Top 5 things anatomy educators need to know about learning analyticsJanet Corral
This document discusses learning analytics and academic analytics for educators. It begins by defining learning analytics as the measurement, collection, analysis and reporting of data about learners and contexts, while academic analytics uses administrative data like grades and attendance with statistics to improve decision making. It then notes that analytics can be used to monitor course, learner or activity progress through dashboards, and to evaluate courses or predict student success. The document also discusses using analytics in clinical disciplines and adapting to changes in analytics over one's career. It concludes by discussing open questions around analytics and their application to research.
This document describes the features and potential market for a functional standalone database-driven electronic health record simulation program. The program would include simulated patient search functions, pre-configured case studies, integrated evidence-based libraries, and health assessment features with knowledge-based rules. It would have over 3000 potential nursing programs as an initial target audience, and an even larger potential market in allied health sciences.
The document discusses various options for assessing information literacy (IL) at the institutional and individual level. It describes competency assessments, inference assessments, assessing background knowledge, and self-reporting as options at the institutional level. For individual assessments, it mentions assessing background knowledge and self-reporting. The document also outlines the 7 CAUL standards for IL and how the Australian Information Skills Survey assesses these standards through self-reported behaviors. It provides details on the survey's validity and plans for further validation and studies relating IL to student outcomes and workplace skills.
The document summarizes key themes from a webinar on developing medical policies and coverage guidelines for next generation sequencing in oncology. It discusses the challenges of evaluating genomic tests and gaining insurance coverage. Recommendations include requiring laboratories to obtain accreditation for analytic validity, covering small gene panels when clinical utility is established, and facilitating data collection to support coverage of larger tests and off-label drug use. The webinar included perspectives from various stakeholders on addressing these issues.
Elizabeth Copello Analytics Visualization Resume Mar2016Elizabeth Copello
Elizabeth Copello is a data visualization analyst with over 15 years of experience in data analysis, statistical programming, and data visualization. She has a MS in Data Analytics and has worked at RTI International since 2001 leading various projects involving national surveys on topics like substance use, education, and early childhood development. These projects involve sampling, statistical modeling, data manipulation in SAS, and producing reports and visualizations to help inform policy decisions. She has strong skills in enterprise data management, presentation/visualization, predictive analytics, and project management.
Terry Cannestra is a registered nurse in Wisconsin with over 35 years of clinical experience. She has held roles as a clinical research nurse, data specialist, case coordinator, and bedside nurse. Her skills include research coordination, project management, data entry, clinical skills such as phlebotomy and IV placement, and strong computer and communication abilities. She is certified as a clinical research coordinator and in basic life support.
Dr Chris May - Healthcare Improvement Unit, Department of HealthInforma Australia
Future emergency departments will need to change to meet increasing demand. Population growth and aging will drive an 8.7% increase in emergency department attendances by 2026. Future emergency departments will feature specialized areas and models of care matched to patient cohorts based on age, socioeconomic status, and acuity level. They will adopt linear or hub-and-spoke designs to optimize patient flow. This tailored approach will improve outcomes while containing healthcare costs in the transition to more integrated healthcare precincts in the future.
The document discusses the power of collaboration in healthcare. It provides examples of how collaboration between different organizations and countries has led to building a shared health care quality infrastructure and conducting research using a common data model and terminology. Challenges include issues with terminology but collaboration has enabled clinical decision support, improved quality of care, and allowed for meaningful comparison of data across settings.
Top 5 things anatomy educators need to know about learning analyticsJanet Corral
This document discusses learning analytics and academic analytics for educators. It begins by defining learning analytics as the measurement, collection, analysis and reporting of data about learners and contexts, while academic analytics uses administrative data like grades and attendance with statistics to improve decision making. It then notes that analytics can be used to monitor course, learner or activity progress through dashboards, and to evaluate courses or predict student success. The document also discusses using analytics in clinical disciplines and adapting to changes in analytics over one's career. It concludes by discussing open questions around analytics and their application to research.
This document describes the features and potential market for a functional standalone database-driven electronic health record simulation program. The program would include simulated patient search functions, pre-configured case studies, integrated evidence-based libraries, and health assessment features with knowledge-based rules. It would have over 3000 potential nursing programs as an initial target audience, and an even larger potential market in allied health sciences.
The document discusses various options for assessing information literacy (IL) at the institutional and individual level. It describes competency assessments, inference assessments, assessing background knowledge, and self-reporting as options at the institutional level. For individual assessments, it mentions assessing background knowledge and self-reporting. The document also outlines the 7 CAUL standards for IL and how the Australian Information Skills Survey assesses these standards through self-reported behaviors. It provides details on the survey's validity and plans for further validation and studies relating IL to student outcomes and workplace skills.
The document summarizes key themes from a webinar on developing medical policies and coverage guidelines for next generation sequencing in oncology. It discusses the challenges of evaluating genomic tests and gaining insurance coverage. Recommendations include requiring laboratories to obtain accreditation for analytic validity, covering small gene panels when clinical utility is established, and facilitating data collection to support coverage of larger tests and off-label drug use. The webinar included perspectives from various stakeholders on addressing these issues.
