Category Archive: Research

Information Processing Theory

Presented by Joe Tise, PhD, Educational Psychology & Senior Education Researcher at CSEdResearch.org

The stark limitations of behaviorist theories of learning gave rise (in part) to cognitive theories of learning, the most prominent of which is information processing theory (IPT) (Atkinson & Shiffrin, 1968). As you will see, IPT is analogous to a computer system in many ways. IPT posits three primary “stores” of memory and three primary cognitive “processes.” The three memory stores include the:

• Sensory register (like a motion detector or thermostat)
• Working memory (like RAM)
• Long-term memory (like a hard drive)

The three processes include:

• Attention (like selecting which folder or drive to work in)
• Encoding (like writing to a disk)
• Retrieval. (like reading a disk)

When a person encounters information (broadly construed), it exists first in the sensory register which is informed by the five physical senses as shown in the following figure.

Information in the sensory register persists only as long as the senses actively perceive the information (e.g., the shapes of words on a page). Once the senses stop perceiving the information, the sensory register is cleared. 

So how does one learn anything, then? The first primary cognitive process must be invoked—attention. Information is transferred from the sensory register to working memory when we direct attention toward the information—and only at this point do we become conscious of it. This is analogous to how a computer transfers information from a physical sensor (sensory register) to its RAM (working memory) for manipulation.

You have likely already heard that working memory (WM) is limited to 7 +/- 2 pieces of information (Miller, 1956), and this fact illustrates one relatively strict limitation of our cognitive system. Working memory is in many ways a “bottleneck” to human learning and cognitive functioning. Information persists in WM for about 20-30 seconds without rehearsal or other cognitive manipulations of the information. As with a computer’s RAM, it is limited in capacity and will be periodically cleared.

If we want the information to persist longer than that, we must apply the second primary cognitive process, encoding, to the information so that it can move from WM to long term memory (LTM), much like writing to a hard drive. LTM capacity is theoretically unlimited and information within LTM can persist forever. 

Finally, if we wish to use information in LTM, we must invoke the third primary cognitive process: retrieval. Retrieval brings information out of LTM and back into WM so that it is once again conscious to us and can be manipulated or articulated via speech, writing, actions, or other means. Drawing the information from LTM into WM is akin to reading information from a hard drive.

Only now can one understand the IPT definition of learning. IPT views human learning as the transfer (i.e., encoding) of information from working memory into long term memory.

Strengths

Information processing theory provides a succinct framework for understanding how the human brain processes information. While behaviorists completely disregard the cognitive domain, IPT attempts to directly explain it. Tenets of IPT are ripe for empirical investigation (e.g., the capacity and duration of working memory has been studied countless times). 

IPT also is directly applicable to many fields beyond just learning, and its tenets are leveraged in domains such as user experience research, driving safety courses, and brain health assessments for sports injuries and dementia screening. 

Limitations

While information processing theory provides explanations for many of the cognitive phenomena we encounter during learning and daily life, some limitations still exist. For example, IPT faces a supposed “homunculus” problem. That is, models of working memory (yes, there are sub theories of IPT to further specify the working memory component) detail a central executive component, which controls the two other components of working memory (see Baddeley, 2003 for more detail). 

But this raises the question—what controls the central executive? Herein lies the problem. Such models of working memory appear to rely on a homunculus—a small imaginary “being” inside our brains that controls the central executive, which in turn controls the other components of working memory. 

This blog post is far too broad to properly detail models of working memory, so for our purposes just know that critics of IPT cite the homunculus problem as at least a needed point of further theoretical refinement. 

Potential Use Cases in Computing Education

• Research: How does a student’s working memory capacity relate to their coding skill/accuracy?
• Practice: Direct attention to salient features of content (to ensure attention is on the correct features of content), provide and model use of learning strategies (to promote encoding), and use low-stakes practice quizzes/questions often to exercise students’ retrieval process. 

