7 Harmful Racial Discourse Practices to Avoid

Backgrounds

This resource identifies and describes seven harmful racial discourse practices that are found not only in mainstream media, but also more broadly throughout our society. They are used by public officials and their staff, by lawyers and judges, and by advocates of various political backgrounds, by cultural and entertainment figures, and by others with power and influence over public perception and behavior.

We provide definitions for the practices and describe the specific negative effects these practices have on racial discourse. Each practice discussion also contains an example or two of its use from recent events—some carried out by news media and others carried out by public officials and their staffs, by lawyers and judges, and by advocates of various political backgrounds, by cultural and entertainment figures, and by others with power and influence over public perception and behavior.

Results

Taken as a whole, we argue that:

  • When these harmful racial discourse practices succeed, either individually or acting collectively within a single narrative, they stifle the general public’s understanding of systemic racism.
  • The seven harmful racial discourse practices reinforce the common misconception that racism is simply a problem of rare, isolated, individual attitudes and actions, and most damagingly, that as a significant barrier to the success of people of color, racism is a thing of the past .
  • Taken together, these harmful discourse practices often ostentatiously promote a blanket standard of “colorblindness,” while simultaneously promoting so-called “race-neutral” policies and practices that reinforce the power of white anxiety and fear in policymaking and decision-making.

Everyday recommendations for how readers can help overcome these harmful racial discourse practices follow this section of the report.

What Is Reinforcement Learning? (Definition, Uses)

Reinforcement learning is a training method in machine learning where an algorithm or agent completes a task through trial and error. An agent must explore a controlled environment and learn from its actions the optimal way to achieve a certain goal. Actions that bring an agent closer to its goal are considered positive while those that end in failure are negative.

Unlike techniques such as supervised and unsupervised learning, reinforcement learning doesn’t involve feeding data into an agent before it tries to perform an action. The agent must rely solely on learning from its experiences, allowing it to improve its decision-making over time as it compiles data from previous attempts.

When to Use Reinforcement Learning

Reinforcement learning has many applications and is used in gaming, recommendation engines, robotics, traffic light control and more.

Reinforcement learning delivers appropriate next actions by relying on an algorithm that tries to produce an outcome with the maximum reward. This allows reinforcement learning to control the engines for complex systems for a given state without the need for human intervention.

Reinforcement learning is the most conventional algorithm used to solve games. A famous example of this is AlphaGo, a reinforcement learning engine that was trained in countless human games and has been able to defeat best-in-class masters of renowned games for their difficulty, such as Go, through the use of the Monte Carlo tree search and neural networks in its policy network.

Personalized recommendation engines use an advanced form of reinforcement learning known as deep reinforcement learning to overcome challenges such as rapidly changing content, content fatigue and click rate to deliver recommendations with the greatest reward (ie a “yes” selection).

More From Built In ExpertsHow AI Teach Themselves Through Deep Reinforcement Learning

Applications of Reinforcement Learning

Healthcare

Reviewing patient data and past visit information, reinforcement learning can find a treatment that best meets the needs of each patient while also factoring in timetables for recovery. This speeds up medical diagnoses and ensures patients receive faster, more personalized treatments.

Energy

Learning models can analyze data gathered from sensors and anticipate how much energy will be spent when mixing and matching different variables. Reinforcement learning then determines the ideal conditions that minimize energy and costs when teams attempt to cool data centers.

Manufacturing

In factories and warehouses, reinforcement learning powers the computer vision systems of robots. Mobile robots can also learn to navigate warehouse aisles, retrieving and transporting inventory while avoiding accidents.

Automotive

Reinforcement learning can train self-driving cars how to operate safely by training in realistic environments. During testing, the algorithm learned how to take into account factors like staying in lanes, watching the speed limit and remaining aware of other drivers and pedestrians.

Transportation

To combat congestion in urban environments, cities are turning to reinforcement learning to control traffic signals. Algorithms are trained on finding the best ways to operate traffic lights by considering variables such as the time of day and number of cars passing through an intersection.

Customer Service (NLP)

Reinforcement learning is a major part of natural language processing and helping customer service agents comprehend and respond to sentences. These approaches make possible various customer service technologies, including chatbots and virtual assistants.