Elizabeth Copello Analytics Visualization Resume Mar2016Elizabeth Copello
Elizabeth Copello is a data visualization analyst with over 15 years of experience in data analysis, statistical programming, and data visualization. She has a MS in Data Analytics and has worked at RTI International since 2001 leading various projects involving national surveys on topics like substance use, education, and early childhood development. These projects involve sampling, statistical modeling, data manipulation in SAS, and producing reports and visualizations to help inform policy decisions. She has strong skills in enterprise data management, presentation/visualization, predictive analytics, and project management.
Terry Cannestra is a registered nurse in Wisconsin with over 35 years of clinical experience. She has held roles as a clinical research nurse, data specialist, case coordinator, and bedside nurse. Her skills include research coordination, project management, data entry, clinical skills such as phlebotomy and IV placement, and strong computer and communication abilities. She is certified as a clinical research coordinator and in basic life support.
Dr Chris May - Healthcare Improvement Unit, Department of HealthInforma Australia
Future emergency departments will need to change to meet increasing demand. Population growth and aging will drive an 8.7% increase in emergency department attendances by 2026. Future emergency departments will feature specialized areas and models of care matched to patient cohorts based on age, socioeconomic status, and acuity level. They will adopt linear or hub-and-spoke designs to optimize patient flow. This tailored approach will improve outcomes while containing healthcare costs in the transition to more integrated healthcare precincts in the future.
The document discusses the power of collaboration in healthcare. It provides examples of how collaboration between different organizations and countries has led to building a shared health care quality infrastructure and conducting research using a common data model and terminology. Challenges include issues with terminology but collaboration has enabled clinical decision support, improved quality of care, and allowed for meaningful comparison of data across settings.
Operations Research: Methods, Challenges, Emerging Lessons, and Opportunities...CORE Group
This document discusses operations research (OR) and provides guidance on choosing an OR concept and methodology. It recommends examining large problems in a small focused way and using evidence to scale up successful innovative ideas. When choosing an OR concept, consider how it could increase access to care and impact key health issues, and whether organizational learnings and experience can support the study. The appropriate methodology depends on the study topic and factors like data availability, technical support, and budget. External partners can help refine ideas, and field trips are valuable. Working through uncertainties around security, literacy, staffing, and gender issues is important. Afghanistan's use of mHealth technologies is mentioned.
Medical Simulation 2.0: Improving value-based healthcare deliveryYue Dong
This document provides an overview of medical simulation and its applications in healthcare delivery. It discusses how simulation can be used as a tool to systematically analyze complex healthcare systems and processes, identify bottlenecks, and test interventions to optimize quality and safety. Specific applications mentioned include using simulation to study workflows like sepsis care, test user interfaces on clinical tasks and performance, and evaluate new system designs before implementation. The goal is to move from traditional education-focused "Simulation 1.0" to a more integrated "Simulation 2.0" approach that leverages simulation throughout healthcare systems and daily practices.
This document summarizes an individual's education and work experience in nursing and healthcare informatics. It includes a Master of Science in Nursing Informatics from Walden University in 2015, an Associate in Science in Nursing from Edison State College in 2011, and various clinical and informatics roles at Naples Community Hospital from 2012 to the present involving design and implementation of electronic medical record systems, clinical documentation, and rehabilitation programs.
This document discusses clinical decision support systems (CDSS) and their impact on diagnostic imaging utilization. It notes that CDSS have been shown to reduce inappropriate imaging orders by up to 13% by providing alerts and reminders. Specifically, one study found a CDSS reduced CT scans for suspected pulmonary embolisms by 13% over 6 years. However, barriers like physician resistance and a fee-for-service payment system can limit CDSS effectiveness. The document advocates for CDSS that are evidence-based, easily actionable, and fit seamlessly into clinical workflows.
Nabil Shamsher has a Bachelor of Arts in Statistics from the University of Virginia. He currently works as a Statistician in Saudi Arabia where he assists with studies and compiles data in Microsoft Excel. Previously, he was a Lab Assistant for an introductory statistics course where he helped students and graded assignments. He also interned with the University of Virginia baseball program, maintaining recruiting databases and assisting with outreach.
A good app is effective, cost-effective, feasible, profitable, user-friendly, safe, relevant, and usable. It also clearly shows any side effects and dose responses. However, many current health apps lack evidence on their effectiveness, safety, and how they were developed. Developing apps through a scientific process that involves users can help address these issues. This includes conceptualizing designs based on theory and user input, testing prototypes, and iterative development and field testing. The goal is to produce evidence-based, innovative health apps that can be safely implemented and improve outcomes.
The document discusses clinical decision support systems (CDSS), which are software designed to aid clinical decision making by matching patient characteristics to a computerized knowledge base. It describes several types of CDSS including knowledge-based systems, alerts and reminders, diagnostic assistance, therapy critiquing and prescribing decision support. It also discusses different knowledge representations, functionally classified systems, benefits and limitations of CDSS, and their future directions.
The Decision Support Unit (DSU) was established in 2002 through a collaboration between UK universities to provide research and training support to NICE's technology appraisal program. The DSU conducts appraisal-specific work on 7-10 appraisals annually, involving rapid evaluation and modeling expertise. It also leads methods development through guidance documents, workshops, and research on topics like evidence synthesis, utilities, and uncertainty analysis. The DSU aims to facilitate methods discussions between NICE and industry and improve approaches to health technology assessment.
MEDICal REsearch Support is a scientific- post graduate- international -life long learning- medical education and publication program for health care professionals aiming to support medical research by ‘Evidence Based Medicine and Medical Decision Making’ tools, especially Biostatistics.
