Why Learner Centered Education Is The Key To Meaningful School Improvement

Effective educators have long known that one-size-fits all approaches to teaching and learning are insufficient. Through extraordinary effort, they have figured out ways to differentiate and personalize learning for their students. They have done so despite an industrial-era education paradigm that makes it very difficult to do so. Over time, some of their efforts were named, systematized, and scaled.

Today, building on these approaches, some believe (count us among them) that a shift to an entirely new education paradigm is within reach. Harnessing new technologies, aided by advancements in transportation and communication, and required in order to adequately respond to deep and disruptive social, economic, environmental, and political forces, we envision a fundamental shift in how learners experience their education.

Specifically, we envision moving from a school-centric, industrial-age model akin to factories and assembly lines, to a learner-centric, networked-age model characterized by lateral connections and flexibility. In short, we envision learner-centered education. But what does the movement towards learner-centered education mean for the many methods for designing learning and differentiating support to students developed in recent decades?

In this piece, we identify some of the most-broadly adopted methods developed by educators to differentiate support, improve learning design, and meet the individual needs of learners. They include Response to Intervention (RTI), Positive-Behavior Intervention Systems (PBIS), Universal Design for Learning (UDL), and Multi-Tiered System of Support (MTSS). Then, we seek to compare learner-centered education to these approaches, exploring the implications for each. Ultimately, we will make the following arguments:

  1. Learner-centered education is about a paradigm shift, not a specific methodology.
  2. Learner-centered education requires learning design that is flexible and adaptive, similar to or expanding upon the principles of UDL.
  3. Learner-centered education may include specific methodologies for differentiating support (e.g. RtI or PBIS), but it is more likely to extend and/or replace them.
  4. Learner-centered education is additive to and inherently strengthens existing systems-level approaches such as MTSS.
  5. Learner-centered education is fundamentally adaptive and outcomes-focused (rather than technical and process-focused).

All of the approaches we name above recognize the same problem. The current industrial model for teaching and learning was designed based on an assembly line metaphor, expecting students to move through school in the same amount of time with more or less the same amount of support regardless of where they enter, unique challenges they may be facing, or strengths they may bring.

Within this rigid system, educators have sought ways to differentiate support. Over time, some of the techniques educators developed to provide each student the support they need have been built upon to create school and systems-level approaches. Tiered systems of support and intervention such as Response to Intervention (RtI) and Positive Behavioral Interventions and Supports (PBIS) were developed to introduce achievable levels of differentiated support (e.g. 3 tiers) within the constraints of the industrial paradigm.

  • Response to Intervention is a multi-tier approach and framework for instruction that screens all students for learning needs, and then provides progressive levels of intervention to students on an as needed basis. Interventions scale-up in the level of intensity such as supplemental instruction within the large group (typically Tier 1), targeted small group instruction (Tier 2), and individualized, intensive instruction aimed at skill deficits (Tier 3), though tier definitions and strategies differ by school. In practice, RtI models may call for individualized interventions (problem-solving models) or preselected interventions (standard protocol models). The three essential components are tired instruction and intervention, ongoing student assessment, and family involvement. RtI originated from the goal of proactively identifying and providing special education interventions to students before they fall too far behind.
  • Positive Behavioral Interventions and Supports is also a three-tier approach and framework but focused on student behavior and social-emotional development. The goal of PBIS is to proactively promote positive behavior. Similar to RtI, PBIS typically scales interventions starting with universal and proactive routines and support provided to the full classroom or school (Tier 1), then targeted behavior support (Tier 2), and lastly individualized, intensive support (Tier 3).

Recent innovations with tiered systems of support by organizations such as Turnaround for Children expand these models to include an understanding of trauma and adversity as well as taking into account how to adjust for hybrid and remote learning options.

These systems were developed based on a recognition that all students are capable of reaching similar outcomes, but require different amounts of time and support to get there. They were helpful steps towards providing each student with different amounts of time, support, and attention based on their needs. They have positively impacted tens of thousands of students in achieving desired standards however this often comes at the cost of removing students from their peers and narrowing the curriculum and will continue in such a manner as long as the traditional paradigm exists.