Influential theorists:

• Richard C. Atkinson (1942 – present)
• Richard M. Shiffrin (1929 – present)
• Alan Baddeley (1934 – present) 

Recommended seminal works:

Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In K. W. Spence & J. T. Spence (Eds.), The psychology of learning and motivation: Advances in research and theory, Vol II (pp. 89–195). Academic Press.

Baddeley, A. D., & Hitch, G. (1974). Working memory. In G. H. Bower (Ed.), Psychology of Learning and Motivation (Vol. 8, pp. 47–89). Academic Press. https://doi.org/10.1016/S0079-7421(08)60452-1

Baddeley, A. (1992). Working Memory. Science, 255(5044), 556–559. https://doi.org/10.1126/science.1736359 

Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81-97. https://psychclassics.yorku.ca/Miller/

Shiffrin, R. M., & Atkinson, R. C. (1969). Storage and retrieval processes in long-term memory. Psychological Review, 76(2), 179–193.

References

Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In K. W. Spence & J. T. Spence (Eds.), The psychology of learning and motivation: Advances in research and theory, Vol II (pp. 89–195). Academic Press.

Baddeley, A. (2003). Working memory: Looking back and looking forward. Nature Reviews | Neuroscience, 4, 829–839. https://www.nature.com/articles/nrn1201

Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81-97. https://psychclassics.yorku.ca/Miller/

Series

• Introduction to Behaviorism
• Social Cognitive Theory
• Constructivism/Sociocultural Theory

Behaviorism Introduction

Presented by Joe Tise, PhD, Educational Psychology & Senior Education Researcher at CSEdResearch.org

At least surface-level familiarity with Pavlov’s experiments and principles of classical and operant conditioning have become almost ubiquitous among the general public. What many may not know, however, is that classical and operant conditioning are the two primary Behaviorist theories of learning. To a behaviorist, only observable behavior is worthy (or even possible) of scientific study. From this philosophy stems the behaviorist definition of learning: a relatively permanent change in behavior that is caused by experience. Behaviorist theories of learning exclude any attempts to examine cognition or cognitive processes because they are not directly observable.

The theory of classical conditioning was born from Pavlov’s experiments with dogs. Pavlov discovered that his dogs began to associate food with the sound of a bell, and that upon ringing the bell, his dogs would salivate. Thus, a change in behavior (salivation) was caused by experience (presentation of food shortly after the bell). Note that classical conditioning only applies to involuntary (reflexive) behavior (e.g., fear, physiological responses). Operant conditioning (think B.F. Skinner) extends the theory into voluntary behavior. In a series of classic experiments, psychologist B.F. Skinner trained rats to perform novel behaviors in exchange for food (reinforcement) or in anticipation of an electric shock (punishment) (Skinner, 2019).

Operant conditioning still relies on the mechanism of association, but accounts for novel (i.e., not innate) behavior. Indeed, operant conditioning is at the heart of nearly all animal training (e.g., dogs, show animals) and has real-world applications for classroom management. For example, a teacher may reward students with a small toy if they participate in class and may punish students (e.g., by issuing a demerit) for acting out. If this reinforcement and/or punishment is successful and the student’s behavior changes, the behaviorist would say the behavior change is evidence of learning. Although purely Behaviorist research studies are less common today than they were 60-70 years ago, elements of Behaviorism are still prevalent in some fields and sub-disciplines, including game-based learning (e.g., though badges, scoring) (Coskun, 2019; Hulsbosch et al., 2023; Leeder, 2022)

Strengths

There are several strengths of behaviorist theories of learning.

• First, research has shown that behaviorist conceptions of learning are generalizable to not just multiple cultures, but indeed a wide variety of animals. That is, learning by association and reinforcement/punishment is not uniquely human, and therefore behaviorist theories of learning are by far the most generalizable.
• And since behaviorists study only what can be observed directly (i.e., behavior), behaviorist theories are arguably the best-positioned to achieve replicability—a known problem in the psychology fields (Open Science Collaboration, 2015).
• Behaviorist principles are directly applicable to the classroom via classroom management techniques. Any experienced K-12 teacher will tell you that classroom management is a top priority, and there is ample opportunity to apply behaviorism throughout the instructional process.
• Behaviorism arose as a direct counter to eugenic philosophies, and therefore was one of the first DEI-minded approaches to psychological/educational research. To this effect, John Watson (1930) famously said: “Give me a dozen healthy infants, well-formed, and my own specified world to bring them up in and I’ll guarantee to take any one at random and train him to become any type of specialist I might select – doctor, lawyer, artist, merchant-chief and, yes, even beggar-man and thief…”