Marketing

Marketing teams often try to target customers with personalized recommendations, and this process becomes easier with reinforcement learning. By analyzing which products and webpages a customer spends the most time viewing, reinforcement learning models can determine other products that may pique a customer’s interest.

Gaming

Reinforcement learning improves the artificial intelligence used to control non-player characters in video games. Applying reinforcement learning, AI characters can adopt different offensive and defensive tactics and figure out new ways to navigate the game’s landscape.

How Does Deep Reinforcement Learning Work?

Deep reinforcement learning combines reinforcement learning frameworks with artificial neural networks.

To help a software agent reach its reward, deep reinforcement learning combines reinforcement learning frameworks with artificial neural networks to map out a series of states and actions with the rewards they lead to, uniting function approximation and target optimization.

The inclusion of artificial neural networks allows reinforcement learning agents to tap into computer vision and time series prediction and facilitate real-time decision-making that is based on a reward and punishment system. Determining the best path to the maximum reward from a series of states and actions is responsible for AlphaGo and deep learning models besting top-tier human players in Atari video games such as Starcraft II and Dota-II, to name just a few examples.

An Introduction to Deep Reinforcement Learning | Video: Arxiv Insights

What Kinds of Problems Can Reinforcement Learning Solve?

Reinforcement learning helps solve problems in expected and probabilistic environments.

In expected environments, an action must be executed in a certain order to produce a reward and will be punished if other orders are pursued.

Rewards in probabilistic environments, however, are harder to determine due to the inclusion of probability, and consequently, determine the action that should be taken through a defined policy. A policy accounts for probability and determines the action that the agent should take based on the conditions of the environment.​

Limitations of Reinforcement Learning

Although reinforcement learning offers many advantages in a variety of fields, it does come with some drawbacks:

  • Time-consuming: Reinforcement learning requires lots of data to learn an action while undergoing a trial-and-error process.
  • Too complex: Reinforcement learning is designed to address more complex issues and is not a great fit for simpler problems.
  • Lacking experience: Training for reinforcement learning often occurs within controlled environments. This means an agent or algorithm may not be prepared for unique circumstances and events that can occur in the real world.
  • Difficult to understand: Reinforcement learning often involves complex neural networks that are difficult to analyze.
  • Potentially harmful: Reinforcement learning can raise tough ethical questions. What if a model develops its own shortcuts or makes decisions that put people in harm’s way?
  • Easily affected: Noisy data, human interactions and dynamic environments can all impact the performance of agents and make them less effective.

What is reinforcement learning?

Reinforcement learning is a machine learning training method that employs a trial-and-error approach to teach an algorithm or agent how to complete a specific task. Based on actions that result in reward or failure, the agent improves over time as it gathers data from each attempt and learns the optimal way for performing an action.

What is an example of reinforcement learning?

An example of reinforcement learning is AlphaGo, which played itself thousands of times to understand Go and how to beat human players.

Changing definition of success in education system a way for S’pore to stay relevant: Chan Chun Sing – Mothership.SG

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Minister for Education Chan Chun Sing gave the opening speech of the Institute of Policy Studies’ (IPS) flagship Singapore Perspectives 2023 conference on Jan. 5.

Chan highlighted five key shifts needed to ensure that Singapore’s education system can remain relevant in an increasingly “connected, yet fragmented world”.

A changing society

Chan opened by talking about the challenging and constantly changing environment that Singapore and Singaporeans find themselves in.

Going forward, society will be shaped by various forces.

As Singapore faces a slowing birth rate, Chan pointed out the growing challenge to integrate non-local citizens born to sustain the country’s economic vibrance and social cohesion.

“For Singapore, managing diversity and being able to connect and collaborate are essentials, not options,” he said.

Singapore will also face increasing competitive pressures as it becomes more integrated with the rest of the world, and will need to “counter the tendencies to turn insular, nativist, and retreat into our own echo-chambers”.

Additionally, Chan acknowledged that there is a greater risk of “us seeing the world as we want it, rather than as it is”, which would threaten Singapore’s ability to connect and remain relevant with the world.

To meet these challenges, Chan said Singapore’s education system needs to “evolve at speed”.

