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      Ten simple rules for designing learning experiences that involve enhancing computational biology Wikipedia articles

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          Introduction Wikipedia is the largest and most visited encyclopedia on the World Wide Web [1]. Wikipedia is frequently accessed as an educational resource in computational biology, with the articles on Bioinformatics and CRISPR being viewed 413,000 and 1.18 million times, respectively, in 2019 [2]. However, academics remain skeptical of Wikipedia as a reliable source of knowledge [3]. A common complaint by educators is the perceived lack of quality of information found on Wikipedia. Some educators also worry that the platform discourages deeper learner engagement, providing learners with a crutch instead of ways to engage in rigorous, secondary research within a discipline. Both of these concerns often overshadow the advantages of free and readily available knowledge. Given that Wikipedia is one of the most visited websites and an established platform for knowledge seekers, it makes sense to address these concerns and help learners make the most of what they would do anyway. Mentored contributions from students to open platforms like Wikipedia offer opportunities for improved rigor, quality, depth, and reliability of the information indexed and make it relatable to a wide audience. There have been stellar examples of such mentored contributions, resulting in well-curated additions to domain-specific knowledge amenable for consumption by both public and specialist audiences. For instance, educators around the world have mentored students to either improve or create Wikipedia articles and enter the annual International Society for Computational Biology (ISCB) Wikipedia Competition, a grassroots initiative designed to improve the coverage and depth of computational biology topics [4,5]. Entries are judged based on the quality of writing, figures, and depth of subject knowledge [6]. Winning entries have included important topics in computational biology, such as chromosome conformation capture [7], molecular phylogenetics [8], and the Ruzzo-Tompa algorithm [9]. Some educators have gone further, replacing the writing of traditional term papers with the rigorous editing or creation of new articles in Wikipedia, resulting in class assignments that enhance a vital, publicly accessible resource of field-specific information. Trainees at 24 United States universities participated in a pilot of the Wikipedia Education Program [10] during the 2010–2011 academic year. A 2012 article reviewed the experiences of four professors who participated, each assigning trainees to write Wikipedia articles on a course topic in place of a term paper [11]. Although each professor tailored assignments to her/his particular class, all found the assignments “extremely useful” in improving trainees’ learning. Similar positive outcomes have been reported in other studies, including larger introductory courses of over 100 learners [12]. While recognizing that there may be an element of confirmation bias in these articles [3], there is an emerging consensus that is highly supportive of editing Wikipedia articles as part of a class curriculum. The scope of this work does not have to be limited to the traditional classroom. Using Wikipedia lends itself to a variety of learning and teaching contexts throughout the professional development path, including single-semester bioinformatics courses, short and intensive bioinformatics courses, and professional community service activities. Although there are advantages to facilitating mentored student contributions to Wikipedia, bioinformatics educators face two main challenges in designing meaningful learning experiences: first, ensuring the quality of students’ contributions to Wikipedia, and second, inculcating meaningful learning activities that align with intended outcomes. In addressing the first challenge, we highlight substantial evidence that shows that writing can be an effective tool to promote learning in the classroom [13]. A “writing-to-learn” pedagogical approach focuses on deepening understanding and improving long-term retention of content and concepts through writing activities [14,15]. The inclusion of varied writing activities in a computer science course compels students to be analytical and critical in their thinking, resulting in improved long-term retention of course content and greater awareness of different writing styles [16]. Therefore, integrating well-planned learning experiences and assignments centered around writing is key to ensuring the quality of Wikipedia contributions. It is especially important for science educators, as written communication is often underemphasized in scientific courses. In addressing the second challenge, we propose that authentic learning may hold the key. Educational researchers have emphasized the value of authentic learning as a part of learner-centered teaching [17]. Authentic learning elements are already commonplace in computational biology curricula through the use of authentic databases, algorithms, and research tools [18,19], and a Wikipedia-based writing project is a natural extension. Themes of authentic learning include (a) using real-world problems to engage learners in professional work, (b) providing inquiry activities that practice thinking skills and metacognition, (c) encouraging discourse among communities of learners, and (d) empowering learners through choices [20]. A Wikipedia-based writing activity offers a more authentic learning experience than a traditional term paper [21,22] and provides trainees with the opportunity to practice disseminating domain-specific knowledge to a broad audience while navigating the complexity and ambiguity of working through a real-life problem. Importantly, these learning outcomes overlap with core competencies from the ISCB’s bioinformatics training and education framework [23,24]. The benefits of authentic learning experiences are well documented; however, preparing for authentic learning may present logistical and instructional challenges [17,25]. Therefore, we present Ten Simple Rules for bioinformatics educators who wish to use the enhancement of Wikipedia articles as a class project. These rules extend previously published rules for editing Wikipedia more generally [26]; we recommend following these previous rules, which remain an excellent foundation for Wikipedia editing. Rule 1: Use Wikipedia to foster ISCB core competencies Since 2014, the Curriculum Task Force of the ISCB Education Community of Special Interest (COSI) has sought to define, apply, evaluate, and refine core competencies: proficiencies that are desirable for learners to attain in order to succeed in a breadth of careers in the field of bioinformatics [23,24,27]. A relevant subset of the ISCB core competencies is presented in Table 1 . You should consider how your courses currently provide training in these competencies and how these competencies could be fostered by incorporating Wikipedia-based elements. 10.1371/journal.pcbi.1007868.t001 Table 1 Selected ISCB bioinformatics core competencies. Label Competency I GUI/web-based computing skills appropriate to the discipline (e.g., effectively use bioinformatics and analysis tools through the web). L Local and global impact of bioinformatics and genomics on individuals, organizations, and society. M Professional, ethical, legal, security, and social issues, and responsibilities of bioinformatics and genomic data in the workplace. N Effective communication of bioinformatics and genomics problems/issues/topics with a range of audiences, including but not limited to other bioinformatics professionals. O Effective teamwork to accomplish a common scientific goal. P Engage in continuing professional development in bioinformatics. This table provides a subset of the current ISCB bioinformatics core competencies relevant to enhancing computational biology Wikipedia articles as a class project (the competency labels correspond to those used by Mulder and colleagues [24]). Abbreviations: GUI, graphical user interface; ISCB, International Society for Computational Biology. The ISCB core competencies were first defined by surveying bioinformatics core facility directors, career opportunities, and existing bioinformatics curricula [27]. In their latest iteration [24], these competencies are divided into three categories: biological, computational, and professional. Without being prescriptive, the ISCB core competencies provide a framework in which best practices in bioinformatics education can be identified. The extent to which domain-specific (i.e., biological and computational) core competencies