Posts tagged ‘engineering education’
In the last couple of months, I have had the opportunity to speak to groups of Engineering Education Researchers. That doesn’t happen often to me, and I feel very fortunate to get that chance.
I was asked to speak about my vision for the future of Engineering Education, from my perspective as a Computing Education Researcher. What I said wasn’t wholly unique–there are Engineering Education Researchers who are already working on some of the items I described. The response suggested that it was at least an interesting vision, so I’m telling the story here in blog form.
For readers of this blog who may not be familiar with Engineering Education Research, the Wikipedia page on EER is pretty good. The most useful paper I read is Borrego and and Bernhard’s “The Emergence of Engineering Education Research as an Internationally Connected Field of Inquiry.” I also recommend looking around the Purdue Engineering Education department website, which is the oldest Eng Ed department in the US.
Engineering has had a long relationship with computing. Engineers made computing part of their practice earlier and more pervasively than scientists or mathematicians. I love how this is described in the motion picture Hidden Figures where Octavia Spencer’s character is part of the effort to use computing as soon as possible in the American space program. Engineering educators have made computing part of the learning goals for all of today’s engineering students, again more pervasively than what I can see in science or mathematics programs.
Much of my work and my students’ work is about embedding computing education (e.g., Media Computation which embeds computing in the digital media context that students value, or Brian Dorn’s work embedding computing in a graphic design context) and tailoring computing education (e.g., high school CS teachers need something different from software developers). Computing education can be embedded in Engineering classes and tailored for Engineering students, of course. My vision is about embedding and tailoring engineering education.
There are three parts to the story below:
- Engineering Education for everyone K-16, especially for STEM learners.
- Reaching a diverse audience for engineering education.
- Recognizing the differences between Engineering Education research and teaching, and the need for more research on learning outside of the engineering classroom.
In January 2016, President Barack Obama launched the “CS for All” initiative. When he said that he wanted students to be “job-ready,” he wasn’t saying that everyone should be a software engineer. Rather, he was reflecting a modern reality. For every professional software developer, there are four-to-nine end-user-programmers (depending on the study and how you count). Most professionals will likely use some form of programming in the future. That’s an argument for “CS for All.”
We also need Engineering for All. Engineering skills like designing, planning, collaboration on diverse teams, and trouble-shooting are needed across STEM. When I look at bench science, I see the need for engineering — to design, plan, collaborate, debug, and test.
Engineering education researchers know a lot about how to teach those skills. I’d love to learn how to inculcate some engineering perspectives in my CS students. When I see Chemical Engineering students designing a plant, or Civil Engineering students designing a bridge, they predict that they made mistakes, and they look for those mistakes. There’s a humility about their process. CS students often run their program once and turn them in. If you write a hundred lines of code, odds are almost 100% that you made errors. How do we get CS students to think that way?
Engineering for All is different than what professional engineers do, in the same way that what a high school teacher needs is different than what a professional software developer needs. Both need a mental model of the notional machine. A high school teacher also needs to know how students get that wrong, and probably doesn’t need to know about Scrum or GitHub.
I believe that there is a tailored part of engineering education which should be embedded throughout K-16 STEM. The American Society of Engineering Education’s mission is focused on professional engineers, and my proposal does not diminish the importance of that goal. We need more professional engineers, and we need to educate them well. But engineering skills and practices are too important to teach only to the professionals.
Engineering should play a significant role in STEM education policy. Engineering education researchers should own that “E” in STEM. There are many research questions that we have to answer in order to achieve Engineering for All.
- What is the tailored subset of engineering that should be taught to everyone? To STEM learners?
- All technically literate US citizens should know far more about engineering than they do today. Here’s a hypothesis: If all US citizens understood what engineering is and what engineers do, we might have less crumbling infrastructure, because we citizens would know that infrastructure is critical and professional engineers design, build, and maintain infrastructure. How do we get there?
- All K-12 students should have the opportunity to fall in love with engineering. How?
