Systematic Review of Research on Teaching Algorithm Design in Undergraduate Computer Science Education

Core Concepts

There is a broad sparsity of research on teaching algorithm design in undergraduate computer science education, with each algorithm topic only being discussed in between 0 and 10 papers. More rigorous research methods, such as controlled trials and qualitative coding, are needed to gain deeper insights into effective pedagogical practices for teaching algorithm design.

Abstract

This systematic literature review examines research on teaching algorithm design in undergraduate computer science education. The authors searched the ACM Digital Library and identified 94 relevant papers published prior to August 2023.
The key findings are:

Topic Coverage:

Most algorithm topics are only covered in 0-10 papers, indicating a broad sparsity of research.
The most covered topics are O(n log n) Sorting Algorithms (10 papers) and Dynamic Programming (10 papers).
Many important topics like Graph Representations and Pattern Matching have little to no coverage.

Research Methods:

Only 38 out of 88 papers presenting quantitative data use statistical tests.
Only 15 out of 45 papers presenting qualitative data use a coding scheme.
Only 17 papers report controlled trials.
The use of rigorous research methods has increased over time, but more is needed.

Pedagogical Insights:

Problem selection is important - real-world, multi-solution, and comparison problems can increase student engagement.
Algorithm visualizations show promise, but their impact on learning outcomes is mixed.
Active learning exercises and collaborative work improve student engagement, but facilitating productive group work remains a challenge.
Little is known about psychological factors like self-efficacy in algorithms courses.

The results highlight the need for more rigorous and comprehensive research to develop a deeper understanding of effective pedagogies for teaching algorithm design in undergraduate computer science education.

Stats

"Algorithm design is a vital skill developed in most undergraduate Computer Science (CS) programs, but few research studies focus on pedagogy related to algorithms coursework."
"Across all papers in the ACM Digital Library prior to August 2023, we only find 94 such papers."
"Only 38 out of 88 papers presenting quantitative data use statistical tests, and only 15 out of 45 papers presenting qualitative data use a coding scheme. Only 17 papers report controlled trials."

Quotes

"Though the design of rudimentary algorithms has been extensively researched in an introductory programming context, most students will take a more in-depth algorithms-focused course later in their degree, as suggested by the ACM curricular guidelines [46]. Research on algorithm design instruction, as taught in upper-level algorithms courses, is critically underrepresented in the literature."
"We find a broad sparsity of papers which indicates that many open questions remain about teaching algorithm design, with each algorithm topic only being discussed in between 0 and 10 papers."
"Almost all authors only contribute to one publication, an indication that few groups have been able to gain deep insights built on their prior work."

Key Insights Distilled From

Teaching Algorithm Design: A Literature Review

by Jonathan Liu... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.00832.pdf

Deeper Inquiries

What are the key barriers that prevent more rigorous research on teaching algorithm design, and how can the computing education community address them

One key barrier to conducting more rigorous research on teaching algorithm design is the complexity and variability of the subject matter itself. Algorithm design involves abstract concepts and problem-solving strategies that can be challenging to measure objectively. Additionally, the diverse range of topics within algorithm design, as highlighted in the literature review, makes it difficult to standardize assessments and interventions across different courses and institutions. This variability can hinder the ability to compare results and draw generalizable conclusions.
Another barrier is the limited availability of standardized assessment tools and metrics specifically tailored to algorithm design education. While student performance and grades are commonly used metrics, they may not capture the full spectrum of learning outcomes in algorithm design courses. Without standardized tools for measuring student understanding, misconceptions, and problem-solving skills, it becomes challenging to conduct rigorous evaluations of teaching interventions.
To address these barriers, the computing education community can collaborate to develop standardized assessment tools and metrics for algorithm design education. By establishing common frameworks for evaluating student learning outcomes, researchers can ensure consistency in research methodologies and facilitate comparisons across studies. Additionally, promoting interdisciplinary collaborations between computer scientists, educators, and cognitive psychologists can bring diverse perspectives to the research process, leading to more comprehensive and robust studies on teaching algorithm design.

How can the findings from research on teaching introductory programming be effectively adapted and applied to the teaching of algorithm design in upper-level courses

The findings from research on teaching introductory programming can be effectively adapted and applied to the teaching of algorithm design in upper-level courses through several strategies:

Active Learning Strategies: Techniques such as peer instruction, group problem-solving, and hands-on coding exercises have been shown to enhance student engagement and learning outcomes in introductory programming courses. These active learning strategies can be adapted to algorithm design courses to promote deeper understanding of complex algorithms and problem-solving techniques.

Problem-Based Learning: Problem-based learning approaches, where students work on real-world problems or case studies, have been successful in introductory programming courses. By incorporating challenging algorithm design problems that mimic real-world scenarios, instructors can help students develop critical thinking skills and practical problem-solving abilities.

Assessment Strategies: Formative assessments, feedback mechanisms, and adaptive learning technologies used in introductory programming can be tailored to assess student understanding and progress in algorithm design courses. By providing timely feedback and personalized learning experiences, instructors can support students in mastering algorithmic concepts and techniques.

Incorporating Visualization Tools: Algorithm visualization tools, which have been effective in enhancing understanding of programming concepts, can also be utilized in algorithm design courses. Visual representations of algorithms, data structures, and problem-solving processes can aid students in comprehending complex algorithms and optimizing their designs.

By leveraging these proven strategies and adapting them to the specific challenges and objectives of teaching algorithm design, instructors can create engaging and effective learning experiences for students in upper-level courses.

What insights can be gained by expanding the literature review to include non-ACM venues, and how might that change the overall understanding of the state of research in this area

Expanding the literature review to include non-ACM venues can provide a more comprehensive and diverse perspective on the state of research in teaching algorithm design. By incorporating studies from a wider range of academic conferences, journals, and publications, researchers can access a broader pool of insights, methodologies, and findings related to algorithm design education.
This expansion may lead to the discovery of innovative approaches, best practices, and novel interventions that have not been captured in the ACM literature alone. It can also shed light on research trends, regional variations in teaching practices, and emerging topics in algorithm design education that may not be prevalent in ACM venues.
Furthermore, including non-ACM venues in the literature review can foster collaboration and knowledge exchange between different academic communities and disciplines. Researchers from diverse backgrounds can contribute their expertise and perspectives to the discourse on algorithm design education, enriching the overall understanding of effective teaching strategies and pedagogical approaches.
Overall, expanding the literature review to non-ACM venues has the potential to enhance the depth and breadth of research in algorithm design education, offering new insights and opportunities for advancing the field.