Limitations

Noteworthy limitations to behaviorism also exist. For example:

• Behaviorist theories cannot account for cognitive processing—and explicitly exclude study of cognition. Cognitive/educational research, and even simple experience, tells us that human learning is much more complex than involuntary associations and reinforcement/punishment schedules.
• The notion of observational learning (i.e., learning by watching someone else) is a prime example of the shortcomings of behaviorism. Behaviorism cannot explain observational learning.
• Finally, experienced students and teachers understand many tasks require complex problem solving, learning strategies, and metacognition to complete. Behaviorism falls short of even conceptualizing these constructs, let alone explaining them.

Potential Use Cases in Computing Education

• Research: An intervention based in classical conditioning designed to reduce negative physiological responses (anxiety) to computers/computer science. These negative physiological responses would also influence students’ self-efficacy, so a link could be made to social-cognitive theory as well.
• Practice: A teacher could begin each class with a pleasant story, song, comment, snack, or even scent to elicit a positive emotional response from their students. After repeated exposure (i.e. conditioning), the students should associate positive feelings with the classroom/subject/teacher.

Influential theorists

• John B. Watson (1878 – 1958)
• B.F. Skinner (1904 – 1990)
• Edward L. Thorndike (1874 – 1949)

Recommended seminal works

• Watson, J. B. (1913). Psychology as the behaviorist views it. Psychological Review, 20(2), 158–177. https://doi.org/10.1037/h0074428
• Skinner, B. F. (1965). Science and human behavior. Simon and Schuster.
• Thorndike, E. L. (1898). Animal intelligence: An experimental study of the associative processes in animals. The Psychological Review: Monograph Supplements, 2(4), i–109. https://doi.org/10.1037/h0092987

References

Coşkun, K. (2019). Conditioning Tendency Among Preschool and Primary School Children: Cross-Sectional Research. Interchange, 50(4), 517–536. https://doi.org/10.1007/s10780-019-09373-1

Hulsbosch, A., Beckers, T., De Meyer, H., Danckaerts, M., Van Liefferinge, D., Tripp, G., & Van Der Oord, S. (2023). Instrumental learning and behavioral persistence in children with attention‐deficit/hyperactivity‐disorder: Does reinforcement frequency matter? Journal of Child Psychology and Psychiatry, 64(11), 1–10. https://doi.org/10.1111/jcpp.13805

Leeder, T. M. (2022). Behaviorism, Skinner, and Operant Conditioning: Considerations for Sport Coaching Practice. Strategies, 35(3), 27–32. https://doi.org/10.1080/08924562.2022.2052776
Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716. https://doi.org/10.1126/science.aac4716

Skinner, B. F. (2019). The Behavior of Organisms: An Experimental Analysis. B. F. Skinner Foundation. https://books.google.com/books?id=S9WNCwAAQBAJ

Watson, J. B. (1930). Behaviorism (Revised edition). University of Chicago Press.

Series

• Information Processing Theory
• Social Cognitive Theory
• Constructivism/Sociocultural Theory

Introduction to Learning Theories Series

Presented by Joe Tise, PhD, Educational Psychology & Senior Education Researcher at CSEdResearch.org

If data is a pile of bricks, theory is a building plan. Used together, a house can be built and a valid representation of truth can be uncovered. 

The traditional view of education research would say data without theory is no more useful than a pile of bricks without a building plan. This understanding is at the heart of traditional quantitative educational and psychological research. Quantitative educational researchers view theory as integral to the relationship between research and practice because it gives rise to causal hypotheses and, in turn, informs action. 

