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DATA DRIVEN

Transcript: DATA DIVE PSRC Student Support Services Facilitator: Aaron Locklear March 1, 2017 Standard Va The number of classroom lessons that were delivered. Topics: e.g. emotions, conflict resolution, friendships Why Data? 85% increased to 90% Fall 2016 Spring 2017 5% How do we use this data? 10 out of 289 students 15 out of 300 students Behavior 10% Academic Analyze student learning Academic goals Other Standard Va: Analyze the impact of the school counseling program. To determine the effectiveness of comprehensive program Aligned to professional standards The number of small group meetings that were facilitated. Coordinated two student council groups for grades 5 & 6. 105 40 23% High School Retentions NC School Counseling Standards Attendance Rates School counselors analyze student achievement, behavior, and school climate data, as well as feedback from students, parents, and other stakeholders to continually develop their program 0 47 20 Classroom Lessons Turn and Talk Activity Directions: Discuss with a shoulder partner and decide on what data you will use. Share out with everyone. To understand the importance of data To learn how to present your data in various formats on end of year report. 18 a. Social workers collect and analyze student data to plan and evaluate the effectiveness of service delivery School Counseling Program End-of-Year Report Sample SO WHAT? 12% What is your FOCUS? NC School Social Worker Standards 100 Personal 14% Behavior 43% Academic 36% Other 7% 20% decrease in suspensions from 1st to 2nd semester 25% of my high school students accepted to college 5% decrease in bullying incidents Referred 10 students to PSRC Mental Health Provider 38 home visits by social worker Assisted 15 displaced families Facilitated 39 MTSS/RTI meetings 90% of the teacher survey results indicated...... Standard Va Personal 18% MORE EXAMPLES OF DATA Friendships Standard V Emotions Today's objectives: 280 120 Individual Counseling 37% 2016-2017 The number of students who met with counselor individually or with a friend. Students were referred by teachers, administrators, and parents. Total Careers 120 105 Conflict Resolution 3.5% Longley Elementary School K-6 PEER MEDIATORS: Peer mediation is facilitated deliberation that helps students in conflict resolve their disputes and create their own solutions, using shared problem-solving within a school setting. 2015-2016

