Post by BCcampus Student Research Fellow Marta Samokishyn
Even as algorithms become an integral part of our socio-cultural digital ecosystem, their impact on our lives often remains invisible. We live in what Pasquale (2016) called a “black box society” and often do not question, acknowledge, or critically reflect on algorithmic decision-making (Shin et al., 2021).
As a BCcampus Research Fellow, my goal was to build a toolkit that could help instructors from different disciplines, institutions, and professional backgrounds integrate algorithmic literacy into their educational practice. Although I geared this toolkit primarily for academic librarians due to my professional background, it can be successfully applied across many educational contexts, since algorithmic literacy transcends disciplinary boundaries and is crucial for all students.
I designed the Algorithmic Awareness Toolkit: Teaching Algorithmic Literacy in Academic Libraries and Beyond to help instructors bring awareness to what algorithms are and how they impact our lives to foster students’ algorithmic awareness and algorithmic literacy skills.
The guide begins with an algorithmic literacy assessment scale, which invites instructors to ask students to self-assess their algorithmic literacy skills in such categories as algorithmic biases and transparency, privacy, and ethical issues about algorithms. Students can complete the same self-assessment at the end of the modules to measure their understanding.
The guide has a modular structure and contains five core units that cover such topics as demystifying algorithms, targeted advertising, algorithmic biases, generative artificial intelligence, and privacy considerations. Each unit contains individual lessons that shed light on issues about algorithms. Each lesson can be used separately, making this guide adaptable to different curricular contexts. The lessons can also be applied to different delivery modes with slight modifications. The section “How to Use this Guide” provides information on audience, technology use, literacies and competencies, and curricular context and adaptability. The guide contains multiple H5P activities that provide interactive elements and self-assessment opportunities for learners at the end of each module. It also includes multimedia, activities, and discussion questions that provoke deeper understanding of concepts.
For example, in Lesson 5: Exploring Algorithmic Biases, learners watch a video called The Coded Gaze: Unmasking Algorithmic Bias , created by Joy Buolamwin, a researcher who investigates racial and gender biases in algorithmic systems. Learners have an opportunity to explore the activity, during which the algorithm judges their faces using the resource How Normal Am I? Note this activity requires the use of a camera and therefore might not be suitable for all educational contexts. Students learn how algorithms might judge them based on their gender, race, and other characteristics. The reflection questions provide an opportunity for discussion about the implication of these systems on personal and societal levels. This is just one example where students might learn more about algorithmic systems and develop awareness about the inequalities and biases perpetuated by these systems.
As algorithmic systems evolve, our understanding of how they impact society must change as well. This resource will remain a dynamic tool and continuously evolve as algorithms change.
Thanks to the BCcampus Research Fellows Program, I gained valuable experience in creating an open educational resource with mentorship and immense support, developed my skills in managing a project, and gained effective skills in algorithmic literacy education.
I will continue my work on algorithmic literacy during my PhD program to research the unique needs of undergraduate students when it comes algorithmic literacy and how Canadian educators can address them.
In a world where algorithms shape our digital experiences, fostering algorithmic literacy is crucial (Cotter & Reisdorf, 2020). It is my hope the Algorithmic Awareness Toolkit can equip post-secondary educators to talk about algorithms in their classrooms. I plan to build on this open educational resource to advance the field of algorithmic literacy in Canadian higher education.
References
Cotter, K., & Reisdorf, B. C. (2020). Algorithmic knowledge gaps: A new dimension of (digital) inequality. International Journal of Communication, 14, 745–765.
Pasquale, F. (2016). The black box society: The secret algorithms that control money and information. Harvard University Press.
Shin, D., Rasul, A., & Fotiadis, A. (2021). Why am I seeing this? Deconstructing algorithm literacy through the lens of users. Internet Research, 32(4), 1214–1234. https://doi.org/10.1108/INTR-02-2021-0087
This research is supported by the BCcampus Research Fellows Program, which provides B.C. post-secondary educators and students with funding to conduct small-scale research on teaching and learning as well as to explore evidence-based teaching practices that focus on student success and learning.