However, one may (convincingly) argue that recent developments in artificial intelligence (AI), data mining, machine learning (ML), and large-language modeling (LLM) uncover deep insights and relationships despite not being driven by a particular a priori theoretical perspective. While this is certainly true, I argue that researchers still must construct theoretical models (broadly construed) to make sense of the patterns and insights uncovered by these empirical methods. There is some inherent utility in using AI or ML to discover, for example, that students’ user log data in a learning management system can predict their eventual GPA or likelihood of dropping the course. But understanding why these relationships exist requires theoretical musing, which so far cannot be accomplished via AI or ML.

Further, as a qualitative researcher may be quick to point out–some research questions are simply too cutting-edge to be grounded in theory a priori. Save for truly exploratory research (where very little or even no prior research exists), educational researchers will tend to engage a theory either as a guide prior to data collection or explanatory mechanism after data analysis–whether that theory is robust with decades of empirical support or more fledgling and known only to the researcher. 

As one manifestation of educational research, computer science education (CSEd) research needs to be grounded in established educational theory and/or generate new theory where established theory falls short. Fortunately, nearly 100 years of educational research have already passed. The fruits of this research are four prominent theories about how learning occurs: Behaviorism, Information-processing, Social-cognitive, and Constructivism. 

In this four-part series, I introduce and briefly overview each theory and in doing so, I forward a paraphrased definition of the (nebulous) term “learning” associated with each theory, outline central assumptions, explicate strengths and limitations, and recommend several seminal works for each theory of learning. 

I encourage education researchers who wish to research the learning phenomenon to pay attention to each. If you have limited time, however, I suggest paying special attention to the posts and subsequent recommended readings on information-processing and social-cognitive theories, as these two theories undergird much of contemporary educational research (whether these theories are explicitly mentioned in publications or not) and have shown prowess in explaining the complicated web of influences on human learning. 

Series:

• Introduction to Behaviorism
• Information Processing Theory
• Social Cognitive Theory
• Constructivism/Sociocultural Theory

Reimagining CS Pathways: High School and Beyond

In the past four years, the proportion of US high schools offering at least one computer science (CS) course increased from one-third to one-half (source), and more growth is expected. Simultaneously, the field of computer science has shifted significantly and we have continued to learn more about what it means to teach computer science with equitable outcomes in mind. One challenge in CS education is ensuring that curriculum and pedagogy adapt to these shifting grounds; it is easy to imagine the frustration of a student who discovers that their high school CS instruction has left them poorly prepared for future opportunities to learn computer sciences. 

We are pleased to announce, in collaboration with the Computer Science Teachers Association (CSTA), our new NSF-funded project to address this issue. With Bryan Twarek (PI) and Dr. Monica McGill (Co-PI) at the helm, the Reimagining CS Pathways: High School and Beyond project has the long-term goal of articulating a shared vision for introductory high school CS instruction that could be used to fill a high school graduation requirement as well as the alignment between that content and the two AP CS courses and college-level CS courses.

Our work will not be in isolation. Reimagining CS Pathways: High School and Beyond includes three convenings of K12 teachers and administrators, instructors at 2- and 4- year colleges, curriculum developers, industry representatives, state CS supervisors, and other vested parties. Written reports of these convenings will be shared with the public. Additionally, the project will create:

• Recommendations for the content of an introductory high school CS course
• Descriptions of high school CS courses beyond an introductory course, including suggested course outcomes
• Recommendations for possible adjustments to the CSTA standards and the AP program
• A framework for the process of creating similar course pathways in the future

Undergirding this work is a commitment to more equitable CS instruction, ensuring that all students – including those who have historically been less likely to study CS – will have access to these CS pathways. A more coordinated approach to high school and college level CS instruction is also more likely to meet the needs of industry and society as a whole.

This project expects to have its recommendations and framework available in the summer of 2024. 

Click here to learn more about this project.

If you are interested in participating, please reach out via our contact form or, for more information, contact julie@csedresearch.org.

“But They Just Aren’t Interested in Computer Science” (Part Three)

Written by: Julie Smith

Note: this post is part of a series about the most-cited research studies related to K12 computer science education.