The document discusses the National Institute for Health Innovation in New Zealand and its goals of developing health technologies, improving health outcomes, and strengthening the health system. It describes several initiatives, including creating a Health Data Interoperability Laboratory to facilitate adoption of interoperability standards, examining use of prediction tools for cardiovascular disease and diabetes, and developing business intelligence tools and quality reporting for healthcare providers. The overall aim is to empower citizens through supported self-management and appropriate consumer technologies.
Pamela Mazzocato discusses how lean practices can be used to improve emergency care by reducing waste and inefficiency. Two case studies are described where lean was used: 1) At Danderyd Hospital, lean reduced the time to surgery for hip fractures from 24.8 to 20 hours and increased the percentage of patients operated on within 24 hours from 47% to 83%. 2) At Karolinska University Hospital, lean reduced non-value adding time and variation, increasing the percentage of patients ready to leave the emergency department within 4 hours by up to 29% and decreasing the waiting time for first physician assessment by up to 56%. Lean focuses on continually improving processes to increase value for patients.
MEDICal REsearch Support is a scientific, post graduate, international, life long learning, medical education and publication program for health care professionals aiming to support medical research by ‘Evidence Based Medicine and Medical Decision Making’ tools, especially Biostatistics.
Examining Pediatric Resuscitation Education Using Simulation and Scripted Deb...JAMA Pediatrics
The study examined the effectiveness of using a scripted debriefing method compared to a non-scripted method in pediatric resuscitation training programs. Novice instructors and medical teams from 14 centers participated in a simulation using either scripted or non-scripted debriefing after. Results showed those receiving scripted debriefing had greater improvement in medical knowledge and team leader behavioral performance compared to non-scripted, but no difference in clinical team performance. The study supports using standardized scripted debriefing to improve learning outcomes for novice instructors.
1) The document discusses how to integrate new technology innovations within healthcare systems using a 6 stage framework: identifying problems/needs, proposing solutions, developing prototypes, piloting, evaluating/iterating, and final launch.
2) Stage 1 involves identifying compelling use cases that have a clear impact and value proposition.
3) Stages 3-4 involve developing prototypes, piloting solutions, and integrating them with workflows while ensuring privacy, usability, and legal compliance.
4) Stages 5-6 focus on refining solutions based on user feedback, fixing issues, realigning with goals, and finally launching at scale with training and champions.
Systematic Reviews: the researcher's perspective and the research question. E...healthlibaust2012
The document discusses the perspectives of researchers conducting systematic reviews and the importance of developing a clear research question. It emphasizes that a well-constructed question using the PICO/PICo framework is fundamental to guiding the review process, including developing inclusion criteria and an effective search strategy. Researchers may have different levels of experience and understanding of systematic reviews, so librarians play an important role in helping them conceptualize the question and properly carry out the various review steps, such as developing a reproducible search strategy.
Mary Edna Parish is a clinical research professional with over 15 years of experience in clinical trials. She has extensive experience in areas such as protocol development, regulatory compliance, patient recruitment, data collection and management, adverse event reporting, and project management. Her background includes positions as a clinical research coordinator at Le Bonheur Children's Hospital and St. Jude Children's Research Hospital, as well as a research nurse at Gastro One and the University of Tennessee Health Science Center. She has strong computer skills and is proficient in various clinical trial software programs and electronic medical records systems.
Case study approach as a pedagogy in managementsmitaj
A case study provides detailed, narrative information about a unique individual, program, or event. It captures what happened and why to highlight both successes and challenges. Case studies are appropriate when there is an interesting story to tell and can provide context to other data. The primary advantage is detailed information from multiple sources. Limitations include potential length, perceived lack of rigor, and inability to generalize. A case study should plan data collection, analyze findings, and disseminate lessons learned. Elements include identifying the problem, steps taken, results, challenges, lessons learned. Presentation includes introduction, methodology, problem, steps, results, challenges, conclusions.
What are we learning from learning analytics: Rhetoric to reality escalate 2014Shane Dawson
This document summarizes a talk about what we are learning from implementing learning analytics (LA) in higher education. It discusses the drivers for interest in LA, perspectives from industry and research, benchmarks of current LA adoption, and emerging models. While industry rhetoric portrays LA as providing easy answers, the reality is more complex. Most universities are still in early stages of basic reporting rather than advanced applications. For LA to meet its potential and have long term impact, a process-focused model is needed that builds organizational capacity, is adaptive, and takes a broad view of LA beyond just retention.
Handout of my presentation on the student perspective of Learning Analytics. Most slides contain a few sentences in the speaker notes (in English) to describe the point I was making there.
Learning Analytics: Seeking new insights from educational dataAndrew Deacon
1) Learning analytics seeks new insights from educational data by measuring, collecting, analyzing and reporting data about learners and learning environments to optimize learning.
2) There are three eras of social science research: collecting simple data on important questions; getting the most from little data; and today's "big data" deluge allowing new questions.
3) Educational data can be analyzed through psychometrics, educational data mining, and learning analytics, typically focusing on assessment, learning over time, and wider contexts respectively.
Operations Research: Methods, Challenges, Emerging Lessons, and Opportunities...CORE Group
This document discusses operations research (OR) and provides guidance on choosing an OR concept and methodology. It recommends examining large problems in a small focused way and using evidence to scale up successful innovative ideas. When choosing an OR concept, consider how it could increase access to care and impact key health issues, and whether organizational learnings and experience can support the study. The appropriate methodology depends on the study topic and factors like data availability, technical support, and budget. External partners can help refine ideas, and field trips are valuable. Working through uncertainties around security, literacy, staffing, and gender issues is important. Afghanistan's use of mHealth technologies is mentioned.
