workshops+2018

1. Bringing a Research Lens into Teaching 2. Data Science in R: A Beginners Workshop for DBER Researchers, 3. Development and Testing of Assessments for Measuring Experimentation Competence in Biology 4. Making the case for tenure: Using evidence to delineate between teaching and research for DBER scholars,
 * Four workshops: each workshop costs $35 and has a limit of 20 participants **

Katelyn Southard and Jonathan Cox (University of Arizona)
 * 1. Bringing a Research Lens into Teaching **

 Adopting a research lens in the classroom can be a difficult process for many instructors interested in conducting research in their own classroom settings. Many faculty who are interested in discipline-based education research (DBER) may be excited to make transformative changes in their classrooms, share interesting approaches to well-known instructional problems, or communicate interesting patterns in student thinking on a particular topic, but might be doing so based on “hunches” or student perspective feedback. The goal of this workshop is to provide insight into DBER methodologies for instructors that are at the beginning stages of pursuing research questions in their own instructional settings. We aim to provide some initial training in collecting and analyzing quality evidence of student thinking from within the classroom setting in order to inform a particular research question or learning process. This workshop is geared toward 1) individuals who are interested in starting biology education research but are approaching it for the first time in their own classrooms or 2) individuals who are currently teaching and would like input on how to use evidence of student thinking patterns from within their own classrooms to improve their instructional practices. Workshop activities will center on a case-based approach to 1) defining research goals, 2) evaluating evidence of student learning using a variety of research methods 3) analyzing collected data by focusing on underlying student reasoning patterns, and 4) considering possible actions based on the evidence collected. Participants will spend time dissecting examples and consider how these principles can be applied to their own DBER work.
 * ABSTRACT: **

 Through participation in the “Bringing a Research Lens into Teaching” workshop, participants will be able to:
 *  PARTICIPANT OUTCOMES: **
 * 1. Identify methods of collecting student thinking that align with research goals.
 * 2. Compare the differences between interpreting student thinking patterns versus evaluating student answers for “correctness”.
 * 3. Reflect on the potential benefits of focusing on student thinking in research and teaching.
 * 4. Apply what they have learned to specific cases.

 Workshop activities will primarily focus on actively engaging participants in evaluating case-based scenarios of DBER research in classrooms. These scenarios will highlight a variety of evidence-based tools and strategies for collecting and analyzing student reasoning in the classroom. In small groups, participants will work to evaluate scenarios by focusing on 1) defining quality research goals, 2) evaluating evidence of student reasoning patterns, 3) applying evidence-based DBER research tools and methodologies, 4) analyzing data for underlying patterns, and 5) considering possible actions and outcomes based on research results.
 *  PARTICIPANT ENGAGEMENT: **

 Small and large group discussions will focus on analyzing patterns in student thinking by noticing key elements of reasoning, creating interpretations of critical features and common patterns in collected evidence, and responsively acting based on results. Specifically, groups will be asked to analyze hypothetical data sets in small groups, while focusing on 1) distinguishing between underlying reasoning patterns vs. potentially distracting variables in the data and 2) exploring the alignment between the observed results and the research aims. The case-based scenarios will be designed to underscore the value of focusing on student reasoning patterns in conducting DBER research and will show differences between teacher-centric evaluative approaches to analysis and learner-centric interpretive approaches to uncovering reasoning patterns in collect student work.

