Computing Education Research Becomes a Research Area in CISE’s CAREER Proposals
What every department should know about CS education research
The 2014 NSF CAREER competition in the Computer and Information Sciences and Engineering (CISE) directorate for the first time actively sought proposals in computing education research. The area of interest was closely aligned with the computing education research goals stated in its CE21 solicitation (NSF 12-609) which solicited proposals developing a research base for computing education: “Projects may conduct basic research on the teaching and learning of computational competencies in face-to-face or online settings; they may design, develop, test, validate, and refine materials, measurement tools, and methods for teaching in specific contexts; and/or they may implement promising small-scale interventions in order to study their efficacy with particular groups.” In March 2015 CISE announced two education research CAREER awardees, Kristy Boyer and R. Benjamin Shapiro. In the coming CAREER cycle, CISE is again inviting computing education research proposals. The topics of interest are highlighted in the STEM+C solicitation (NSF 15-537). Of special interest for CAREER proposals is the track focused on Computing Education Knowledge and Capacity Building.
This article briefly describes the proposed research in the two new CAREER awards. These two awards represent a new area of exploration for CISE. Computing education research offers new opportunities for computer science departments and schools, and we also describe some of them.
Kristy Elizabeth Boyer is an Assistant Professor of Computer Science at North Carolina State University. Her research focuses on how to support learning with natural language dialogue and intelligent systems. Her interest in computer science education research focuses on collaborative learning and on how machine learning can help us understand social, cognitive, and affective phenomena in human interactions. She received a Ph.D. in Computer Science from North Carolina State University in 2010. She holds an undergraduate degree in Mathematics and Computer Science from Valdosta State University and an M.S. in Applied Statistics from the Georgia Institute of Technology. Kristy was the recipient of a 2007 NSF Graduate Research Fellowship award.
Kristy Boyer’s CAREER project is titled “CS-CLIMATE: Collaborative Learning for Identity, Motivation, and Technology Engagement.” A rich body of evidence suggests that collaborative learning holds many benefits for computer science students, yet there is growing recognition that neither collaborative learning itself, nor the innovative curricula in which it may be situated, are “magic bullets” capable of single-handedly broadening the participation of students belonging to underrepresented groups. In contrast to being a one-size-fits-all solution, collaborative learning is highly dependent upon characteristics of the collaborators and on fine-grained interactions.
The proposed research will explore the fine-grained facets of collaborative dialogue known to be particularly effective for diverse computer science learners and build theoretically informed models that capture collaborative dialogue and problem solving phenomena associated with learning, identity development, motivation, and engagement. The project will leverage a learning environment built by the PI to support remote collaboration with textual natural language dialogue, synchronized code editing, and integrated repository control. It will implement and evaluate evidence-based pedagogical support for fostering effective collaborative dialogue by extracting a set of evidence-based pedagogical strategies for fostering effective collaborative dialogue tailored to student characteristics. Pedagogical support is expected to significantly improve learning, sense of identity, motivation, and continued engagement for students overall, and for women and African American students in particular. The research will draw upon collaborative learning data from computer science students at North Carolina State University, Meredith College, and Florida A&M University.
Benjamin Shapiro is the McDonnell Family Assistant Professor of Engineering Education and an Assistant Professor in the departments of Computer Science and Education at Tufts University. He received his Ph.D. in Learning Sciences from Northwestern University in 2009 and was a postdoctoral fellow at the University of Wisconsin-Madison. He holds an undergraduate degree in Independent Studies in Symbolic Systems (Computer Science & Cognitive Science) from the University of California, San Diego. His research focuses on the design of playful and constructionist learning environments. He studies how engineering computational systems can help learners to further their personal interests. To do so, he creates new technologies for learning and investigates how people, including students and teachers, use them to learn together.
Ben Shapiro’s CAREER project is titled “Constructing Modern and Inclusive Trajectories for Computer Science Learning.” His research will explore how youth building distributed cyber-physical systems offers possibilities of broadening participation and producing new theoretical and practical insights into the development of computational thinking.
Using data from middle and high school students solving problems by building networks of devices and mobile applications that communicate with each other, the project will develop new empirically-supported theories of the development of student thinking about distributed computing, as well as new tools to enable that development. Underrepresented minority youth will be partners in the co-design of the tools and supporting curriculum and the effects of their participation on interest, self-efficacy, and projective identity within computer science will be evaluated. A learning environment will be constructed to support youths’ transitions from using beginner-specific programming environments (e.g., Scratch) into techniques and tools that are commonly found in university-level computer science education and in industry and open-source community practice. The research will describe and assess the development of student thinking in these transitions.
Why computing education research is important to computer science. Undergraduate enrollments are at a record high level. University administrators are unsure whether this is another high in the CS enrollment cycle to be followed by a steep drop or whether this is the new status quo. A time of burgeoning enrollments may not seem like an obvious time to focus on research in computing education, but the reality is that computer science students are changing and computing education practices are needed. Increased class sizes make many faculty re-evaluate how to teach and how to assess learning at scale. While MOOCS courses had significant promise three years ago, completion rates have been disappointing. But MOOCS demonstrated a new approach and model for a scalable teaching method that could make novel use of continuously collected data on learning.
