Teomara Rutherford

Educational videos found online can be a useful learning resource. Teachers use videos in the classroom to engage students and clarify concepts. At home, students routinely search for videos on topics that remain unclear to them after class. These two modes of learning are fundamentally different. In one, the material is assigned, in the other, students actively seek out the information. Based on self-determination theory, we predicted the active process would improve learning outcomes. We test whether choosing an educational video from a curated set, rather than being assigned one, affects student motivation, attention, and academic performance. We conducted a multi-session experiment as part of an introductory college physics class (n=114, quasi-randomized by lab section) and a large single-session online experiment (n=462). We measured quiz scores in each session, final class exam performance, standardized physics knowledge tests, task-related motivation state, overt attention via webcam-based eye tracking, and assessed individual short-term memory capacity and academic motivation. Video choice did not statistically significantly affect immediate gains measured with quizzes after individual sessions, nor were task-related motivation states affected in either cohort. However, choice did show longer term effects, with higher final exam performance in the classroom cohort. Amotivation and extrinsic motivation predicted lower exam performance, as expected. These findings suggest that small benefits of student choice can accumulate over the course of a full semester, but motivation and attention effects of individual sessions may be too small to observe.

Generative artificial intelligence (GenAI) is entering children’s lives, often before they learn how to use it. Computational thinking (CT) may offer a foundation for this learning, yet research integrating CT and GenAI typically involves older learners. This study examined the feasibility of integrated CT and GenAI lessons with 22 second graders at a Title I school in the Mid-Atlantic United States. The study included ten lessons on CT, GenAI, or their combination. CT scores on a validated paper-and-pencil measure increased statistically significantly from pre- to post-test with an effect size larger than similar CT interventions; observations showed students applied CT concepts when writing prompts and identifying errors generated by GenAI. In a GenAI prompting post-test asking students to recreate an image, students described image content in detail but rarely used directive prompt language. Still, ChatGPT largely produced images matching the target image and students’ intent, possibly creating an illusion of competence. Self-efficacy and intrinsic value were rated high across lessons, with some differences reflecting strengths of lessons in supporting student interests and participation. These findings offer evidence that CT can serve as a framework for GenAI literacy in early elementary grades and that the combined approach can produce gains in each. Lessons learned from implementation present opportunities for improvement to the studied program and others.

Computational thinking (CT) skills are essential for navigating daily life, but how best to teach these skills is still being explored. Educational programming games can provide engaging environments to develop CT, yet uncovering student struggles during gameplay remains challenging. This study analyzed 15 undergraduates with varying programming experience playing the CT game, Fox and Field, utilizing gameplay videos, heart rate data, and surveys. Results revealed common struggle points in understanding the character's perspective, degree angles, and debugging, with notable differences between novice and experienced players. Findings suggest support targeting abstraction, pattern recognition, and debugging skills to improve future game design and scaffolding.

Coding for computer programming is a common way to support and assess computational thinking (CT) skills, a set of problem-solving skills essential for computer science (CS) and STEM more broadly. We designed a coding platform, Fox and Field, to determine what kinds of scaffolds can encourage novice coders to behave more like experts. We compared actions between 221 upper-division undergraduate CS/engineering (expert, n=106) and social science (novice, n=115) majors randomized to scaffolding conditions from four universities in the United States. Overall, experts used statistically significantly more practices aligned with CT skills, such as those that increased code efficiency (e.g., non-right angle turns, loops). This difference disappeared when novices were scaffolded by being told to use fewer codes or by priming with critical features. Both experts and novices were equally likely to be swayed by purposefully distracting features within the platform, such as a drawn path to deflect them from the most efficient solution. Results present initial evidence regarding which features of coding platforms can direct students to exercise CT-linked practices, leading to recommendations regarding platform development to better support learning.

Despite rising enrollment, CS Education struggles with training adequate educators, leading to increased teaching loads. Open-source teaching materials alleviate this by streamlining course preparation. Yet, there is a scarcity of free, open curricula that offer a contemporary coding experience while covering CS fundamentals. To address this gap, we introduce the CS1 Python Bakery curriculum with a "Batteries Included" approach, aiming to furnish instructors with comprehensive teaching resources. This curriculum refines an earlier open-source CS1 with detailed lesson plans, slides, rubrics, reference answers, student answers, and more. We present the learning content in a cross-platform, autograded textbook format and embrace modern Python features such as Dataclasses and static types. We deployed the curriculum in multiple university CS1 courses and collected data on the tradeoffs of our approach. This paper offers a thorough self-assessment based on student learning outcomes, code snapshot analyses, and reflection via the TEC Rubric for curriculum evaluation. Although we improved teacher accessibility, the change in student learning outcomes was unexpectedly minimal. Recognizing room for advancement, we conclude with recommendations for our next iteration to emphasize Equity, Community, and Identity.