At the same time that these methodologies proliferated for differentiating and targeting support by pulling students out, complementary methodologies were developed for designing learning in a way that was flexible enough to meet the needs of learners with different motivations, interests, (dis)abilities, and needs. One example is Universal Design for Learning (UDL). UDL is an approach and framework for designing instruction and learning environments that are accessible to all students.

UDL emphasizes providing flexibility in how students access content (e.g., visual, audio, hands-on) engage with it, and demonstrate knowledge or mastery. The goal is to remove barriers to learning. UDL is rooted in the premise that while accommodations and flexibility are necessary to ensure learning accessibility for some individuals, they in fact benefit all individuals (sometimes in unforeseen ways) and therefore should always be in play.

More recently, attempts have been made to create overarching systems that build on and integrate these into an overall coherent framework for systems change. One example that has gained widespread interest and adoption is Multi-Tiered System of Support (MTSS). MTSS is a framework for meeting the academic, social, and emotional needs of students. It builds upon and may include data-driven, tiered intervention strategies such as RtI and PBIS as part of the approach.

However whereas RtI primarily focuses on academic learning and PBIS focuses on behavior and social/emotional development, MTSS aims to bring a more comprehensive lens and integrated approach to meeting the needs of learners. Moreover, MTSS is often described as a system-level approach with implications for aligned leadership, resource allocation, professional development and more.

This now brings us to the term that is at the center of our inquiry: learner-centered education. Like MTSS, learner-centered education has been growing in popularity. Learner-centered attempts to define an alternative to the industrial-era education model itself. The graphic below, borrowed from Education Reimagined, makes this clear.

Learner-centered education is about a paradigm shift, not a specific intervention methodology. It pushes education leaders to critically consider the purpose of school and to re-envision how the complete education ecosystem prepares students for the future. Learner-centered education demands that we move away from the traditional industrial model towards a transformative one that designs learning in response to the diverse needs of students.

This future-oriented paradigm requires a new set of student outcomes and aligned success metrics as part of its vision, whereas most of the above can function within the traditional set of outcome metrics. Lastly, learner-centered education goes beyond schools as the unit of change. Instead, it looks at the needs and goals of the individual learner and macroscopically at opportunities for learning within an education ecosystem.

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Source: Why Learner-Centered Education is the Key to Meaningful School Improvement | Getting Smart

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What AI Practitioners Could Learn From A 1989 MIT Dissertation

Child at laptop

More than thirty years ago, Fred Davis developed the Technology Acceptance Model (TAM) as part of his dissertation at MIT. It’s one of the most widely cited papers in the field of technology acceptance (a.k.a. adoption). Since 1989, it’s spawned an entire field of research that extends and adds to it. What does TAM convey and how might today’s AI benefit from it?

TAM is an intuitive framework. It feels obvious yet powerful and has withstood the test of time. Davis started with a premise so simple that it’s easy to take it for granted: A person will only try, use and ultimately adopt technology if they are willing to exert some effort. And what could motivate users to expend this effort?

He outlined several variables that could motivate users, and many researchers have added to his list over the years, but these two variables are the ones that were most important: 1. Does it look easy to use? 2. Will it be useful? If the learning curve doesn’t look too steep and there’s something in it for them, a user will be inclined to adopt. Many researchers have added to this foundation over the years. For example, we’ve learned that a user’s intention can also be influenced by subjective norms.

We’re motivated to adopt new tech at work when senior leadership thinks it’s important. Perceived usefulness can also be influenced by image, as in, “Does adopting this tech make me look good?” And lastly, usefulness is high if relevance to the job is high.

TAM can be a powerful concept for an AI practitioner. It should be front-of-mind when embedding AI in an existing tool or process and when developing an AI-first product, as in, one that’s been designed with AI at the center of its functionality from the start. (Think Netflix.) Furthermore, AI can be used to drive adoption by levering TAM principles that increase user motivation.

Making AI more adoptable

With the proliferation of AI in sales organizations, AI algorithms are increasingly embedded in tools and processes leveraged by sales representatives and sales managers. Adding decision engines to assist sales representatives is becoming increasingly common. A sales organization may embed models that help determine a customer’s propensity to buy or churn, recommend next best actions or communications and more. The problem is, many of these initiatives don’t work because of a lack of adoption.