Medical Simulation 2.0: Improving value-based healthcare deliveryYue Dong
This document provides an overview of medical simulation and its applications in healthcare delivery. It discusses how simulation can be used as a tool to systematically analyze complex healthcare systems and processes, identify bottlenecks, and test interventions to optimize quality and safety. Specific applications mentioned include using simulation to study workflows like sepsis care, test user interfaces on clinical tasks and performance, and evaluate new system designs before implementation. The goal is to move from traditional education-focused "Simulation 1.0" to a more integrated "Simulation 2.0" approach that leverages simulation throughout healthcare systems and daily practices.
This document summarizes an individual's education and work experience in nursing and healthcare informatics. It includes a Master of Science in Nursing Informatics from Walden University in 2015, an Associate in Science in Nursing from Edison State College in 2011, and various clinical and informatics roles at Naples Community Hospital from 2012 to the present involving design and implementation of electronic medical record systems, clinical documentation, and rehabilitation programs.
This document discusses clinical decision support systems (CDSS) and their impact on diagnostic imaging utilization. It notes that CDSS have been shown to reduce inappropriate imaging orders by up to 13% by providing alerts and reminders. Specifically, one study found a CDSS reduced CT scans for suspected pulmonary embolisms by 13% over 6 years. However, barriers like physician resistance and a fee-for-service payment system can limit CDSS effectiveness. The document advocates for CDSS that are evidence-based, easily actionable, and fit seamlessly into clinical workflows.
Nabil Shamsher has a Bachelor of Arts in Statistics from the University of Virginia. He currently works as a Statistician in Saudi Arabia where he assists with studies and compiles data in Microsoft Excel. Previously, he was a Lab Assistant for an introductory statistics course where he helped students and graded assignments. He also interned with the University of Virginia baseball program, maintaining recruiting databases and assisting with outreach.
A good app is effective, cost-effective, feasible, profitable, user-friendly, safe, relevant, and usable. It also clearly shows any side effects and dose responses. However, many current health apps lack evidence on their effectiveness, safety, and how they were developed. Developing apps through a scientific process that involves users can help address these issues. This includes conceptualizing designs based on theory and user input, testing prototypes, and iterative development and field testing. The goal is to produce evidence-based, innovative health apps that can be safely implemented and improve outcomes.
The document discusses clinical decision support systems (CDSS), which are software designed to aid clinical decision making by matching patient characteristics to a computerized knowledge base. It describes several types of CDSS including knowledge-based systems, alerts and reminders, diagnostic assistance, therapy critiquing and prescribing decision support. It also discusses different knowledge representations, functionally classified systems, benefits and limitations of CDSS, and their future directions.
The Decision Support Unit (DSU) was established in 2002 through a collaboration between UK universities to provide research and training support to NICE's technology appraisal program. The DSU conducts appraisal-specific work on 7-10 appraisals annually, involving rapid evaluation and modeling expertise. It also leads methods development through guidance documents, workshops, and research on topics like evidence synthesis, utilities, and uncertainty analysis. The DSU aims to facilitate methods discussions between NICE and industry and improve approaches to health technology assessment.
MEDICal REsearch Support is a scientific- post graduate- international -life long learning- medical education and publication program for health care professionals aiming to support medical research by ‘Evidence Based Medicine and Medical Decision Making’ tools, especially Biostatistics.
The document discusses the National Institute for Health Innovation in New Zealand and its goals of developing health technologies, improving health outcomes, and strengthening the health system. It describes several initiatives, including creating a Health Data Interoperability Laboratory to facilitate adoption of interoperability standards, examining use of prediction tools for cardiovascular disease and diabetes, and developing business intelligence tools and quality reporting for healthcare providers. The overall aim is to empower citizens through supported self-management and appropriate consumer technologies.
Pamela Mazzocato discusses how lean practices can be used to improve emergency care by reducing waste and inefficiency. Two case studies are described where lean was used: 1) At Danderyd Hospital, lean reduced the time to surgery for hip fractures from 24.8 to 20 hours and increased the percentage of patients operated on within 24 hours from 47% to 83%. 2) At Karolinska University Hospital, lean reduced non-value adding time and variation, increasing the percentage of patients ready to leave the emergency department within 4 hours by up to 29% and decreasing the waiting time for first physician assessment by up to 56%. Lean focuses on continually improving processes to increase value for patients.
MEDICal REsearch Support is a scientific, post graduate, international, life long learning, medical education and publication program for health care professionals aiming to support medical research by ‘Evidence Based Medicine and Medical Decision Making’ tools, especially Biostatistics.
Examining Pediatric Resuscitation Education Using Simulation and Scripted Deb...JAMA Pediatrics
The study examined the effectiveness of using a scripted debriefing method compared to a non-scripted method in pediatric resuscitation training programs. Novice instructors and medical teams from 14 centers participated in a simulation using either scripted or non-scripted debriefing after. Results showed those receiving scripted debriefing had greater improvement in medical knowledge and team leader behavioral performance compared to non-scripted, but no difference in clinical team performance. The study supports using standardized scripted debriefing to improve learning outcomes for novice instructors.