It’s discouraging to learn that children as young as age six express the belief that boys are better than girls at programming and at robotics, and girls have less interest in or belief in their ability to succeed in computing.

But the good news from the study Programming experience promotes higher STEM motivation among first-grade girls is that it was, in their experiment, actually not that difficult to improve girls’ interest and belief in their self-efficacy: all it took was twenty minutes in the lab with a cute robot that they could program with a smartphone. After that intervention, their interest and self-efficacy were statistically indistinguishable from boys; the same was not the case for girls who engaged in another activity unrelated to technology. 

There’s a reason this article is one of the most commonly-cited in the computer science education literature: the representation rates of women in computing – from high school courses through college majors and into the workforce – remains stubbornly low. This article suggests that, while stereotypes are adopted early, a relatively simple intervention for young children could perhaps be enough to overcome the effects of those stereotypes on girls’ interest in computing.

Further Reading

• The role of recognition in disciplinary identity for girls
• Subtle Linguistic Cues Increase Girls’ Engagement in Science

Series

“But They Just Aren’t Interested in Computer Science” Part One

“But They Just Aren’t Interested in Computer Science” Part Two

“But They Just Aren’t Interested in Computer Science” (Part Two)

Written by: Julie Smith

Note: this post is part of a series about the most-cited research studies related to K12 computer science education.

The study’s title says it all: “Gender stereotypes about interests start early and cause gender disparities in computer science and engineering.” It’s worth noting that the careful design of their studies bolsters the case: this work includes both surveys and experiments, allowing the researchers to comment on causality. The combination of surveys and interventions makes it possible to conclude that the stereotype drives the lower interest rate, not a student’s inherent lower rate of interest causing them to generate a stereotype by imputing their attitude onto others. Additionally, their diverse subject pool makes it more likely that their findings are widely applicable.

The researchers found that stereotypes suggesting that boys are more interested in computer science exist from at least the third grade. Further, these stereotypes make it less likely for girls to study computer science, an effect mediated by the girls’ decreased sense that CS is for them. 

Significantly, stereotypes about interest in computer science were a stronger predictor of a student’s intent to study computer science than stereotypes about ability. The authors do point out that there is a stronger cultural norm against expressing ability stereotypes than interest stereotypes, which may make it harder to root out the interest stereotypes. At the same time, the finding that student interest in studying computer science could be impacted by their experiences in an experiment imply that interventions designed to counteract stereotypes may very well be effective. 

The fact that this study is one of the most-cited K12 computer science education research studies suggests that its message of the importance of recognizing the role of interest stereotypes has resonated with many other researchers. The next step is to determine which types of interventions are most effective at breaking down interest stereotypes.

Further Reading

• Gender Stereotypes Influence Children’s STEM Motivation
• Beyond students: how teacher psychology shapes educational inequality

Series

“But They Just Aren’t Interested in Computer Science” (Part One)

“But They Just Aren’t Interested in Computer Science” (Part Three)

Emerging Promising Practices for CS Integration

Our recently accepted paper, Emerging Practices for Integrating Computer Science into Existing K-5 Subjects in the United States, will be presented at WIPSCE 2023 in Cambridge, England. 

This particular qualitative work, conducted by Monica McGill, Laycee Thigpen, and Alaina Mabie of CSEdResearch.org, included interviews with researchers and curriculum designers (n=9) who have engaged deeply in K-5 CS integration for several years. Their perspectives were analyzed and synthesized to inform our results.

Several promising practices emerged for designing curriculum, creating assessments, and preparing teachers to teach in a co-curricular manner. These include ways for teachers to vary instruction, integrating into core (and oft tested) language arts and mathematics, and simplifying assessments. Many of the findings are borne from the need to help new teachers become comfortable teaching a new subject integrated into their other subjects.

Generally, promising practices that emerged included adopting Universal Design for Learning practices, include ways for teachers to take the curriculum and vary instruction to fit their comfort levels as they learn to teach CS integration, and co-design lessons with teachers. They also suggest capitalizing on integrating into language arts since it is a highly-tested and critical subject for learning.