- Are there limits to what we can teach about engineering in K-16? What learning and cognitive disabilities interfere with learning engineering, and what parts of engineering? I also wonder about the kinds of bias that prevent someone from succeeding in engineering, besides race and gender. For example, here in the South, there are a lot of students who don’t believe in evolution. I’m pretty sure that belief in evolution isn’t necessary for designing a bridge or a distillation column. But someone who believes in intelligent design is going to face a lot of barriers to getting through basic science to become an engineer. Is that how it should be?
- Engineering should aim to influence K-12 STEM education nationally, in every state.
The American University (particularly the Land Grant University, developed in the late 1800’s) was supposed to blend the German University focus on research and the British focus on undergraduate education. My favorite history of that story is Larry Cuban’s How Scholars Trumped Teachers, but Michael Crow also tells the story well in his book Designing the New American University. We believed that there were synergies between research and teaching. It’s not clear that that’s true.
Research and teaching have different measures of success and don’t feed directly into one another.
Teaching should be measured in terms of student success and at what cost. Cost is always a factor in education. We know from Bloom’s two-sigma 1984 study (and all the follow-ups and replications) that the best education is an individual human tutor for each subject who works with a student to mastery. But we as a society can’t afford that. Everything else we do is a trade-off — we are trying to optimize learning for the cost that we are willing to bear.
Research should be measured in terms of impact — on outcomes, on the research community, on society.
It’s quite likely that the education research on a given campus doesn’t influence teaching practice on the same campus.
I see that in my own work.
- The best of Media Computation is no longer at Georgia Tech. Beth Simon and Leo Porter at UC San Diego have done better studies and are inventing cool interventions like MediaComp art galleries. Cynthia Lee at Stanford has created MediaComp for multiple languages. Celine Latulipe built on Beth and Leo’s work to implement lightweight teams in her MediaComp course.
- Subgoal labeling totally works (see Lauren’s dissertation or Briana’s dissertation). Coursera uses it in some of their videos. Rob Miler at MIT has picked it up. But there are very few CS classes using it.
We can see the transition for education research idea to impact in teaching practice as an adoption curve. Boyer’s “Scholarship Reconsidered” helps to explain what’s going on and how to support the adoption. There is traditional Scholarship of Discovery, the research that figures out something new. There is Scholarship of Teaching that studies the practice of teaching and learning.
Then there’s Scholarship of Application, which takes results from Discovery into something that teachers can use. We can’t expect research to influence teaching without scholars of application. Someone has to take the good ideas and carry them into practice. Someone has to figure out what practitioners want and need and match it to existing research insights. Done well, scholarship of application should also inform researchers about the open research questions, the challenges yet to be faced.
High-quality teaching for engineering education should use the most effective evidence-based teaching methods.
Good teachers balance teaching for relevance and motivation with teaching for understanding. This is hard to do well. Students want authenticity. They want project-based learning and design. I was at the University of Michigan as project-based learning for science education was first being developed, and we knew that it very often didn’t work. It’s often too complex and leads to failure, in both the project and the learning. Direct instruction is much more efficient for learning, but misses out on the components that inspire, motivate, and engage students. We have to balance these out.
We have to teach for a diverse population of students, which means teaching differently to attract women and members of under-represented groups. In our ICER 2012 paper, we found that encouragement and self-perception of ability are equally important for white and Asian males in terms of intention to persist in computing, but for women and under-represented group students, encouragement matters more than ability in terms of how satisfied they are with computing and intention to persist. This result has been replicated by others. Encouragement of individual students is critical to reach a diverse audience.
An important goal for a first year Engineering program is to explain the relevance of the classes that they’re taking. Larry Cuban tells us that a piece of the British system that got lost by the early 1920’s in the American University was having faculty advisors who would explain how all the classes fit together for a goal. The research on common first year Engineering courses (e.g., merging Physics, Calculus, Engineering in a big 12 credit hour course) shows that they worked because they explained the relevance of courses like Calculus to Engineering students. I know from my work that relevance is critical for retention and transfer.