1) The document discusses how to integrate new technology innovations within healthcare systems using a 6 stage framework: identifying problems/needs, proposing solutions, developing prototypes, piloting, evaluating/iterating, and final launch.
2) Stage 1 involves identifying compelling use cases that have a clear impact and value proposition.
3) Stages 3-4 involve developing prototypes, piloting solutions, and integrating them with workflows while ensuring privacy, usability, and legal compliance.
4) Stages 5-6 focus on refining solutions based on user feedback, fixing issues, realigning with goals, and finally launching at scale with training and champions.
Systematic Reviews: the researcher's perspective and the research question. E...healthlibaust2012
The document discusses the perspectives of researchers conducting systematic reviews and the importance of developing a clear research question. It emphasizes that a well-constructed question using the PICO/PICo framework is fundamental to guiding the review process, including developing inclusion criteria and an effective search strategy. Researchers may have different levels of experience and understanding of systematic reviews, so librarians play an important role in helping them conceptualize the question and properly carry out the various review steps, such as developing a reproducible search strategy.
Mary Edna Parish is a clinical research professional with over 15 years of experience in clinical trials. She has extensive experience in areas such as protocol development, regulatory compliance, patient recruitment, data collection and management, adverse event reporting, and project management. Her background includes positions as a clinical research coordinator at Le Bonheur Children's Hospital and St. Jude Children's Research Hospital, as well as a research nurse at Gastro One and the University of Tennessee Health Science Center. She has strong computer skills and is proficient in various clinical trial software programs and electronic medical records systems.
Case study approach as a pedagogy in managementsmitaj
A case study provides detailed, narrative information about a unique individual, program, or event. It captures what happened and why to highlight both successes and challenges. Case studies are appropriate when there is an interesting story to tell and can provide context to other data. The primary advantage is detailed information from multiple sources. Limitations include potential length, perceived lack of rigor, and inability to generalize. A case study should plan data collection, analyze findings, and disseminate lessons learned. Elements include identifying the problem, steps taken, results, challenges, lessons learned. Presentation includes introduction, methodology, problem, steps, results, challenges, conclusions.
What are we learning from learning analytics: Rhetoric to reality escalate 2014Shane Dawson
This document summarizes a talk about what we are learning from implementing learning analytics (LA) in higher education. It discusses the drivers for interest in LA, perspectives from industry and research, benchmarks of current LA adoption, and emerging models. While industry rhetoric portrays LA as providing easy answers, the reality is more complex. Most universities are still in early stages of basic reporting rather than advanced applications. For LA to meet its potential and have long term impact, a process-focused model is needed that builds organizational capacity, is adaptive, and takes a broad view of LA beyond just retention.
Handout of my presentation on the student perspective of Learning Analytics. Most slides contain a few sentences in the speaker notes (in English) to describe the point I was making there.
Learning Analytics: Seeking new insights from educational dataAndrew Deacon
1) Learning analytics seeks new insights from educational data by measuring, collecting, analyzing and reporting data about learners and learning environments to optimize learning.
2) There are three eras of social science research: collecting simple data on important questions; getting the most from little data; and today's "big data" deluge allowing new questions.
3) Educational data can be analyzed through psychometrics, educational data mining, and learning analytics, typically focusing on assessment, learning over time, and wider contexts respectively.
Open Learning Analytics Strategy for Student Success: The North Carolina Stat...Joshua
The document discusses open learning analytics strategies to improve student success. It provides context on the Open Academic Analytics Initiative (OAAI), which developed an open-source early alert system. A research study found predictive models can be portable across institutions and a library of open models could be shared. The Apereo Learning Analytics Initiative is working to develop modular components for an open learning analytics platform. North Carolina State University is moving toward enterprise learning analytics by validating predictive models on their data and planning to implement a larger-scale, automated system to provide more frequent early alerts.
Educational Data Mining/Learning Analytics issue brief overviewMarie Bienkowski
An overview of the Draft Issue Brief prepared by SRI International for the US Department of Education on Educational Data Mining and Learning Analytics
Advances in Learning Analytics and Educational Data Mining MehrnooshV
This presentation is about the state-of-the-art of Learning Analytics and Edicational Data Mining. It is presented by Mehrnoosh Vahdat as the introductory tutorial of Special Session 'Advances in Learning Analytics and Educational Data Mining' at ESANN 2015 conference.
Sdal air education workforce analytics workshop jan. 7 , 2014.pptxkimlyman
The American Institutes for Research (AIR) and Virginia Tech are collaborating to explore and develop new approaches to combining, manipulating and understanding big data. The two are also looking at how big data analytics can help answer questions critical to solving issues in education, workforce, health, and human and social development. They held two workshops on January 7 and 27, 2014- the first on Education and Workforce Analytics and the second on Health and Social Development Analytics.
This document discusses big data and the opportunities and challenges it presents. It covers key aspects of the data lifecycle like proposal, infrastructure, acquisition, management, dissemination and preservation. It also discusses issues with data integrity that have arisen, like studies with small sample sizes that are hard to replicate. Changes are needed to improve data quality, including raising standards for data collection and analysis. Funding agencies, publishers and institutions need to ensure best practices are followed to maximize the benefits of big data while minimizing risks to data integrity.
