J. Phil Gibson|Teresa Mourad
The growing importance of data literacy in life science education
University of Oklahoma|Ecological Society of America
science datadriven process biologists use many types data ranging genomic geospatial research advances research technology rapidly increased amount diversity complexity data available researchers hampton et al 2013 likewise tools collect analyze share data never readily available due similar progress computer technology driven big data era marx 2013 baker 2017 unprecedented data collection analysis capabilities data skills widely acknowledged increasingly important role future workplace familiarity data science necessity nas 2018 addition learning core content experimental methods field science technology engineering mathematics stem students levels also need develop data literacy ie ability create use different forms data skill learned studies agnotology shown data literacy key component critical thinking protect weaponization data sow doubt cultivate misinformation scientifically valid research falsely elevating pseudoscience cook et al 2014 thus prepare students datarich future undoubtedly lies ahead imperative stem educators rise meet challenge promote development strong data literacy students donovan 2008 marx 2013 data core component science students need develop full appreciation significance data scientific evidence understand correctly analyze interpret explore current state nature data literacy life science education botanical society america ecological society america society study evolution collectively life discovery partners convened 4th life discovery conference university oklahoma theme data discover investigate inform conference brought together biology educators high schools higher education stem education organizations share datafocused educational modules discuss best approaches improving data literacy stem students conference attendees specifically addressed latter point networking session focused identifying specific characteristics data literacy striving classes best approaches achieve httpwwwesaorgldcpastconferences2017conferencenetworking central ideas suggestions conclusions discussion summarized stimulate individual reflection promote conversations data literacy biology education among colleagues departments programs although fundamental skills using arithmetic operations mathematical functions modeling data presentation form foundation data literacy much numeracy graphing data literacy broader concept also includes understanding 1 quantitative analytical tools use 2 apply tools biological context integrated component experiment 3 interpret analyzed data light specific question hypothesis 4 effectively communicate results different platforms discussants identified key features data literacy characteristic quantitative skills conceptual understanding associated different levels data literacy features table 1 different levels data literacy ability understanding matrix broadly applied students educational level starting point assist educators identifying specific aspects data literacy want emphasize learning outcomes assessment hoffman et al 2016 identifying components levels data literacy relatively straightforward approaches developing skills less clear participants suggested two general areas faculty emphasize largest overall beneficial effect courses across curricula fig 1 first widely shared opinion students must understand collecting certain data data provide evidence address question exploring students levels often adept process collecting data numerous peerreviewed datarich classroom laboratory experiences available portals life discoveryed digital library httpslifediscoveryedorg resources promote development quantitative experimental skills imperative educators also help students understand specific data collected recognize data provide appropriate evidence answer specific question additional steps often overlooked given full attention deserve absolutely necessary promote students understanding fundamental importance considering data collect analyzed integrated essential part exploratory hypothesisdriven scientific inquiry elliott et al 2016 second educators provide frequent opportunities develop specific skills use data technology better communicate data science increased availability computers classrooms laboratories allowed instructors move beyond dedicating valuable instructional time lengthy calculations instead place greater emphasis giving students meaningful practice data analysis interpretation instructors similarly provide multiple opportunities students use technology produce graphics effectively communicate information data sentiment expressed many participants students levels struggle constructing interpreting figuresa severe data literacy weakness group unanimously agreed educators provide repeated opportunities students explore presenting data using appropriate software develop technical skills figure graph construction also potentially improve ability interpret diverse ways data presented figures encounter maltese et al 2015 experience summarizing data help foster understanding research deeper understanding interpret data presented researchers live rapidly expanding universe data collection analysis possibilities task face educators determine effective ways implement educational changes improve students data literacy ability succeed dynamic environment unfortunately simple singular answer conference participants recommended initial step individual instructors identify specific data skills concepts teach decide balance teaching methods data collection teaching methods analysis integrate skills concepts courses curricula speth et al 2010 identified specific learning outcomes instructors find develop resources achieve outcomes implement courses although developing new resources daunting task times educators feel compelled create activities de novo wealth peerreviewed datafocused modules representing diversity degrees engagement readily available implemented almost immediately sites hhmi biointeractives data points httpswwwhhmiorgbiointeractivedatapoints data nuggets httpdatanuggetsorg used quickly bring data primary literature classroom develop data analysis interpretation skills mentioned previously planted portals life discovery education library sources wide variety botanical ecological evolution educational resources teaching guides support use teaching issues experiments ecology tiee httptieeesaorgindexhtml another webbased collection peerreviewed teaching resources stem educators explore even broader array resources addressing different quantitative skills resources professional development teachers also available quantitative undergraduate biology education synthesis project qubes qubes project provides access peerreviewed open educational resources httpsqubeshuborgqubesresources also supports professional development communities educators want develop pedagogical skills faculty mentoring networks fmns participants successfully complete fmns sponsored bsa esa earn designation bsa esa education scholars httpesaorgfedfmn gaining familiarity existing resources discussion participants strongly encourage educators develop unique resources share different venues full tapestry scientific inquiry available teachers choose students experience achieving lasting change students data literacy cannot achieved instructors working alone however also require coordinated sequential development data literacy across biology curriculum necessary departments identify define articulate quantitative skills programs learning outcomes coordinate scaffolding different aspects data literacy skills implemented throughout curriculum manduca mogk 2002 ambitious suggestion discussion life science departments begin communicating across stem programs unify efforts improve data literacy achieve greater levels success identify different aspects data literacy skills implemented throughout individual departmental curricula also communicate programs promoting data literacy fields manduca mogk 2002 likely great deal overlap desired learning outcomes partnering efforts improve data literacy across stem programs help unify efforts stimulate institutional change improves critical quantitative abilities data literacy students given goal scientists use data provide evidencebased explanations world live provide answers serious biological questions efforts improve data literacy students must highest priority encourage colleagues engage discussions levels consider effectively educate students stem majors nonmajors alike data literacy surely need meet challenges changing face science future thank c adams g hamerlinck k jenkins lamar j noor p strode b wu assistance facilitating discussion ldc also thank p diggle two anonymous reviewers comments earlier versions manuscript ldc conference supported part nsf grant due 1742980 esa mourad pi j p gibson copi