Do students see relevance of first year Engineering programs? Most first year programs emphasize design and team problem-solving. First year Engineering students don’t know what engineers do. When they’re told “This is Engineering” in their first year, do they believe it? Do they cognitively index it as “real Engineering”? Do they remember those experiences and that learning in their 3rd and 4th years when they are in the relevant classes? I hope so, but I don’t know of evidence that shows us that they do.
Engineering education research, like most discipline-based education research (DBER), is focused on education. I see the study of “education” as being about implementation in a formal system. Education is a design discipline, one of Simon’s Sciences of the Artificial. Robert Glaser referred to education as psychology engineering.
We need more research on Engineering Learning. How do students learn engineering skills and practices, even outside of Engineering classes? How do those practices develop, even if it’s STEM learners and teachers using them and not professional engineers? How should we best teach engineering even if it’s not currently feasible?
That last part is much of what drives my work these days. We’re learning a lot about how great Parsons Problems are for learning CS. Very few CS classes use them. There are reasons why they don’t (e.g., they’re emphasizing the project side of the education spectrum). I’m figuring out how to teach CS well, even if it’s not feasible in current practice. CS teaching practice will eventually hit a paradigm shift, and I’ll have evidence-based practices to offer.
To focus on engineering learning requires work outside the classroom, like Multi-Institutional, Multi-National (MIMN) studies that we use in computing education research, or even laboratory studies. A focus on Engineering Learning creates new opportunities for funding, for audience, and for impact. For example, I could imagine engineering education researchers seeking science education funding to figure out how to teach high school science teachers the engineering that they ought to teach their students — not to introduce engineering, but to make their students better in science.
My vision for engineering education has three parts:
- K-16 STEM learners need Engineering for All. Engineering education has more to contribute than just for producing more professional Engineers. Engineering education ought to own the “E” in STEM education policy. Engineering skills and practices can be tailored to different audiences and embedded in STEM education.
- Reaching a diverse audience is critical for both research and teaching. For me, that diversity includes the people who need engineering education who aren’t going to become professional engineers, but also people who look different or even have different beliefs.
- Finally, research and teaching are different activities, with different measures of success. Teaching should be informed by evidence and be as efficient and effective as possible for a given cost. We need evidence for what we’re doing, and we should gather evidence if we don’t know if what we’re doing is working. Research should focus on what’s possible and on having impact, even if that impact isn’t in the on-campus classrooms. We shouldn’t expect research to impact teaching without explicit investment in adaptation to support adoption.
(Thanks to Barb Ericson, Beth Simon, Leo Porter, and Wendy Newstetter for advice on drafts of this piece.)
Why ‘U.S. News’ should rank colleges and universities according to diversity: Essay from Dean Gary May #CSforAll
Georgia Tech’s Dean of Engineering Gary May was one of the advisors on “Georgia Computes!” He makes a terrific point in his essay linked below. Want broadened participation in computing (BPC)? CS for All? Make diversity count — and rankings are what “counts” in higher education today.
U.S. News & World Report, that heavyweight of the college rankings game, recently hosted a conference focused partially on diversity in higher education. I did an interview for the publication prior to the forum and spoke on a panel at the event.I was happy to do it. As dean of one of the country’s most diverse engineering schools, I am particularly invested in these issues. My panel focused on how to help women and underrepresented minority students succeed in STEM fields, and I’m grateful to U.S. News for leading the discussion.But the publication, for all its noble intentions, could do more to follow through where it counts. Diversity is currently given no weight in the magazine’s primary university and disciplinary rankings, and it’s time for that to change. As U.S. News goes, so goes higher education.
One reason we have so much engineering and so little computer science taught at US high schools. | ACM Inroads
Joe Kmoch wrote an interesting follow-on to my blog post about why we have so little CS ed in the US. Why is that engineering is succeeding so much more than CS in high schools in the US? He suggests that (in part) it’s because engineering is getting the PD right.
