As many school districts nationwide continue to incorporate Computer Science (CS) and Computational Thinking (CT) instruction at the K-8 level, it is crucial that we understand the factors and skills, such as reading and math proficiency, that contribute to the success of younger learners in a computing curriculum and are typically developed at this age. Yet, little is known about the relationship between reading and math proficiency, and the learning of key CS concepts at the elementary level. This study focused on 4th-grade students (ages 9-10) who were taught events, sequence, and repetition through an adaptation of the Creative Computing Curriculum. While all students benefited from access to such a curriculum, there were statistically-significant differences in learning outcomes, especially between students whose reading and math proficiency are below grade-level, and students whose proficiency are at or above grade-level. This performance gap suggests the need for curricular improvement and learning strategies that are CS specific for students who struggle with reading and math.
more »
« less
Understanding the Link between Computer Science Instruction and Reading & Math Performance
Worldwide, national initiatives have led to many school districts implementing computing curricula at the primary level. At that age, students are learning the foundational skills of reading and math. It is important to understand how computing can influence the development of these skills. While some argue that learning computing sharpens problem-solving skills that are applicable to other subjects, evidence supporting this belief is thin. In a quasi-experimental study of fourth-grade (ages 9-10) students, we compared state reading and math test scores of students receiving computing instruction with students who did not. Our findings demonstrated that a more open-ended, less scaffolded form of computing instruction was linked to performance gains in math, but not in reading (đš (2, 232) = 11.08, đ < .01, đđ2 = .0625). When looking at students who face academic challenges that can impact reading and math, the same trend applied to students with economic disadvantages and students with limited English proficiency, but not for students with disabilities. These results suggest that moderately scaffolded computing instruction supports the development of skills applicable to math, a step towards better understanding the relationship between learning opportunities in computing and outcomes in other subjects.
more »
« less
- PAR ID:
- 10256866
- Date Published:
- Journal Name:
- The 52nd ACM Technical Symposium on Computer Science Education (SIGCSE â21)
- Page Range / eLocation ID:
- 408 to 414
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Quantitative reasoning (QR) is the ability to apply mathematics and statistics in the context of real-life situations and scientific problems. It is an important skill that students require to make sense of complex biological phenomena and handle large datasets in biology courses and research as well as in professional contexts. Biology educators and researchers are responding to the increasing need for QR through curricular reforms and research into biology education. This qualitative study investigates how undergraduate biology instructors implement QR into their teaching. The study used pedagogical content knowledge (PCK) and a QR framework to explore instructorsâ instructional goals, strategies, and perceived challenges and affordances in undergraduate biology instruction. The participants included 21 biology faculty across various institutions in the United States, who intentionally integrated QR in their instruction. Semi-structured interviews were used to collect data focusing on participantsâ beliefs, experiences, and classroom practices. Findings indicated that instructors adapt their QR instruction based on course level and student preparedness. In lower-division courses, strategies emphasized building foundational skills, reducing math anxiety, and using scaffolded instruction to promote confidence. In upper-division courses, instructors expected greater math fluency but still encountered a wide range of student abilities, prompting a focus on correcting misconceptions in integrating math knowledge and fostering deeper conceptual understanding in biology. Many instructors reported that their personal and educational experiences, especially struggles with math, often shaped their inclusive and empathetic teaching practices. Additionally, instructorsâ research backgrounds influenced instructional design, particularly in the use of authentic data, statistical tools, and real-world applications. Instructorsâ teaching experiences led to refinement in lesson planning, pacing, and active learning strategies. Despite their efforts, instructors faced both internal and external challenges in implementing QR, including discomfort with teaching math, time limitations, student resistance, and institutional barriers. However, affordances such as departmental support, interdisciplinary collaboration, and curricular flexibility helped to overcome some of these challenges. This study highlights the complex relationships between instructorsâ experiences, beliefs, and contextual factors in shaping QR instruction. This calls for professional development that supports reflective practice, builds interdisciplinary competence, and promotes instructional strategies that bridge biology and mathematics and will help instructors design a learning environment that better support studentsâ development of QR skills. These findings offer valuable guidance for professional development aimed at helping biology instructors incorporate quantitative reasoning into their teaching. Such efforts can better equip students to meet the quantitative demands of modern biology and promote their continued engagement in STEM fields through more inclusive and integrated instructional approaches.more » « less
-
null (Ed.)With the growth of Computer Science (CS) and Computational Thinking (CT) instruction in the primary/elementary domain, it is important that such instruction supports diverse learners. Four categories of students Ĺ students in poverty, multi-lingual students, students with disabilities, and students who have below-grade-level proficiency in reading and math, may face academic challenges that can hinder their learning in CS/CT curricula. However, little is known about how to support these students in CS/CT instruction, especially at this young age. TIPP&SEE, a meta-cognitive strategy that scaffolds learning by proceduralizing engagement through example code, may offer some support. A quasi-experimental study revealed that the gaps between students with and without academic challenges narrowed when using the TIPP&SEE strategy, indicating its promise in providing equitable learning opportunities in CS/CT.more » « less
-
Mills, Caitlin; Alexandron, Giora; Taibi, Davide; Lo_Bosco, Giosuè; Paquette, Luc (Ed.)Students' reading ability affects their outcomes in learning software even outside of reading education, such as in math education, which can result in unexpected and inequitable outcomes. We analyze an adaptive learning software using Bayesian Knowledge Tracing (BKT) to understand how the fairness of the software is impacted when reading ability is not modeled. We tested BKT model fairness by comparing two years of data from 8,549 students who were classified as either "emerging" or "non-emerging" readers (i.e., a measure of reading ability). We found that while BKT was unbiased on average in terms of equal predictive accuracy across groups, specific skills within the adaptive learning software exhibited bias related to reading level. Additionally, there were differences between the first-answer mastery rates of the emerging and non-emerging readers (M=.687 and M=.776, difference CI=[0.075, 0.095]), indicating that emerging reader status is predictive of mastery. Our findings demonstrate significant group differences in BKT models regarding reading ability, exhibiting that it is important to considerâand perhaps even modelâreading as a separate skill that differentially influences students' outcomes."]}more » « less
-
The quantum computing curriculum developed in the Freshman Research Initiative at The University of Texas at Austin caters to students who have not yet studied advanced math and science. We lower the barrier to entry by simplifying notation and teaching through application of the concepts, only covering math methods as they become necessary. Physical motivation and simulation connect with students, and embedded examples and practice problems help cement their understanding. Through learning to program in Python and completing creative technical projects, students gain valuable, transferable skills while exploring quantum information and computing.more » « less
An official website of the United States government