 Reflection activities will provide participants with the opportunity to voice ideas for their own classroom research and engage in actively providing and receiving specific feedback on these ideas. To assess workshop learning outcomes and to allow participants additional practice, the workshop will close with a small group activity that assesses the participants’ ability to transfer the principles used in the workshop to their own unique settings  Dr. Katelyn Southard received her BER-focused PhD from the University of Arizona in the Department of Molecular and Cellular Biology. Her two primary areas of research focus include understanding undergraduate biology students’ ideas about molecular mechanisms and improving learning opportunities for students in large enrollment STEM courses through effective instructional teams. Katie served as program coordinator, team facilitator, and presenter for the HHMI/NAS Mountain West Regional Summer Institute on Undergraduate Education in Biology (2011-2015). Currently, she is an Assistant Research Scientist at the UA for the NSF-IUSE project Developing Instructional Teams for Evidence-Based Instruction in Large Collaborative Learning Environments. Here she heads the research arm of the project focusing on evaluating a new model of effectively using instructional teams to increase learning opportunities for students in undergraduate STEM.
 * FACILITATOR DESCRIPTION: **

 Dr. Jonathan Cox received his PhD in Epidemiology from Yale University. His interest in teaching and learning was sparked during a Postdoctoral Excellence in Research and Teaching fellowship, part of the NIH’s IRACDA program, at the UA. During this time, he transitioned out of basic Epi research and moved entirely into undergraduate education after joining the UA’s AAU Undergraduate STEM Education Leadership Team. He has extensive experience working with faculty training and evaluation projects across the UA campus. For example, he worked with faculty to establish common learning objectives across an introductory biology curriculum and evaluated the implementation of the Chemical Thinking curriculum serving roughly 2500 students per year. He has experience teaching in-person and online introductory courses including molecular and cellular biology and epidemiology courses. Currently, as a Research Associate for the same NSF-IUSE project, he heads the professional development training of participating STEM faculty and students.

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<span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;">Jordan Harshman (Auburn University)
 * <span style="font-family: Arial,Helvetica,sans-serif; font-size: 16.2pt;">2. Data Science in R: A Beginners Workshop for DBER Researchers, **

<span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> In discipline-based education research (DBER), quantitative analyses can be performed in a variety of programs. Arguably, none is more powerful, customizable, efficient, and cheaper than R, a free open-source program. R offers a number of advantages. Firstly and perhaps most importantly, coding in R is always reproducible because in order to run analyses, the proper code must be documented fully in order to produce the targeted results. Secondly, because it is open-source, an international audience has contributed over 12,400 packages (additional features and analyses), meaning virtually any analysis can be done in R. Thirdly, programming customizable functions and loops makes monotonous, repetitive tasks more reliable and efficient. Additional advantages include completely customizable visualizations, transparency, ability to build interactive documents, and many more. <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> However, a high learning curve often encourages researchers to use point-and-click programs, like SPSS, as opposed to coding languages like R. This workshop is designed to help researchers learn the basic concepts behind programming in R for DBER. The target audience is beginners, meaning participants should have only a little or no experience with R. The workshop will be utilizing a front-end interface (called RStudio) and new coding syntax (referred to as “tidyverse”) that makes coding in R significantly easier to read and understand. To make the coding easier to learn, physical materials (i.e. folders to represent objects in a global environment), kinesthetic learning techniques (i.e. acting out a for loop), and group learning techniques (i.e. challenge coding tasks) will be employed throughout the workshop <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;">While this is a workshop to develop R skills, the primary objective of this workshop is to also challenge participants ideas of how they should manipulate and explore by asking reflective questions like “what (dis)advantages does this way offer over that way?” An example is to consider the differences between displaying scores across groups as a bar plot showing average and standard deviation versus distributions via boxplots. The coding objectives for participants are to a) understand object-based coding in R, b) be able to manipulate and visualize data in R, and c) find and interpret sources that allow users to independently increase their abilities in R. Specifically, this includes vector-index nature of R, working in the script versus console, importing and manipulating data, visualizing data with the grammar of graphics syntax, and basic programming in for loops and functions. Throughout the workshop, segues will be implemented to point out the benefits of using good coding practices (i.e. always use relative references versus absolute values).
 * <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;">Abstract - **

<span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> At the conclusion of this workshop, participants will be able to…
 * <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> Outcomes - **