are achieved by learners depends on the scope of your course or training sequence. Incorporating a Wikipedia-based writing element affords learners the opportunity to develop core competencies that emphasize professional skills while enhancing both domain knowledge and writing abilities. These core competencies have transferable elements that will be beneficial in a trainee’s career, regardless of her eventual chosen field. Each of these competencies is further discussed below. However, here we highlight competency N, a competency that is often neglected in the bioinformatics classroom. Leveraging the worldwide reach of Wikipedia as a platform for trainees’ writing will be beneficial in promoting effective communication of bioinformatics topics to an audience of varying expertise. Rule 2: Draw inspiration from the experiences of other educators Several academic reviews of class projects involving editing Wikipedia articles are available for inspiration and guidance. In addition to the Wikipedia Educational Program, other reviews include that of a month-long undergraduate class project in chronobiology at Washington University in St. Louis [28] and a graduate seminar in plant–animal interactions at the University of Florida [29]. The latter article presents a flowchart illustrating a potential process for editing Wikipedia articles as a semester-long class project. These reviews provide useful guidance for educators planning Wikipedia-based writing tasks. For further inspiration, here we briefly detail the experience of one of the authors. In place of writing a final term paper, students in an upper-level, one-semester Bioinformatics Tools course at Ohio University were asked to enhance a Wikipedia article relating to computational biology. The project began with an introduction to the Wikipedia writing genre (see Rule 7 ). Subsequently, students were required to make three iterative revisions to their chosen article, receiving feedback from both their coach and their peers after each revision. Peer feedback considered the clarity of writing, the perceived depth of knowledge of the subject area, and the quality of additional media (e.g., figures) used to enhance the article. At the end of the project, students were encouraged to submit their articles to the ISCB Wikipedia Competition. In addition to the overall goal of learning about a bioinformatics-related topic, students learned how to critique articles, enhanced their writing and editing skills, improved their awareness of available resources, and contributed to the international bioinformatics community. Feedback from the students at the end of the semester was positive overall. One student who edited an article relating to a bioinformatics algorithm reported that, “[I] felt that many subtle details of the problems relat[ing to] my topic only really came to my attention when I attempted to write [them] down in words. It was after I finished editing the article that I truly understood every bit of the [algorithm].” Another commented that writing for Wikipedia’s nonexpert audience “encouraged me to develop a broader knowledge of the subject, and to think about how I would explain it clearly to other people.” Two articles developed in the class won awards in the 2016–2017 ISCB Wikipedia Competition [30], with enhancements to the Smith-Waterman algorithm article [31], which included a novel animated illustration of how the algorithm is applied to example data, awarded first prize. Rule 3: Design a learning experience that aligns with your curriculum Maximizing the potential of using Wikipedia requires careful, intentional planning. Designing experiences for learners is best done when attention is paid to alignment with overall teaching goals. Backward design approaches to curriculum development (i.e., beginning with the end goal in mind) ensure that your curriculum design decisions match your intended outcomes [32]. We caution against trying to fit a Wikipedia-based writing assignment into your existing curriculum without considering whether there is enough time for each component of the project to be done well. A helpful exercise in thinking about how trainees may benefit from a Wikipedia-based assignment is to picture a student standing in front of you at the end of your course. This student received the best possible grade. What can the student do now, as a result of completing your course? What new skills can this student demonstrate? How did a Wikipedia-based project contribute to this change in the student? The answers to these questions will form your goals, or learning objectives, for the experience. Working backward from there, you can determine what materials, practice, and feedback learners will need to reach these goals. For example, if your goal for the assignment is that learners will be able to describe, infer, compare, or synthesize genomics concepts, then as part of your approach to helping learners reach this goal, you may wish to have learners edit and contribute to Wikipedia articles in genomics, with a particular emphasis on articles that compare genomic concepts. If Wikipedia editing is to form a significant portion of your course assessment, it will be wise to plan a multiphase, scaffolded learning process, which builds from article review and minor edits, towards independently editing or creating an article. Rules 4–8 provide guidelines for such a multiphase process. Rule 4: Select specific articles The choice of Wikipedia articles to be edited is an important decision that depends greatly on your course outcomes. If your desired outcomes are more writing focused, then it will make sense to choose less fully developed articles that would benefit greatly from editing. In this case, reviewing and assessing the quality of existing articles could be a useful collaborative classroom activity. Alternatively, if the course is focused on a narrower topic within computational biology, it makes sense to choose articles that fall within this topic. Article selection may be guided by Wikipedia’s Release Version Tool, which ranks articles relating to computational biology by quality and importance [33]. Article quality is graded according to Wikipedia’s content assessment scale and judged by Wikipedia editors [34]. Computational biology articles may also be ranked by popularity, based on recent page views [35]. You may prefer to curate a focused set of articles for learners to choose from rather than allowing free choice from all computational biology articles. However, allowing learners to choose their own articles may encourage greater participation than if they are assigned an article. The Wikipedia-based assignment itself meets characteristics “b,” “c,” and “d” of authentic learning activities [36] because it responds to real-life contexts that do not have single or unique solutions, is directed toward a real audience, and provides new information. Alignment with characteristic “a”—the personal frame of reference—can also be satisfied when learners are allowed to choose their articles, define the problem, and select a solution path [20]. For example, a student in the Bioinformatics Tools course described in Rule 2 chose to enhance a biographical article about bioinformatics pioneer Margaret Oakley Dayhoff. The human-interest element of this project allowed the learner to connect with bioinformatics technology and with the Wikipedia editing project in a very personal way, leading to a significant volume of high-quality edits to the article. In particular, an outstanding request for additional citations dating from February 2013 was resolved and relevant images were added to the article [37]. One additional consideration may be the languages spoken by your trainees, because improving articles in multiple languages will maximize public accessibility of their chosen topics. For instance, the enhancements made to the Smith-Waterman algorithm article mentioned in Rule 2 were simultaneously made to the corresponding article in the English and Chinese Wikipedias, and previous ISCB Wikipedia Competition winners have made contributions to the Spanish Wikipedia. Beyond computational biology, the Wikipedia Education Program has also published case studies for translation assignments [38]. Although it is difficult to assess the veracity or quality of work done in a language you do not know, contributions made in tandem to English and non-English articles (or in any pair of languages) encourage discourse on computational biology topics among those who do not speak English, which is considered to be the scientific lingua franca [39]. Rule 5: Set clear assessment expectations In keeping with Wikipedia’s philosophy of openness and its guidelines regarding article quality, we recommend designing and distributing