An official website of the United States government
The expansion of computer science (CS) into K-12 contexts has resulted in a diverse ecosystem of curricula designed for various grade levels, teaching a variety of concepts, and using a wide array of different programming languages and environments. Many students will learn more than one programming language over the course of their studies. There is a growing need for computer science assessment that can measure student learning over time, but the multilingual learning pathways create two challenges for assessment in computer science. First, there are not validated assessments for all of the programming languages used in CS classrooms. Second, it is difficult to measure growth in student understanding over time when students move between programming languages as they progress in their CS education. In this position paper, we argue that the field of computing education research needs to develop methods and tools to better measure students' learning over time and across the different programming languages they learn along the way. In presenting this position, we share data that shows students approach assessment problems differently depending on the programming language, even when the problems are conceptually isomorphic, and discuss some approaches for developing multilingual assessments of student learning over time.
more »
« less
Linguistic Pedagogical Approaches to Transfer in Computer Science
More students are encountering computer science at multiple grade levels and so are learning more than one programming language. There is an ever-growing body of research describing how students transfer knowledge from one language to another. Current research shows that transfer helps students learn a second programming language in the interim and improves attitudes and retention of students in computer science. While novice programmers generally struggle with the same concepts [1, 12], the exact difficulties and benefits of the transition to a second programming language differ depending on the programming languages the student is learning. In order to best serve students of different backgrounds and maintain their interest in the subject, the details of transfer for different programming language combinations must be understood. This poster surveys and analyzes the current research on transfer and provides a summary of the variety of challenges and advantages students face in learning a second programming language. Additionally, interdisciplinary pedagogical approaches are discussed, integrating perspectives from applied linguistics as novel solutions to the specific issues faced in programming language transfer.
more »
« less
- Award ID(s):
- 2201209
- PAR ID:
- 10590338
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400704246
- Page Range / eLocation ID:
- 1776 to 1777
- Format(s):
- Medium: X
- Location:
- Portland OR USA
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
There has been significant progress in increasing the access to computing education for many K-12 students, including states adopting computer science (CS) standards and/or requiring CS courses. This includes the creation of block-based programming languages to make programming more accessible to younger students. Despite this progress, a new challenge has emerged: Students often struggle to transfer conceptual knowledge when transitioning to a new programming language (e.g., transitioning to a text-based programming after learning a block-based programming language). This poster presents the results of teacher interviews regarding the examples of knowledge transfer they observe in their classrooms. These interviews are part of an overarching project that aims to address the challenge of knowledge transfer between programming languages by developing a framework to support such transfer and deliver curricular supports that can be used to aid students’ productive knowledge transfer between programming languages.more » « less
-
Expertise in programming traditionally assumes a binary novice-expert divide. Learning resources typically target programmers who are learning programming for the first time, or expert programmers for that language. An underrepresented, yet important group of programmers are those that are experienced in one programming language, but desire to author code in a different language. For this scenario, we postulate that an effective form of feedback is presented as a transfer from concepts in the first language to the second. Current programming environments do not support this form of feedback. In this study, we apply the theory of learning transfer to teach a language that programmers are less familiar with-such as R-in terms of a programming language they already know-such as Python. We investigate learning transfer using a new tool called Transfer Tutor that presents explanations for R code in terms of the equivalent Python code. Our study found that participants leveraged learning transfer as a cognitive strategy, even when unprompted. Participants found Transfer Tutor to be useful across a number of affordances like stepping through and highlighting facts that may have been missed or misunderstood. However, participants were reluctant to accept facts without code execution or sometimes had difficulty reading explanations that are verbose or complex. These results provide guidance for future designs and research directions that can support learning transfer when learning new programming languages.more » « less
-
Emile Shehada (Ed.)Learning the system of estates in land and future interests can seem like learning a new language. Scholars and students must master unfamiliar phrases, razor-sharp rules, and arbitrarily complicated structures. Property law is this way not because future interests are a foreign language, but because they are a programming language. This Article presents Orlando, a programming language for expressing conveyances of future interests, and Littleton, a freely available online interpreter (at https://conveyanc.es) that can diagram the interests created by conveyances and model the consequences of future events. Doing so has three payoffs. First, formalizing future interests helps students and teachers of the subject by allowing them to visualize and experiment with conveyances. Second, the process of formalization is itself deeply illuminating about property doctrine and theory. And third, the computer-science subfield of programming language theory has untapped potential for legal scholarship: a programming-language approach takes advantage of the linguistic parallels between legal texts and computer programs.more » « less
-
Historically, female students have shown low interest in the field of computer science. Previous computer science curricula have failed to address the lack of female-centered computer science activities, such as socially relevant and real-life applications. Our new summer camp curriculum introduces the topics of artificial intelligence (AI), machine learning (ML) and other real-world subjects to engage high school girls in computing by connecting lessons to relevant and cutting edge technologies. Topics range from social media bots, sentiment of natural language in different media, and the role of AI in criminal justice, and focus on programming activities in the NetsBlox and Python programming languages. Summer camp teachers were prepared in a week-long pedagogy and peer-teaching centered professional development program where they concurrently learned and practiced teaching the curriculum to one another. Then, pairs of teachers led students in learning through hands-on AI and ML activities in a half-day, two-week summer camp. In this paper, we discuss the curriculum development and implementation, as well as survey feedback from both teachers and students.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3626253.3635566