Big Data: Big Opportunities or Big Trouble?Shea Swauger
Big data is changing how research is being conducted and allowing new kinds of questions to be asked. Meanwhile, data management has enabled a rapid increase in the dissemination and preservation of research products and many funding agencies like the National Science Foundation and National Institute of Health now require data management plans in their grant applications. The combination of big data applications and data management processes has created new opportunities and pitfalls for researchers. In the past year, prominent scientists including the Director of the NIH have suggested that inappropriate methodology for data acquisition, analysis and storage has led to a gap in the translation of basic research findings to clinical cures. In this session we will track data through all research stages, describe best practices and university resources available to faculty grappling with these important issues.
Preparing for Informatics Careers and Trends in the Age of Meaningful Use - I...Nawanan Theera-Ampornpunt
The document summarizes a career panel discussion on preparing for informatics careers and trends in health information technology (HIT) in the age of meaningful use. The panel included experts from academia and industry with clinical, technical, and health IT vendor backgrounds. They discussed their career experiences and trajectories, key factors that influenced their informatics careers, upcoming trends in HIT like interprofessional education and personalized clinical decision support, and implications for students. The panel provided advice to students on gaining experience through volunteering, networking, obtaining credentials, and participating in practical projects to prepare for emerging opportunities in HIT.
This document discusses existing measures of information literacy (IL) as psychometric tests. It reviews IL tests in terms of their context dependency, domain specificity, and representation of the IL construct. The author finds that current tests often focus on specific educational contexts and skills rather than broadly measuring IL. Two main recommendations are made: 1) Developing IL measures for other contexts beyond education to show IL is measurable more widely. 2) Creating a general IL measure that is not context-dependent and tests all aspects of IL to establish it as a construct across populations. This would allow more comprehensive research on IL.
This document discusses the history and potential uses of learning analytics in education. It traces the growth of analytics in higher education from 2010-2017. Learning analytics can utilize feedback, prediction, and coaching to support student learning outcomes. Effective analytics requires multiple data sources and will impact the entire educational system. The document suggests analytics could enhance virtual patient learning by adding data metrics to measure feedback effectiveness, predict learning, and enable coaching across a series of virtual patients.
Bigger data as better data an exploration in the context of distance educatio...Elizabeth Archer
This document discusses the potential of "bigger data" or large datasets in distance education. It uses a case study of the University of South Africa (Unisa) to explore the necessary conditions and frameworks for leveraging big data. Unisa has a large number of students taking modules over many years, resulting in a substantial amount of student data. However, this data currently resides in different systems and extracting and analyzing it poses challenges regarding skills, data quality, representation and ethics. Institutional stakeholders must address these challenges to better understand students and support learning through big data.
Technology, Innovation, and Education Presentation to Emerging Technologies C...Philip Piety
This document discusses the educational data movement. It begins by comparing how data is used across different levels in education from national to classroom levels. It then analyzes why using data in education is both necessary and difficult due to issues like imprecise measurements and fragmentation. The document also examines quantitative and qualitative shifts in education brought about by more data, including a reorientation around competencies and blended learning. Finally, it explores viewing education as an entire sector and mapping innovations across different educational levels and scales.
The document discusses the promises and challenges of academic analytics in higher education. It outlines stakeholders' expectations for data strategies and concerns regarding privacy, resource allocation, and how data is collected and used. Academic analytics aims to mine institutional data to produce actionable intelligence for informing policy, enhancing responsiveness, and improving learner choice and governance. However, concerns remain around who determines what data is collected and how profiles are used ethically while balancing accountability and performance indicators.
Education Data Sciences and the Need for Interpretive Skills Philip Piety
The document discusses the emerging field of education data science. It asks what kind of profession it will be, what related disciplines influence it, and what skills are important for practitioners. Education data science draws upon fields like statistics, learning sciences, and information sciences. Practitioners need skills in qualitative and quantitative methods, technology, and reasoning from imperfect data, with an emphasis on ethics. The document argues education data science can help improve educational practices and decisions if practitioners recognize data as socially situated and imperfect lenses for understanding contexts.
The document discusses Response to Intervention (RTI) models for identifying learning disabilities and providing early intervention for struggling students. It describes the shift from an IQ-achievement discrepancy model to an RTI model focused on monitoring student response to evidence-based interventions. The RTI model uses multiple tiers of instruction with increasing intensity. Teachers require skills in progress monitoring, assessment, evidence-based instruction, and designing and evaluating behavioral interventions. Issues discussed include over-identification of students for special education and the need for early intervention to prevent failure.
Big data has the potential to transform nursing education and healthcare. It allows analysis of large, diverse datasets to reveal patterns and trends. Nursing has a long history of using data to improve patient care. Now, with big data and analytics, insights can be gained from vast amounts of structured and unstructured data from various sources. This can help personalize learning and predict outcomes. However, challenges include technical issues, privacy concerns, and developing a data-driven culture. With collaboration across sectors and letting the data speak, big data can advance nursing knowledge and the learning healthcare system.
This document discusses the importance of data in education and provides an overview of key topics related to data use. It defines different types of data, sources of data, and how data can be used at various levels within the education system. The goal is to shift toward using data in strategic and thoughtful ways to inform decisions and improve student outcomes. Leaders are encouraged to develop a culture of inquiry and data-informed decision making.
The document discusses using artificial intelligence technologies in healthcare, noting opportunities for AI to enhance diagnosis, treatment planning, and research, but also challenges regarding governance, privacy, bias, and other issues. It provides an overview of different applications of AI in healthcare management, clinical decision-making, and patient data analysis, and emphasizes that AI should augment rather than replace human experts in medical fields. The workshop aims to educate participants on utilizing AI, specifically generative AI, in healthcare and medical education.