a rubric specific to assessing the Wikipedia contributions of learners. Just as the editing or creating of a Wikipedia article is different from a traditional writing assignment, so the assessment will also be different [40]. Providing your learners with an assessment rubric will help them to understand your expectations for the assignment and will allow them to self-evaluate and reflect on their performance. For educators, having such a rubric also removes a significant amount of guesswork from assessment. In addition to the aforementioned guidelines for assessing article quality on Wikipedia, the Wiki Education Foundation (which promotes the integration of Wikipedia into coursework by educators in Canada and the US) provides a sample assessment rubric [41], which defines characteristics of assignments from an educational perspective, from “poor” through “excellent.” You will likely want to tailor this sample rubric to your particular assignment; however, the rubric should still reinforce, rather than contradict, Wikipedia’s own rules and style [42]. It is important to emphasize that the unique features of this textual genre offer learners the opportunity to focus on the quality of their message rather than meeting an arbitrary word count. ISCB core competency N, relating to the effective communication of bioinformatics and genomics topics with a range of audiences, is particularly relevant here (see Table 1 ). If the rubric has been consulted throughout the assignment, then there should be few surprises when it comes to the final grading. A vital difference in assessment is that, because Wikipedia articles are “living” documents, the edits of trainees may not be reflected in the article at the time of assessment. However, nothing is ever lost on Wikipedia [40]! We recommend using the article history to extract the contributions made by the trainee and carefully and fairly assess her contributions, not only on edits that remain in the current article, but also edits that may have been deleted or modified [42]. This approach may take longer than assessing a traditional term paper, which is wholly the work of the learner; one solution is to have the learner also document her edits, and present this as part of the submitted assignment. Rule 6: Provide learners with informative examples Once you have set the expectations for the assignment, it is important to make sure learners understand those expectations and how to meet them. To do so, we recommend a two-step process. First, provide examples of both good and bad contributions, and second, have learners use your evaluation criteria to evaluate current articles. Providing robust models that illustrate specific features (see Rule 7 ) is an important part of writing pedagogy [43], and this is a first step in the process. A helpful resource for locating examples of good Wikipedia articles specific to computational biology is Wikipedia’s Release Version Tool [33]. The article quality grades in this list are generated based on appraisals using the Wikipedia Content Assessment Scale discussed in Rule 4. From here, it is possible to find articles that are already in a good state (or in a poor state). A guided exercise to help learners understand the evolution of an article might include finding a good article, or even an ISCB award-winning article [44], and dissecting its editing history. Such an activity can help learners visualize potential improvements to currently underdeveloped articles. A next step might be to have learners practice using your rubric or evaluation criteria to evaluate good and bad articles, either as they currently appear or as they did at some point in the past. Additionally, it may be helpful to introduce learners to the Wikipedia “talk” pages, which are administrative pages associated with each article. Editors use these pages to discuss the content, edits, and needs of articles. For example, the talk page for the Margaret Oakley Dayhoff article referenced in Rule 4 contains justifications for past edits as well as recommendations for improvement that would be appropriate for an article that is of interest not only to those in computational biology but also to those who follow biographies or women’s history. Although they are sometimes underused, it is helpful to highlight these resources to learners so they can understand the expectations of different kinds of articles and their editors. The degree of depth required at this stage depends on your learners. Students with more advanced writing skills may be able to readily appraise the current state of a Wikipedia article, while other students may require additional practice with more frequent teacher- or mentor-directed feedback. Rule 7: Offer guidance on genre-specific writing for Wikipedia Learning how to write in a new genre or with different conventions can be a daunting task. While your learners may have significant experience writing in an academic setting, it is important to note that a Wikipedia-based writing assignment has some unique aspects—intended audience, source selection, and discourse patterns, for example—which contrast with a traditional term paper. There is also an inherent difference in the approach to collaborative writing and the types and immediacy of feedback (see Rule 8 ). A learner’s awareness of the differences between writing styles relates to ISCB core competency N (see Table 1 ). The “Five pillars” are Wikipedia’s most fundamental principles [45,26]; the second “pillar” covers writing in a Wikipedia-appropriate style. We recommend that your trainees begin by reading the Ten Simple Rules for Editing Wikipedia [26] and completing one of the online courses for new Wikipedia editors [46,47]. The Wikimedia Foundation has also provided guidance on why writing for Wikipedia requires a different skill set than writing a traditional term paper [42]. Sharing these resources with your learners at the start of their learning experience will help to underline the differences and commonalities when writing for Wikipedia. One important feature that distinguishes Wikipedia articles from academic articles or term papers is that original research is strictly not allowed, and primary sources are rarely used. To the surprise of some learners and especially educators (usually more accustomed to writing for journal papers), this policy may extend to primary source articles appearing in peer-reviewed journals, which should still be cited with care. Information added to Wikipedia should be based on reliable and published secondary or tertiary sources. For scientific topics, these are usually review articles. For computational biology, in particular, this creates a dilemma due to the fast-moving pace of the field. However, waiting for secondary sources on a particular topic helps to establish its notability (without which, articles may be swiftly deleted). The absence of review articles may also identify writing opportunities for educators and for advanced learners. The technical differences when writing for Wikipedia necessitate additional considerations and learner training activities. For instance, some Wikipedia editing features can only be accessed through Wikipedia’s markup language, Wikitext, which is different from traditional word-processing software. We recommend suggesting to learners that they learn to use Wikitext, in the same way they may learn other similar tools, such as LaTeX; note that such web-based computing skills relate to ISCB core competency I (see Table 1 ). Learners can take low-stakes opportunities to practice the technical requirements of Wikipedia editing by experimenting in the Wikipedia Sandbox [48]. Rule 8: Encourage learners to recognize that feedback is a gift Good feedback is a gift [49]. Wikipedia-based writing projects offer a rare opportunity for learners to receive authentic feedback on their writing from subject-matter experts who serve as Wikipedia editors. Unlike a traditional term paper, which often is reviewed only by an instructor and/or peers, a Wikipedia article is a “living document,” monitored by editors who strive to provide consistent, accurate, and appropriate domain knowledge to readers. In doing so, the editors offer a form of evaluative (rather than descriptive) feedback that learners may not be accustomed to receiving. To equip learners with the capacity to accept and make effective use of this authentic feedback, we recommend four strategies: (a) engage learners in the selection of articles to be edited (see Rule 4 ), (b) create an iterative cycle of editing and feedback with multiple revisions, similar to the revision cycles for scientific journal articles, (c) help learners discern feedback that is useful from feedback that is not useful, and (d) create opportunities for