Figure 1. General findings.

For more specific findings, the experts suggested integrating focusing on fractions for math, leveraging cause and effect in science to teach conditional logic, and reflecting upon how language is similarly used in English and in computing.

Figure 2. Subject specific findings.

You can read the full paper (including our methodology and profiles of our experts) here.

Monica M. McGill, Laycee Thigpen, and Alaina Mabie. 2023. Emerging Practices for Integrating Computer Science into Existing K-5 Subjects in the United States. In The 18th WiPSCE Conference on Primary and Secondary Computing Education Research (WiPSCE ’23), September 27–29, 2023, Cambridge, United Kingdom. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3605468.3609759 (effective after September 27th, 2023).

This material is based upon work supported by Code.org. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of Code.org.

We acknowledge and thank Brenda Huerta for her assistance with the literature review.

Conducting High-quality Education Research in Computing (2025 SIGCSE Affiliated Event)

Join us on Wednesday, February 26, 2025, from 1-5pm EST, in Pittsburgh, Pennsylvania, United States (the day before ACM SIGCSE) for a workshop on conducting high-quality education research designed to help address disparities in computing education.

If you attended last year’s session, you are welcome to attend this session as well with four different topics. See below for more details.

Register using this form.

This event will be for computer science education researchers who want to learn more about:

• Characteristics of high-quality education research,
• How to conduct research that meets these characteristics, and
• How to center the participants and their lived experiences throughout the research process.

The workshop will be held Wednesday, February 26, 2025, 1-5pm EST at ACM SIGCSE Technical Symposium as an affiliated event*.

Participants will learn about the guidelines and associated resources, discuss their application to current or proposed research projects, and gain a new appreciation for how to embed broad perspectives in each phase of their research. Specifically, participants will develop a broader perspective of literature reviews, ways to approach writing well-crafted abstracts, strategies for engaging with participants, and how to incorporate ethics into research. As a bonus, we’ll also dive into publication strategies, so when an article is rejected, you can reset, revise, and resubmit to other publication venues.

This interactive workshop will be geared towards those studying computing education and who want to learn more. We welcome submissions from those with any level of education research. For this particular event, graduate students will be prioritized for the limited spaces available.

This presentation is supported by a National Science Foundation grant (#2122212). Graduate students who are U.S. citizens, nationals, and permanent residents will receive a $150 stipend for participating.

Facilitators for this event will include:

• Monica McGill, Institute for Advancing Computing Education
• Sarah Heckman, North Carolina State University
• Julie Smith, Institute for Advancing Computing Education
• Jennifer Rosato, National Center for Computer Science Education
• Isabella Gransbury, North Carolina State University

This workshop is based in part on guidelines for conducting education research that were created during a 2023 ITiCSE workshop (McGill, M. M., Heckman, S., Chytas, C., Diaz, L., Liut, M., Kazakova, V., Sanusi, I. T., Shah, S. M., & Szabo, C. Conducting Sound Computing Education Research. (Working Group Report).

*This event is an in-person event only. We are aware that attending in-person is not feasible for all researchers. Therefore, we hosted an 8-part webinar September – December 2024 to accommodate those who may not be able to attend in person in 2024 or 2025.

For questions about this event, please email monica@csedresearch.org.

Register using this form.

“But They Just Aren’t Interested in Computer Science” (Part One)

Written By: Julie Smith

Note: this post is part of a series about the most-cited research studies related to K12 computer science education.

When discussions about the lack of women in tech occur, it is sometimes observed that the disparities exist because girls just aren’t as interested in studying computer science in school and women just choose not to work in the tech industry. 

This sentiment is horribly misleading. It is true that research shows that girls and women are, on average, not as interested in studying or working in computing. But what is important to understand is that interest isn’t like eye color: it’s not an inherent, biological attribute that simply reflects human differences. Rather, what we choose to be interested in is strongly influenced by what our culture conveys is appropriate for ‘people like us.’ This may seem to be an unusual way of thinking about the issue; we often assume that our interests are simply pure reflections of our personality and volition. But, at least for the case of interest in computer science, the evidence suggests otherwise.

