 course at a large North American university. We measured programming self-efficacy, implicit theories of intelligence, need for cognition, sense of belonging, motivation and learning strategies, and intentions to major in computing. NNES students began the course believing intelligence is fixed and had lower self-efficacy on language-dependent tasks, gaps that persisted throughout the term. However, despite lower confidence, NNES students were more willing to choose challenging assignments and consistently used more strategic learning approaches. While NNES and native English-speaking (NES) students reported similar overall belonging, NNES students experienced greater belonging uncertainty, specifically when encountering difficulties, and were more likely to want to fade into the background within the CS community. These findings reveal language background as a persistent motivational cost in introductory computing and underscore the need for instructional designs that explicitly support belonging, self-efficacy, and adaptive strategy use for linguistically diverse learners.

Motivation: Database courses focus on SQL querying (DQL) while treating schema definition (DDL) and data manipulation (DML) as side topics, even though real-world database work begins with understanding schema design and data updates. This misalignment leaves students underprepared for authentic data management practice. Method: We integrated scaffolded DDL and DML exercises as a core concept in a third-year data course across three offerings (2023-2025). Students completed structured weekly tasks in an LMS that provides immediate feedback and unlimited attempts, encouraging low-stakes, iterative practice. We analyzed student interaction data (number of attempts and performance) to examine learning patterns across DDL/DML and DQL. We analyzed 9,071 total exercise submissions from 669 students, examining both the number of LMS exercise attempts and assignment performance across DDL/DML and DQL. Results: Students required fewer attempts on DDL/DML tasks than on traditional DQL tasks, indicating strong receptiveness when these topics were properly scaffolded. Early performance on schema-definition tasks was moderately correlated with later SQL performance, suggesting that schema competence supports subsequent query learning. Implications: We encourage database educators to teach schema design and data manipulation as core topics to strengthen students' conceptual foundations, as our results suggest these skills are learnable with scaffolding and may support subsequent query learning.

This paper describes the instructor and student experience of a ''just-in-time'', blended approach to prerequisite review, implemented in a machine learning course but applicable elsewhere. Although pre-requisites are commonly used to structure university curricula, both the literature and our experience show that students sometimes forget prerequisite knowledge when it is needed in subsequent courses. This challenge is especially pronounced in courses where diverse prerequisite concepts are applied throughout the semester, with different concepts required for different units. Our approach consisted of short prerequisite review quizzes due before each lecture, where the quiz questions assessed mastery of key prerequisite concepts needed for that lecture. Moreover, each quiz was accompanied by a brief instructional video that provided a targeted review of the content. We evaluated this approach across two course implementations, based on perspectives from 2 instructors and 353 students. Both instructors and students felt positively: a reduction in prerequisite related questions during lectures was observed, and students reported that the approach helped bridge gaps in preparedness, improved self-efficacy, and was efficient. More interestingly, the responses showed key tradeoffs regarding the timing, modality, and level of support in a prerequisite review intervention. While we believe this approach to be applicable for other courses with diverse requirements, our results lead us to believe that there is no one-size-fits-all for prerequisite review, and that it is highly context-dependent.

Self-explanation promotes active learning by having students articulate their conceptual understanding in their own words. This study investigates whether the timing of self-explanations (before vs. after solving an exercise) relates to performance in a flipped, second-year computer organization course. Although students who self-explained before the exercises achieved higher course marks, the difference was not significant. Still, these findings suggest that early self-explanation may better prepare students for problem-solving when learning new concepts.

Self-explanation deepens understanding by giving learners an opportunity to reflect on what they are learning in a structured way. However, many students struggle to engage in it effectively. We investigate whether large language models (LLMs) can scaffold self-explanations in a flipped computer organization course. In an A/B test, one group used a fixed prompt to compare their explanations with an expert's, while another engaged in an interactive dialogue with an LLM to identify gaps. Although the overall quality of the explanation did not differ significantly between conditions, some students (non-native English speakers and women) reported greater comfort and perceived value when using the LLM.