Faculty are teaching freshmen with increasingly diverse computing backgrounds, both in-person and in-MOOCs. We see an increasing number of non-majors are taking computing courses. We do not always know how to engage, motivate, and retain a diverse student body. We know surprisingly little about how students understand foundational concepts in computer science. We know even less about effective methods for teaching parallel and distributed computing concepts. The field of computing education research draws on other disciplinary-based education research (DBER) areas, like science, mathematics, and engineering education. The history of DBER fields tell us computer science education is different, with unique challenges.
The percentage of students from underrepresented groups in computing has changed little in the last decade. While many departments have outreach and recruiting events that manage to attract more students from underrepresented groups, the retention of those students is often a challenge. To engage more diverse students, departmental outreach activities increasingly engage the entire K-12 range. However, little is known about validated practices for the earlier ages.
The computing community has recently seen a number of new activities at the high school level. A new AP course called CS Principles has been developed in an NSF-funded effort between computer scientists, higher education and high school educators, and the College Board. The CS Principles course is currently piloted in hundreds of schools across the country. The course will “introduce students to creative aspects of programming, using abstractions and algorithms, working with large data sets, understandings of the Internet and issues of cybersecurity, and impacts of computing that affect different populations.” The course was designed to be engaging and inspiring for all students. With only 19% of high school students currently taking a single CS course, the CS Principles course aims to attract a more diverse student to computing. The first AP Computer Science Principles Exam is taking place in May 2017.
Computer science has the unique position of teaching the medium that we can also use to teach. Can computing revolutionize how we teach and how students learn computer science? We can create specialized development environments tuned to the learning needs of our students. As we teach with MOOCs and other new media, we are challenged to explore the advantages of these platforms. Are there effective and scalable methods available to instructors for automated assessment of learning? Do we have validated data and evidence driven analysis tools for student learning? Many of these questions are at the heart of computing education research.
The research proposed by the two exceptional CS education researchers in their CAREER awards highlights a number of characteristics of computing education research. First, the field of computing education research is interdisciplinary. While grounded in computer science, the research area draws expertise and knowledge from learning sciences, education research, behavior and social scientists, psychology and sociology. We expect successful researchers to have a strong background in computer science as well as a clear understanding of the techniques and tools of education and learning science research. Many promising computing education research projects explore the application of the methods used in computer science research on itself. Common are methods in the areas of machine learning, big data/analytics, delivery of software as a service, human computer interaction. Computing education research has a number of similarities with HCI research. HCI research relies on the computing discipline to develop new methods and techniques and it also uses methods from the social sciences for assessment and validation.
Computing education research topics include understanding how students with different backgrounds learn computing, understanding how to effectively teach computing to audiences with different interests and backgrounds, and how to make use of personalized learning approaches and validated learning progressions. Research focuses on inventing, developing, assessing and validating ways to teach computing at all levels, from elementary school to a scientist. Computing education research aims to transform how learning happens in the traditional classroom as well as on-line. Based on new understandings on how learning happens, new systems and tools supporting teaching at scale are developed and assessed. Computing education research also includes effective teacher preparation and training, both as pre-service and in-service.
Numerous funding opportunities for computing education research exist. At NSF, the CISE and the EHR directorates both offer solicitations that can support work in computing education research. The most recent STEM + Computing Partnerships (STEM+C) solicitation is such an example. Other opportunities include Science of Learning (NSF 15-532), Cultivating Cultures for Ethical STEM (CCE STEM, NSF 15-528), and Cyberlearning and Future Learning Technologies (NSF 14-526). The Department of Education is also a source for computing education research funding. Funding opportunities from foundations include the MacArthur Foundation, Gates Foundation, and Sloan Foundation. Companies with a critical workforce computing needs including Microsoft Research, Google, Intel, IBM have various programs and opportunities for support.
In the long run, computing education research will strengthen our field and it has the potential to broaden participation of underrepresented groups. Understanding how students think about computing and how we can improve their learning will have impacts in how we design the interface between humans and computers. Teachers will have access to validated and established pedagogical instruments and assessment tools. The training of K-12 computer science teachers will follow established guidelines, principles, and methods. Departments with faculty interested in computing education research or interested in hiring in this area, will realize that a number of models for successful appointments and collaborations exist. Faculty often have joint appointments, especially in CS departments that are not in a College/School of Computing environment. Departments with computing education research as focus will train graduate students in the field of computing education research and will develop curricula. We expect graduates to get hired by CS/I/CE departments with active computing education research, Education departments, teaching focused institutions, providers of on-line and MOOCS courses, high schools, and organizations like Code.org, the Computer Science Teachers Association (CSTA), the National Center for Women in Information Technology (NCWIT), or Project Lead the Way (PLTW). This is an exciting time to get involved in computing education research, a research area shaped by the advances in our field which will help shape the future of our field.