<span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> 1. …import and manipulate data. This fundamental skill is required in virtually all DBER projects and includes filtering, creating, modifying, naming, and many other common tasks in data manipulation. <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> 2. …visualize data and customize it (basic customization only). The importance of effective visualizations is an often overlooked aspect of science; emphasis in this area will only include basics of customization with segues into more advance manipulation. <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> 3. …program basic for loops (doing something over and over again) and functions (defining a set of instructions for which arguments can be easily adjusted). <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> 4. …conduct the previous three goals in a manner that is completely reproducible. This will involve understanding how to code in scripts that are always reproducible. <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> 5. …independently search for, read, and implement R documentation on packages that allow users to conduct additional analyses.

<span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> Keeping participants engaged in an abstract process like coding can be difficult. To make these concepts more concrete and to maintain participation from all participants, a mixture of physical models and scaffolded group work tasks will be used throughout the workshop. All participants will be required to bring their own laptops with R/RStudio previously installed and a script containing all of the examples will be given to participants from the beginning to avoid typing time and errors due to type-o’s.
 * <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> Engagement - **

<span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> Physical Models: Examples of physical models include how to store objects into the global environment using a file in a banker’s box demonstration/analogy, acting out for loops and functions, and attempting to draw graphs from verbal descriptions. These models can help make what R is doing behind the scenes (invisible and abstract) more accessible to participants.

<span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> Scaffolded Group Activities: To help participants get used to the frustration and troubleshooting that comes with coding in R, several small-scale tasks will be assigned to groups randomly determined. An example progression of scaffolds is as follows. First task; given this ggplot (syntax used to graph things) code, predict what it will produce. Second task: Display a different variable on the x-axis. Third task, Determine the most effective visualization and code it.

<span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> Facilitator - <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> Workshop will be led by an Assistant Professor in Discipline-Based Education Research (DBER). The facilitator has been coding in R for the past 6 years and has authored a book chapter on using R in chemistry education research. He coded an interactive web application entirely in R that allows users to easily analyze data generated from COPUS (Classroom Observation Protocol for Undergraduate Science) observations. He has also led two 8-hour R workshops at two of his previous institutions; and is very passionate about the benefits to the field that could be realized if DBER researchers learn and use R for their analyses. The facilitator has published papers that contain analyses generated exclusively in R in Science, Journal of Chemical Education, Chemistry Education Research and Practice, and International Journal of Science Education. He has also taught 3 sections of large enrollment courses while facilitating active learning strategies. While he could accommodate more, he prefers that enrollment be capped at 50 to ensure every group has a chance to receive some individualized attention during group work activities.

<span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;">Stephanie Gardner (Purdue University), Kristy Wilson (Marian University), Dina Newman (Rochester Institute of Technology), Michelle Harris (University of Wisconsin Madison)
 * <span style="font-family: Arial,Helvetica,sans-serif; font-size: 140%;">3.Development and Testing of Assessments for Measuring Experimentation Competence in Biology **

<span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;">The Advancing Competence for Experimentation in Biology (ACE-Bio) Network was formed with funding from the National Science Foundation to tap the expertise of a cross-disciplinary group of faculty with the overarching goal of developing a set of competencies, assessments and recommendations for undergraduate education in the area of biological experimentation. The lack of reliable assessments to measure basic competence in biology experimentation precludes researchers from validating claims about whether particular Anticipated Learning Outcomes (ALOs) are indeed developed (Verified Learning Outcomes, VLOs) in students who are taught about scientific inquiry and creative problem solving through biological experimentation. The ACE-Bio network has developed a framework describing the competencies and skills required for expertise in biology experimentation (http://docs.lib.purdue.edu/pibergiim/4). Workshop participants will use the framework to review areas where assessments have been developed already and identify where work is needed to address the gaps. Participants will initiate the development of new assessments, with the potential for future collaboration to continue the work for publication. The workshop leadership team consists of experienced investigators from the ACE-Bio project who will help the participants advance research addressing the following Research Question: How well are we helping undergraduate students along their path to learn the process of scientific inquiry by thinking creatively about experimentation in biology? The participants will share (a) their own experiences with teaching experimentation, including student difficulties and the results and observations from new instructional or mentoring approaches (b) any preliminary assessments/assignments and/or individual assessment questions that address student understanding and skills with biological experimentation. Further, this collaboration will enable participants to explore (c) how to adapt assessments that were created for a different discipline or context or targeting a different student demographic. The workshop leaders come from a variety of biology sub-disciplines, are at different career levels, and come from a wide range of institutions. By providing a framework of the competencies, the workshop aims to support faculty from liberal arts colleges, masters-degree-granting institutions, and major research institutions, working together, to provide input to the development of assessments that can positively impact their students. A stipend will be granted to defray travel costs for about 15 participants who contact the authors and provide a draft assessment with an answer key and typical examples of flawed answers with a statement no more than 200 words summarizing how the assessment builds on what the ACE-Bio Network has achieved before the workshop.
 * <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;">Abstract **