He said Singapore’s education system needs to deliver curiosity, collaboration, and confidence at the individual level.

At the industry level, it needs to help companies connect better across geographies, geopolitics, and culture, and to remain competitive.

At the societal level, Singapore’s education system will also need to be cohesive enough to move “against the forces that threaten to fragment us”.

Five key shifts

In order to achieve this, Chan highlighted five “key shifts” to deliver these outcomes.

  • Moving beyond mass access to education to mass customization
  • Defining success beyond the first 15 years of education, but also in the next 50 years
  • More closely intertwining the Academia-Industry partnership into a relationship
  • Going beyond the efforts of the Ministry of Education (MOE) to the efforts of society as a whole
  • Investing in lifelong learning and innovation for the teaching fraternity

1. Mass access to quality education

Lauding Singapore’s strong basic system that allowed mass access to quality education, Chan noted three things that the education system needed to improve on in this area.

Firstly, stronger investments in early year education, especially for less privileged children of families with higher needs.

Chan cited evidence which showed that it was important not to let the learning and developmental gap to grow too wide in young children, as it would be difficult to rectify, and remediation required in the future would be extremely high.

While celebrating the progress made in closing the gap over the past 15 years, he said there is still more to do, and shared that the government will examine news ways to provide support for these less privileged children.

Second, the government will use more adaptive learning technologies and pedagogies to “stretch the top, while freeing up resources to uplift the disadvantaged”.

New technologies such as artificial intelligence would allow Singapore to relook its pedagogies to enable even better mass customization of the education system.

Thirdly, Singapore will continue to diversify its success pathways for students, through programs like Full Subject Based Appeal, and customization of degree programs, among other things.

Chan said a more diverse education system would better serve Singapore, but would require a mindset shift away from constant comparison and benchmarking of students and institutions.

There is also a need to keep Singapore’s meritocracy broad and continuous, and not allow the “system to degenerate into credentialism”.

2. Beyond schooling years

Chan said that success cannot be defined by the 15 years spent in formal education, but would have to shift to be defined by the 50 years beyond that.

Disruptions and uncertainty meant that it would not be possible to frontload education, and the first 15 years of education would be to build learning foundations.

Instead, the new benchmarks for success will be “the spirit of inquiry, the desire to create new knowledge and value, the ability to discover, discern and distill”.

Chan said industry would have a part to play in achieving lifelong learning — industry cannot wait for the “perfect worker”, but must be an active partner in shaping students’ interests and skillsets before they even enter the workforce.

Industry will also need to work with academia to keep training workers after joining the workforce.

The government would also review funding and support for lifelong learning, to better guide those in the middle of their careers through challenges and opportunities, to defray costs, help smooth transitions in and out of jobs, and skills acquisition.

Chan said that more ideas would be put forward during the Forward Singapore deliberations.

3. Academia-industry relations

Chan said the third shift would be in the relationship between academia and industry in Singapore.

Singapore cannot outcompete other countries in scale, but can be a pioneer at intersections of disciplines, and thus create new value, he added.

This means being adequate at forming interdisciplinary teams across students, faculty, and alumni, and collaboration between institutions and faculties.

Chan spoke about the Research-Innovation-Enterprise cycle, saying Singapore had done well in research, but needed to improve in the latter two aspects.

Other than universities, Chan also highlighted the connection between industry and polytechnics/Institute of Technical Education.

He said that they would need to work together to help students and lifelong learners to better integrate work and study, and allow a better flow of research and practices between learners and industry.

4. Beyond the efforts of MOE to the whole of society

Chan said that the MOE never believed it alone could change society or develop future generations.

To broaden the definition of success, MOE needs to work closely with parents, community partners and industries, or its efforts would ultimately be “undone”, and Singapore’s speed of change would be measured in generations, not years.

To that end, he said that only by rallying together could Singapore build a culture that appreciates diverse learners.

Industry also has to close the remuneration gap, compensating according to skills and contributions, not credentials.

Success, he said, was “everyone doing justice to their blessings, rather than everyone chasing the same yardstick.”

5. Teaching the teachers

The final shift to improve the education system will be how Singapore equips and organizes the teaching faculty.

Teachers are not just academics, transmitters of information, or only engaging mainstream students with established pedagogies.

