A large global change in data protection law is about to hit the tech industry, thanks to the EU’s General Data Protection Regulations (GDPR). GDPR affects any company, wherever they are in the world, that handles data about European citizens. It becomes law on 25 May 2018, and as such includes UK citizens, since it precedes Brexit. It’s no surprise the EU has chosen to tighten the data protection belt: Europe has long opposed the tech industry’s expansionist tendencies, particularly through antitrust suits, and is perhaps the only regulatory body with the inclination and power to challenge Silicon Valley in the coming years.
So, no more harvesting data for unplanned analytics, future experimentation, or unspecified research. Teams must have specific uses for specific data.
a learning management system (LMS) is never the solution to every problem in education. Edtech is just one part of the whole learning ecosystem and student experience.
Therefore, the next generation digital learning environment (NGDLE), as envisioned by EDUCAUSE in 2015 … Looking at the NGDLE requirements from an LMS perspective, I view the NGDLE as being about five areas: interoperability; personalization; analytics, advising, and learning assessment; collaboration; accessibility and universal design.
Interoperability
Content can easily be exchanged between systems.
Users are able to leverage the tools they love, including discipline-specific apps.
Learning data is available to trusted systems and people who need it.
The learning environment is “future proof” so that it can adapt and extend as the ecosystem evolves.
Personalization
The learning environment reflects individual preferences.
Departments, divisions, and institutions can be autonomous.
Instructors teach the way they want and are not constrained by the software design.
There are clear, individual learning paths.
Students have choice in activity, expression, and engagement.
Analytics, Advising, and Learning Assessment
Learning analytics helps to identify at-risk students, course progress, and adaptive learning pathways.
The learning environment enables integrated planning and assessment of student performance.
More data is made available, with greater context around the data.
The learning environment supports platform and data standards.
Collaboration
Individual spaces persist after courses and after graduation.
Learners are encouraged as creators and consumers.
Courses include public and private spaces.
Accessibility and Universal Design
Accessibility is part of the design of the learning experience.
The learning environment enables adaptive learning and supports different types of materials.
Learning design includes measurement rubrics and quality control.
The core analogy used in the NGDLE paper is that each component of the learning environment is a Lego brick:
The days of the LMS as a “walled garden” app that does everything is over.
Today many kinds of amazing learning and collaboration tools (Lego bricks) should be accessible to educators.
We have standards that let these tools (including an LMS) talk to each other. That is, all bricks share some properties that let them fit together.
Students and teachers sign in once to this “ecosystem of bricks.”
The bricks share results and data.
These bricks fit together; they can be interchanged and swapped at will, with confidence that the learning experience will continue uninterrupted.
Any “next-gen” attempt to completely rework the pedagogical model and introduce a “mash-up of whatever” to fulfil this model would fall victim to the same criticisms levied at the LMS today: there is too little time and training to expect faculty to figure out the nuances of implementation on their own.
The Lego metaphor works only if we’re talking about “old school” Lego design — bricks of two, three, and four-post pieces that neatly fit together. Modern edtech is a lot more like the modern Lego. There are wheels and rocket launchers and belts and all kinds of amazing pieces that work well with each other, but only when they are configured properly. A user cannot simply stick together different pieces and assume they will work harmoniously in creating an environment through which each student can be successful.
As the NGDLE paper states: “Despite the high percentages of LMS adoption, relatively few instructors use its more advanced features — just 41% of faculty surveyed report using the LMS ‘to promote interaction outside the classroom.'”
But this is what the next generation LMS is good at: being a central nervous system — or learning hub — through which a variety of learning activities and tools are used. This is also where the LMS needs to go: bringing together and making sense of all the amazing innovations happening around it. This is much harder to do, perhaps even impossible, if all the pieces involved are just bricks without anything to orchestrate them or to weave them together into a meaningful, personal experience for achieving well-defined learning outcomes.
