Developing Course-Level Learning Goals for Basic Data Structures in CS2

Porter, Leo; Zingaro, Daniel; Lee, Cynthia; Taylor, Cynthia; Webb, Kevin C.; Clancy, Michael

doi:10.1145/3159450.3159457

Cited by 37 publications

(25 citation statements)

References 10 publications

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“…The second topic covered in this paper is that of Data Structures and Algorithms (DSA), which is one of the key learning goals in some CS2 courses [65]. An introductory data structures course typically covers classic structures -stacks, queues, hash tables and binary trees, as well as learning about abstract data types and the application of these structures, while advanced data structure courses introduce topics on balanced search trees, graphs and sorting algorithms.…”

Section: Data Structures and Algorithmsmentioning

confidence: 99%

Differentiated Assessments for Advanced Courses that Reveal Issues with Prerequisite Skills

Nelson

Strömbäck

Korhonen

et al. 2020

Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education

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Computing learners may not master basic concepts, or forget them between courses or from infrequent use. Learners also often struggle with advanced computing courses, perhaps from weakness with prerequisite concepts. One underlying challenge for researchers and instructors is determining the reason why a learner gets an advanced question wrong. Was the wrong answer because the learner lacked prerequisite skills, has not mastered the advanced skill, or some combination of the two? We contribute a design investigation into how to create differentiated questions which diagnose prerequisite and advanced skills at the same time. We focused on tracing and related skills as prerequisites, and on advanced object-oriented programming, concurrency, algorithm and data structures as the advanced skills. We conducted an inductive qualitative analysis of existing assessment questions from instructors and from a concept inventory with a validity argument (the Basic Data Structures Inventory). We found dependencies on a variety of prerequisite knowledge and mixed potential for diagnosing difficulties with prerequisites. Inspired by this analysis, we developed examples

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Section: Data Structures and Algorithmsmentioning

confidence: 99%

Differentiated Assessments for Advanced Courses that Reveal Issues with Prerequisite Skills

Nelson

Strömbäck

Korhonen

et al. 2020

Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education

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“…The topics discussed in AITC 351 are equivalent to most CS2 courses as reported in [13]. The assignments in this course were already structured in a way that students are exposed to a single, semesterlong project that they develop incrementally.…”

Section: Aitc 351 Object-oriented Programmingmentioning

confidence: 99%

Open Challenges as a Way to Engage Students

Vincenti

2020

Proceedings of the 21st Annual Conference on Information Technology Education

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New approaches to problem-solving through crowdsourcing offer great potential for project-based learning opportunities in higher education. This experience report details how students in three upper-division courses were exposed to an open design challenge sponsored by NASA. Each course had a different level of involvement in the project, documented in the paper, which looks at different levels of involvement from the instructor's perspective. At the end of the semester, students reported on their engagement in the course as well as how these assignments compared to traditional ones. The findings support that open challenges, and NASA SUITS in particular, offer a great opportunity to engage students with computer programming. CCS CONCEPTS • Social and professional topics → Information technology education; Computing education.

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“…We were encouraged to find that all of the courses we looked at covered the topics we classified as basic data structures, that basic data structures were often a significant portion of the course, and that the expert panel felt basic data structures were important for student success in CS 2 and follow-on courses. As a result, we narrowed our focus to a CI just for the topics within that group (10 of our original 49 subtopics; see [23]).…”

Section: Step 1: Establishing Topicsmentioning

confidence: 99%

“…We were advised by our external evaluator that 6 goals was appropriate given the focus and depth of our assessment; many other CI projects have between 4 and 10 course-level learning goals that they seek to address [2,4,5,16,27,31]. More details about our topics and learning goals are available in prior work [23].…”

Section: Learning Goalsmentioning

confidence: 99%

STOIC: Streaming operating systems in the cloud

Collins

Gelles

Marks

et al. 2017

2017 IEEE International Conference on Communications (ICC)

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Concept inventories (CIs) allow researchers and practitioners to measure student conceptual learning within a course or topic area. While they have enabled meaningful pedagogical change in other disciplines, there are relatively few CIs in computer science. In this paper, we report on our experiences as recent developers of a CI for basic data structures. We discuss each step along the route to a CI and offer tips based on what we have learned. We encourage others to create CIs, and we hope that this paper will serve as a practical guide through the process.

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Developing Course-Level Learning Goals for Basic Data Structures in CS2

Cited by 37 publications

References 10 publications

Differentiated Assessments for Advanced Courses that Reveal Issues with Prerequisite Skills

Differentiated Assessments for Advanced Courses that Reveal Issues with Prerequisite Skills

Open Challenges as a Way to Engage Students

STOIC: Streaming operating systems in the cloud

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