I think the reason is that groups like Project Lead the Way (PLTW) offer an “off the shelf” high quality program, vetted by engineers. The attractiveness of this is that the school and students get access to a number of well-written up-to-date courses and they also get access to intensive professional development for teachers who want to teach a particular PLTW course. Teachers must not only take but also pass the two-week intensive summer course before being allowed to teach a particular course. There is regular monitoring of schools in terms of offering a minimal 3-course sequence of engineering courses and evaluating how well these courses are being taught.
In computer science we have really never had such a program available. The AP is not such a program. If a school wants to teach a computer science course, they have to find a teacher who is willing to put together a course syllabus, and then teach that course. (For AP, the course must be audited for fidelity). There really isn’t any professional development required to teach any kind of computer science course in most states.
In one week, I found two articles about all female programming academy (first quote and link below) and engineering high school class (second link and quote below). Both of them talk about issues of sexism and intimidation that they hope an all-female cohort will help to avoid.
Why just for women? Because some hiring managers, in response to these statistics, are particularly interested in hiring women, Worthy says. “The need is very top-of-mind,” she says. In addition, there are other training options for men, though they aren’t free like the Ada Developers Academy is, she admits. Moreover, some women and girls have encountered sexism in school and training programs themselves; an all-female class may forestall that problem.
The Ada Developers Academy isn’t the only such effort to challenge this trend. A number of other parallel training opportunities for women are also springing up, some for students and some for working women, to help fill jobs and address the growing gender gap in programming.
Seventeen female students are enrolled in Wisconsin’s first high school class aimed at women in engineering.
Women comprise more than 20% of engineering school graduates but only 11% of practicing engineers, according to the National Science Foundation. Only about 30% of the 14 million Americans who work in manufacturing are women, a study from the National Women’s Law Center noted.
“If we are going to have any hope of replacing all of the retiring baby boomers, we have to get women involved,” Moerchen said.
“It’s a pretty wide gender gap,” Moerchen said, adding that only about three of 35 students in computer-aided machining courses are female.
“The data show that female students are easily intimidated by technology and engineering classes that are traditionally dominated by male students,” Moerchen said.
After researching programs in other states, the Kewaskum teachers said they believed they could create an engineering class specifically for girls that would prepare the students for advanced courses.
Posted to the SIGCSE-Members list — I really like this idea! Our work on DCCE showed that communities of teachers was an effective way of improving teacher’s sense of belonging and desire to improve. Will it work for faculty? ASEE is the organization to try!
This is a great opportunity for CS faculty to work with like-minded faculty from across the country to explore and share support for introducing new instructional practices into your classroom. Please consider this for yourself and pass it on to your colleagues.
Engineering education research has shown that many research-based instructional approaches improve student learning but these have not diffused widely. This is because (1) faculty members find it difficult to acquire the required knowledge and skills by themselves and (2) sustaining the on-going implementation efforts without continued encouragement and support is challenging. This project will explore ways to overcome both obstacles through virtual communities.
I spent a couple days at Michigan State University (July 11-12) learning about integrated engineering education. The idea of integrated engineering education is to get students to see how the mathematics and physics (and other requirements) fit into their goals of becoming engineers. In part, it’s a response to students learning calculus here and physical principles there, but having no idea what role they play when it comes to design and solving real engineering problems. (Computer science hasn’t played a significant role in previous experiments in integrated engineering education, but if one were to do it today, you probably would include CS — that’s why I was invited, as someone interested in CS for other disciplines.) The results of integrated engineering education are positive, including higher retention (a pretty consistent result across all the examples we saw), higher GPA’s (often), and better learning (some data).
But these programs rarely last. A program at U. Massachusetts-Dartmouth is one of the longest running (9 years), but it’s gone through extensive revision — not clear it’s the same program. These are hard programs to get set up. It is an even bigger challenge to sustain them.
The programs lie across a spectrum of integration. The most intense was a program at Rose-Hulman that lasted for five years. All the core first year engineering courses were combined in a single 12 credit hour course, co-taught by faculty from all the relevant disciplines. That’s tight integration. On the other end is a program at Wright State University, where the engineering faculty established a course on “Engineering Math” that meets Calculus I requirements for Physics, but is all about solving problems (e.g., using real physical units) that involve calculus. The students still take Calculus I, but later. The result is higher retention and students who get the purpose for the mathematics — but at a cost of greater disconnect between Engineering and mathematics. (No math faculty are involved in the Engineering Math course.)