Making a commitment to build easy, flexible, and smart technology
Working with colleges and universities to remove barriers to adopting new tools in the ecosystem
Standardizing the vetting of accessibility compliance (the Strategic Nonvisual Access Partner Program from the National Federation of the Blind is a great start)
Advancing standards for data exchange while protecting individual privacy
Building integrated components that work with the institutions using them — learning quickly about what is and is not working well and applying those lessons to the next generation of interoperability standards
Letting people use the tools they love [SIC] and providing more ways for nontechnical individuals (including students) to easily integrate new features into learning activities
My note: something just refused to be accepted at SCSU
Technologists are often very focused on the technology, but the reality is that the more deeply and closely we understand the pedagogy and the people in the institutions — students, faculty, instructional support staff, administrators — the better suited we are to actually making the tech work for them.
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Under the Hood of a Next Generation Digital Learning Environment in Progress
The challenge is that although 85 percent of faculty use a campus learning management system (LMS),1 a recent Blackboard report found that, out of 70,000 courses across 927 North American institutions, 53 percent of LMS usage was classified as supplemental(content-heavy, low interaction) and 24 percent as complementary (one-way communication via content/announcements/gradebook).2 Only 11 percent were characterized as social, 10 percent as evaluative (heavy use of assessment), and 2 percent as holistic (balanced use of all previous). Our FYE course required innovating beyond the supplemental course-level LMS to create a more holistic cohort-wide NGDLE in order to fully support the teaching, learning, and student success missions of the program.The key design goals for our NGDLE were to:
Create a common platform that could deliver a standard curriculum and achieve parity in all course sections using existing systems and tools and readily available content
Capture, store, and analyze any generated learner data to support learning assessment, continuous program improvement, and research
Develop reports and actionable analytics for administrators, advisors, instructors, and students
To say that someone is or is not intelligent has never been merely a comment on their mental faculties. It is always also a judgment on what they are permitted to do. Intelligence, in other words, is political.
The problem has taken an interesting 21st-century twist with the rise of Artificial Intelligence (AI).
The term ‘intelligence’ itself has never been popular with English-language philosophers. Nor does it have a direct translation into German or ancient Greek, two of the other great languages in the Western philosophical tradition. But that doesn’t mean philosophers weren’t interested in it. Indeed, they were obsessed with it, or more precisely a part of it: reason or rationality. The term ‘intelligence’ managed to eclipse its more old-fashioned relative in popular and political discourse only with the rise of the relatively new-fangled discipline of psychology, which claimed intelligence for itself.
Plato conclude, in The Republic, that the ideal ruler is ‘the philosopher king’, as only a philosopher can work out the proper order of things. This idea was revolutionary at the time. Athens had already experimented with democracy, the rule of the people – but to count as one of those ‘people’ you just had to be a male citizen, not necessarily intelligent. Elsewhere, the governing classes were made up of inherited elites (aristocracy), or by those who believed they had received divine instruction (theocracy), or simply by the strongest (tyranny).
Plato’s novel idea fell on the eager ears of the intellectuals, including those of his pupil Aristotle. Aristotle was always the more practical, taxonomic kind of thinker. He took the notion of the primacy of reason and used it to establish what he believed was a natural social hierarchy.
So at the dawn of Western philosophy, we have intelligence identified with the European, educated, male human. It becomes an argument for his right to dominate women, the lower classes, uncivilised peoples and non-human animals. While Plato argued for the supremacy of reason and placed it within a rather ungainly utopia, only one generation later, Aristotle presents the rule of the thinking man as obvious and natural.
The late Australian philosopher and conservationist Val Plumwood has argued that the giants of Greek philosophy set up a series of linked dualisms that continue to inform our thought. Opposing categories such as intelligent/stupid, rational/emotional and mind/body are linked, implicitly or explicitly, to others such as male/female, civilised/primitive, and human/animal. These dualisms aren’t value-neutral, but fall within a broader dualism, as Aristotle makes clear: that of dominant/subordinate or master/slave. Together, they make relationships of domination, such as patriarchy or slavery, appear to be part of the natural order of things.
Descartes rendered nature literally mindless, and so devoid of intrinsic value – which thereby legitimated the guilt-free oppression of other species.
For Kant, only reasoning creatures had moral standing. Rational beings were to be called ‘persons’ and were ‘ends in themselves’. Beings that were not rational, on the other hand, had ‘only a relative value as means, and are therefore called things’. We could do with them what we liked.
