The document provides an introduction to monitoring and evaluation (M&E) for the board of Lwala Community Alliance. It discusses why M&E is important for setting objectives and outcomes, engagement of stakeholders, and detecting problems earlier. It outlines the key components of the M&E cycle including planning, data collection and reporting. Examples are provided around setting objectives and outcomes for a girls' education program and identifying relevant indicators that are SMART. The importance of collecting data and timeline is also highlighted.
Ecosphere Technologies Annual Shareholder Meeting PresentationThe WSR Group
Ecosphere Technologies (OTCBB:ESPH) is a diversified water engineering, technology licensing and environmental services company that designs, develops and manufactures wastewater treatment solutions for industrial markets. The Company provides environmental services and technologies for use in large-scale and sustainable applications across industries, nations and ecosystems. Ecosphere is driving clean water innovation with its patented Ozonix advanced oxidation technologies and its mobile, low maintenance water treatment systems. Ecosphere's patented Ozonix technology is a high volume, advanced oxidation process designed to recycle water while reducing liquid chemicals used during water treatment applications. For more information, please visit www.EcosphereTech.com
Model-Driven Research in Social Computing
Abstract:
Research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. Our approach to creating this augmentation or enhancement is primarily model-driven. Our system developments are informed by models such as information scent, sensemaking, information theory, probabilistic models, and more recently, evolutionary dynamic models. These models have been used to understand a wide variety of user behaviors, from individuals interacting with social bookmark search in Delicious and MrTaggy.com to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behaviors.
By studying online social systems such as Google Plus, Twitter, Delicious, and Wikipedia, we further our understanding of how knowledge is constructed in a social context. In this talk, I will illustrate how a model-driven approach could help illuminate the path forward for research in social computing and community knowledge building
Bio: Ed H. Chi is a Staff Research Scientist at Google, working on the Google+ project. Very recently, Ed was the Area Manager and a Principal Scientist at Palo Alto Research Center's Augmented Social Cognition Group. He led the group in understanding how Web2.0 and Social Computing systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and has been doing research on user interface software systems since 1993. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press.
With 20 patents and over 80 research articles, his most well-known past project is the study of Information Scent — understanding how users navigate and understand the Web and information environments. Most recently, he leads a group of researchers at PARC to understand the underlying mechanisms in online social systems such as Wikipedia and social tagging sites. He has also worked on information visualization, computational molecular biology, ubicomp, and recommendation/search engines. He has won awards for both teaching and research. In his spare time, Ed is an avid Taekwondo martial artist, photographer, and snowboarder.
Presentation at UMAP, held in Montreal, July 2012.
Our research goal is to provide a better understanding of how users engage with online services, and how to measure this engagement. We should not speak of one main approach to measure user engagement – e.g. through one fixed set of metrics – because engagement depends on the online services at hand. Instead, we should be talking of models of user engagement. As a first step, we analyzed a number of online services, and show that it is possible to derive effectively simple models of user engagement, for example, accounting for user types and temporal aspects. This paper provides initial insights into engagement patterns, allowing for a better understanding of the important characteristics of how users repeatedly interact with a service or group of services.
This work was done in collaboration with Mounia Lalmas, Georges Dupret, Elad Yom-Tov and Ricardo Baeza-Yates.
A summary of an HSSi social impact evaluation system design project and initial data gathering, including challenges in evaluation and quotes from qualitative data gathering. Note that this initial round of data gathering did not yield statistically significant quantitative results (as it was undertaken primarily to test the evaluation system process), and we expect the numbers to change somewhat in upcoming rounds of data collection.
Ecosphere Technologies Annual Shareholder Meeting PresentationThe WSR Group
Ecosphere Technologies (OTCBB:ESPH) is a diversified water engineering, technology licensing and environmental services company that designs, develops and manufactures wastewater treatment solutions for industrial markets. The Company provides environmental services and technologies for use in large-scale and sustainable applications across industries, nations and ecosystems. Ecosphere is driving clean water innovation with its patented Ozonix advanced oxidation technologies and its mobile, low maintenance water treatment systems. Ecosphere's patented Ozonix technology is a high volume, advanced oxidation process designed to recycle water while reducing liquid chemicals used during water treatment applications. For more information, please visit www.EcosphereTech.com
Model-Driven Research in Social Computing
Abstract:
Research in Augmented Social Cognition is aimed at enhancing the ability of a group of people to remember, think, and reason. Our approach to creating this augmentation or enhancement is primarily model-driven. Our system developments are informed by models such as information scent, sensemaking, information theory, probabilistic models, and more recently, evolutionary dynamic models. These models have been used to understand a wide variety of user behaviors, from individuals interacting with social bookmark search in Delicious and MrTaggy.com to groups of people working on articles in Wikipedia. These models range in complexity from a simple set of assumptions to complex equations describing human and group behaviors.
By studying online social systems such as Google Plus, Twitter, Delicious, and Wikipedia, we further our understanding of how knowledge is constructed in a social context. In this talk, I will illustrate how a model-driven approach could help illuminate the path forward for research in social computing and community knowledge building
Bio: Ed H. Chi is a Staff Research Scientist at Google, working on the Google+ project. Very recently, Ed was the Area Manager and a Principal Scientist at Palo Alto Research Center's Augmented Social Cognition Group. He led the group in understanding how Web2.0 and Social Computing systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and has been doing research on user interface software systems since 1993. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press.