My most significant insight was: The greater the integration, the greater the need for incentives. And the greater the need for the incentives, the higher in the organization you need support. If you just want to set up a single course to help Engineers understand problem-solving with mathematics, you can do that with your department or school, and you only have to provide incentives to a single faculty member each year. If you want to do something across departments, you need greater incentives to keep it going, and you’ll need multiple chairs or deans. If you want a 12 credit hour course that combines four or five disciplines, maybe you need the Provost or President to make it happen and keep it going.
Overall, I wasn’t convinced that integrated engineering education efforts are worth the costs. Are the results that we have merely a Hawthorne effect? It’s hard to sustain integrated anything in American universities (as Cuban told us in “How Scholars Trumped Teachers”). (Here’s an interesting review of Cuban’s book.) Retention is good and important (especially of women and under-represented students), but if Engineering programs are already over-subscribed (which many in the workshop were), then why improvement retention of students in the first year if there is no space for them in the latter years? Integration probably leads to better learning, but there are deeper American University structural problems to fix first, which would reduce the costs in doing the right things for learning.
I met Jeff Froyd at the MSU Workshop in Integrated Engineering Education, and he asked me to share this call for a special issue of IEEE Transactions on Education. The whole notion of a “systemic review” is pretty interesting, and relates to the Blog@CACM post I wrote recently. His call has detailed and interesting references at the bottom.
Request for Proposals
2015 Special Issue on Systematic Reviews
The IEEE Transactions on Education solicits proposals for a special issue of systematic reviews on education in electrical engineering, computer engineering, computer science, software engineering, and other fields within the scope of interest of IEEE to be published in 2015. The deadline for 2,000‐word proposals is 9 September 2013. Proposals should be emailed as PDF documents to the Editor‐in‐Chief, Jeffrey E. Froyd, at email@example.com. Questions about proposals should be directed to the Editor‐in‐ Chief, Jeffrey E. Froyd, at firstname.lastname@example.org.
Special Issue Timeline
- 9 September 2013: Interested interdisciplinary, global teams of authors should submit proposals for full papers by 9 September 2013.
- 14 October 2013: The editorial team for the special issue will review proposals and notify authors of the status of their submission by 14 October 2013.
- 31 December 2014: For proposals that are accepted, the authors will be asked to prepare manuscripts that will go through the standard review process for the IEEE Transactions on Education in the Scholarship of Integration. Completed draft manuscripts will be due on 31 December 2014. Papers are expected to be between 8000‐10,000 words in length.
- Xxx – 31 December 2014: Plan (timeline, milestones, activities…) will be collaboratively developed to support manuscript completion by 31 December 2014. Steps in the process of preparing a systematic review include: (i) establishing the research questions, (ii) selecting the databases to be searched and the search strings, (iii), establishing inclusion/exclusion criteria, (iv) selecting articles to be studied, etc. Meetings, in‐person or virtual, will be scheduled to provide support for systematic review methodologies. Meetings will be intended to help develop systematic review expertise across the teams and to improve quality of published systematic reviews.
- 2015: Manuscripts accepted for publication are expected to be published in 2015.
Proposals for systematic review manuscripts must provide the following sections:
- (i) Contact information and institutional affiliation of the lead author
- (ii) An initial list of the team members who will prepare the systematic review, indicating howthese team members provide requisite expertise and global representation. Given the requirements for systematic reviews, it is expected that a qualified, interdisciplinary team will include one or more individuals with expertise in library sciences, one or more individuals with expertise in synthesizing methodologies (qualitative, quantitative, mixed method, or combinations of the three), and one or more individuals with domain expertisein the proposed content area. Given the need to promote global community in the fields in which ToE publishes, it is expected that a qualified team will represent the diverse global regions that comprise the IEEE.