Data- Driven

Transcript: Best-Practices in Collecting Data Informed Consent Confidentiality Frequency of Collection Volume Needed Source of Data Limitations Dissemination of Findings Avoid Over-Generalizing Degrading Empty Promises Dunning and Kruger – “Unskilled and Unaware of It” Findings Across 4 Studies: Knowns – There are things we know about quality treatment (Family Systemic Involvement) Known Unknowns – There are things we know we don’t know about quality treatment (Sensory Checklist: Sensory-Informed Interventions) Unknown Unknowns – There are things that are unknown unknowns; things we don’t know that we don’t know about quality treatment Mediocre Doctor is not aware of the diagnostic possibility or treatments never considered Run-Of-The-Mill Lawyer fails to recognize the winning legal argument that is out there People fail to reach their potential as professionals, lovers, parents and people simply because thy are not aware of the possible. Dunning: “ So, yes, the idea resonates. I would write more, and there’s probably a lot more to write about, but I haven’t a clue what that all is”  Organizational Social Context (OSC) Organizational Social Context (OSC) Thank You! Pragmatic tracker Outcometools.com OQ Analyst Myoutcomes.com Fit-outcomes.com DBT Diary card –Tracker app mFIT mobile app System norms and values “The way things are done” What is expected and rewarded Data-Driven Treatment Measure Examples Practice based evidence is essential for our field. We will focus on the use of a program’s own outcomes studies in driving treatment decision making. Outcomes / Feedback informs treatment, and informed treatment produces improved outcomes. Contextual model Fit bit ( Health and wellness) Lume (I phone mood tracker) Jawbone UP -( steps taken, calories burned, eating habits, sleep quality and sleep cycle vibration alarm; behavioral nudges (water consumption, movement, sleep) Wake mate (sleep specific) Nike+ Fuelband ( Steps, calories burned) Utilization of measures - opportunities and stumbling blocks for data collection What are the barriers for data collection in your program? Create an action plan for overcoming. One study found those who lost more then 60 pounds and maintained the weight loss for more then five years weighed themselves daily (Klem et al, 1997) More and more we are finding ways to quantify our lives by measuring more then weight. Organizational Readiness for Change Examples Three main purposes for data collection in your program Types of assessment measures that are best fit for your program Action plan for beginning to collect data Proper utilization and dissemination of findings Climate Perceptions of work environment’s psychological impact on employees Affective response “Is this place good or bad for me” Rejection or adoption of new technologies It can predict sustainability and success of EBP implementation Assimilation vs. Accommodation Information fitting versus developing fields Positive or negative outcomes: Turnover, organizational commitment, staff wellbeing, client wellbeing, EBP success, quality of care Individual persistence vs. apathy/resistance Feelings of autonomy, decision control, energy, motivation, tenacity Data-Driven Treatment Data- Driven Treatment 1. Proficiency – expectation that service providers will be competent, have up-to-date knowledge, and place the well-being of clients first 2. Rigidity – expectation that service providers will have limited discretion and flexibility, and closely follow extensive bureaucratic rules and regulations 3. Resistance – expectation that service providers will show no interest in change or new ways of providing services Feedback Informed Treatment Dunning and Kruger – “Unskilled and Unaware of It” Findings Across 4 Studies: Participants scoring in the bottom quartile on tests of humor, grammar, and logic grossly overestimated their test performance and ability. Although their test scores put them in the 12th percentile, they estimated themselves to be in the 62nd” “Put simply, people tend to do what they know and fail to do that which they have no conception of. In that way, ignorance profoundly channels the course we take in life.” See Justin Kruger and David Dunning, “Unskilled and Unaware of It: How Difficulties in Recognizing One’s Own Incompetence Lead to Inflated Self-Assessments,” Journal of Personality and Social Psychology, Dec. 1999, 1121–34) Client Questionnaires (eg. YOQ, RADS, ORS, SRS) http://www.screencast.com/t/y4ALQWNsc5u Organizational Questionnaires / Measures (eg. OSC) (incident reporting, Staff turnover) Organizational Trackers (eg. Salesforce, ) Feedback forms (Staff, Students, Families, Referral partners) Employee training and level advancement Prioritizing Items to be Assessed: What is the potential utilization of outcomes data in your program? Marketing Screening Treatment Planning Projecting Training Risk Assessments More? Examine current areas of a programs strength and weakness Program “fit” for incoming clients

Data Driven

Transcript: WHY Data Driven Decision Making Porshe' Chapman Present your ideas WHAT Professional Learning Communities WHY? Professional Learning Commitee Strong professional learning communities can foster an environment of academic risk-taking and growth, allowing teachers and students to benefit. Professional Learning Commitee give teachers the chance to directly enhance their students' learning and teaching. Professional Learning Commitee provides teachers with simple approach to enhance best practices and come up with creative ideas to enhance instruction and raise student progress. For educators to exchange perspectives and believe that what they are doing in the classroom matters, effective communication is essential. These learning communities allow teacher more critically thinking on their own teaching methods and students' learning outcomes. When you meet with your PLC, you may share student progress, and when the data is shared between grade levels in the same building, administrators and teachers take responsibility for every student's education. My IQ? How When Open your mind for new ideas Healhty diet? No Thanks! School Wide Data Digs Educators Through the effective implementation of Data Dugs, Teachers will be prepared to: Review and understand Data Utilize Data to Plan for instruction SEt Professional Goals Engage in Data Driven Civersation Through the effective implementation of School and District Data Digs, students will be prepared to engage in the following Students Review Indivdualized Data Goal Setting track success Data Dialog Data Digs Data Digs All stakeholders will be present at Data Dig meetings. To improve data competence, school leaders, instructional coaches, teachers, and students will take part in data-driven dialogs. Common planning periods will be scheduled by school leadership teams to give teachers time to have organized data dialogues based on the most recent data for their respective grade levels or subject areas. These meetings will run concurrently with PLCs and be a part of weekly grade team meetings that are incorporated with regard to individual classroom data. Monthly Data Digs only look at data that affect the entire school to decide what to do next. Student data will be collected in the classroom and will follow implementation guidelines for educators. District Wide Meeting Where WHY Principals, instructional coaches, parents, community partners, and students would all have access to district-wide data in addition to these individuals. This will serve as the starting point for data-driven talks that will spread to every student's school, classroom, and home. The relevance of data, what it means, and how it is used to decide what is NEXT will be explained to stakeholders through the usage of this technique. We will evaluate and discuss all recent assessment data in novel and interesting ways. All employees and students will be expected to attend this training, which will be held four times a year. On a monthly basis, school leaders and instructional coaches will continue to execute school-wide data digs.