In a recent report, Allison Master, Sapna Cheryan, and Andrew N. Meltzoff describe two experiments which show how (dis)interest in computer science can be influenced by very simple interventions. They created photographs of stereotypical (think: Star Trek posters) and non-stereotypical computer science classrooms and showed them to high school students and asked which classroom they would prefer. Girls were significantly more likely to express interest in the course in the non-stereotypical classroom. (Boys’ interest was not impacted.). In their second experiment, the researchers provided participants with a written description of a computer science classroom, some stereotypical and some not. Again, girls  were much more interested in a course in the non-stereotypical classroom. 

These two experiments are important because they show that interest in computer science isn’t hard-wired. Rather, it appears to be strongly influenced by whether computing is presented as conforming to stereotypes that aren’t as welcoming to girls. For those of us concerned with the negative effects of the lack of women in tech – not just on the women themselves but on a society that is ever-increasingly shaped by technology – these results are good news because they show that relatively simple interventions can increase girls’ interest in the study of computing.

Further Reading

• Girls in STEM: Is It a Female Role-Model Thing?
• Promising Practices for Addressing the Underrepresentation of Women in Science, Engineering, and Medicine

Series

“But They Just Aren’t Interested in Computer Science” (Part Two)

“But They Just Aren’t Interested in Computer Science” (Part Three)

National Intern Day- Intern Shout Out

Last week was National Intern Day, which gave me another reason to reflect on the many students I’ve worked with over the last seven years contributing to the K-12 CS Education Research Resource Center on our site.

The Resource Center has been under development since 2017. Originally funded under a National Science Foundation grant and now from Amazon Future Engineer, we owe much to the many interns who have worked on our project over the course of this time. This is true whether they provided support to our mission over one semester or three years. I’m also thrilled to say that I was fortunate enough to publish several articles together with one-third of our interns in an effort to engage them in computing education research. (Why, yes, that was my attempt to bring them to the dark side!)

It’s really incredible and I am personally grateful for their contributions and camaraderie. I’m also thankful that so many have stayed in touch with me after graduating and starting their post-college careers.

So, HUGE SHOUT OUT to all of you! And many, many thanks from me and a grateful research community who still uses your contributions today.

Monica McGill, President & CEO, CSEdResearch.org

Media

• Emily Nelson, Undergraduate Student, Bradley University (current)

Data Curation (2017-2023)

• Alia Saadi El Hassani, Undergraduate Student, Knox College
• Alaina Mabie, Undergraduate Student, Bradley University
• Arsalan Bin Najeeb, Undergraduate Student, Knox College
• Ava Lu, Undergraduate Student, Knox College
• Bishakha Awale, Undergraduate Student, Knox College
• Bishakha Upadhyaya, Undergraduate Student, Knox College
• Brenda Huerta, Undergraduate Student, Bradley University
• Emily Schroeder, Undergraduate Student, Knox College
• Jessica Potter, Undergraduate Student, Bradley University
• Joey Reyes, Undergraduate Student, Knox College
• Ma’Kiah Holliday, Undergraduate Student, Rochester Institute of Technology
• Olivia Lu, Undergraduate Student, Bradley University
• Sarah Wallenfelsz, Undergraduate Student, Knox College
• Sean Mackay, Graduate Student, University at Buffalo
• Shebaz Chowdhury⁺, Undergraduate Student, Knox College
• Tavian James, Undergraduate Student, Knox College
• Zachary Abbott, Undergraduate Student, Bradley University

Software Development (2017-2023)

• Bishakha Upadhyaya, Undergraduate Student, Knox College
• Hung Vu, Undergraduate Student, Knox College
• Momin Zahid, Undergraduate Student, Knox College
• Nate Blair, Graduate Student, Rochester Institute of Technology
• Nhan Thai, Undergraduate Student, Knox College
• Thu Nguyen, Undergraduate Student, Knox College
• Trang Tran, Undergraduate Student, Knox College

⁺ Deceased