8 Matching Annotations
  1. Apr 2019
    1. Please annotate how you have felt about the current situation with the Team-preneurship class here. I will compile your annotations into a document that I can present at the next organizational meeting. I would like our needs and feelings to be represented in this process. Thanks, guys. 

      Well given how Scott showed me how there are a ton of moving parts in the teampreneurship program, along with the fact that there are misunderstandings on how the research team will go about with the research onto how would the teampreneurship program at Evergreen proceed, I found there are three things that needs to happen: the first is that the teampreneurship and other programs need to understand the difference between the action research and traditional research, the second is to make sure that all the faculties are on the same page about what they are doing with the teampreneurship program while making sure that they are for the interest of the group (and have to learn to leave aside the personal interest that does not align or build the teampreneurship pursuit), and the third is to build awareness with the administrators on the roles they play in the program. The program on Thursday was a sign to help us understand that there are some parts that needs to be on the same page with one another and unfortunately, what I found was there is a need for communication before proceeding. If this does not work out well within the couple of days or weeks into the program, this could jeopardize the money that the administration poured into in order to understand the impact of teampreneurship and the millions of dollars of donations made for the program.

  2. May 2018
    1. C. Gopinath and themembers of Cividep, who have been working with garment workers,and Sister Celia, who was instrumental in forming the first tradeunion of domestic workers in the country

      Garment workers and work being done with them for Min. Wages

  3. Nov 2017
    1. One of the primary uses of a model like this one is to improve the conversation between stakeholders and managers. The model can be valuable in helping managers and citizens arrive at realistic goals and to realize that there will be inherent risks associated with meeting those goals. For example, our analysis shows that reducing the probability of transmission by one half in five years using vaccination is not likely when we include uncertainty in the ability of managers to treat a targeted number of seronegative females. Forecasts suggested that there was virtually no chance of meeting that goal (Table 12). Similarly there was a 7% chance of reducing adult female seroprevalence below 40% using vaccination. We can nonetheless use this work to articulate what level of brucellosis suppression is feasible given current technology. For example, managers and stakeholders might agree that it is enough to be moving in the right direction with efforts to reduce risk of infection from brucellosis. In this case, a reasonable goal might be “Reduce the probability of exposure by 10% relative to the current median value.” The odds of meeting that goal using vaccination increased to 26%. With this less ambitious goal, vaccination increases the probability that the goal would be met relative to no action by a factor of only 1.4. This illustrates a fundamental trade-off in making management choices in the face of uncertainty: less ambitious goals are more likely to be met, but they offer smaller improvements in the probability of obtaining the desired outcome relative to no action.

      Great description of the value of forecasting models for improving conversations between stakeholders and managers in the development of goals and expectations of outcomes.

  4. Sep 2016
    1. Application Modern higher education institutions have unprecedentedly large and detailed collections of data about their students, and are growing increasingly sophisticated in their ability to merge datasets from diverse sources. As a result, institutions have great opportunities to analyze and intervene on student performance and student learning. While there are many potential applications of student data analysis in the institutional context, we focus here on four approaches that cover a broad range of the most common activities: data-based enrollment management, admissions, and financial aid decisions; analytics to inform broad-based program or policy changes related to retention; early-alert systems focused on successful degree completion; and adaptive courseware.

      Perhaps even more than other sections, this one recalls the trope:

      The difference probably comes from the impact of (institutional) “application”.

    1. who will (and will not) control and define the learning process, who will (and will not) profit from the ways that learning processes are enacted, who will (and will not) have access to science and scholarship and the infrastructure necessary for creating it, who will (and will not) participate in the design of curriculum and assessment and learning spaces, who will (and will not) profit from the benefits of science and artistry, and who will (and will not) have opportunities to attend schools and colleges.

      Several (though not all) of these questions relate to the core sociological one: Who Decides? The list sounds, in part, like a call for deeper and more nuanced “stakeholders” thinking than the typical case study. The apparent focus (at least with parenthetical mentions of those excluded) is on the limits of inclusion. From this, we could already be thinking about community-building, especially in view of a strong Community of Practice.

    2. ownership in educational systems
  5. Jul 2016
    1. Data collection on students should be considered a joint venture, with all parties — students, parents, instructors, administrators — on the same page about how the information is being used.
  6. Nov 2013
    1. The design of interactions is driven by user requirements and their impact on the choices made in the implementation process. It is constrained by resource and technical system properties and by social and legal requirements.

      It would have been better if we'd talked about interactions with a broader notion of "stakeholder" instead of just "user" because the latter term tends to be conflated with customer, end user, consumer types and is too narrow. Many of the interactions in organizing systems are designed to support its operators or managers or other "non-user" stakeholders