Data Driven

Transcript: Mean , Mode , Median GOAL! Our mean is 36.05. Our mode is 48. Our median is 37.5. Our race measures are from greatest to least are 48 in, 48 in, 46 in, 46 in, 44 in, 43 in, 43 in, 43 in, 41 in, 38 in, 37 in, 37 in, 34 in, 34 in, 33 in, 30 in, 26 in, 23 in, 21 in, and 6 in. Our range for our distances is 42. Our M.A.D for our distances is Our I.Q.R for our distance is 36.5. Data Driven Our race measures (in inches) By Chris and Justin Range, M.A.D, I.Q.R

data driven

Transcript: Les Madeleines de Christophe TISSIER Emma - VIOLANTE Salomé - ROOSENS Théa - PAPI Délia AGENDA AGENDA 01. 02. 03. NEWS WHERE WE ARE WHERE WE ARE Update 1 Update 1 Update 2 Update 2 ANNOUNCEMENTS COMPANY NEWS COMPANY NEWS NEWS NEWS EVENTS EVENTS NEW EMPLOYEES NEW EMPLOYEES NEW HIRE NEW HIRE Name Department Start date NEW HIRE Name Department Start date NEW HIRE SPECIAL DATES SPECIAL DATES Department Name FEB MAR Name Birthday Department Years with Company JAN WHAT'S NEXT WHAT'S NEXT JAN MAR FEB KEEP IN MIND KEEP IN MIND 02. 01. 03. CHANGES CHANGES

