Learning from the living class room

 

Legal Tech Broke Investment Record in 2019 as Sector Matures — from biglawbusiness.com by Sam Skolnik

Excerpts:

  • Investments in legal tech soared past $1 billion in 2019
  • Key legal tech conference boasted record attendance

Legal technology deals and investments stayed on a fast track in 2019 as the sector becomes increasingly relevant to how Big Law firms and corporate legal divisions operate. Legal tech investments flew past the $1 billion mark by the end of the third quarter. It hit that mark for the first time the year before.

Also see:

“E-discovery sits at the intersection of two industries not known for diversity: legal and high-tech. Despite what can feel like major wins, statistics paint a bleak picture—most U.S. lawyers are white, managing partners are primarily male, and only 2% of partners in major firms are black; leadership at e-discovery companies has historically reflected this demographic. The next decade will see a major shift in focus for leadership and talent development at e-discovery providers as we join law firms and corporate legal departments in putting our money where our mouths are and deliver recruitment and retention programs that fight discrimination and actively recruit, retain, and promote women, minority, and underserved talent.” 

Sarah Brown, director of marketing, Inventus

 

On the care and handling of student ratings — from rtalbert.org by Robert Talbert
Student evaluations of teaching are not true evaluations. We should call them what they are — perception data — and use them accordingly.

Excerpts:

How to handle student perception data as a department

  • Never use student perception data as the sole, or even the main source of information about a faculty member’s teaching. Teaching, as I said, is a wickedly complex problem. It simply cannot be reduced to a set of data, or in some cases to a single number. To get an accurate picture of faculty teaching, you need more than just student perceptions. Use faculty self-evaluations, peer evaluations via class visits, faculty-initiated data collected through pre- and post-testing… Insist on using multiple sources of data for faculty evaluations and make it easy to include them.
  • Never compare one faculty member to another based on student perception data.
  • Look at trends over time and how faculty respond to their data.

 

 

Companies Say Blockchain Could Have Prevented College Admissions Scandal — from edsurge.com by Jeff Young

Excerpt:

One of the most eye-catching aspects of the recent Varsity Blues admissions scandal was that fake athletic profiles were created for students to help them get into highly-selective colleges through so-called “side doors.”

Now, several companies that sell student-record systems based on blockchain—the technology behind Bitcoin—are pitching their products as a way to prevent that kind of fraudulent record tampering in the future.

 

Stepping Back from the Cliff: Facing New Realities of Changing Student Demographics — from evoLLLution.com by Jim Shaeffer
Most universities that plan to stick to the status quo and serve exclusively traditional learners are facing a cliff. CE divisions can help their institutions avoid a potential drop, but only if they’re empowered.

Excerpt:

Demographics of students enrolling at colleges and universities are evolving. And students’ expectations are evolving as well. As the numbers of 18-22 year olds fresh out of high school drop, the recruitment of non-traditional students is becoming more important than ever. In this interview, James Shaeffer discusses the role continuing education (CE) departments can play as drivers of innovation and reflects on how CE leaders can help their main campus colleagues embrace transformational change.

Addendum on 1/4/20:

 

7 Artificial Intelligence Trends to Watch in 2020 — from interestingengineering.com by Christopher McFadden

Excerpts:

Per this article, the following trends were listed:

  1. Computer Graphics will greatly benefit from AI
  2. Deepfakes will only get better, er, worse
  3. Predictive text should get better and better
  4. Ethics will become more important as time goes by
  5. Quantum computing will supercharge AI
  6. Facial recognition will appear in more places
  7. AI will help in the optimization of production pipelines

Also, this article listed several more trends:

According to sources like The Next Web, some of the main AI trends for 2020 include:

  • The use of AI to make healthcare more accurate and less costly
  • Greater attention paid to explainability and trust
  • AI becoming less data-hungry
  • Improved accuracy and efficiency of neural networks
  • Automated AI development
  • Expanded use of AI in manufacturing
  • Geopolitical implications for the uses of AI

Artificial Intelligence offers great potential and great risks for humans in the future. While still in its infancy, it is already being employed in some interesting ways.