This document discusses supporting neurodiverse postgraduate students. It notes the growing number of autistic students in higher education and higher dropout rates compared to non-autistic students. Supervision and viva voce exams are identified as two key areas to provide inclusive support. The document provides checklists of considerations for both, focusing on structure, clear expectations, limiting uncertainty, and capitalizing on students' strengths. It emphasizes student voice and reasonable adjustments to ensure intellectual abilities are examined, not social differences.
The document discusses efforts by Johnson & Wales University librarians Joe Eshleman and Richard Moniz to improve students' ability to evaluate information sources. They designed class exercises where students individually evaluated sources for a research assignment and received feedback. Student and instructor feedback indicated the exercises improved students' critical evaluation skills. The librarians shared their approach and findings to help other instructors implement similar exercises in their courses.
The document summarizes a presentation about assessing the information literacy of health students. It discusses differences between students and experts, across academic disciplines, and between computer skills and information literacy skills. It also explores how educators can increase the quality of references cited in student papers. The presentation shares past research findings on these topics and results from the presenters' Research Readiness Self-Assessment (RRSA) tool, which provides students individual feedback to improve their skills.
This workshop is a comprehensive introduction to the application of Generative AI in healthcare. It provides healthcare professionals, educators, and researchers with practical experience in using Generative AI for data analysis, predictive modeling, and personalized treatment planning. The workshop also explores the use of Generative AI in medical education and research. No prior AI experience is required, making this a unique opportunity to learn about the latest advancements in Generative AI and its healthcare applications.
Deriving value from analytics requires much more than purchasing technology. University of Kentucky's analytics journey utilized fostering a bottom-up emergent community of practice as well as top-down organizational maneuvers. This presentation shares different aspects of the University of Kentucky score.
Similar to Learning analytics Student considerations (20)
General AI for Medical Educators April 2024Janet Corral
Learn how to consider Artificial Intelligence as augmentation, to enhance your work. In this presentation we cover augmentation, cyborgs and critically appraise examples of #AI in #MedEd. We then discuss faculty development and can #AI be an #instructionaldesinger.
AI & VR for Academy of Medical Educators.pptxJanet Corral
The document discusses the use of artificial intelligence (AI) and virtual reality (VR) in clinical education. It provides examples of how VR can be used for anatomy learning, patient case simulations, and physical therapy skills training. Studies show that while VR increases a student's sense of presence, it does not necessarily improve learning compared to traditional methods. VR works best when it allows for deliberate practice with specific feedback. The document also discusses how AI could serve as a cognitive partner to guide students' clinical decision making. Overall, the key takeaways are that education technologies like VR and AI can enhance learning if they are designed based on principles from the learning sciences and allow for skills practice with feedback.
Developed for the University of Arizona Tucson COMPAS program, this interactive session helps junior educators align their teaching methods and assessment with their goals.
This document welcomes students to the University of Arizona College of Medicine and provides tips for success as a medical student. It emphasizes engagement, asking questions, supporting others, developing a growth mindset, reflecting on learning, and transitioning study skills. It outlines the curriculum, which includes distinction tracks and a scholarly project. It also discusses incorporating themes of health disparities, racism, and structural racism throughout the pre-clerkship and clerkship phases. Faculty are trained on bias and cultural competency is emphasized. Students are encouraged to reach out for support from administrators.
Tips on how to write great abstract for conference Janet Corral
This document provides guidance on writing conference submissions for medical education conferences like AAMC GEA or AMEE. It outlines basic guidelines and deadlines, emphasizing the importance of focusing the submission on the conference theme. It provides examples of refining a conference focus to better align with the theme. The document also shares example abstracts and lessons learned from reviewing abstracts. Great resources on writing effective abstracts and titles are recommended.
Orientation to UACOM-T MD Curriculum July 21 2020Janet Corral
This document provides an orientation for new medical students at the University of Arizona College of Medicine. It discusses shifting mindsets from learning to growing as a learner and relating to others. It emphasizes engaging actively in learning sessions, asking questions, and developing a growth mindset. Students are encouraged to be curious, empathetic advocates. The curriculum overview shows a focus on societies, pathways, clerkships and distinguishing tracks in areas like rural health. Combating racism in medicine is highlighted as an important curricular focus. Resources like the digital learning platform and health sciences library are also outlined.
Creative Disruption in Medical Education: 4 ExamplesJanet Corral
Keeping educational evidence and theory at the forefront, this presentation asks health professions educators to re-imagine how health professions education might evolve while judiciously incorporating technology into clinical and classroom experiences. Focusing on competent health care providers as our graduates, we don't let tech rule - we rule tech!
Tips for higher quality large group teaching in 2020Janet Corral
Need quick tips to improve your lecture or large group teaching? This short presentation walks through examples from the science of learning and applies them to lecture structure. Be prepared - the presentation asks you to apply what you learn to your own teaching! Catch me on Twitter to let me know what you changed in your teaching world for 2020! @edtechcorral
Digital Education for Clinical EducationJanet Corral
A presentation given to University of Colorado Dept of Anesthesia Grand Rounds on April 29, 2019. Designed to be interactive and follow principles of active learning, where slides ask a question, this was a time for the audience to pause & discuss with each other what they had learned to that point, as a way of co-constructing knowledge, bringing in critical appraisal, and application of concepts to their own teaching & learning practice. Meant to reach a broad audience, only some of whom are educators, the presentation also remains at an introductory level to ensure broad applicability. Email me if you are interested in a higher level of engagement around digital education options for clinical education!