learners to provide quality feedback via peer review activities. These strategies engage learners in types of teamwork that align with ISCB core competency O (see Table 1 ). Educators will benefit from the authentic feedback of other Wikipedia editors by partially “crowdsourcing” their assessment of learners’ knowledge, because obvious errors will generally be swiftly reverted and plagiarized text will be removed (usually automatically, by one of Wikipedia’s software “bots”). While most feedback will be constructive, it is important to remember that occasional conflicts may arise despite good faith on both sides. Indicating to regular editors that articles are being modified as part of a class assignment by using Wikipedia’s “educational assignment template” [50] may encourage those editors “not to bite the newbies” [51]. In a previous set of Ten Simple Rules, Dashnow and colleagues asked “how would Darwin have handled a Wikipedia edit war?” [52]. While this was asked in jest, we recommend that you give some forethought as to how you will deal with potential conflicts involving your learners and other Wikipedia editors. Wikipedia has a comprehensive dispute resolution procedure [53]; we suggest becoming familiar with this resource before such conflicts arise. It should be emphasized that embracing constructive feedback and resolving conflicts in an ethical and considered manner are important parts of learners’ continuing professional development and are useful skills for interacting with journal article reviewers and thesis examiners, as well as in many contexts outside of academia. These skills fall under ISCB core competency M (see Table 1 ). Rule 9: Connect learners with the wider Wikipedia community Wikipedia is almost entirely community led. Instead of having learners edit in a bubble, encourage them to connect with the wider Wikipedia community, including across languages. Making these connections will demonstrate to learners that their writing will be seen globally and should encourage them to consider the impact of their work in a wider context. Indeed, it has been observed that once a learner’s edits become live on Wikipedia and indexed in search engine results, they begin to realize that “there is agency to sharing their scholarship with the world” [54]. Of course, if you are not already registered with Wikipedia, we recommend doing so and spending some time editing Wikipedia and connecting with the community. The Wikipedia School and University projects page collects information about Wikipedia class projects and is home to a community of Wiki-friendly educators [55]. We also recommend joining the Computational Biology task force of WikiProject Molecular Biology. This task force, previously known as WikiProject Computational Biology [30], is an international community of Wikipedia editors formed in 2007 to organize and improve the roughly 1,500 Wikipedia articles relating to all aspects of computational biology and bioinformatics. We encourage educators and learners alike to remain engaged with Wikipedia after their project, since they have subject-specific expertise that is enormously valuable for Wikipedia. For learners, in particular, remaining engaged with Wikipedia can provide an additional channel to keep up to date with new developments in a topic area in which they are interested. Topic Pages are a collaborative initiative between PLOS journals and Wikipedia [56] for review-type articles on subjects that are not covered in Wikipedia. These articles are published simultaneously on Wikipedia and in a PLOS journal and would be an ideal follow-up activity, especially for advanced learners. These connections and longer-term engagement with the Wikipedia community, and the consideration of the wider impact of a learner’s work, align with ISCB core competencies L and P (see Table 1 ). Rule 10: Share outcomes with other educators After your learning experience is complete, take some time to reflect on the activity and share the outcomes with other educators. There are three major outcomes to be considered: (i) the impact of the project on the learner and the scale of the contribution to the public’s knowledge of computational biology, (ii) the design of the learning experience and how it can be improved, and (iii) how to share what you have learned to aid future educators. The principal focus of your reflection should be on what learners have gained from completing a Wikipedia-based assignment. As discussed in Rule 3, it is helpful to consider how learners would benefit from such an assignment; this benefit may be measured. In addition to the specific learning outcomes of your course (e.g., the improved depth of knowledge about bioinformatics algorithms in a technical course), consider the growth of learners in general and transferable skills attained. The ISCB core competencies presented in Table 1 may be useful for measuring these, especially when paired with a hierarchical model for classifying learning objectives (such as Bloom’s taxonomy [24,57]). After completing their assignment, each trainee will have made some concrete contributions to the public’s knowledge of computational biology [3]. Wikipedia’s article quality ratings [34] may help quantify the improvements. Due to the nature of Wikipedia, the learner’s edits may last for years or may be changed by tomorrow. A high turnover of edits may be expected when writing about a fast-paced field such as bioinformatics; however, it is worth attempting to define the qualities of lasting edits and promoting these in future learning experiences, including future iterations of the course. This previous point may guide reflection on the design of the learning experience. Inevitably, some aspects of the project will have been more successful than others, and we encourage an iterative approach to refining your learning experience. For instance, was there a significant variation in the quality of edits made by different learners, which might be remedied by a group-based element in future assessments? This iterative refinement of the project will be easier if the details of the entire process have been documented. Here, identifying and improving a single aspect of the project will be more successful than modifying many features at once. Documenting and sharing your experiences and course materials with other educators demonstrates a commitment to Wikipedia’s ethos of openness and may encourage other educators to implement similar projects, continuing and expanding the cycle of knowledge transfer. This documentation may range from an informal write-up on Wikipedia, through resource sharing via the open Zenodo repository, to a short report submitted to a journal such as the ISCB Community Journal or presentation at the annual meeting of the ISCB Education COSI [58]. A deeper evaluation of your learning experience may be submitted as an educational review; we suggest involving your trainees as coauthors: the review by Chiang and colleagues is an excellent example of this [28]. We also recommend that trainees submit improved articles to the ISCB Wikipedia Competition [4,5]; as well as recognition from an international scholarly society, there is an additional financial incentive in the form of awards presented (at the Intelligent Systems for Molecular Biology conference) to the editors who have made the best improvements to their chosen article. Conclusion There is an emerging consensus that editing Wikipedia articles as part of a class curriculum has lasting benefits far beyond the classroom. The scientific academic community remains wary of its relationship with Wikipedia [59], yet Wikipedia continues to be a first reference for many learners searching for information on unfamiliar topics. Both academia and Wikipedia would benefit from a stronger relationship and we believe that, for academics, improving Wikipedia articles represents not only an educational opportunity but a professional responsibility [29,60]. The increasing pace of the field of computational biology means that the many important Wikipedia articles are outdated or incomplete; as of July 2017, 80% of relevant articles had a Wikipedia quality rating of “start” class (articles that are developing but are essentially incomplete) or lower [30]; this state has persisted through the time of writing this article. We hope educators in computational biology will adopt the simple rules set out above to simultaneously enhance the learning of their students and improve a vital public resource for their profession. We believe that the replacement of traditional class projects with Wikipedia-based learning experiences is something to be embraced, and marks “[t]he end of throwaway assignments and the beginning of real-world impact for student editors” [10].