According to sources like Forbes, some of the next “big things” in technology include, but are not limited to:

  • Blockchain
  • Blockchain As A Service
  • AI-Led Automation
  • Machine Learning
  • Enterprise Content Management
  • AI For The Back Office
  • Quantum Computing AI Applications
  • Mainstreamed IoT

Also see:

Artificial intelligence predictions for 2020: 16 experts have their say — from verdict.co.uk by Ellen Daniel

Excerpts:

  • Organisations will build in processes and policies to prevent and address potential biases in AI
  • Deepfakes will become a serious threat to corporations
  • Candidate (and employee) care in the world of artificial intelligence
  • AI will augment humans, not replace them
  • Greater demand for AI understanding
  • Ramp up in autonomous vehicles
  • To fully take advantage of AI technologies, you’ll need to retrain your entire organisation
  • Voice technologies will infiltrate the office
  • IT will run itself while data acquires its own DNA
  • The ethics of AI
  • Health data and AI
  • AI to become an intrinsic part of robotic process automation (RPA)
  • BERT will open up a whole new world of deep learning use cases

The hottest trend in the industry right now is in Natural Language Processing (NLP). Over the past year, a new method called BERT (Bidirectional Encoder Representations from Transformers) has been developed for designing neural networks that work with text. Now, we suddenly have models that will understand the semantic meaning of what’s in text, going beyond the basics. This creates a lot more opportunity for deep learning to be used more widely.

 

 

Get Smart About Going Online: Choosing the Right Model to Deliver Digital Programming — from evolllution.com by Charles Kilfoye
A veteran online educator looks at the benefits and pitfalls for each of the three main ways to launch an online program.

Excerpt:

Online learning is making headlines again with big players such as University of Massachusetts and California Community College Online launching high profile online initiatives recently. Some would argue that if you haven’t made it in online education already, you’ve missed your opportunity.

However, my sense is it’s never too late. You just have to be smart about it. It all boils down to asking yourself the basic problem-solving questions of Why, What and How to determine if online education is right for your institution. To illustrate my point, I will briefly discuss major considerations you should make when exploring an online strategy and I will examine the pros and cons of the three most common models of delivering online programs in higher education today.

Be aware that differentiated pricing may indicate to prospective students that one format is more valuable or better than another. My personal opinion is that a degree earned online should be considered the same degree as one earned on-ground. It is the same program, same faculty, same admissions requirements, same relevance and rigor, so why not the same cost?

 

From DSC:
Regarding the topic of pricing, it would be my hope that we could offer online-based programs at significantly discounted prices. This is why I think it will be the larger higher education providers that ultimately win out — or a brand new player in the field that uses a next gen learning platform along with a different business model (see below article) — as they can spread their development costs over a great number of students/courses/program offerings.

If the current players in higher ed don’t find a way to do this (and some players have already figured this out and are working on delivering it), powerful alternatives will develop — especially as the public’s perspective on the value of higher education continues to decline.

 

Learning from the living class room

I’d also like to hear Charles’ thoughts about pricing after reading Brandon’s article below:

If it’s more expensive, it must be better. That, of course, has been the prevailing wisdom among parents and students when it comes to college. But that wisdom has now been exposed as an utter myth according to a new study published in The Journal of Consumer Affairs. It turns out the cost of a college does not predict higher alumni ratings about the quality of their education. In fact, the opposite is true: total cost of attendance predicts lower ratings.

Quality matters. Price does not. Quality and price are not the same things. And this all has enormous implications for the industry and its consumers.

 

 

The Secret to Student Success? Teach Them How to Learn. — from edsurge.com by Patrice Bain

Excerpt:

Abby’s story is hardly unique. I often teach students who react with surprise when they do well in my class. “But I’ve never done well in history,” they say. This is almost always followed by a common, heartbreaking confession. “I’m not smart.” Every time I hear this, I am faced with the gut-wrenching realization that the student has internalized failure by age eleven. Yet every year I see these same students soar and complete the class with high grades.

This raises two questions for me: How can we turn eleven-year-olds who have internalized failure into students like Abby who retain information for years? And how can we teach that poor grades don’t indicate failure, but rather that we haven’t found the correct learning strategy?

Enter research.

 

Merry Christmas!

 

From DSC:
To those who celebrate it, Merry Christmas to you and to yours!

 

 

Art-filled journeys into the future — methods of futures education for children in lower stage comprehensive school — from kultus.fi by Ilpo Rybatzki and Otto Tähkäpää

Art-filled futures education

 

See this PDF file which contains the following excerpt:

In art, futures literacy plays a significant role. Art has the ability to point elsewhere; to fool and mess around with things and shake up conventions without needing to achieve measurable benefits (Varto, 2008). Art ensures a solid background for imagining alternative worlds. It is important to support a permissive atmosphere that supports experimentation! From the perspective of art pedagogy, activities focus on the idea of art experience as meeting place (Pääjoki, 2004) where people can see themselves in a new light beside another person’s thoughts and imagination. Strengthening futures literacy means supporting transformative learning that aims for change. Through this type of learning, we can question norms, roles, identities and the concept of what is ‘normal’ (Lehtonen et al., 2018).