<span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> Participants will receive: (1) a template for aligning competency categories with assessments; (2) specific examples of high quality assessments for measuring what students learn about biological experimentation; (3) opportunity to work on constructing their own assessment in a collaborative environment; (4) feedback on their assessments for detecting student difficulties with experimentation.
 * <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> Participant outcomes **

<span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> A stipend will be granted to defray travel costs for about 15 participants who provide the following prior to the workshop: (1) a draft assessment with an answer key and typical examples of flawed answers; (2) a list of the co-investigators and detailed affiliations; (3) a statement no more than 200 words summarizing how the assessment builds on what the ACE-Bio Network has achieved; (4) a reasonable travel budget with details of logistics; and (5) a brief explanation of how the participant’s success in the workshop will be gauged in terms of answering the research question.

<span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;">Prior to the workshop: <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> 1. All attendees will be invited to (a) review Basic Competencies of Biological Experimentation framework as an Anticipated Learning Outcomes (ALOs) guideline, and (b) bring examples of intellectually-demanding activities and students’ work that have allowed them to observe difficulties the students have encountered with experimentation and how they used their knowledge and scientific reasoning to resolve the issues. (Attendees receiving a stipend will complete additional tasks prior to the workshop) <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> During the workshop: <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> 2. Discuss our Basic Competencies of Biological Experimentation framework as an Anticipated Learning Outcomes (ALOs) guideline. <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> 3. Intro: open-ended conversation - find out what the participants understand about students’ difficulties with biological experimentation and how they know. <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> 4. Think pair share – find out how they would assess the difficulties discussed to get an image of their assessment practices. <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> 5. Participants will divide into working groups to: <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> a. Fill out Assessment Coversheet for to address specific experimentation ALOs. <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> b. Metatag the participant-provided assessments and/or individual assessment questions they previously designed, according to ACE-Bio ALOs, then look for examples of VLOs in the answer key and typical responses provided, where student data exist. <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> c. Examine assessments from other participants and give feedback on the various assessments for detecting student difficulties with experimentation. <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> 6. Based on mutual interest within the ACE-Bio competencies and with support and guidance from the ACE-Bio network, working groups will set up an asynchronous post-workshop timeline to complete assessments.
 * <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> Participant engagement **