With 20 patents and over 80 research articles, his most well-known past project is the study of Information Scent — understanding how users navigate and understand the Web and information environments. Most recently, he leads a group of researchers at PARC to understand the underlying mechanisms in online social systems such as Wikipedia and social tagging sites. He has also worked on information visualization, computational molecular biology, ubicomp, and recommendation/search engines. He has won awards for both teaching and research. In his spare time, Ed is an avid Taekwondo martial artist, photographer, and snowboarder.
Presentation at UMAP, held in Montreal, July 2012.
Our research goal is to provide a better understanding of how users engage with online services, and how to measure this engagement. We should not speak of one main approach to measure user engagement – e.g. through one fixed set of metrics – because engagement depends on the online services at hand. Instead, we should be talking of models of user engagement. As a first step, we analyzed a number of online services, and show that it is possible to derive effectively simple models of user engagement, for example, accounting for user types and temporal aspects. This paper provides initial insights into engagement patterns, allowing for a better understanding of the important characteristics of how users repeatedly interact with a service or group of services.
This work was done in collaboration with Mounia Lalmas, Georges Dupret, Elad Yom-Tov and Ricardo Baeza-Yates.
A summary of an HSSi social impact evaluation system design project and initial data gathering, including challenges in evaluation and quotes from qualitative data gathering. Note that this initial round of data gathering did not yield statistically significant quantitative results (as it was undertaken primarily to test the evaluation system process), and we expect the numbers to change somewhat in upcoming rounds of data collection.
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A mathematical model and a heuristic memory allocation problemDiego Montero
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Place de la quinine par voie intrarectale pour le traitement précoce du paludisme grave de l'enfant - Conférence de la 4e édition du Cours international « Atelier Paludisme » - Hubert BARENNES - Institut Francophone de Médecine Tropicale, Vientiane, LAO PDR - hubert.barennes@auf.org
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1. Monitoring & Evaluation
an introduction for the board of
Lwala Community Alliance
Jessica Lyon & Sydney Nehrig
Peabody College, Vanderbilt University
2. Why Monitoring & Evaluation?
When broad level objectives and outcomes
are set:
Engagement & investment for stakeholders
Clarity surrounding roles, responsibilities, and
the usage of resources Greater success
rate
Strategic data collection and monitoring
problems detected earlier
3. Why is M&E important NOW
Hiring of new DD
On boarding of future staff members
Going after targeted funds
Can set LCA up for a future impact evaluation
of programs
4. The M&E Cycle: Planning
OUTCOMES
Short to ACTIVITIES
medium term Tasks
change for the undertaken to
IMPACT/OBJEC development produce
TIVE situation OUTPUTS outputs
resulting from Products and
Ultimate, long
outputs services,
term benefit for
the target delivered or
population provided by your
activities
7. Setting Objectives and
Outcomes
Objectives vs. Outcomes
Who sets the objectives and outcomes?
Are your objectives and outcomes SMART?
Specific
Measurable
Achievable
Relevant
Time-Bound
8. Example: Girls’ Education
Program
Program Objective:
To empower the girls of North Kamagambo to achieve school
completion, career advancement, and the capacity to make
smart life decisions, and to educate the community as a
whole about the importance of girls’ and women’s education
and empowerment
Program Outcomes:
To increase the number of girls that completes primary
school with exam scores that will allow them to continue
to secondary school.
1.1: Increase in the number of girls who are passing the KCPE
1.2: Increase in the number or girls who are sitting for the KCPE
1.3: Increase in primary school attendance and retention rate of girl
students
1.4: Increase in the gender parity ratio of girls to boys in primary
school
9. Setting Outputs and Activities
Outputs vs. Activities
Outputs are goods & services produced in order
to reach specified outcomes
Sample Output: Uniforms and sanitary towels distributed to
girls in Classes 6-8
Activities are the tasks undertaken in order to
produce the above goods & services (outputs)
Sample Activity: New Visions women measure girls, sew and
distribute uniforms on an annual basis
* Why is setting Outputs and Activities
important?
10. Example: Girls’ Education
Tasks
Knowledge sets, undertaken to
attitudes, produce the
products, & goods &
services services
11. Indicators
What are indicators?
Quantitative & Qualitative indicators
Are your indicators SMART?
Specific
Measurable*
Attainable
Relevant
Time-bound
* Means of verification
Example: Girls’ Education logframe
12. Data Collection within LCA
Data Timeline example: Girls’ Education (Ed.
Dept. & New Visions)
S Specific: Impacts, outcomes, and outputs must use change language—they must describe a specific future condition M Measurable: Results, whether quantitative or qualitative, must have measurable indicators, making it possible to assess whether they were achieved or not A Achievable: Results must be within the capacity of the partners to achieve R Relevant: Results must make a contribution to selected priorities of the national development framework T Time-bound: Results are never open-ended—there is an expected date of accomplishment Adapted from the UNDP Handbook ________.
S- Is the indicator specific enough to measure progress towards the results? M-Is the indicator a reliable and clear measure of results A-Are the results in which the indicator seeks to chart progress realistic? R-Is the indicator relevant to the intended outcomes and outputs? T-Are data available at reasonable cost and effort? **means of verification– tells us where we will find the data for our indicators: Ministry of Health, Hospital records, School attendance records?