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          Bioinformatics Curriculum Guidelines: Toward a Definition of Core Competencies

          Introduction Rapid advances in the life sciences and in related information technologies necessitate the ongoing refinement of bioinformatics educational programs in order to maintain their relevance. As the discipline of bioinformatics and computational biology expands and matures, it is important to characterize the elements that contribute to the success of professionals in this field. These individuals work in a wide variety of settings, including bioinformatics core facilities, biological and medical research laboratories, software development organizations, pharmaceutical and instrument development companies, and institutions that provide education, service, and training. In response to this need, the Curriculum Task Force of the International Society for Computational Biology (ISCB) Education Committee seeks to define curricular guidelines for those who train and educate bioinformaticians. The previous report of the task force summarized a survey that was conducted to gather input regarding the skill set needed by bioinformaticians [1]. The current article details a subsequent effort, wherein the task force broadened its perspectives by examining bioinformatics career opportunities, surveying directors of bioinformatics core facilities, and reviewing bioinformatics education programs. The bioinformatics literature provides valuable perspectives on bioinformatics education by defining skill sets needed by bioinformaticians, presenting approaches for providing informatics training to biologists, and discussing the roles of bioinformatics core facilities in training and education. The skill sets required for success in the field of bioinformatics are considered by several authors: Altman [2] defines five broad areas of competency and lists key technologies; Ranganathan [3] presents highlights from the Workshops on Education in Bioinformatics, discussing challenges and possible solutions; Yale's interdepartmental PhD program in computational biology and bioinformatics is described in [4], which lists the general areas of knowledge of bioinformatics; in a related article, a graduate of Yale's PhD program reflects on the skills needed by a bioinformatician [5]; Altman and Klein [6] describe the Stanford Biomedical Informatics (BMI) Training Program, presenting observed trends among BMI students; the American Medical Informatics Association defines competencies in the related field of biomedical informatics in [7]; and the approaches used in several German universities to implement bioinformatics education are described in [8]. Several approaches to providing bioinformatics training for biologists are described in the literature. Tan et al. [9] report on workshops conducted to identify a minimum skill set for biologists to be able to address the informatics challenges of the “-omics” era. They define a requisite skill set by analyzing responses to questions about the knowledge, skills, and abilities that biologists should possess. The authors in [10] present examples of strategies and methods for incorporating bioinformatics content into undergraduate life sciences curricula. Pevzner and Shamir [11] propose that undergraduate biology curricula should contain an additional course, “Algorithmic, Mathematical, and Statistical Concepts in Biology.” Wingren and Botstein [12] present a graduate course in quantitative biology that is based on original, pathbreaking papers in diverse areas of biology. Johnson and Friedman [13] evaluate the effectiveness of incorporating biological informatics into a clinical informatics program. The results reported are based on interviews of four students and informal assessments of bioinformatics faculty. The challenges and opportunities relevant to training and education in the context of bioinformatics core facilities are discussed by Lewitter et al. [14]. Relatedly, Lewitter and Rebhan [15] provide guidance regarding the role of a bioinformatics core facility in hiring biologists and in furthering their education in bioinformatics. Richter and Sexton [16] describe a need for highly trained bioinformaticians in core facilities and provide a list of requisite skills. Similarly, Kallioniemi et al. [17] highlight the roles of bioinformatics core units in education and training. This manuscript expands the body of knowledge pertaining to bioinformatics curriculum guidelines by presenting the results from a broad set of surveys (of core facility directors, of career opportunities, and of existing curricula). Although there is some overlap in the findings of the surveys, they are reported separately, in order to avoid masking the unique aspects of each of the perspectives and to demonstrate that the same themes arise, even when different perspectives are considered. The authors derive from their surveys an initial set of core competencies and relate the competencies to three different categories of professions that have a need for bioinformatics training. Survey of Directors of Bioinformatics Core Facilities Bioinformatics educational programs face the risk of producing students who have skills that are primarily academic in nature, thereby limiting the utility of program graduates. To investigate this risk, the ISCB Curriculum Task Force sought to capture the perspectives of directors of bioinformatics core facilities as representatives of employers of professional bioinformaticians. Specifically, the core facility directors were asked what skills are needed for success in the field of bioinformatics and what skills are lacking in recently hired bioinformaticians. In general, these lists were very similar (i.e., skills needed are often lacking). Twenty-nine core facility directors responded to the survey. The respondents were from Europe (six), Israel (one), and the United States and Canada (21). (One respondent did not indicate geographic location.) The results are divided into general skills and domain-specific skills and are categorized by level of training: bachelors (ten respondents), masters (22 respondents), and PhDs (25 respondents). Hiring at the bachelor level appears to be a less frequent occurrence than hiring people with graduate degrees. At the bachelor level, managers are looking for people who can work independently, have good communications and consulting skills, are organized, and are passionate about their work. The most frequently mentioned domain-specific skills needed for bachelor-level candidates were technical in nature and included programming, software engineering, system administration, and databases. New hires for such positions at the bachelor level typically lack time management skills and project management skills and are unable to manage multiple projects. They also lack knowledge in biology and statistics. The responses for hiring at the master level were far more numerous and varied. General skills needed include those that are more interpretative and problem solving, as well as personal traits, such as being independent, curious, and self-motivated. These same skills are considered lacking in many master-level hires. With respect to domain-specific skills, directors need people well versed in biology, bioinformatics, statistics, and programming, essentially needing people with technical experience in both biological sciences and computational methods. New hires often lack experience in the analysis of real biological data. Not surprisingly, general skills needed at the PhD level include those skills necessary at the master level, as well as communications skills, management skills, and the ability to help others. Skills most frequently found lacking in individuals with PhDs include communications skills, ability to synthesize information, ability to complete projects, and leadership skills. The domain-specific skills were similar to those needed at the master level, but emphasized more prior experience in bioinformatics, data analysis, and statistics. What is lacking among candidates at this level is experience specific to work done by the hiring group. The responses of the core facility directors can be summarized as follows: everyone wants smart, motivated people with good critical thinking skills and deep domain knowledge. It is clear that training in both general skills and domain-specific skills is necessary at all professional levels, both while in a degree program and throughout one's career. Table 1 presents the skill sets synthesized from the bioinformatics core facility directors' survey and the bioinformatics career opportunity survey. 