- (iii) Description of the proposed content area, why a systematic review of education in the proposed content area is timely, why a systematic review will enhance development of the field, and how future initiatives might build on the systematic review.
- (iv) Initial description of the proposed systematic review methodology. The project will provide support to promote development of systematic review methodology across all participating teams. However, demonstration of initial familiarity with systematic review methodology will strengthen a proposal.
Brief Overview of Systematic Review Methodology
Diverse fields are developing systematic review, a study of primary (and other) studies to address a crafted set of questions, as a research methodology in and of itself. With risks of considerable oversimplification, systematic review methodology rests on two basic ideas. First, interdisciplinary systematic review teams can use large databases of journals, conference proceedings, and grey literature that have been constructed to search the literature using keywords. Then, the team systematically evaluates returned items using explicit criteria to identify the set of articles that will be reviewed. The first basic idea provides a transparent, unbiased, replicable process to identify relevant articles. Second, teams can apply synthesizing methodologies that have been developed in the last 50 years to extract trends, patterns, themes, relationships, gaps… from the identified set of articles. Synthesizing methodologies draw from a wide variety of quantitative (e.g., statistical meta‐analysis, network meta‐analysis), qualitative (e.g., meta‐ethnography, content analysis), mixed method approaches, and combinations of the three. Systematic, transparent use of literature search and synthesizing methodologies can produce systematic reviews of the literature that may be seminal contributions to the community that has created the literature. Good introductions to systematic reviews can be found at:
- Texas A&M University Libraries, Research Guides, Systematic Reviews,
- Oxford LibGuides, Subject resources. Information Skills. Research Guides. Systematic Reviews, http://ox.libguides.com/systematic‐reviews
- University of Toronto, Gerstein Science Information Centre, Systematic Reviews in the Sciences & Health Sciences, http://guides.library.utoronto.ca/systematicreviews
ToE has already established review criteria for the scholarship of integration, the area addressed by the proposed special issue. These review criteria can be found at http://sites.ieee.org/review‐criteria‐toe/.
This section offers examples of systematic reviews that have been done in STEM education. Generally, topics for these examples are outside topical areas that would be considered for the IEEE Transactions on Education, but they show examples of good practices for some steps in systematic reviews.
L. Springer, M. E. Stanne and S. S. Donovan, “Effects of small‐group learning on undergraduates in science, mathematics, engineering, and technology: A meta‐analysis.” Review of Educational Research, vol. 69, no. 1, pp. 21‐51. 1999 (doi: 10.3102/00346543069001021)
F. B. V. Benitti, “Exploring the educational potential of robotics in schools: A systematic review,” Comput. & Educ., vol. 58, no. 3, pp. 978‐988, 2012
N. Meese, and C. McMahon, ”Knowledge sharing for sustainable development in civil engineering: A systematic review,” AI and Soc., vol. 27, no. 4, pp. 437‐449, 2012
N. Salleh, E. Mendes, Emilia, and J. Grundy, “Empirical studies of pair programming for CS/SE teaching in higher education: A systematic literature review,” IEEE Trans. Softw. Eng., vol. 37, no. 4, pp. 509‐525, 2011
R. M. Tamim, R. M. Bernard, E. Borokhovski, P. C. Abrami, and R. F. Schmid, “What forty years of research says about the impact of technology on learning: A second‐order meta‐analysis and validation study,” Review of Educ. Research, vol. 81, no. 1, pp. 4‐28, 2011
These resources provide guides to systematic review methodologies:
E. Barnett‐Page, and J. Thomas, “Methods for the synthesis of qualitative research: A critical review,” BMC Medical Research Methodology, vol. 9, no. 1, p. 59, 2009
Background: In recent years, a growing number of methods for synthesising qualitative research have emerged, particularly in relation to health‐related research. There is a need for both researchers and commissioners to be able to distinguish between these methods and to select which method is the most appropriate to their situation.
Discussion: A number of methodological and conceptual links between these methods were identified and explored, while contrasting epistemological positions explained differences in approaches to issues such as quality assessment and extent of iteration. Methods broadly fall into ‘realist’ or ‘idealist’ epistemologies, which partly accounts for these differences.
