Visiting the ORI website, one gets surprised that many cases of misconduct are related to medical research. This may affect the credibility of all findings that seem proposing new medicine or causes of a disease. One recent case in 2015 is the case of Dr. Fujita from Columbia University, which is a prestigious institution.
It is hard to uncover the true reasons that push a researcher to make up and fabricate data of protein expressions of human cells realted to Alzheimer’s disease patients. These fabricated results were published in Nature and Cell!! So what can we trust after those! It is nice that this misconduct was discovered but what makes anyone believe that any results published will be produced honestly through scientific research.
What makes a researcher do that? The goal of medical research is to help people fight their sickness and live with good health. When a researcher is fabricating, what is the value for Alzheimer’s disease patients from Dr. Fujita work? Maybe reviewing of submitted papers may require the peer reviewers to watch all experiment steps and revise everything, for the sake of diseased people whose tax money fund this research.
A nice info-graphic that I saw can be reached at http://elearninginfographics.com/how-to-flip-a-classroom-infographic/
Flipping the classroom has always been a dream for me. I can see how it increases the interaction with students and make them prepared at time of class to completely understand the material. The info-graphic highlights how this technique turns the learning process to be student-centered. It also meets a desire of students as 60% of them like to use technology in the class. The info-graphic mentions a study where 200 teachers flipped their classes, and 85% of them noticed an increase in grades.
According to a case study, 95% of students believe video lectures enhance their learning. Flipping a class makes those students more comfortable and at the same time provide the instructor with a chance to make sure no students are suffering or misunderstanding any important concepts covered by the class.
73% of academic staff are already using digital content in their classes. I need to be one of those 🙂
The current university system is not suiting the needs of many people around the world. A person who finished high school and did not continue his way through university education may in many cases be forced to do that. Governments and university leaders should seriously work to make it possible for people with different needs and life conditions to join university if they choose to. A person who cannot commit to be a full time undergraduate student does not mean he or she is lacking the passion to learn. Also a person that needs to work and has very limited time to study should be allowed to do so. Self-study is good for some but not suitable for all. Universities can do more than offering online classes. Campus experience and being among other students in classroom with a professor is so important for a student. Self-paced classes for people who need them can provide an opportunity for older people who like to learn but cannot spend the time needed for regular semester classes. Such classes need to have separate certificates so students don’t feel they are forced to take a specified number of classes to have a university degree. In last few years, MOOCs were joined by tens of thousands of senior citizens whose learning needs are not taken care of. Yes, it will be challenging for a professor to lead a class with students proceeding at different paces, a class that allows a student to enroll at any time, be certified when she finishes the material , and many other challenges, but for sure this will be a window for many people to make their dreams true.
In my future research, I will always give priority to publish in open access journals, One of the most important things I achieved from this class. I am really surprised about the big number of computer science open access journals, more taken by the high impact factors of many of these journals.
A good example is JMLR (Journal of Machine Learning Research). As a researcher in Machine Learning, I am very proud of the founders of this journal in 2000. Back then, in what seems like a revolution, forty members of the editorial board of a prestigious Machine Learning journal resigned to work in this open access journal. Their resignation letter (http://sigir.org/files/forum/F2001/sigirFall01Letters.html) is a nice piece that Machine Learning researchers should use to show how we learn and adapt and exploit technological advancements as humans before we look for ways to inject learning behavior to machines.
They stated it is detrimental for researchers to continue publishing in expensive journals with pay-access archives in the Internet era.
That was 15 years ago! Since then, JMLR continued to publish cutting-edge research that can be accessed online for free while authors’ copyright is retained. JMLR has been always in connection with MIT which used to publish paper issues of the journal papers till 2004. It is really one of the best journals in the field. In 2008, it was included in top 20 computer science journals in terms of average citations per paper, where each paper is cited more than 6 times on average. In 2004, its impact factor was the second highest in all computer science journals in general.
JMLR gave students and researchers around the world easy and free access to top research in the field. It has been steadily growing from 17 accepted papers (from 143 submitted) in 2003 to 135 papers accepted in 2013 after one decade (from 764 submitted in the same year). But for students and researchers, JMLR is more than a place to download a paper to read for free by supporting open-source software for research!
Like the lesson of the editorial board, humans should learn and allow free access to their publications online, also machine learning researchers should make their software developed for ML research available for other researchers to start from where they ended. Through JMLR, any student or researcher can easily access more than 80 open-source software tools and programs that can be used for machine learning and related artificial intelligence and statistics fields.
JMLR is a successful example of a scientific community that respects copyright but knows that it does not mean that accessing scientific achievements should be paid for.