10.1371/journal.pcbi.1003496.t001 Table 1 Summary of the skill sets of a bioinformatician, identified by surveying bioinformatics core facility directors and examining bioinformatics career opportunities. Skill Category Specific Skills General time management, project management, management of multiple projects, independence, curiosity, self-motivation, ability to synthesize information, ability to complete projects, leadership, critical thinking, dedication, ability to communicate scientific concepts, analytical reasoning, scientific creativity, collaborative ability Computational programming, software engineering, system administration, algorithm design and analysis, machine learning, data mining, database design and management, scripting languages, ability to use scientific and statistical analysis software packages, open source software repositories, distributed and high-performance computing, networking, web authoring tools, web-based user interface implementation technologies, version control tools Biology molecular biology, genomics, genetics, cell biology, biochemistry, evolutionary theory, regulatory genomics, systems biology, next generation sequencing, proteomics/mass spectrometry, specialized knowledge in one or more domains Statistics and Mathematics application of statistics in the contexts of molecular biology and genomics, mastery of relevant statistical and mathematical modeling methods (including experimental design, descriptive and inferential statistics, probability theory, differential equations and parameter estimation, graph theory, epidemiological data analysis, analysis of next generation sequencing data using R and Bioconductor) Bioinformatics analysis of biological data; working in a production environment managing scientific data; modeling and warehousing of biological data; using and building ontologies; retrieving and manipulating data from public repositories; ability to manage, interpret, and analyze large data sets; broad knowledge of bioinformatics analysis methodologies; familiarity with functional genetic and genomic data; expertise in common bioinformatics software packages, tools, and algorithms Survey of Career Opportunities The context in which bioinformaticians employ their talents is an important consideration for defining bioinformatics curricular guidelines. Thus, we analyzed the ISCB - Membership Job Board postings (see http://www.iscb.org/iscb-careers) to determine the responsibilities and required skills of bioinformaticians. We examined job listings from a four-month period, sampling 75 listings (of 130) from diverse geographic locations. Specifically, job listings from the following locations were analyzed: Australia, Austria, Canada (London, Ottawa, Toronto), China (Hong Kong, Shanghai), Denmark, France, Germany, Israel, Italy, Japan, Kenya, Singapore, South Africa, South Korea, Sweden (Stockholm, Uppsala), the United Kingdom (Cambridge, London, Norwich), and the United States (Arizona, Georgia, Texas, Delaware, North Carolina, California, Colorado, Iowa, Illinois, Indiana, Kansas, Massachusetts, Maryland, New York, Pennsylvania, Michigan). The remainder of this section summarizes the duties and skills required for the bioinformatics positions considered. The responsibilities of a bioinformatician include data analysis, software development, project support, and computational infrastructure support in biological contexts (such as next generation sequencing, medical research, regulatory genomics, and systems biology). A bioinformatician analyzes and manages data as a member of an interdisciplinary research team composed of members from disciplines that span the biological, medical, computational, and mathematical sciences. This involves several activities: working in a production environment managing scientific data; modeling, building, and warehousing biological data; using and/or building ontologies; and retrieving, manipulating, and managing data from public data repositories. To successfully perform the duties of a bioinformatician, one must possess an array of bioinformatics skills: ability to manage, interpret, and analyze large data sets; broad knowledge of bioinformatics analysis methodologies; familiarity with functional genetic and genomic data; and expertise in common bioinformatics software packages and algorithms. A bioinformatician must apply statistics in contexts such as molecular biology, genomics, and population genetics. Thus, a bioinformatician must have mastery of relevant statistical and mathematical modeling methods, including descriptive and inferential statistics, probability theory, differential equations and parameter estimation, graph theory, epidemiological data analysis, and programming and analysis of next generation sequencing data using software such as R and Bioconductor. The ability to employ computer science methods is critical in the discipline of bioinformatics because custom software tools and databases often need to be created. Therefore, a bioinformatician must have the ability to apply software engineering methodologies to successfully design, implement, and maintain systems and software in scientific environments. The ability to employ modern software engineering processes (such as object-oriented analysis, design, and implementation) is important. In order to develop efficient and effective software systems, it is valuable to have a detailed understanding of the methods of algorithm design and analysis, machine learning, data mining, and relational databases. A bioinformatician should be proficient in the use of one or more scripting languages (such as Perl, Python, Java, C, C++, C#, .NET, and Ruby), database management languages (e.g., Oracle, PostgreSQL, and MySQL), and scientific and statistical analysis software (such as R, S-plus, MATLAB, and Mathematica). Additionally, a bioinformatician should be able to incorporate components from open source software repositories into a software system. The ability to effectively utilize distributed and high-performance computing to analyze large data sets is essential, as is knowledge of networking technology and internet protocols. A bioinformatician should be able to utilize web authoring tools, web-based user interface implementation technologies, and version control and build tools (e.g., subversion, Ant, and Netbeans). While it is important for a bioinformatician to have a suite of computational, mathematical, and statistical skills, this alone is insufficient. Throughout their careers, bioinformaticians usually contribute to a variety of scientific projects, such as variant detection in human exome resequencing; human genetic diversity; genomic and epigenomic mechanisms of gene regulation; viral diversity; neurodegeneration and psychiatric disorders; drug discovery; the role of transcription factors and chromatin structure in global gene expression, development, and differentiation; and cancer/tumor biology. To be a fully integrated member of a research team, a bioinformatician must possess detailed knowledge of molecular biology, genomics, genetics, cell biology, biochemistry, and evolutionary theory. Furthermore, it is necessary to understand related technologies, including next generation sequencing and proteomics/mass spectrometry. It is also desirable for a bioinformatician to have modeling experience or background in one or more specialized domains, such as systems biology, inflammation, immunology, cell signaling, or physiology. Additionally, a bioinformatician must have a high level of motivation, be independent and dedicated, possess strong interpersonal and managerial skills, and have outstanding analytical ability. A bioinformatician must have excellent teamwork skills and have strong scientific communication skills. As a bioinformatician progresses through his or her career, it is helpful to develop managerial and programmatic skills, such as staff management and business development; understanding of or experience with grant funding and/or access to finance; awareness of research and development (R&D) and innovation policy and government drivers; the use of modeling and simulation approaches; ability to evaluate the major factors associated with efficacy and safety; and ability to answer regulatory questions related to product approval and risk management. It is also important to have familiarity with presenting biological results in both oral and written forms. In summary, a senior bioinformatician will benefit from strong analytical reasoning capabilities, as evidenced by a track record of innovation; scientific creativity, collaborative ability, mentoring skills, and independent thought; and a record of outstanding research. Table 1 summarizes the skill sets identified by (1) surveying bioinformatics core facility directors and (2) examining bioinformatics career opportunities. Preliminary Survey of Existing Curricula An important step in developing guidelines for bioinformatics education is to gain a comprehensive understanding of current practices in bioinformatics and computational biology education. To this end, the task force surveyed and catalogued existing curricula used in bioinformatics educational programs. As a first step, the task force began a manual