Cyborg Learners and Adopting Tech Well in Health Professions EducationJanet Corral
1. Learners have become "cyborgs" as they engage with digital education resources and technology in daily life, which has disrupted traditional higher education.
2. When learners use technologies like mobile devices, it allows their "minds and selves [to be] spread across biological brain and nonbiological circuitry."
3. Educators should adopt technologies that make sense to support learning, but also help structure how learners use digital tools and devices to ensure they actually facilitate learning rather than replacing it. The role of educators is to guide learners' development and use of technologies, not be ruled by them.
Adopting Educational Technology in Medical SchoolsJanet Corral
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This document summarizes the development of learning analytics in higher education and medical schools from 2010-2017. It discusses the continuum of analytics approaches from basic visualizations to feedback and nudging/coaching. It notes that to effectively support learning outcomes, educational analytics need multiple data sources and will impact the whole system of stakeholders. The document raises questions about what types of analytics could benefit learners using the Entrada data system and engages attendees in a discussion.
AME Education Innovation and Scholarship Symposium 5th AnniversaryJanet Corral
Celebrating the 5th Annual Education Innovation and Scholarship Symposium lead by the Academy of Medical Educators at the University of Colorado. We've had increased numbers of submissions for posters and oral presentations over our first five years, and are pleased to share 2017 was our biggest yet!
Active learning for Residency TeachingJanet Corral
Learn 3 times of the day when you can use active learning techniques for short-burst teaching encounters with small groups of residents.
For longer teaching sessions (e.g. 1 hr talk), please see other presentations on the multiple types of active learning for longer teaching sessions.
Active Learning: 3 Easy Ways for Higher Education LecturesJanet Corral
This short faculty development session covers 3 easy ways in which faculty may use active learning strategies in their lectures. I present some of the evidence base in support of each strategy, and give tips on how to successfully incorporate these strategies into your teaching.
The document discusses active learning strategies for teaching anatomy. It defines active learning as involving students in activities that require thinking about what they are doing. Some examples of active learning include using cases or problems in lectures, flipping the classroom, and team-based learning. The benefits of active learning are that it engages students, helps with attention spans, and provides practice and feedback. Challenges for faculty include adequately covering content and increased preparation time. Sustaining active learning requires faculty development, design support, evaluation, and addressing resource needs. The document encourages readers to try different active learning approaches like modifying lectures, using problems, flipping lectures, or team-based learning.
How to Use Social Media at Conferences & to Build your PLNJanet Corral
An updated version of how to rock your academic presentations at conferences. This new & updated module covers the concepts of PLNs (personal learning networks) and PKM (personal knowledge management). Spacer slides also included for presenters to pause and move to live demos of using Slideshare and Prezi to upload & share academic presentations.
Teaching Scholars Program Curriculum Design to Enable Tech IntegrationJanet Corral
Dr. Janet Corral from the University of Colorado presented on a new Teacher Scholar Program (TSP) curriculum that uses a hybrid model integrating 21st century tools. The new model addresses needs like allowing more time for projects by meeting less frequently and providing more structure. It incorporates pre-activities, evaluations, and tools like learning management systems, surveys, and calendars. Early feedback showed students needing help with the learning management system initially and later with finding posts and completing projects. The design-based approach allows ongoing improvements based on feedback.
Keeping Education in Learning Analytics AAMC 2013Janet Corral
This document discusses various topics related to learning analytics including personalized feedback, recommendations for studying, measuring student success over time, curriculum design and pedagogy, understanding achievement more deeply, and the social aspects of learning. It raises questions about what learning analytics can and cannot cover, including topics like humanism, empathy, and deep learning.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
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LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
3. What are “Academic Analytics”?
• Mining data associated with the
administration of education
See Campbell & Oblinger, 2007
4. What are “learning analytics”?
Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S.,
Shum, S. B., Ferguson, R., . . . Baker, R. S. J. D. (2011)
5. What is medical education gathering?
LMS
Exams
ePortfolio
Lecture
Capture
Admissions
7. Meaningfulness
• Learning has social and personal components
• What does the data mean?
• How might the data guide/predict your
learning?
• Beware of the bubble – what are you not
learning? How do you know?
8. Transparency and Literacy
• Of data collection
• Of reporting the data back to user (faculty, student)
• Helping user (faculty, student) make sense of the
data
9. What skills & literacies might you need?
As a Student
• Formative progress
• Summative evaluation
• Predictive
• Algorithms,
Transparency
• Meaning
Student
Education
As a Doctor
• EVBM
• Rx
• Predictive medicine
10. Analytics could look across the web
• Across institutional and personal web
applications (Retalis et al, 2006)
– Limited view of learning
– Expanded view of learning
Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S.,
Shum, S. B., Ferguson, R., . . . Baker, R. S. J. D. (2011)
Limited view of learning (e.g. discussion postings, sharing links)Expanded view of learning (e.g. students don’t use the LMS exclusively; learning happens across personal and institutional contexts)
Educate students on where data obtained, how mined, how used. Also show them how it might be used to assess their own students (teachers), is changing EVBM (MDs), might change how Pts are treated (MDs),