TAM can help us design these initiatives more carefully, so that we maximize the chances of acceptance. For example, if these models surface recommendations and results that fit seamlessly into reps’ tools and processes, they would perceive them as easy to use.

And if the models make recommendations that help a sales person land a new customer, prevent one from leaving and help them upsell or cross-sell when appropriate, reps would perceive them as useful. In other words, if the AI meets employees where they are and offers timely, beneficial support, adoption becomes a no-brainer.

We also see many new products and services that are AI first. For these solutions, if perceived ease of use or perceived usefulness are not high, there would be no adoption. Consider a bank implementing a tech-enabled solution like mobile check deposits. This service depends on customers having a trouble-free experience.

The Newark airport’s global entry system uses facial recognition to scan international flyers’ faces. It’s voluntary, and the experience is fantastic. The kiosk recognizes my face, and a ticket is printed for me to take to the immigration officer. Personally, I find this AI-first process a better experience than the previous system that depended on fingerprints, and now I will always opt for the new one.

Using AI to drive adoption

And perhaps counter intuitively, what if AI was used to drive elements of TAM within existing technology? Can AI impact perceived usefulness? Can AI impact perceived ease of use? Consider CRM. It has been improved and refined over the years and is in use within most sales organizations, yet the level of dissatisfaction with CRM is high and adoption remains a challenge.

How can AI help? A machine learning algorithm that uses location services can recommend that a rep visit a nearby customer, increasing the perceived usefulness of their CRM solution. Intelligent process automation can also help reps see relevant information from a contracting database as information on renewals are being entered. Bots can engage customers on behalf of the representatives to serve up more qualified leads. The possibilities are numerous. All these AI features are designed to ensure that CRM lives up to its promise as a source of value to the sales representative.

Outside of sales, consider patients. In the past few years, many new technologies have been introduced to help diabetics. Adoption of this technology is critical to self-management, and self-management is critical to treating the disease. For any new technology in this space, patients need to see that it’s useful to them.

AI can play a role in gathering information such as glucose levels, activity and food intake and make recommendations on insulin dosing or caloric intake. Such information gathering could go a long way toward reducing the fatigue that diabetics feel while they make countless health and nutrition decisions throughout the day.

AI’s algorithmic nature makes it easy to forget that it’s another technology and that it can aid technology. Its novelty can convince us that everything about it is new. TAM holds up because it’s intuitive, straightforward and proven. While we boldly innovate a path forward in the world of AI, shed convention and think like a disruptor, let’s keep an eye on our history too. There’s some useful stuff in there.

Follow me on Twitter or LinkedIn. Check out my website.

Arun provides strategy and advisory services, helping clients build their analytics capabilities and leverage their data and analytics for greater commercial effectiveness. He currently works with clients on a broad range of analytics needs that span multiple industries, including technology, telecommunications, financial services, travel and transportation and healthcare. His areas of focus are AI adoption and ethics, as well as analytics organization design, capability building, AI explainability and process optimization.