search for educational programs. Due to the large number of education programs, the decision was made to initially restrict the search to programs awarding a degree or certificate and explicitly including “computational biology,” “bioinformatics,” or some close variant in the name of the degree or certificate awarded. The search thus excluded non-degree tracks or options within more traditional programs, non-degree programs of study, or programs in related fields that might have high overlap with bioinformatics (e.g., biostatistics or biomedical informatics). Although this was a controversial decision even within the task force, this narrow scope and definition of programs was intended to keep the search from becoming too unfocused or being sidetracked over questions of which programs should be included as belonging to the field. A search by committee members produced a preliminary collection of two programs awarding degrees of associate of arts or sciences; 72 awarding bachelor of science, arts, or technology; 38 awarding master of science, research, or biotechnology; 39 awarding doctor of philosophy; and 15 awarding non-degree certificates. However, it provided a basis for manual examination of trends in educational practice. Attempts to identify common practices among this narrow subset revealed substantial challenges. First, differences in types of degrees and regulations for awarding them proved challenging in making a precise but inclusive definition of a bioinformatics degree program, especially across international boundaries. Differences in how specific topics are partitioned among courses and limited information on the contents of specific courses likewise hindered analysis. For example, multiple programs may have a class called “Bioinformatics I,” yet one cannot assume these classes cover comparable material. Furthermore, the number of extant programs and the lack of any central repository of information or standard reporting format make it difficult to make any comprehensive statements about current accepted practices or variations. Finally, the preliminary surveys revealed an extraordinary diversity of requirements across programs, even at a given degree level. Consequently, it was extremely difficult to catalog the requirements for an individual program and a greater challenge to identify the commonalities between programs. Given the challenges of conducting a committee-directed survey, the task force concluded that self-reporting of program features by cognizant program officials would be the best mechanism to produce a survey that is comprehensive, inclusive, and accurate. The task force hopes to have, in the future, a central system in which program officials can identify their programs and describe the coursework they require, yielding a database that can be mined to uncover common practices and variations across programs at multiple levels. Such a repository could be made available for public viewing, as we expect it will have incidental benefits for others, such as potential students looking to compare programs. A key obstacle to creating such a repository has been identifying a format that allows the coursework to be categorized in a way that is specific enough to meaningfully distinguish among programs but general enough to allow one to identify commonalities among classes that are never identical across institutions. To this end, a decision was made to produce a controlled vocabulary in which programs can report their required courses. Figure 1 provides an initial draft of such a controlled vocabulary, which was developed manually, based on the initial task force survey of existing curricula. We note that this is not intended to be a finished product but rather a starting point for discussion. We hope for feedback, to improve this vocabulary in order to represent the range of variation in classes offered by such programs. 10.1371/journal.pcbi.1003496.g001 Figure 1 Draft of a controlled vocabulary for identifying specific requirements of computational biology and bioinformatics degree and certificate programs. The terms are drawn from requirements observed in a manual survey of a subset of existing educational programs in order to allow identification of recurring requirements while also allowing for the wide variation between programs. The task force intends to incorporate the final controlled vocabulary into a website to which individual program officials can add their programs, providing identifying information and a description of the curriculum in terms of the vocabulary. This is a task that will require community participation, and it is our hope that a shared desire to identify best practices and the benefits of having a program listed in a central repository will encourage broad participation. Discussion Toward a definition of core competencies In the discipline of bioinformatics and computational biology, there are numerous ways in which curricula can be designed to achieve the desired educational outcomes. However, analysis of our survey results suggests that there is a common set of desired proficiencies for bioinformaticians. We have organized these desired proficiencies into a set of core competencies to provide guidance for bioinformatics educational programs. These guidelines synthesize the results of our surveys (see preceding sections of this manuscript). While we acknowledge that we are dealing with small samples of responses, not randomly surveyed, the resulting competencies do not contravene previously published recommendations (see introduction and references [1]–[16]), and they comport with the experiences of the task force members. The wording for the core competencies is modeled after the Accreditation Board for Engineering and Technology (ABET) criteria for computer science programs [18], using the terminology and concepts of Bloom's Taxonomy [19]–[21]. Our recommendation is that bioinformatics programs enable students to attain the competencies shown in the rows of Table 2. 10.1371/journal.pcbi.1003496.t002 Table 2 Core competencies for each bioinformatics training category. Bioinformatics User Bioinformatics Scientist Bioinformatics Engineer (a) An ability to apply knowledge of computing, biology, statistics, and mathematics appropriate to the discipline. X X (b) An ability to analyze a problem and identify and define the computing requirements appropriate to its solution. X X (c) An ability to design, implement, and evaluate a computer-based system, process, component, or program to meet desired needs in scientific environments. X (d) An ability to use current techniques, skills, and tools necessary for computational biology practice. X X X (e) An ability to apply mathematical foundations, algorithmic principles, and computer science theory in the modeling and design of computer-based systems in a way that demonstrates comprehension of the tradeoffs involved in design choices. X (f) An ability to apply design and development principles in the construction of software systems of varying complexity. X (g) An ability to function effectively on teams to accomplish a common goal. X X X (h) An understanding of professional, ethical, legal, security, and social issues and responsibilities. X X X (i) An ability to communicate effectively with a range of audiences. X X X (j) An ability to analyze the local and global impact of bioinformatics and genomics on individuals, organizations, and society. X X X (k) Recognition of the need for and an ability to engage in continuing professional development. X X X (l) Detailed understanding of the scientific discovery process and of the role of bioinformatics in it. X X X (m) An ability to apply statistical research methods in the contexts of molecular biology, genomics, medical, and population genetics research. X X X (n) Knowledge of general biology, in-depth knowledge of at least one area of biology, and understanding of biological data generation technologies. X X X It is not the intention of the authors to imply that the skill set of one category is entirely subsumed by the skill set of another category. The focus of this document is on bioinformatics; thus, the authors did not attempt to define the full set of competencies that are required in the medical, legal, and scientific contexts. The columns of Table 2 indicate core competencies for three different types of individuals that have a need for bioinformatics training. (The three categories of bioinformatics training are not meant to capture all possible types of bioinformatics training needed but to describe three common categories.) Bioinformatics users access data resources to perform job duties in specific application domains. Bench-based researchers, both in academia and in industry, provide the classic example of a bioinformatics user, but this group is broadening in scope. For example, medical professionals (e.g., physicians and genetic counselors) utilize bioinformatics resources in medical