Summary: Methods for qualitative synthesis vary across a range of dimensions. Commissioners of qualitative syntheses might wish to consider the kind of product they want and select their method – or type of method – accordingly.
M. Borrego, , E.P. Douglas and C.T. Amelink, “Quantitative, qualitative, and mixed research methods in engineering education“ Journal of Eng. Educ., vol. 98, no. 1, pp. 53‐66, 2009
Abstract: The purpose of this research review is to open dialog about quantitative, qualitative, and mixed research methods in engineering education research. Our position is that no particular method is privileged over any other. Rather, the choice must be driven by the research questions. For each approach we offer a definition, aims, appropriate research questions, evaluation criteria, and examples from the Journal of Engineering Education. Then, we present empirical results from a prestigious international conference on engineering education research. Participants expressed disappointment in the low representation of qualitative studies; nonetheless, there appeared to be a strong preference for quantitative methods, particularly classroom‐based experiments. Given the wide variety of issues still to be explored within engineering education, we expect that quantitative, qualitative, and mixed approaches will be essential in the future. We encourage readers to further investigate alternate research methods by accessing some of our sources and collaborating across education/social science and engineering disciplinary boundaries.
D.A. Cook and C.P. West, “Conducting systematic reviews in medical education: a stepwise approach,” Medical Education, vol.46, pp. 943‐952, 2012
Objectives: As medical education research continues to proliferate, evidence syntheses will become increasingly important. The purpose of this article is to provide a concise and practical guide to the conduct and reporting of systematic reviews.
Results: (i) Define a focused question addressing the population, intervention, comparison (if any) and outcomes. (ii) Evaluate whether a systematic review is appropriate to answer the question. Systematic and non‐ systematic approaches are complementary; the former summarise research on focused topics and highlight strengths and weaknesses in existing bodies of evidence, whereas the latter integrate research from diverse fields and identify new insights. (iii) Assemble a team and write a study protocol. (iv) Search for eligible studies using multiple databases (MEDLINE alone is insufficient) and other resources (article reference lists, author files, content experts). Expert assistance is helpful. (v) Decide on the inclusion or exclusion of each identified study, ideally in duplicate, using explicitly defined criteria. (vi) Abstract key information (including on study design, participants, intervention and comparison features, and outcomes) for each included article, ideally in duplicate. (vii) Analyse and synthesise the results by narrative or quantitative pooling, investigating heterogeneity, and exploring the validity and assumptions of the review itself. In addition to the seven key steps, the authors provide information on electronic tools to facilitate the review process, practical tips to facilitate the reporting process and an annotated bibliography.
M. Petticrew and H. Roberts, Systematic Reviews in the Social Sciences: A Practical Guide. Malden, MA: Blackwell Publishing, 2006
A. C. Tricco, J. Tetzlaff and D. Moher, “The art and science of knowledge synthesis,” Journal of Clinical Epidemiology, vol. 64, no. 1, pp. 11‐20, 2011
Objectives: To review methods for completing knowledge synthesis.
Study Design and Setting: We discuss how to complete a broad range of knowledge syntheses. Our article is intended as an introductory guide.
Results: Many groups worldwide conduct knowledge syntheses, and some methods are applicable to most reviews. However, variations of these methods are apparent for different types of reviews, such as realist reviews and mixed‐model reviews. Review validity is dependent on the validity of the included primary studies and the review process itself. Steps should be taken to avoid bias in the conduct of knowledge synthesis. Transparency in reporting will help readers assess review validity and applicability, increasing its utility.
Conclusion: Given the magnitude of the literature, the increasing demands on knowledge syntheses teams, and the diversity of approaches, continuing efforts will be important to increase the efficiency, validity, and applicability of systematic reviews. Future research should focus on increasing the uptake of knowledge synthesis, how best to update reviews, the comparability between different types of reviews (eg, rapid vs. comprehensive reviews), and how to prioritize knowledge synthesis topics.