Recent work has explored the interests that draw learners to Machine Learning (ML), aiming to support their success and broaden participation in the field. However, whether strategies used in textbooks align with these interests is unexplored. We perform a thematic analysis of the introductions from ten openly available ML textbooks to identify their motivational strategies and compare them with student interests documented in prior research. We find that textbooks frequently motivate learners in their introductions by setting learning goals, previewing core ML topics to be covered, showcasing applications and current successes, and, less often, by using learner-centered strategies such as reassurance or curiosity prompts. We group these motivations into three overarching themes: theoretical, practical, and learner-centered. These motivations largely align with student interests, particularly in theory and applications, even in textbooks published before the recent surge of ML and Artificial Intelligence. These findings reveal how textbooks frame ML’s value and offer evidence-based guidance for developing future materials that better engage and support diverse learners.

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.

Machine learning (ML) has become increasingly important for students, yet university-level ML courses are often perceived as challenging and time-intensive. This study explores the perceived challenges and motivations of students in a university ML course to inform curricular and teaching strategies. Through 5 surveys conducted in two instances of a 12-week introductory ML course, we examined students' engagement with both theoretical and practical aspects of ML. Results indicate that while students initially express strong interest in applying ML concepts, their reported interests can shift toward theoretical foundations. Challenges in both theory and practice are reported, including difficulties in mathematical notation and vectorization of gradient components, as well as model implementation. Students also discuss the time commitment required in a course with both theoretical and practical content. We recommend aligning course content with student motivations, providing targeted support for mathematical notation and vectorization, and balancing theoretical depth with practical application.

Creating a supportive community in introductory programming courses is vital to student success, yet forums meant to facilitate this can cause stress due to social comparison. According to social identity theory, students are more likely to engage and feel a sense of belonging when they perceive connections with their peers. This study investigates whether peer-modeled posts that simulate students exhibiting desirable engagement behavior can reduce feelings of isolation and foster social connection among students. We introduced curated posts modeling expected student behavior -- covering content, providing emotional support, and offering study tips -- into Q&A forums for two introductory computing courses. These posts were inserted using different student accounts. Surveys and forum data were analyzed to measure the impact on students' feelings of isolation. Students responded positively to the seeded posts, reporting a significant reduction in feelings of isolation. Notably, women reported feeling less isolated after seeing the posts more than men, and many students reported feeling relieved that other students had the same worries and concerns as them. Seeding peer-modeled posts can significantly reduce student isolation and foster a greater sense of belonging in competitive academic contexts. However, future work may explore alternative delivery mechanisms, such as instructor posts framed as ''questions from last year,'' to determine if they can achieve similar effects.