When discussing the future, we are always discussing values: what kind of future is desirable for any one person? Artistic activity can produce materials through which human meanings can be communicated from one person to another and questions about values in life can be discussed (Varto, 2008; Valkeapää, 2012). Encounters create opportunities for dialogue and enriching one’s perspectives. Important aspects include creating safe settings, the individual expression of the participants, the courage to open up and thrown oneself into the centre of an experience, as well as the courage to question or even completely let go of presumptions. In the age of the environmental crisis, art has a critical role in all of society. We cannot solve difficult problems using the same kind of thinking that created the problems in the first place.

 

Active Learning on the Uptick?— from LinkedIn.com by Carrie O’Donnell

Excerpt:

The evidence is overwhelming that employing active learning strategies leads to deeper learning, increased retention and higher performance. In fact, the EDCAUSE Horizon Report: 2019 Higher Education Edition states 73 percent of universities surveyed indicate active learning classrooms are in the planning process or being implemented in 2020.

Active learning is an instructional approach that puts the student in the center of the learning. This teaching methodology actively engages the learner and is a contrast with the traditional lecture-based approaches where the instructor does most of the talking and students are passive. Some of the many strategies that instructors use to promote active learning include group discussions, peer instruction, problem-solving, case studies, role playing, journal writing and structured learning groups.

Several trends we’ve seen on campuses across the country bode well for active learning:

 

Using a Research-Based Approach – It’s Up to Us  — from wcetfrontiers.org by Andria Schwegler

Excerpt (emphasis DSC):

This discrepancy suggests that perceptions are heavily influenced by idiosyncratic, personal experiences instead of by research.

Nearly a decade ago, a meta-analysis of studies comparing student learning in online, blended, and face-to-face contexts revealed no significant differences in learning across course modality (Means, Toyama, Murphy, Bakia, & Jones, 2010). Today, a growing body of research corroborates no significant differences exist (National Research Center for Distance Education and Technological Advancements, 2019). That some faculty attitudes are not aligned with this information suggests that concerns regarding course delivery are confounded with beliefs about course modality. Leveraging existing research on teaching and learning and conducting new research to address gaps can clarify how to address concerns with course delivery to facilitate students’ ability to meet learning outcomes instead of assuming course modality is the problem.

 

 

White Bread Mold Experiment Teaches the Importance of Washing Hands — from interestingengineering.com by Donna Fuscaldo
An elementary school teacher used an experiment with white bread to show how important is to wash your hands.

Excerpts:

Flu season is around the corner and Jaralee Metcalf, a behavioral specialist who works with autistic students in elementary school wanted to teach the importance of washing hands to stave off the influenza virus.

“As somebody who is sick and tired of being sick and tired of being sick and tired. Wash your hands! Remind your kids to wash their hands! And hand sanitizer is not an alternative to washing hands!! At all!.” wrote Metcalf in his Facebook post.

 

Kids! Wash your hands!

 

Don’t trust AI until we build systems that earn trust — from economist.com
Progress in artificial intelligence belies a lack of transparency that is vital for its adoption, says Gary Marcus, coauthor of “Rebooting AI”

Excerpts:

Mr Marcus argues that it would be foolish of society to put too much stock in today’s AI techniques since they are so prone to failures and lack the transparency that researchers need to understand how algorithms reached their conclusions.

As part of The Economist’s Open Future initiative, we asked Mr Marcus about why AI can’t do more, how to regulate it and what teenagers should study to remain relevant in the workplace of the future.

Trustworthy AI has to start with good engineering practices, mandated by laws and industry standards, both of which are currently largely absent. Too much of AI thus far has consisted of short-term solutions, code that gets a system to work immediately, without a critical layer of engineering guarantees that are often taken for granted in other field. The kinds of stress tests that are standard in the development of an automobile (such as crash tests and climate challenges), for example, are rarely seen in AI. AI could learn a lot from how other engineers do business.

The assumption in AI has generally been that if it works often enough to be useful, then that’s good enough, but that casual attitude is not appropriate when the stakes are high. It’s fine if autotagging people in photos turns out to be only 90 percent reliable—if it is just about personal photos that people are posting to Instagram—but it better be much more reliable when the police start using it to find suspects in surveillance photos.

 
 

Looking for something?

Use the form below to search the site:

Still not finding what you're looking for? Drop a comment on a post or contact us so we can take care of it!

© 2020 | Daniel Christian