This line of thinking was extended to become a core part of the logic of colonialism. The argument ran like this: non-white peoples were less intelligent; they were therefore unqualified to rule over themselves and their lands. It was therefore perfectly legitimate – even a duty, ‘the white man’s burden’ – to destroy their cultures and take their territory.
The same logic was applied to women, who were considered too flighty and sentimental to enjoy the privileges afforded to the ‘rational man’.
Galton believe that intellectual ability was hereditary and could be enhanced through selective breeding. He decided to find a way to scientifically identify the most able members of society and encourage them to breed – prolifically, and with each other. The less intellectually capable should be discouraged from reproducing, or indeed prevented, for the sake of the species. Thus eugenics and the intelligence test were born together.
From David Hume to Friedrich Nietzsche, and Sigmund Freud through to postmodernism, there are plenty of philosophical traditions that challenge the notion that we’re as intelligent as we’d like to believe, and that intelligence is the highest virtue.
From 2001: A Space Odyssey to the Terminator films, writers have fantasised about machines rising up against us. Now we can see why. If we’re used to believing that the top spots in society should go to the brainiest, then of course we should expect to be made redundant by bigger-brained robots and sent to the bottom of the heap.
Natural stupidity, rather than artificial intelligence, remains the greatest risk.
1. They have self- awareness. Emotionally intelligent leaders understand their own emotions and know how to manage them. They don’t speak out of frustration or anger; they control their emotions and wait to speak up until their feelings have settled and they have processed their thoughts. They don’t react in the heat of the moment but wait to respond.
2. They respond to criticism and feedback. Every leader faces feedback, some of it negative. Emotionally intelligent leaders don’t become defensive or take it personally. They listen, process, and genuinely consider other points of view, and because they’re always looking to improve, they know how to accept sincere critiques.
3. They know how to generate self-confidence. Emotionally intelligent leaders share a healthy dose of confidence but never cross the line into arrogance. When they don’t understand something, they ask open-ended questions that aim to gather information, not challenge or argue. They know how to give and take in a way that generates confidence.
4. They know the importance of checking their ego.Leaders who have to demonstrate their own importance or value are not yet connected to true leadership or emotional intelligence. Those who are know how to speak and act out of concern of others. They don’t always have to be the center of attention, and they would never take credit for the work of others. Secure in their own abilities, they’re generous and gracious to others.
5. They know how to embody empathy. Leaders with emotional intelligence can put themselves in others’ shoes. They listen with genuine interest and attention and make it a point to understand, then give back in a way that benefits themselves and others. They know how to create win-win situations.
6. They know how to engage with empowerment. The best leaders–the ones with the highest EQs–make it their mission to believe in others and empower them to believe in themselves. Instead of focusing on themselves they know it’s the power of the people that makes leadership successful, so that’s where they focus their efforts.
Weiler, A. (2005). Information-Seeking Behavior in Generation Y Students: Motivation, Critical Thinking, and Learning Theory. Journal Of Academic Librarianship, 31(1), 46-53.
The research indicates that only a very small percentage of the general population prefer to learn by reading.
members of “Generation Y,” the generation born between 1980 and 1994.
The first model for study of information-seeking behavior in the general population was developed by James Krikelas in 1983. This model suggested that the steps of information seeking were as follows: (1) perceiving a need, (2) the search itself, (3) finding the information, and (4) using the information, which results in either satisfaction or dissatisfaction.
A second model developed by Carol C. Kuhlthau of Rutgers University stresses a process approach with an emphasis placed on cognitive skills; as they increase, so does information-seeking effectiveness. This model is one of the few that was developed based on actual research and not simply on practical experience.
Eisenberg and Berkowitz proposed a model based on the “Big Six Skills”—task definition, information seeking, implementation, use, synthesis, and evaluation. Their model is flexible and nonlinear in the same way that hypertext is, allowing for different areas and avenues to be explored out of sequence. In addition, seekers can go back to refine and reidentify the information need, implementing new strategies.
Critical thinking is a process that is widely acknowledged in the literature to be crucial to the learning process, to cognitive development, and to effective information seeking.
A more effective lesson on Internet information then, rather than specifically dwelling on “good” and “bad” Web sites, would be to present actual examples and to raise questions rather than giving answers, opening the student up to the next level intellectual development, “multiplicity.” Multiplicity is the ability to acknowledge that the world contains knowledge that the student cannot yet classify as right or wrong, knowledge which requires further study and thought (the so-called “gray area”).