Data-Driven

Transcript: Automatic Movement Generation Applications Style Support: Mean values of the p.d.f is a function of factors (style variable) Calculated by multiple regression Supervised Models state duration Allows for defining factors that control the state duration: walking pace What to Evaluate? Data-Driven Human Movement Generation Using Statistical Models High-level factors that affects the movement Gait Identity Effort ... Often referred to ask 'style variable' or 'style space' in the literature Data-Driven Movement Generation Statistical Movement Generation: Overview Style transitions Stride length and speed DBN Learned Style Machine Describing data in statistical terms Each feature vector (frame) contains 60-120 dimensions: multivariate Multivariate probability distribution functions Multivariate Gaussian Parametric Multivariate Gaussian Decomposable Triangulated Graph Mixture Models Data Multiple regression Hidden Semi-Markov Models Wang et al. [2006b] Parameterizing rotations with 3 Degrees of Freedom 3x3 Rotation Matrix Euler Angles 4D Quaternion Exponential Maps Bone Rotations vs Joint Angles Depending on MOCAP format Can be converted to each other Hierarchical representation of joins Rotations relative to parents Temporal: RBMs with direct connections from a number of its past visible units to its hidden and visible units Real-valued visible variables representing joint angles CRBMs as building blocks of Deep Belief Networks Generation based on initialization No style blending or interpolation Factored CRBM Experiments X=strutting+(running-walking) Yamazaki et al. [2005] Semantics Conditional Restricted Boltzmann Machines (CRBM) Yamazaki et al. [2005] Fall October 2013 Applications Discussion Style Extrapolation Pure data modeling No knowledge of human movement involved No physical constraints Having a good data is important! Need a collection of training data Record our own Use a movement database What we can generate mainly depends on what we have as data Multivariate Probability Distribution Functions 3D Rotation of joints or bones Full body movements An skeleton can be used for visualization purposes Joint positions Sources Motion Capture Kinect, Leap Motion Accelerometer (not good for generation) ... Not violating body constraints Aesthetics Realistic/Believable Variance from the training data Is it generation new examples? How different are examples from each other? Prediction How well can the model predict the movements given an initial set of frames Movement Data Physics based Data-driven Machine Learning Models - Differences Style-Content Separation: Motivation Wang et al. [2007] Style-Content Separation: Techniques Movement database with proper annotations Represent a range of different movement semantics Approaches for extracting and representing semantics Choice of the contents and semantics affects the data collection requirements Data source Marker placements for mocap Framerate SHMM http://www.uoguelph.ca/~gwtaylor/thesis/4/ Multifactor Gaussian Process Model Collection of motion capture files Used for training Need to be labeled/annotated for supervised methods Ideally includes Multiple takes of the same movements with same and different subjects Variations of the same movement type: stylized walks Follows a set of proper semantics Wang et al. [2006b] Style Content Separation Data Representation Defining the style dimensions Supervised Unsupervised Incorporating style into the model Conditional Probability Distribution Model Architecture Multivariate Gaussian Correlation between all dimensions Parametric Multivariate Gaussian The mean value of the distribution is affected by a parameter (style variable/factor) Decomposable Triangulated Graph HMM/Mix-SDTG HMM/Mix-SDTG Different models, different architectures, different representational capabilities Hidden Markov Models: Parametric HMM, HSMM Artificial Neural Networks: Feed-forward, CRBM, Factored CRBM Gaussian Process Models ... Specialized learning algorithms Generation Algorithms Probability Distribution Functions Taylor et al. [2006] Wang et al. [2007] Between 60 to 120 Includes root position and orientation 1, 2, or 3 DOFs for each joint/bone Depends on Type of the rotation parametrization Number of joints/bones defined in skeleton Number of markers used (marker based mocap) Dimensionality Reduction Algorithms Principal Component Analysis Hard to evaluate the works No methodological evaluation method Usually no videos available Not replicable Lack of a comprehensive database Repeated variations of the same movements Valid annotations No semantics defined in current databases or models No study on agent model integration Mostly using motion capture Approaches: Concatenation Blending and interpolation Transforming existing movements Statistical models Style Machines Multifactor Gaussian Process Model: Experiments Extending a MOCAP database Can be used by animators Useful for other animation technique Cost and time Direct Animation Current

Data-Driven

Transcript: Target Who not to target 6-7x Opportunity in churn Understand Marketing has changed 3 Lessons Use your data to... Keep Customers Skeptical of "averages" Ted Paff, CEO tpaff@customerlobby.com 510-230-0588 Customer Lobby Very Unlikely Beat Them At Their Own Game Data-Driven Customer Communication Very Likely No silver bullet

Data-Driven

Transcript: Data-Driven Design & Creativity Qualitative Data Quantitative Data Abandoning breakthrough thinking just because no statistics exist to back it up is flawed. This dependency can severely hinder one of the greatest values we can bring to our clients. Who do we want to be? Data in the hands of smart, creative and strategic people can be the input that drives creativity to a new level. When applied correctly and meaningfully it can generate new insight and creative opportunities. Data can be a tool that fuels inspiration. Adam Kleinberg - CEO of Traction, Douglas Bowman - Visual Design Leader upon leaving the company in 2009. ddy focuses on numbers and/or statistics dobe Common Forms Data isn't the enemy. web analytics A/B testing Customer Satisfaction Scores "Creativity is more important than knowledge." "When all think alike, then no one is thinking." Walter Lippman "I'll miss working with the incredibly smart and talented people I got to know there. But I won't miss a design philosophy that lives or dies strictly by the sword of data." Insight & Instincts are vital. "Design is not just what it looks like & feels like. Design is how it works." Common Forms Can Data-Driven Creative lead to Mediocre Creative? Aha! Did you know? usability testing focus groups open ended feedback surveys conversation client brief Who are we? = comes from peoples expressions of their opinions, feelings & behaviors. Data can provide insight. It can also cause confusion. Insight can come just as easily in the shower, from a copywriter or HTML programmer as from scouring mounds of spreadsheets. Where does it all come from?