Houston, we have a problem.

Here’s the punch line from Mark Ackerman’s The Intellectual Challenge of CSCW: The Gap Between Social Requirements and Technical Feasibility:

If CSCW (or HCI) merely contributes “cool toys” to the world, it will have failed its intellectual mission. Our understanding of the gap is driven by technological exploration through artifact creation and deployment, but HCI and CSCW systems need to have at their core a fundamental understanding of how people really work and live in groups, organizations, communities, and other forms of collective life. Otherwise, we will produce unusable systems, badly mechanizing and distorting collaboration and other social activity.

This “social-technical” gap is the space between how human behavior and activity actually work and our ability to understand, model/represent, and design for human behavior and activity in human-computer interactions. And, coming to grips with this gap presents, for Ackerman, the primary challenge for computer-supported cooperative work as a field.

Ackerman borrows Simon’s idea of sciences of the artificial to build a case for an approach toward better studying, understanding, and addressing the social-technical gap in CSCW. Simon differentiates between the artificial (those things that exist as the products of “human design and agency”), and the natural (those things that exist apart from human intervention). For Simon, the existing sciences focused on understanding the natural, and engineering focused on synthesizing the artificial. Between these two, Simon proposed a space for new sciences–those that seek to understanding the nature of design and engineering. Ackerman places CSCW squarely in the realm of these new sciences:

CSCW is at once an engineering discipline attempting to construct suitable systems for groups, organizations, and other collectivities, and at the same time, CSCW is a social science attempting to understand the basis for that construction in the social world (or everyday experience).
CSCW’s science, however, must centralize the necessary gap between what we would prefer to construct and what we can construct. To do this as a practical program of action requires several steps-—palliatives to ameliorate the current social conditions, first-order approximations to explore the design space, and fundamental lines of inquiry to create the science.

I’m most interested by Ackerman’s call for fundamental lines of inquiry to create this new science of the artificial, primarily because I believe this approach to CSCW holds implications not only for CSCW, but for the broader field of human-computer interaction. The lack of focus we tend to have in HCI (exhibited by the never-ending stream of “cool toys” presented at conference after conference) desperately needs to be addressed, and the identification of and careful examination through fundamental lines of inquiry could go long way in bringing this focus.

I’m just as guilty of this lack of focus as anyone else. I’ve got what I think are “cool” ideas, and I’ve built up my own research around what I’m afraid are thrown together, not-so-fundamental lines of questioning. It’s difficult for me to backtrack, as I know it would be for anyone else. However, in order to genuinely contribute to the progress of HCI as a field, I must take the time establish my work in such a way that it is both prompted by that work that has gone before, and is at least situated to inform that which may follow. If I and others don’t, then fourteen years after Ackerman wrote his article, we’re still failing our mission.

Picture this…

Does visualization help to more effectively communicate concepts in computer science education to learners? The answer seems, to me at least, to very clearly be yes. It seems as though the jury was out in 2003 when Naps et al. published Exploring the Role of Visualization and Engagement in Computer Science Education.

In this paper, Naps et al. summarize the results of the Working Group on Improving the Educational Impact of Algorithm Visualization. Based on a survey they conducted, the group points out two key reasons that the use of visualization in computer science education may have not yet gained widespread acceptance. These were:

  • “From the learner’s perspective, …visualization technology may not be educationally useful.”
  • “From the instructor’s perspective, …visualization technology may simply incur too much overhead to make it worthwhile.”

They go on to conclude that “learners who are actively engaged with…visualization technology have consistently outperformed learners who passively view visualizations.” And, in fact, that:

Visualization technology, no matter how well it is designed, is of little educational value unless it engages learners in an active learning activity.

I’m fairly confident in stating that this holds true for any teaching materials, not simply visualization technology. Nevertheless, the authors maintain that visualization is more or less worthless unless it actively engages the learner. To be clear, they refer not only to carefully constructed, animated, interactive visualization tools. They also include diagrams–even those found in textbooks–under the heading of “visualization technology”. To this end, the authors provide an exhaustive accounting (or rather, an extraordinarily verbose data dump) of the results of their survey. Based on these results, they both provide a set of best practices for visualization design, as well as a framework for further research around the effectiveness of visualization technology. This framework aims to explore the effectiveness of visualization along the lines drawn by Bloom’s taxonomy.

On the one hand, I’m all for some real science in computer science. Far be it from me to groan when an HCI researcher takes the bold step of demonstrating and calling for additional academic rigor in research. For this, thank you, Naps. On the other hand, I’m stumped by their statements that motivate this call. In their introduction, the authors state that “intuition suggests that…graphical representations would help one” in understanding computer science concepts. Yes, intuition does indeed suggest this. Not only that, the piles of scholarship cited here and elsewhere bear this out–graphical representations do aid in learning. In other words, our intuition doesn’t just suggest this–this is reality. Very little the citations in this paper draw on general education literature. Rather, most of the cited literature deals directly with the use of visualizations in teaching algorithms and data structures. This seems like a non-negligible oversight to me.

In the end, I’m happy to see these sorts of endeavors in our community, dated as they may be. However, their foundational argument doesn’t have me sold. Certainly, the quality of visualization technologies runs a wide gamut. Please don’t exclude diagrams from your articles and books just because Naps told you they’re worthless if they don’t actively engage me–I, for one, appreciate them and find them extremely useful…