Behavior Theory, first developed by B. F. Skinner in the 1950s, uses the concepts of “positive” and “negative” reinforcement to control behavior. This theory explains learning behavior very simply: Reward students who perform well, and punish students who do not.
The “Control Theory” of behavior was developed by William Glasser. The theory states that, rather than being a response to outside stimulus, behavior is determined by what a person wants or needs at any given time, and any given behavior is an attempt to address basic human needs such as love, freedom, power, etc.
The Myers–Briggs Personality Analysis test, developed by Isabel Myers and Katherine Briggs, was developed using Jung’s theory of personality types in an effort to determine what type any given individual is. The personality type then determines the learning style of a given individual.
Multiple Intelligences
Gardner’s theory relates more directly to intelligence rather than to personality. Gardner states that intelligence is comprised of a group of different abilities, which originate in the stages of development each person passes through as they grow to adulthood. He identifies seven such intelligences—verbal–linguistic, logical–mathematical, visual–spatial, body–kinesthetic, musical–rhythmic, interpersonal, and intrapersonal—but he suggests that there are probably more.
Information seeking is a highly subjective process, one which students approach with prior knowledge, strongly held opinions, and differing levels of cognitive development. From the research it is apparent that, aside from personal preconceptions, issues of time and levels of difficulty in obtaining information are usually of more concern to students than issues of accuracy. It is still unclear, however, whether this is because they are not concerned about the accuracy unless their instructor is, or because they are assuming most information is by nature accurate.
This presentation will begin on Thursday, July 28, 2016 at 11:00 AM Pacific Daylight Time.
Audience members may arrive 15 minutes in advance of this time.
Public sector data centers have unprecedented challenges and opportunities, and tomorrow’s demands remain uncertain. We know stakeholders, students, and citizens are all demanding more (e.g. modern services, innovative applications, cost-cutting efficiency), putting even greater strain on an organization’s infrastructure and expertise. It’s up to IT to make it all happen, and there’s simply no “one size fits all” solution to optimize data center efficiency. But can hyperconvergence help?
Dr. Scott Barry Kaufman When he was young, Kaufman had central auditory processing disorder, which made it hard for him to process verbal information in real time. He was asked to repeat third grade because he was considered a “slow” learner.
Kaufman thinks the traditional IQ test does a good job of measuring general cognitive ability, but says it misses all the ways that ability interacts with engagement. An individual’s goals within the learning classroom and excitement about a topic affect how he or she pursues learning, none of which is captured on IQ tests. Worse, those tests are often used to filter people in or out of special programs.
FOUR PRACTICES TO CULTIVATE CHILDREN’S CREATIVITY
allowing more solitary reflective time in kids’ schedules. Whether it’s the constant demands on attention at school or in after-school activities, there often isn’t enough time in a child’s day when she can switch off the executive functioning network and tap into the imagination network.
“We support obsessive passion, but not harmonious passion,” Kaufman said. He defines harmonious passion as a core part of people’s identity that makes them feel good about themselves. Harmonious passion is characterized by flexible engagement, where a child can abandon the pursuit if it isn’t paying dividends.
give young kids a diverse set of experiences in order to increase the chances of inspiration. “Lots of things add meaning to our lives,” he said.
educators, parents, and policymakers need to reset their mindsets around student ability. “Kids who think differently are not appreciated in our school system at all
it’s even worth measuring imagination, but Kaufman believes that measurement is important so researchers can see how changing behavior affects creative achievement. But he hopes the measurements are never used as another sorting mechanism.
My note: Kaufman makes a new call for an old trend. The futility of testing is raging across the United States K12 system. Higher education is turned into the last several decades (similarly to the United States health care system) into a cash cow. When the goal is profit, then good education goes down the drain. Cultivating children’s creativity cannot happen, when the foremost goals to make more money, which inevitably entails spending less cash (not only on teacher’s salaries).
bibliography on the impact of music on intellectual development.
Does music help learn better? get smarter? advance in life?
keywords: music, education, intelligence.
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Costa-Giomi, E. (2015). The Long-Term Effects of Childhood Music Instruction on Intelligence and General Cognitive Abilities. Update: Applications Of Research In Music Education, 33(2), 20-26.