They need to be facilitators of discovery and learning, to support “higher needs” children and families, reaching and nurturing students with special education needs, by exploring and developing new pedagogies.

As new skillsets cannot be frontloaded, teachers would also need to upskill and reskill continuously.

Educators would need to understand the world beyond the education system, understand its changes, and bring back new perspectives to teaching.

Chan cited the Teacher Work Attachment Plus program as a way to facilitate that.

In a similar way, Singapore needs to ensure teaching ability and pedagogical practices of teaching faculties in institutes of higher or continuous learning.

Chan said that the Institute of Adult Learning would join the National Institute of Education and National Institute of Early Childhood Development; providing investment, research, and training to the teaching faculties at all levels.

For Institutes of Higher Learning, there is also a need to define success beyond research.

Chan said that there should be complementary pathways to success in teaching as well as leadership, in addition to research.

In closing, Chan highlighted the need for Singapore universities and education system to reach their fullest potential. He spoke about a conceptual “Education 4.0”.

Where the first three versions of education moved from catering to the privileged, then to industry, then to universal access, “Education 4.0” would need to equip Singaporeans to thrive in an uncertain, fragmented, diverse and competitive, yet more connected world.

The mentioned five key shifts are required for this, but Chan also noted the importance of the government remaining committed to “build the best system possible to enable future generations of Singaporeans to do even better than this generation”.

“What will also not change is our goal to distinguish ourselves as a nation that defines success not just by our achievements, but by our contributions,” he concluded.

Top image via Jacky Ho, for the Institute of Policy Studies, NUS

What Is Digital Literacy?

While the word “literacy” alone generally refers to reading and writing skills, when you tack on the word “digital” before it, the term encompasses much, much more.

Sure, reading and writing are still very much at the heart of digital literacy. But given the new and ever-changing ways we use technology to receive and communicate information, digital literacy also encompasses a broader range of skills—everything from reading on a Kindle to gauging the validity of a website or creating and sharing YouTube videos.

The term is so broad that some experts even stay away from it, preferring to speak more specifically about particular skills at the intersection of technology and literacy.

The American Library Association’s digital-literacy task force offers this definition: “Digital literacy is the ability to use information and communication technologies to find, evaluate, create, and communicate information, requiring both cognitive and technical skills.”

More simply, Hiller Spiers, a professor of literacy and technology at North Carolina State University, views digital literacy as having three buckets: 1) finding and consuming digital content; 2) creating digital content; and 3) communicating or sharing it.

Finding and Consuming

In some formats, “consuming” digital content looks pretty much the same as reading print. Reading a novel on a basic e-reader requires knowing how to turn the device on and flip pages back and forth, but other than that, it isn’t so different from reading a book. A PDF of a New York Times article looks a lot like the page of a printed newspaper, except that it appears on a screen.

Donald Leu, an education professor at the University of Connecticut and a recognized authority on literacy and technology, describes this kind of digital reading as “offline reading.”

“It’s not interactive, … there’s one screen, and you just have to read it,” he explained. “It’s the same as reading a [paper] page.”

"I read on my iPad when I'm in a car, or when I'm on a plane when I'm going on a trip.  When I'm at home, I read regular books," says Shota, a 3rd grade student at Indian Run Elementary School in Dublin, Ohio.

The added skills needed for this kind of reading take just a few minutes to teach.

In comparison, what Leu calls “online reading,” in which a digital text is read through the internet, requires a host of additional skills. For example, a New York Times piece viewed on the web may contain hyperlinks, videos, audio clips, images, interactive graphics, share buttons, or a comments section—features that force the reader to stop and make decisions rather than simply reading from top to the bottom.

“The text is designed so that no two readers experience it in the exact same way,” said Troy Hicks, a professor of literacy and technology at Central Michigan University.

The reader determines, among other things, when to click on videos or hyperlinks, how long to stray from the initial text, and whether and how to pass the information along to others.

The process of finding digital content to read also requires different skills than finding printed texts. In seeking print materials, students might flip through magazines or head to the library and search through stacks of books. They learn to use a table of contents and an index to locate information within a book.

But part of digital literacy is learning to search for content in an online space. Students have to query a search engine using keywords and navigate those results, including assessing the reliability of particular authors and websites.

