172,292 research outputs found

    Applying the interaction equivalency theorem to online courses in a large organization

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    Finding effective ways of designing online courses is a priority for corporate organizations. The interaction equivalency theorem states that meaningful learning can be achieved as long as courses are designed with at least a high level of one of three types of interactions (learner-content, learner-teacher or learner-learner). This study aimed to establish whether the interaction equivalency theorem applies to online learning in the corporate sector. The research was conducted in a large Mexican commercial organization, and involved 147 learners (sales supervisors), 30 teachers (sales managers and directors) and 3 academic assistants (course designers, or Education support staff). Three courses of an existing Leadership Program (Situational Leadership, Empowering Beliefs and Effective Performance) were redesigned and developed to test three course designs, each emphasizing a different type of interaction (learner-content, learner-teacher or learner-learner). Data were collected through surveys (for diagnostic and evaluation purposes) and exams. All courses yielded high levels of effectiveness, in terms of satisfaction, learning, perceived readiness for knowledge transfer and return on expectations. This suggests that the interaction equivalency theorem not only applies in a business setting but might also include other indicators of course effectiveness, such as satisfaction, learning transfer and return on expectations. Further research is needed to explore the possible expansion of the theorem

    Ethical Implications of Predictive Risk Intelligence

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    open access articleThis paper presents a case study on the ethical issues that relate to the use of Smart Information Systems (SIS) in predictive risk intelligence. The case study is based on a company that is using SIS to provide predictive risk intelligence in supply chain management (SCM), insurance, finance and sustainability. The pa-per covers an assessment of how the company recognises ethical concerns related to SIS and the ways it deals with them. Data was collected through a document review and two in-depth semi-structured interviews. Results from the case study indicate that the main ethical concerns with the use of SIS in predictive risk intelli-gence include protection of the data being used in predicting risk, data privacy and consent from those whose data has been collected from data providers such as so-cial media sites. Also, there are issues relating to the transparency and accountabil-ity of processes used in predictive intelligence. The interviews highlighted the issue of bias in using the SIS for making predictions for specific target clients. The last ethical issue was related to trust and accuracy of the predictions of the SIS. In re-sponse to these issues, the company has put in place different mechanisms to ensure responsible innovation through what it calls Responsible Data Science. Under Re-sponsible Data Science, the identified ethical issues are addressed by following a code of ethics, engaging with stakeholders and ethics committees. This paper is important because it provides lessons for the responsible implementation of SIS in industry, particularly for start-ups. The paper acknowledges ethical issues with the use of SIS in predictive risk intelligence and suggests that ethics should be a central consideration for companies and individuals developing SIS to create meaningful positive change for society

    The Irreplaceables: Understanding the Real Retention Crisis in America's Urban Schools

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    "Irreplaceables" are teachers who are so successful they are nearly impossible to replace, but who too often vanish from schools as the result of neglect and inattention.To identify and better understand the experience of these teachers, we started by studying 90,000 teachers across four large, geographically diverse urban school districts. We also examined student academic growth data or value-added results for approximately 20,000 of those teachers. While these measures cannot provide a complete picture of a teacher's performance or ability on their own -- and shouldn't be the only measure used in realworld teacher evaluations -- they are the most practical way to identify trends in a study of this scale, and research has demonstrated that they show a relationship to other performance measures, such as classroom observations.We used the data to identify teachers who performed exceptionally well (by helping students make much more academic progress than expected), and to see how their experiences and opinions about their work differed from other teachers' -- particularly teachers whose performance was exceptionally poor.So who are the Irreplaceables? They are, by any measure, our very best teachers. Across the districts we studied, about 20 percent of teachers fell into the category. On average, each year they help students learn two to three additional months' worth of math and reading compared with the average teacher, and five to six months more compared to low-performing teachers.Better test scores are just the beginning: Students whose teachers help them make these kinds of gains are more likely to go to college and earn higher salaries as adults, and they are less likely to become teenage parents.Teachers of this caliber not only get outstanding academic results, but also provide a more engaging learning experience for students. For example, when placed in the classroom of an Irreplaceable secondary math teacher, students are much more likely to say that their teacher cares, does not let them give up when things get difficult and makes learning enjoyable.Irreplaceables influence students for life, and their talents make them invaluable assets to their schools. The problem is, their schools don't seem to know it
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