Authored by Adjua N. K. McNeil
When joining a professional community-of-practice, the goal of the novice is to learn the history, culture, and methods valued by insiders. This can be challenging in any field, but particularly in disciplines that primarily exist in the digital or virtual space. Despite its ubiquity in modern life, the average person knows little about the history of computer programming and even less about programming culture save the stereotypes of socially inept “nerds” or lawless hackers that are prevalent in the media. Thus, training programs like the Digital Research Incubator provide a supportive environment for newcomers from diverse backgrounds to become viable members of the larger Python community.
To me, the Incubator’s 2025-2026 cohort is unique in that members represent each learner population within the academic community: undergraduate, graduate, doctoral, staff, and faculty. Our proposed projects are drawn from diverse fields including psychotherapy, library science, mathematics, and literacy development, among others. Despite differences in professional experience and areas of interest, all cohort members are novice programmers, and this provides a context for our shared learning journey.
In my case, I see the Incubator as an opportunity to increase my value as a professional. I consider myself an “educational engineer” and have dedicated my career to creating tools that make the fruits of research accessible to teachers, parents, and learners. Whether I am participating in instruction, research, or program administration, my work has been guided by the premise that unfettered access to high quality literacy instruction is an inalienable right. Without literacy, individuals are unable to fully comprehend the extent to which printed words shape their lives. So, it is ironic that my current efforts to support print literacy are dependent on my ability to master an artificial language. Nevertheless, like other seasoned domain experts, I recognize that maintaining my professional standing is heavily dependent on my ability to demonstrate my expertise with digital platforms.
Lucky for me, my focus is literacy research—a field that has become synonymous with machine learning and computational models of human behavior. For more than two decades, the Science of Reading has revolutionized our understanding of reading development. In the process, it has also reframed our assumptions about the skills needed to teach reading effectively. Although I welcome the clarion call to upskill teachers, I sometimes worry that viewing reading instruction as a “science” has given the impression that its methods are not accessible to the mainstream public. This would be an unfortunate assumption considering the amount of time that children spend within their home and community literacy environments. Accounting for seasonal breaks and holidays, the average child only spends about 20% of their waking hours receiving direct instruction over the course of a school year. The remaining time is spent with caregivers, friends, and community members, each serving as a literacy role model for the young readers in their care. Making the Science of Reading accessible to these stakeholders could give rise to a genuinely universal culture of literacy. And, for today’s consumers, accessible means free or low-cost, web-based, and easy to operate.
My initial project plan called for a search engine that could provide simple instructional information about the words found in books and instructional materials for K-3 learners. In addition to a searchable database of words and their characteristics, the project deliverables included a graphic user interface (GUI) that allowed users to select word features of interest and generate simple reports. During a previous study of Grade 1 word reading fluency assessments, I was able to use a Python script and the Carnegie Mellon University Pronouncing Dictionary to examine the decodability of test items. For my Incubator project, I had planned to extract word features from several linguistic databases and adapt my script to these additional resources. However, while I was searching for sources of child language data, a research team led by Don Compton at the Florida Center for Reading Research released the Developmental English Lexicon Project (Compton et al., 2025), a search engine that provides a variety of metrics for words used in K-12 academic and literacy texts. In other words, the very thing I wanted to create but to the nth degree.
I chose to use this bittersweet development as a self-teaching moment and an opportunity to observe the unwritten principles of computer programming. For one, every “original” idea has been thought of by others, and it is not uncommon for the same innovation to emerge from two different lines of inquiry or research teams. On the practical side, even the most gifted polymath cannot outpace the combined efforts of a skilled research team. So, I am mindful that my newly acquired programming skills will not make me a one-person band but a more valuable team member. Lastly, and most importantly, one must always be prepared for the dynamic pace of digital development and to pivot according to the needs of the field.
Thus, my new objective is to create a text analyzer that provides details about features to which developing readers are particularly sensitive. These include word-level features like frequency and spelling patterns, sentence-level features like complexity and passive voice, and text-level features like paragraph structure and function, all of which are contained within the resources I have already collected or accessed. A quick Google search reveals a plethora of web-based or downloadable apps that can perform each of these tasks, but none designed to accomplish all of them. Moreover, literacy experts like Alfrieda Hiebert have shown that traditional text difficulty formulas (and the digital tools that rely on them) are less accurate when used with texts for developing readers. So, there is room in the field for a text evaluator that pays special attention to these learners.
When mapping out my design, I thought deeply about potential end users with the least experience analyzing texts: novice teachers reviewing instructional materials, librarians matching books to readers, parents building their home library. They would benefit from a tool that highlights the teachable moments within a text, like recurring spelling patterns or unfamiliar words. At the same time, consumers of educational technology have certain expectations about the functionality of apps that claim to evaluate or manipulate texts. For one, users expect the ability to submit a text for analysis either by copying and pasting into a text field or uploading a file. Similar apps, like the Editor feature in Microsoft Word, use checkboxes or similar mechanisms that allow users to select features of interest. Lastly, results should be reported using a combination of plain language and easily decodable graphics. In short, the common denominators of the user experience guide the application’s design.
Mapping out the app’s features also provides an outline for my learning within the Incubator. Each aspect of the user experience represents an object or method I must learn to create. For example, now that we have learned how to perform a simple word frequency analysis on texts submitted through copy-paste or file upload, I can combine this with code that requests user input, thanks to the practice exercises in Python Essentials. My next step will be to improve my sequencing of methods within a script, followed by creating a format for the graphic interface.
In hindsight, I realize that the process of revising my project plan served as my initiation into the Python community. In addition to applying technical skills like text extraction and managing data input, I have honed the soft skills that make a successful computer programmer: critical thinking, cognitive and emotional resilience, and ingenuity. These skills are not taught in our coursework per se but are nurtured by the process of becoming Pythonistas.
References:
Compton, D. L., Rueckl, J. G., Steacy, L. M., Siegelman, N., Edwards, A. A., Petscher, Y., Patton-Terry, N., Cooper Borkenhagen, M. J., & Starke, C. (under review). The developmental English Lexicon Project (d-ELP): Continuous word reading difficulty estimates of the 9,961 most frequently printed English words for US children. Behavioral Research Methods.