Creating Content

Digital literacy also refers to content creation. That includes writing in digital formats such as email, blogs, and Tweets, as well as creating other forms of media, such as videos and podcasts.

Renee Hobbs, a professor of communication studies at the University of Rhode Island, talks about digital authorship as “a form of social power.” At a weeklong professional-development institute on digital literacy held at URI this past summer, she showed examples of student activists sharing their messages about the Black Lives Matter movement through YouTube videos.

Creating digital content is a “creative and collaborative process that involves experimentation and risk-taking,” she said. There’s more risk-taking than in print writing because digital writing is so often meant to be shared.

Sharing and Communicating

While traditional writing can be a personal endeavor, digital writing is generally intended to be communicated with others. And digital-writing tools are designed to make that easy to do.

As North Carolina State’s Spiers and her co-author, Melissa Bartlett, wrote in a 2012 white paper about digital literacy and learning, “Web 2.0 tools are social, participatory, collaborative, easy to use, and are facilitative in creating online communities.”

"It's on a book, on a paperback book because I've been reading like that since I was kid," says Hareem, a 10th grade student at Mineola High School, Mineola, NY

That makes digital writing a potentially powerful lever for social good, allowing students to “actively participate in civic society and contribute to a vibrant, informed, and engaged community,” as the ALA notes.

It also makes digital writing a potentially dangerous tool—decisions about when and what to share online can have repercussions for a student’s safety, privacy, and reputation.

For that reason, learning about appropriate internet behavior is also a part of digital literacy, many say.

“We need to help kids see they can use digital tools to create things and put things out into the world, but there’s a responsibility that comes with that,” said Lisa Maucione, who attended the URI institute and who is a reading specialist for the Dartmouth public schools in Massachusetts.

Evolving Technology

Because the term “digital literacy” is so wide-ranging, it can cause confusion. What exactly is someone talking about when he or she refers to digital literacy? Is it the consumption, creation, or communication of digital materials? Or is that person discussing a particular digital tool? Do technology skills like computer coding fall under the digital-literacy umbrella as well?

Some experts prefer the term “digital literacies,” to convey the many facets of what reading and writing in the modern era entails.

“The concept should instead be considered plural—digital literacies—because the term implies multiple opportunities to leverage digital texts, tools, and multimodal representations for design, creation, play, and problem solving,” Jill Castek, a research assistant professor with the Literacy , Language, and Technology Research Group at Portland State University, wrote in an email.

Leu of UConn avoids the term altogether.

“Is someone who is ‘digitally literate’ equally literate when searching for information, when critically evaluating information, when using Snapchat, when using email, when using text messaging, when using Facebook, or when using any one of many different technologies for literacy and learning?” asked Leu in an email. “I don’t think so.”

He prefers the term “new literacies,” which he says better conveys how rapidly technology is changing. Other experts have used terms like “literacy and technology,” “multiliteracies,” and “21st century literacies.”

But for now, digital literacy seems to be the prevailing term among educators. “I understand this is the term that is popular today,” Leu said, “just as I understand a newer term will appear in the future that will replace it.”

How Do We Define and Measure “Deeper Learning”?

“Students can’t learn in an absence of feedback,” Pellegrino said. “It’s not just assessing, but providing feedback that’s actionable on the part of students.”

HOW TO SUPPORT DEEPER LEARNING THROUGH POLICY

In order for deeper learning to become the norm rather than the exception, it has to be a priority for local, state, and national policymakers, said Linda Darling-Hammond, professor of education at the Stanford and advocate for education reform. Common Core State Standards, which began to push towards critical reasoning and problem solving and application of knowledge, are only being applied to math and literacy, she said. “What about other subjects?”

What’s more, social-emotional skills have to be taken into account anytime we address deeper learning, she said. Some states have developed standards for social emotional skills, and it could be a good strategy for others to follow as well.

The way to achieve deeper learning is through curriculum and instruction, in assessments, and teachers’ professional development, she said.

The curriculum schools use now was created by a 10-member committee of men in 1893, Darling-Hammond said.”We need a new committee,” she said. “Maybe with women and with people of color, and maybe even with 20 people.”

