An uncomfortable fact that policy makers and intelligence analysts do not want to confront is that lack of integration and disillusionment is caused by inbred racism in Western societies to non-conformance of the Caucasian or Judeo-Christian mould. Thanks to regulation, explicit racism is banned, but implicit racism exists and is enabled by both economics as well as technology. Unless you confront racism inherent in some societies or geographies, you will be doing post mortems on events rather than pre-emptive cures. Why does India have much lower cases of home grown terror with 150 million Muslims. It is because they fit well here. Muslim males are not fitting well in Florida or California or on the French Riviera. The golden age of surveillance and the cooperation between technology service providers and government agencies cannot solve the problems of lack of integration due to racism.
Motivating students for online education is a dilemma. Students come from diverse cultures with different levels of communication, hierarchy, expectations. Exit barriers to dropping out also make some students drop out too easily by giving up since the course is free or at a nominal price. I believe one way to motivate students is to keep them involved by constant quizzes, feedback mechanisms as well as ensure how to maximize the rate of knowledge gained by student per hour invested. Time is a key investment by a student. Unfortunately one of the reasons of very good content by many MOOCs still continues to have a high dropout rate is they focus on revenue (verified certificates for a small price), and content (better projects and industry interaction) and course breadth (more courses or bundling them into a specialization) than the key underlying principle of motivating students for a global audience
Abstract The advent of massive open online courses (MOOCs) poses new learning opportunities for learners as well as challenges for researchers and designers. MOOC students approach MOOCs in a range of fashions, based on their learning goals and preferred approaches, which creates new opportunities for learners but makes it difficult for researchers to figure out what a student’s behavior means, and makes it difficult for designers to develop MOOCs appropriate for all of their learners. Towards better understanding the learners who take MOOCs, we conduct a survey of MOOC learners’ motivations and correlate it to which students complete the course according to the pace set by the instructor/platform (which necessitates having the goal of completing the course, as well as succeeding in that goal). The results showed that course completers tend to be more interested in the course content, whereas non-completers tend to be more interested in MOOCs as a type of learning experience. Contrary to initial hypotheses, however, no substantial differences in mastery-goal orientation or general academic efficacy were observed between completers and non-completers. However, students who complete the course tend to have more self-efficacy for their ability to complete the course, from the beginning.
Massive Open Online Courses (MOOCs) have recently experienced rapid development and garnered significant attention from various populations. Despite the wide recognition of MOOCs as an important opportunity within educational practices, there are still many questions as to how we might satisfy students’ needs, as evidenced by very high dropout rates. Researchers lack a solid understanding of what student needs are being addressed by MOOCs, and how well MOOCs now address (or fail to address) these needs. To help in building such an understanding, we conducted in-depth interviews probing student motivations, learning perceptions and experiences towards MOOCs, paying special attention to the MOOC affordances and experiences that might lead to high drop rates. Our study identified learning motivations, learning patterns, and a number of factors that appear to influence student retention. We proposed that the issue of retention should be addressed from two perspectives: retention as a problem but also retention as an opportunity.
a data scientist is simply a person who can write code in a few languages (primarily R, Python and SQL) for data querying, manipulation , aggregation, and visualization using enough statistical knowledge to give back actionable insights to the business for making decisions.
Since this rather practical definition of a data scientist is reinforced by the accompanying words on a job website for “data scientists” , ergo, here are some tools for learning the primary languages in data science- Python, R and SQL.
A cognitive bias refers to a systematic pattern of deviation from norm or rationality in judgment, whereby inferences about other people and situations may be drawn in an illogical fashion.[1] Individuals create their own “subjective social reality” from their perception of the input.[2] An individual’s construction of social reality, not theobjective input, may dictate their behaviour in the social world.[3] Thus, cognitive biases may sometimes lead to perceptual distortion, inaccurate judgment, illogical interpretation, or what is broadly called irrationality.
some involve a decision or judgement being affected by irrelevant information (for example the framing effect where the same problem receives different responses depending on how it is described; or the distinction bias where choices presented together have different outcomes than those presented separately)
others give excessive weight to an unimportant but salient feature of the problem (e.g., anchoring)
Logical Fallacies
A hacker knows how to recognize the logical fallacies that are used to counter his scientific and systematic research
A fallacy is an incorrect argument in logic and rhetoric which undermines an argument’s logical validity or more generally an argument’s logical soundness.
A logical fallacy is a flaw in reasoning. Logical fallacies are like tricks or illusions of thought, and they’re often very sneakily used by politicians and the media
You attacked your opponent’s character or personal traits in an attempt to undermine their argument. Ad hominem attacks can take the form of overtly attacking somebody
A hacker is educated and well rounded to understand Politics, Economics, Society, Technology, Legal and Environmental terms as well as their intersection for a country or an organization. He also understands the term regulatory arbitrage as well understand how law enforcement and financial regulators work.
The PESTLE Analysis is a framework used to scan the organization’s external macro environment. The letters stand for Political, Economic Socio-cultural, Technological, Legal and Environmental. Some approaches will add in extra factors, such as International, or remove some to reduce it to PEST
Of the three proofs–logos, pathos, and ethos–ethos is associated with the character of the speaker or writer. People are persuaded by people they trust, even if the argument is not terribly strong. Conversely, if the argument is strong, but the reader does not trust the writer or does not like the writer, the appeal to logic alone will seldom persuade. Therefore, it is essential to consider ethos.
Aristotle said that ethos consists of three sub parts: (1) good moral character, (2) good sense, (3) good will. If the writer or speaker can project an image of good moral character, then the audience will think that he or she can be trusted because the person has a conscience that will keep him or her on the up and up.
Lastly hackers have a great sense of humour and humility and a mock insult culture that thrives on intellectual freedom
DECISION STATS 