Source: What AI Practitioners Could Learn From A 1989 MIT Dissertation

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Workshops List (AAAI-21) | AAAI 2021 Conference
aaai.org – February 9
[…] (AAAI-21) builds on the success of last year’s AAAI PPAI to provide a platform for researchers, AI practitioners, and policymakers to discuss technical and societal issues and present solutions related to privacy […]
1
What is Responsible AI?. “It’s not artificial intelligence I’m… | by Yash Lara | Analytics Vidhya | Jan, 2021
medium.com – February 8
[…] But there is something called as ‘Ethical AI Practitioners’ […]
1
Playing games, gamification, and the gulf between them
[…] Today, AI practitioners have a rich inventory of hundreds of games, with a myriad of variations […]
N/A
What I Learned From Attending TWIMLcon 2021 —
jameskle.com – February 8
[…] There was a wide range of both technical and case-study sessions curated for ML/AI practitioners […]
0
A Startup’s Journey Towards Artificial Intelligence With AI101 | by Jojo Anonuevo | The Startup | Jan, 2021
medium.com – February 7
[…] I highly recommend these for those who want to be AI practitioners and those tasked to build a team to help them understand the skills needed to recruit and interview […]
0
“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI –
research.google – February 6
[…] In this paper, we report on data practices in high-stakes AI, from interviews with 53 AI practitioners in India, East and West African countries, and USA […]
4
AI is Only Going to Get Smarter. How people are already “cyborgs”… | by Michel Kana, Ph.D | Feb, 2021
michel-kana.medium.com – February 5
[…] a critical mass of AI practitioners […]
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Home
mesumrazahemani.wixsite.com – February 5
Karachi.AI is a premier community of Applied AI practitioners. Founded in 2017, the community has staggering 4000+ members from wide variety of domains.   The unique diversity embodies our vision to educate masses towards Artificial Intelligence and upcoming Machine First era, where Jobs of the future will change drastically. ​ Our vision carries around three pillars of execution: 1. Awareness 2. Engagement 3. Empowerment
N/A
2021 will be the year of MLOps
[…] The implementation of MLOps and closer collaboration of software developers and AI practitioners will bring a maturity to the market in 2021 […]
N/A
What’s with the “Cambrian-AI” theme?
cambrian-ai.com – February 4
[…] Hence, I created Cambrian AI Research, where investors, media, and AI practitioners can keep up with the latest AI innovations, and communicate their plans and innovations, with 100’s […]
0
HPE data science experts help customers navigate the new AI accelerator landscape
community.hpe.com – February 3
[…] AI practitioners want competitive alternatives to CPUs and GPUs […]
N/A
Hands-on Guide to AI Habitat: A Platform For Embodied AI Research –
analyticsindiamag.com – February 3
[…] Unlike the strictly algorithm-led approach of such traditional AI practices, embodied AI practitioners try to first understand the working of biological systems, then develop general principles o […]
1
Projects To Know – Issue #67
eepurl.com – February 3
[…] that occur due to data quality issues arising from technical debt) through interviews with 53 AI practitioners across the world. They find that AI practitioners are not properly incentivized to address data quality problems – instead, they are motivated t […]
N/A
AI Strategies and Roadmap: Systems Engineering Approach to AI Development and Deployment | Professional Education
professional.mit.edu – February 3
[…] Communicate your value proposition to stakeholders Receive practical experience from the “voice of AI practitioners” across various industries Formulate a strategic vision and development plan focused on AI products […]
N/A
Designing Ethics Frameworks for AI with Dr. Willie Costello Tickets, Thu, Feb 11, 2021 at 4:00 PM
[…] about AI ethics education, and has designed original courses, workshops, and frameworks to help AI practitioners learn how to think critically about the ethics of their work […]
N/A
Artificial Intelligence: Week #4 | 2021
sixgill.com – February 1
[…] Notable Research Papers: Connect with AI practitioners of all levels Stay connected with artificial intelligence and machine learning practitioners around […]
1
RCV at CVPR 2021
sites.google.com – February 1
[…] policy implications to Consider while constructing representative datasets and training models by AI practitioners […]
N/A
Project Manager — Village Data Analytics (VIDA) | by Nabin Raj Gaihre | Work with TFE Energy | Feb, 2021
medium.com – February 1
[…] The team includes both top-notch AI practitioners, as well as frontier market entrepreneurs with backgrounds in engineering, renewable energy, an […]
N/A
Software engineering intern / Working student / Master thesis | by Nabin Raj Gaihre | Work with TFE Energy | Feb, 2021
medium.com – February 1
[…] The team includes both top-notch AI practitioners, as well as frontier market entrepreneurs with backgrounds in engineering, renewable energy, an […]
N/A
[Proposal] Ocean Academy: Project Oyster �� – Round 2
port.oceanprotocol.com – February 1
[…] series targets business people and organizations dealing with data, data architects, scientists and AI practitioners […]
0
Environmental data justice
http://www.thelancet.com – February 1
[…] there is growing pressure from a community of researchers, activists, and artificial intelligence (AI) practitioners to make Environmental Data Justice (EDJ) a top priority […] One overarching and fundamental concern in the data justice field is the ability of data and AI practitioners to decide what and whose knowledge and data is counted as valid, and what goes ignored an […] As industry and governments increasingly look to AI practitioners and researchers for the solutions to important societal issues, understanding the systemic an […]

Four Education Blogs to Explore this Back-to-School Season — Discover

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Whether they tackle tough topics or inspire better learning habits, these sites prompt readers to think, question, and engage.

via Four Education Blogs to Explore this Back-to-School Season — Discover

 

 

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