Curriculum should go deeper into application of skills, cover fewer topics that are more carefully selected and more deeply taught, and she said Common Core tries to do this. She repeated the mantra of many progressive educators: “Teach less, learn more.”

As for assessment, Darling-Hammond said our goals must be far more ambitious than they are now. Policymakers should follow the lead of schools that have used digital portfolios and projects as assessments, rather than relying on standardized tests. “Students are able to take feedback and revise their work,” she said. “Their conscientiousness is tested. We know that in contexts like that, we have evidence that students are making it through college in higher numbers.”

Our current standardized tests focus on recall of facts and procedures, the lowest levels of types of learning, Pellegrino added. “They’re easily scored and quantified for accountability procedures. They’re not optimal in measuring the kinds of competencies that represent deeper learning,” he said.

But in order to use assessments that are valuable to students, we need to invest more money and time. “The kinds of tasks we need to assess take kids more time to act and more time to score,” she said. Currently, the US spends $10 to $20 per child on assessments, but in other countries where children are doing deep inquiries and investigations, assessments cost about $200 per student.

“We need to rethink the way we make those investments, as part of our policy agenda,” she said, because, as Pellegrino put it, what gets tested governs what gets taught.

Another big component of deeper learning involves collaboration, she said, and “collaboration is not cheating… it’s part of problem-solving. Collaboration is a skill not a deficit.”

Professional development is another key part of bringing deeper learning to students. School principals, who play a big role in curriculum adoption, as well as educators, must learn about problem-solving, child development, and content pedagogy in order to understand how to set up collaborative and project-based learning.

But in order to do their jobs well, educators must be given enough time to create a thoughtful curriculum. In other countries, Darling-Hammond said, educators are allocated 15 to 20 hours a week just dedicated to curriculum creation.

For those interested in pursuing deeper learning strategies in class, she suggested pulling out the key ideas from current standards and going deep into those subjects, such as ratio and proportion in math. She also suggested reading books and learning more about complex instruction and how to develop collaborative group work, even in classes where there’s a wide range of student skills.

BYPRODUCT OF DEEPER LEARNING

From an Edweek article that reported on findings from the same study:

The committee pointed to a 2008 five-year longitudinal study of 700 California students in three high schools: one urban and one rural school, each with large proportions of minority and English-language learner students, and another overwhelmingly wealthy, white school. While at the start of the study, incoming 9th graders in the diverse urban school performed significantly below the students in the other schools in mathematics, the school designed its algebra and geometry courses to highlight multiple dimensions of mathematics concepts and approaches to problem-solving, self- and group-assessment and developing good questions. When tested at the end of the first year, the students exposed to the “deeper learning” mathematics had caught up with their peers in algebra, and they performed significantly better than students in the other schools in the following year. By the 4th year of the study, 41 percent of students at the urban diverse school were taking calculus, in comparison to only 27 percent at the other two schools.

The study was partially funded by the William and Flora Hewlett Foundation.

What exactly is an ‘ineffective teacher?’ California’s definition doesn’t include measures of performance

Even after years of debate and litigation over teacher evaluations and tenure, California has no official definition of what constitutes a bad educator — until now.

Under the federal Every Student Succeeds Act, states must report on whether disadvantaged students have a higher proportion of ineffective, out-of-field or inexperienced teachers than their peers. But to supply that answer, California needed to define, concretely, what an ineffective teacher looks like.

On Wednesday, the Board of Education approved a profile that does not touch on teacher performance: An “ineffective” teacher is now officially one who is improperly assigned or does not have proper credentials.

In less than two months, the board must submit its plan to satisfy the federal law — which replaced the No Child Left Behind Act — to Education Secretary Betsy DeVos. Its members opted to address the requirement dealing with teachers Wednesday, but leave until later the completion of a formula for identifying low-performing schools, as the law also requires.


California’s new education ratings tool paints a far rosier picture than in the past »

What, exactly, is an “ineffective teacher?”

The state’s new definition mirrors language in the Local Control Funding Formula law, as well as a proposal from the California Teachers Assn. union.

Tom Adams, deputy superintendent of public instruction, said California was using it “because that’s the system we have in place.”

But some education advocates were critical of the decision.

