Can describe areal-worldexample of data-driven decision-making ineducation.Can discussthe role ofpredictivemodeling ineducation.Has workedwith datarelated tostudentengagement.Has useddata toevaluatestudentperformance.Has experiencewitheducationaldatamanagementsystems.Has aninterest indata ethicsand privacy.Hascollaborated ona team projectinvolving dataanalysis.Candemonstrate abasic datavisualizationusing any tool.Has contributedto a researchprojectinvolving dataanalysis.Has attendeda dataanalyticsconference orwebinar.Has usedMicrosoftExcel fordataanalysis.Has used datato identifypatterns instudentbehavior.Has applieddata analyticsto improveteachingstrategies.Can discusschallenges andopportunitiesin educationaldata analytics.Has created adatadashboard foreducationalpurposes.Has knowledgeof differenttypes of data(qualitative,quantitative).Has applieddata analyticsin aneducationalresearch paper.Has usedGoogleSheets forcollaborativedata projects.Has an interestin machinelearningapplications ineducation.Can namethree dataanalyticstools orsoftware.Can explainthe conceptof datavisualization.Hasparticipated in adata analyticsworkshop ortraining.Has knowledgeof basicstatisticalconcepts(mean, median,etc.).Can explaintheimportance ofdata qualityin analytics.Can describe areal-worldexample of data-driven decision-making ineducation.Can discussthe role ofpredictivemodeling ineducation.Has workedwith datarelated tostudentengagement.Has useddata toevaluatestudentperformance.Has experiencewitheducationaldatamanagementsystems.Has aninterest indata ethicsand privacy.Hascollaborated ona team projectinvolving dataanalysis.Candemonstrate abasic datavisualizationusing any tool.Has contributedto a researchprojectinvolving dataanalysis.Has attendeda dataanalyticsconference orwebinar.Has usedMicrosoftExcel fordataanalysis.Has used datato identifypatterns instudentbehavior.Has applieddata analyticsto improveteachingstrategies.Can discusschallenges andopportunitiesin educationaldata analytics.Has created adatadashboard foreducationalpurposes.Has knowledgeof differenttypes of data(qualitative,quantitative).Has applieddata analyticsin aneducationalresearch paper.Has usedGoogleSheets forcollaborativedata projects.Has an interestin machinelearningapplications ineducation.Can namethree dataanalyticstools orsoftware.Can explainthe conceptof datavisualization.Hasparticipated in adata analyticsworkshop ortraining.Has knowledgeof basicstatisticalconcepts(mean, median,etc.).Can explaintheimportance ofdata qualityin analytics.

Data Explorer BINGO - Call List

(Print) Use this randomly generated list as your call list when playing the game. There is no need to say the BINGO column name. Place some kind of mark (like an X, a checkmark, a dot, tally mark, etc) on each cell as you announce it, to keep track. You can also cut out each item, place them in a bag and pull words from the bag.


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  1. Can describe a real-world example of data-driven decision-making in education.
  2. Can discuss the role of predictive modeling in education.
  3. Has worked with data related to student engagement.
  4. Has used data to evaluate student performance.
  5. Has experience with educational data management systems.
  6. Has an interest in data ethics and privacy.
  7. Has collaborated on a team project involving data analysis.
  8. Can demonstrate a basic data visualization using any tool.
  9. Has contributed to a research project involving data analysis.
  10. Has attended a data analytics conference or webinar.
  11. Has used Microsoft Excel for data analysis.
  12. Has used data to identify patterns in student behavior.
  13. Has applied data analytics to improve teaching strategies.
  14. Can discuss challenges and opportunities in educational data analytics.
  15. Has created a data dashboard for educational purposes.
  16. Has knowledge of different types of data (qualitative, quantitative).
  17. Has applied data analytics in an educational research paper.
  18. Has used Google Sheets for collaborative data projects.
  19. Has an interest in machine learning applications in education.
  20. Can name three data analytics tools or software.
  21. Can explain the concept of data visualization.
  22. Has participated in a data analytics workshop or training.
  23. Has knowledge of basic statistical concepts (mean, median, etc.).
  24. Can explain the importance of data quality in analytics.