<span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> Stephanie M. Gardner is an assistant professor at Purdue University. She is a physiologist, DBER scholar, and ACE-Bio Network co-PI. She infuses experimentation into her courses including upper division physiology inquiry labs and introductory biology lab CUREs. Her research interests intersect with the ACE-Bio competencies, specifically student competence with data analysis and visualization. <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;">Kristy J. Wilson is an assistant professor at Marian University, a small private undergraduate intuition. She is co-editor for the LSE feature Evidence-Based Teaching Guides and published an assessment to evaluate student understanding and visualization of the scientific process. She also teaches a Molecular Genetics CURE and heads an initiative for Research Across the Curriculum at Marian. <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;">Dina L. Newman is an associate professor at the Rochester Institute of Technology, where she leads the Science and Mathematics Education Research Collaborative and co-directs the Molecular Biology Education Research group. She taught inquiry-based laboratories to freshmen biology majors for many years, and she now teaches experimentation in Genetics through deep reading of primary literature. She is the co-designer of the Central Dogma Concept Inventory Assessment. <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;">Michelle A.Harris is a Faculty Associate in the UW-Madison Biocore Program, where she has 19 years of experience mentoring and assessing students in CURE lab courses. She has collaborated with the MSOE Center for Biomolecular Modeling on several projects to develop and assess instructional materials that help students understand relationships between molecular structure and function.
 * <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;"> Facilitator description **

<span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;">Erika Offerdahl (Washington State University) The number of new hires of discipline-based education researchers into STEM departments is an encouraging indicator of the growth of this emergent, interdisciplinary field of scholarship. While there are some resources available for senior STEM faculty members and administrators to provide guidance on the evaluation of the scholarly contributions of DBER faculty members during tenure and promotion reviews, there are fewer (if no) resources for DBER faculty members to inform their efforts in assembling tenure and promotion documents. In this workshop, tenure-track (and aspiring tenure-track) DBER faculty will be introduced to and apply a promotion packet inventory to reflect on and effectively communicate their efforts in teaching, mentoring and advising, curriculum development, assessment, educational leadership, and educational research for tenure and promotion. This workshop is designed for pre-tenure researchers as well as associate professors compiling their materials for promotion to full professor. Participants are strongly encouraged to bring a laptop and/or paper copies of their current CV and/or annual review documents. At the end of the workshop, participants will be able to: The majority of this workshop will involve participant engagement. Specifically, this workshop will engage the participants in the following activities: 1. Dissection of an excerpt of a “traditional” tenure and promotion dossier with the goal of emphasizing
 * <span style="font-family: Arial,Helvetica,sans-serif; font-size: 140%;">4. Making the case for tenure: Using evidence to delineate between teaching and research for DBER scholars <span style="color: windowtext; font-family: &#39;Times New Roman&#39;; font-size: 12pt;">, **
 * Abstract: **
 * Participant outcomes: **
 * Explain at least **3 ways in which they can more effectively communicate** their teaching activities and outcomes.
 * ** Identify evidence of their impact ** in each of five domains (teaching, mentoring and advising, curriculum development, assessment, educational leadership, and educational research)
 * Take away **resources** to guide them as they prepare the documents to be submitted for annual review and/or promotion review.
 * ** Appreciate the challenges ** in both documenting and delineating impact in teaching and education research activities.
 * Participant engagement: **

The facilitator is a tenured faculty member at a research-intensive university in an academic unit with an active teaching academy. The facilitator has direct experience with the promotion and tenure policies at more than one institution, has served as external reviewer for multiple DBER faculty tenure and promotion dossiers, and is the advisory board member for an NSF ADVANCE grant (a program that creates programming to recruit and retain women faculty in STEM). The facilitator has previously presented workshops for junior faculty on issues related to tenure and promotion as well as workshops on teaching and learning.
 * 1) Introduction to the Promotion Packet Inventory, an instrument that was created based on the Toolbox for Evaluating Educator published by the American Association of Medical Colleges. This instrument articulates 5 domains (teaching, mentoring and advising, curriculum development, assessment, educational leadership, and educational research), which facilitates the delineation of DBER research from other teaching and learning scholarship.
 * 2) Reflection on current activities and promotion and tenure documents. Participants will be contacted prior to SABER and instructed to bring a laptop or physical printout of their existing documents (e.g. CVs, annual review portfolios) and will use a reflection checklist to sort activities into one of the 5 domains.
 * 3) Exploring the impact of their teaching and research activities through discussion of alternatives to h-index, impact factors, and research expenditures.
 * Facilitator description: **