A Preliminary Report of Integrating Science and Computing Teaching Using Logic Programming

Abstract

This paper presents a framework to integrate Science and Computing teaching using Logic Programming. We developed two modules: one for chemistry and the other for chemistry and physics. They are implemented in an elective course for 8th graders. Through clinical interviews, video taped class observations, exit interviews and our own experiences with the class, Logic Programming based approach is accessible to the students.

Designing AI Learning Experiences for K-12: Emerging Works, Future Opportunities and a Design Framework

“…To teach students about cognitive systems, curricula generally discussed representations, like data structures [6] or visualizations [29], and how AI makes decisions [6,73]. Many works also engaged students in developing AI systems and observing how they make decisions based on particular datasets.…”

“…These curricula engaged students in the process of developing ML systems, teaching them the ML Steps as well as about the Human Role in AI through experience. Other works taught students about the human role through engaging them in programming AI systems [17,52,60,73]. Many of the curricula that taught students about the ML Steps also taught students about Learning from Data, Critically Interpreting Data and Contextualizing Data through encouraging students to collect data and critically analyze how the AI system learns from it.…”

“…The AI literacy framework presents a number of design considerations to promote learner engagement. Many works provided students with Opportunities to Program [7,13,34,57,73], which engaged students in the Programmability and Human Role in AI competencies.…”

“…Fourteen of the 49 works considered students' developmental stage to better attune curricula to their cognitive abilities, addressing the Milestones consideration. Some works did so by referring to developmental psychology research or educational standards [13,17,43,64,68,73], and others did so through working with teachers who had experience engaging with students of specific age levels [41,63]. Cognimates, a curriculum for younger students, additionally considered how students may interact with their parents, and developed a learning guide for them [9].…”

“…The fifth guideline addresses this barrier. For example, several strategies to overcome this challenge in existing works include 1) using block-based visual programming to train and test ML models [2,9,21,22]; 2) unveil the complex ML concepts gradually through GUI/TUI/VUIs and visualizations for students to engage with and develop AI systems without programming [13,18,29,64] (Future Opportunity 5); 3) facilitating the progression of AI learning with starter codes, worked examples [16] or following the "Use-Modify-Create" cycle [53,57,63,75]; and 4) providing detailed workbook or guided tutorials [34,50,73,74].…”