contexts for the purposes of diagnosis, treatment, and counseling of patients. As the practices of genomic and personalized medicine increase, we anticipate a growing need for training clinicians in the use of bioinformatics data and tools. Other bioinformatics users include legal professionals and K-12 biology teachers. The authors use personas to refine their understanding of different types of computational biologists and the competencies that they require to perform their roles. Designers of commercial products frequently create “personas”—archetypes based on data and research on the users for whom a product is being designed—to facilitate the design process. This technique is beginning to pervade the design of bioinformatics resources [22], [23]. The use of personas can also be extremely powerful in educational contexts. Personas have two important functions. First, they can help to guide decisions about the appropriateness of the course or curriculum under development: we can ask questions such as, “How might the removal of module A affect the workflow of trainee B?” Second, they can create empathy, reminding the course developer (and ultimately the trainer) that the trainee might have different end goals than her/his own. An example persona for a bioinformatics user is provided in Figure 2. This persona, based on a typical “bioinformatics user,” can help a curriculum designer to interpret the core competencies in Table 2. For example, in training for the competency, “(d) An ability to use current techniques, skills, and tools necessary for computing practice,” one should consider including adequate time for familiarization with the command line, data management practice, and statistical analysis tools. 10.1371/journal.pcbi.1003496.g002 Figure 2 A persona based on a typical “bioinformatics user.” QA: Quality Assurance, GUI: Graphical User Interface. Image credit: Jenny Cham, Mary Todd Bergman, and Cath Brooksbank, EMBL-EBI. Bioinformatics scientists are biologists who employ computational methods in order to advance the scientific understanding of living systems. Both bioinformatics users and bioinformatics scientists should have a basic understanding of the nature of the computational tools they employ, especially when making conclusions based on statistical inference. For example, the E-value output of the BLAST software [24] depends on the sequence statistics of the database against which a search is conducted. As many uses of BLAST require search of customized databases, different searches can lead to difficulties in result interpretation and comparison. Thus, basic knowledge of modeling assumptions and how methods were “trained” is critical. A persona for an archetypal “bioinformatics scientist” is provided in Figure 3. 10.1371/journal.pcbi.1003496.g003 Figure 3 A persona based on a typical “bioinformatics scientist.” GUI: Graphical User Interface. Image credit: Jenny Cham, Mary Todd Bergman, and Cath Brooksbank, EMBL-EBI. Bioinformatics engineers create the novel computational methods needed by bioinformatics users and scientists [25], [26]. Thus, a bioinformatics engineer must have strengths in computational and statistical sciences and must have general competency in biomedical sciences. Bioinformatics engineers design the infrastructure and systems for bioinformatics analysis, integrating software, databases, and hardware. This can involve the choice or design of hardware and software for the storage and management of diverse and distributed data, selection or development of tools and algorithms for integration and analysis of these data, and design of suitable user interfaces. The critical and complex nature of bioinformatics software and the growing volume of associated data require the development of reliable and maintainable systems in an environment where requirements can be complex, vague, and volatile, and budgets and schedules are often tight. In addition to strong scientific foundations and technical skills the bioinformatics engineer needs to bring to bear engineering competencies such as systems design and project management to ensure the quality, viability, and sustainability of the software systems developed. A persona for a representative “bioinformatics engineer” is provided in Figure 4. 10.1371/journal.pcbi.1003496.g004 Figure 4 A persona based on a typical “bioinformatics engineer.” GUI: Graphical User Interface. Image credit: Jenny Cham, Mary Todd Bergman, and Cath Brooksbank, EMBL-EBI. Conclusions ISCB's Education Committee Curriculum Task Force considered bioinformatics and computational biology training and education in a variety of contexts, resulting in the definition of a broad set of core competencies for three different types of individuals. We hope the concepts presented in the article will be valuable for trainers and educators who wish to design courses and curricula to meet the needs of today's bioinformaticians. The task force will continue to refine and update the curricular guidelines as a service to the bioinformatics community.
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            The development and application of bioinformatics core competencies to improve bioinformatics training and education

            Bioinformatics is recognized as part of the essential knowledge base of numerous career paths in biomedical research and healthcare. However, there is little agreement in the field over what that knowledge entails or how best to provide it. These disagreements are compounded by the wide range of populations in need of bioinformatics training, with divergent prior backgrounds and intended application areas. The Curriculum Task Force of the International Society of Computational Biology (ISCB) Education Committee has sought to provide a framework for training needs and curricula in terms of a set of bioinformatics core competencies that cut across many user personas and training programs. The initial competencies developed based on surveys of employers and training programs have since been refined through a multiyear process of community engagement. This report describes the current status of the competencies and presents a series of use cases illustrating how they are being applied in diverse training contexts. These use cases are intended to demonstrate how others can make use of the competencies and engage in the process of their continuing refinement and application. The report concludes with a consideration of remaining challenges and future plans.
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              Writing-to-Learn in Undergraduate Science Education: A Community-Based, Conceptually Driven Approach

              Despite substantial evidence that writing can be an effective tool to promote student learning and engagement, writing-to-learn (WTL) practices are still not widely implemented in science, technology, engineering, and mathematics (STEM) disciplines, particularly at research universities. Two major deterrents to progress are the lack of a community of science faculty committed to undertaking and applying the necessary pedagogical research, and the absence of a conceptual framework to systematically guide study designs and integrate findings. To address these issues, we undertook an initiative, supported by the National Science Foundation and sponsored by the Reinvention Center, to build a community of WTL/STEM educators who would undertake a heuristic review of the literature and formulate a conceptual framework. In addition to generating a searchable database of empirically validated and promising WTL practices, our work lays the foundation for multi-university empirical studies of the effectiveness of WTL practices in advancing student learning and engagement.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                14 May 2020
                May 2020
                : 16
                : 5
                : e1007868
                Affiliations
                [1 ] MRC Centre for Regenerative Medicine, University of Edinburgh, Edinburgh BioQuarter, Edinburgh, United Kingdom
                [2 ] Office of Instructional Innovation, Ohio University, Athens, Ohio, United States of America
                [3 ] School of Computer Science and Electrical Engineering, Ohio University, Athens, Ohio, United States of America
                Carnegie Mellon University, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-4795-8799
                http://orcid.org/0000-0001-6419-9416
                Article
                PCOMPBIOL-D-19-02149
                10.1371/journal.pcbi.1007868
                7224448
                32407308
                6dd28da6-ff3f-4006-8d1c-d3edf88a73bc
                © 2020 Kilpatrick et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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                The authors received no specific funding for this work.
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