Research Statement

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Research Projects

Toward an Equitable Computer Programming Practice Environment for All (2022)

Carl Haynes-Magyar

Traditional introductory computer programming practice has included writing pseudocode, code-reading and tracing, and code-writing. These problem types are often time-intensive, frustrating, cognitively complex, in opposition to learners’ self-beliefs, disengaging, and demotivating—and not much has changed in the last decade. Pseudocode is a plain language description of the steps in a program. Code-reading and tracing involve using paper and pencil or online tools such as PythonTutor to trace the execution of a program, and code-writing requires learners to write code from scratch. In contrast to these programming practice problems, mixed-up code (Parsons) problems require learners to place blocks of code in the correct order and sometimes require the correct indentation and or selection between a distractor block and a correct code block. Practice problems such as these can increase the diversity of programmers who complete introductory computer programming courses by improving the efficiency with which they acquire knowledge and the quality of knowledge acquisition itself.
But not all programmers are alike in their affinity for programming problems; some programmers reject code-tracing while others prefer it, and programmers with prior experience can have strong adverse reactions to Parsons problems. Relevance (i.e., goals, interest, and purpose) mediate learning outcomes and computing education researchers posit:

Equitable assessments in CS should be responsive to student identities and needs, participatory in their design, and educative in their outcomes. Considering the purposes, processes, places, parts, power, people, and products of assessments can help achieve these principles (Davidson & Ko, 2022).

Hence, this project aims to decenter cognitive and other norms about learning how to program through the development of a computer programming practice environment called Codespec. To start, it features a unique problem space area that offers learners the option to switch between solving a problem as either (1) a pseudocode problem (akin to subgoals and programming plans), (2) a Parsons problem, (3) a Faded Parsons problem, (4) a fix code problem, or (5) a write-code problem.

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On Learning How to Program via an Interactive eBook with Adaptive Parsons Problems (2019-2022)

Carl Haynes-Magyar

This multi-manuscript presents studies aimed at exploring the problem-solving efficiency of Parsons problems that optimize cognitive load as a substitute for traditional computer programming practice. Mixed methods are used to understand how learners think, behave, and feel when learning how to program via an interactive eBook with adaptive Parsons problems and equivalent write-code problems. First, I conducted field experiments to evaluate the design of these problems for active learning during lecture. Second, I redesigned these problems and tested hypotheses about cognition and learning to understand cognitive, behavioral, and affective learning outcomes impacted by these design changes. And third, I explored access and equity issues for neurodiverse learners.

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Toward Accessible Introductory Computer Programming Practice (2020)

Carl C. Haynes

Dynamically adaptive Parsons problems are pieces of code that must be ordered and indented correctly. Performance on prior problems determines the difficulty of subsequent ones. These problems comprise an adaptive learning system that dynamically adapts the learning experience to an individual’s ability. But how do novice programmers with learning (dis)abilities experience solving them? And, how can we make this learning experience more accessible? To understand the learning experience, researchers suggest testing the hypothesis that cognitive load is less for solving Parsons problems than writing equivalent code. Furthermore, researchers propose using an ability-based design approach to increase the accessibility of adaptive learning systems such as dynamically adaptive Parsons problems. This is a research proposal to explore these topics.

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Learning Analytics and Interactive Computing eBooks (2020)

Barbara Ericson, Carl C. Haynes, Julia Cope, David Li

Learning analytics has been used to identify at-risk learners, help learners develop self-regulated learning skills, and improve curricula. We take a discipline-based learning analytics approach in the context of computer science learning to analyze clickstream data from two interactive eBooks: one for the Advanced Placement (AP) Computer Science Principles (CSP) course and one for the AP Computer Science A (CSA) course.

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My Learning Analytics (MyLA) (2018-2019)

Stephanie Teasley, Stuart Karabenick, Matthew Kay, Carl C. Haynes, Shannon Elkins, Reed Coots, Jennifer Love, Zhen Qian, Pushyami Gundala, John Johnston, Matthew Jones

The My Learning Analytics (MyLA) project supports research into student use of learning analytics. We investigated the extent to which MyLA supports the development of students' self-regulated learning skills.

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Investigating Enterprise-Level Learning Technologies in Higher Education (2018-2019)

Carl C. Haynes, Stuart Karabenick, Stephanie Teasley

Students, teachers, staff, and others within institutions of higher education use a multitude of enterprise-level learning technologies. We investigate how these technologies mediate learning amongst students to inform the development and continued improvement of these technologies based on student feedback and use.

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Evaluating Student-Facing Dashboards in Learning Management Systems (2017-2018)

Carl C. Haynes, Stephanie Teasley, Stephanie Haley, Meghan Oster, John Whitmer

Student-facing dashboards are becoming standard tools within learning management systems (LMSs) with little empirical guidance about the impact of these tools, particularly on various subpopulations of students (e.g., first-generation, minorities, transfer students). In collaboration with Blackboard Inc., we investigated students' perceptions of textual and visual information provided by the LMS.

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Searching as Learning (2016)

Carl C. Haynes, Soo Young Rieh, and Kevyn Collins Thompson

People often use search systems to retrieve information during learning tasks. We investigated the effect of different query results on learning and search satisfaction.

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Assessment Proficiencies in Library and Information Science (LIS) Education (2016)

Carl C. Haynes, Megan Oakleaf, and Samantha Settimio

The need for libraries to assert their value has led to the creation of new roles for librarians. We investigated the extent to which the current library and information science curriculum addressed the proficiencies needed by assessment librarians.

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