Pelayo, J. M. G., & Galang, E. (2013). Social and Emotional Dynamics of College Students with Musical Intelligence and Musical Training: A Multiple Case Study. Retrieved from http://eric.ed.gov/?id=ED542664
Neves, V., Tarbet, V. (2007). Instrumental Music as Content Literacy Education: An Instructional Framework Based on the Continuous Improvement Process. Retrieved from http://eric.ed.gov/?id=ED499123
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Juchniewicz, J. (2010). The Influence of Social Intelligence on Effective Music Teaching. Journal Of Research In Music Education, 58(3), 276-293.
Silvia, P. J., Thomas, K. S., Nusbaum, E. C., Beaty, R. E., & Hodges, D. A. (2016). How Does Music Training Predict Cognitive Abilities? A Bifactor Approach to Musical Expertise and Intelligence. Psychology Of Aesthetics, Creativity, And The Arts, doi:10.1037/aca0000058
Rickard, N. S., Bambrick, C. J., & Gill, A. (2012). Absence of Widespread Psychosocial and Cognitive Effects of School-Based Music Instruction in 10-13-Year-Old Students. International Journal Of Music Education, 30(1), 57-78.
Munsey, C. (2006). Music lessons may boost IQ and grades. American Psychological Association, 37(6), 13.
Schellenberg, E. G. (2011). Music lessons, emotional intelligence, and IQ. Music Perception, 29(2), 185-194. doi:10.1525/mp.2011.29.2.185
Kaviani, H., Mirbaha, H., Pournaseh, M., & Sagan, O. (2014). Can music lessons increase the performance of preschool children in IQ tests?. Cognitive Processing, 15(1), 77-84. doi:10.1007/s10339-013-0574-0
Degé, F., Kubicek, C., & Schwarzer, G. (2011). Music lessons and intelligence: A relation mediated by executive functions. Music Perception, 29(2), 195-201. doi:10.1525/mp.2011.29.2.195
Sharpe, N. N. (2014). The relationship between music instruction and academic achievement in mathematics. Dissertation Abstracts International Section A, 75.
keywords: music, education, multimedia.
Crappell, C., Jacklin, B., & Pratt, C. (2015). Using Multimedia To Enhance Lessons And Recitals. American Music Teacher, 64(6), 10-13.
le Roux, I., & Potgieter, H. M. (1998). A Multimedia Approach to Music Education in South Africa.
Ho, W.-C. (2007). Music Students’ Perception of the Use of Multi-Media Technology at the Graduate Level in Hong Kong Higher Education. Asia Pacific Education Review, 8(1), 12–26.
Ho, W. (. (2009). The role of multimedia technology in a Hong Kong higher education music program. Visions Of Research In Music Education, 1337.
Bolden, B. (2013). Learner-Created Podcasts: Students’ Stories with Music. Music Educators Journal, 100(1), 75-80.
Pao-Ta, Y., Yen-Shou, L., Hung-Hsu, T., & Yuan-Hou, C. (2010). Using a Multimodal Learning System to Support Music Instruction. Journal Of Educational Technology & Society, 13(3), 151-162.
Malone, K. (2007). The bubble‐wrap generation: children growing up in walled gardens. Environmental Education Research, 13(4), 513–527. http://doi.org/10.1080/13504620701581612http://www.tandfonline.com/doi/abs/10.1080/13504620701581612
some of the changes in childhood environmental behaviours I explore children and parent relationships, in particular, the phenomena of ‘bubble‐wrapping’ children to appease the anxieties of some middle class parents.
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Lynch, K., & Hogan, J. (2012). How Irish Political Parties are Using Social Networking Sites to Reach Generation Z: an Insight into a New Online Social Network in a Small Democracy. Irish Communication Review, 13. Retrieved from http://arrow.dit.ie/cgi/viewcontent.cgi?article=1124&context=buschmarart
Parker, K., Czech, D., Burdette, T., Stewart, J., Biber, D., Easton, L., … McDaniel, T. (2012). The Preferred Coaching Styles of Generation Z Athletes: A Qualitative Study. Journal of Coaching Education, 5(2), 5–97.
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