“It refuses to consider teacher effectiveness … as something related to performance and impact on students,” the Education Trust — West, an Oakland-based nonprofit focused on closing the achievement gap, wrote in a letter.

Similarly, the Assn. of California School Administrators wrote that the definition misses “a teacher who is fully credentialed but ineffective in instructional practices.”

Some, including Carrie Hahnel of EdTrust-West, have suggested considering teacher turnover and absentee rates to get at how well they are performing — without using the controversial, quantitative evaluation systems that rely on students’ standardized test scores.

Board President Mike Kirst said that he was interested in some of those ideas, but that there wasn’t enough data to justify their use. One board member said the conversation gave her “heartburn.”

How does California identify underperforming schools?

Where No Child Left Behind used a strict system to reward and punish schools for their standardized test performance, Every Student gives states much less.

At the bare minimum, the federal law requires that states identify the lowest-performing 5% of their high-poverty schools, as well as high schools with persistently low graduation rates, and help them improve.

The state recently created the California School Dashboard, a website that uses a variety of metrics to analyze schools and displays the results in a color-coded scheme: red is the worst, blue is the best. A Times analysis found that it was possible to have more than half of students underperforming on standardized tests and still be classified as “good” under this system.

California plans to use the dashboard color ratings to identify its lowest-performing schools: Those deemed red across all measures, or all red with one orange category, will be flagged. But by using that method, experts say, the state will be able to identify only one-third of the number of schools it would need to reach the full 5%.

So the board Wednesday also voted on a motion that said it needed one more year of testing and dashboard data to figure that out, thus missing the federal deadline. After a January meeting, they plan to flesh out their strategy and send it to the government as an addendum.

Board member Feliza Ortiz-Licon voted no, saying she wasn’t convinced that the plan showed precisely how the state would close achievement gaps. And Children Now, an education advocacy group, said using the dashboard model was a bad idea because it collapsed nuanced information into blunt categories.

The whole ineffective teacher definition gives me heartburn.

— California State Board of Education member Ting Sun

What will California do to help low-performing schools improve?

The state has proposed letting county education officials take the lead on holding districts accountable.

The Equity Coalition, an umbrella group representing more than 20 California education advocacy organizations, wrote in a long critique that the state’s education plan “offers far too few details regarding how school improvements will occur.”

Kirst said the lack of detail was deliberate, and part of a long-running effort to not let the federal government direct California’s schools. “The state plan is essentially a contract with the federal government,” he said. “The more details we include, the less flexibility that we have to adjust.”

Times staff writer Howard Blume contributed reporting.

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The role of evidence in teaching and learning

The role of evidence in teaching and learning

©Shutterstock/M-SUR

Research Conference 2018, hosted by the Australian Council for Educational Research, took place in Sydney this month. In his keynote, ACER CEO Professor Geoff Masters AO explored the role of evidence in teaching and learning.

Evidence-based teaching involves the use of evidence to: (1) establish where students are in their learning; (2) decide on appropriate teaching strategies and interventions; and (3) monitor student progress and evaluate teaching effectiveness.

The term ‘evidence-based’ is now firmly entrenched in the education lexicon. And with good reason; improvements in student learning and educational outcomes depend on the wider use of reliable evidence in classroom practice. However, much discussion of evidence-based teaching is based on a narrow definition that would benefit from a broader recognition of the role of evidence in teaching and learning.

The concept of evidence-based practice has its origins in medicine. The essential idea is that decisions made by medical practitioners should be based on the best available evidence collected through rigorous research – ideally, through randomized controlled trials. Research studies in the form of carefully controlled experiments are seen as providing the strongest and most reliable form of evidence to guide practice.

However, everyday medical practice uses multiple forms of evidence. In addition to evidence from external research studies, medical practitioners gather and use evidence relating to patients’ presenting conditions and symptoms – for example, by taking patient history and ordering diagnostic tests. Evidence of this kind is essential for informed decision making. So, too, is evidence about the subsequent effectiveness of a practitioner’s decisions. Such evidence plays a crucial role in monitoring a patient’s progress and evaluating the impact of treatments and interventions.

Most definitions of evidence-based medicine recognize the role and importance of these different forms of evidence. One of the earliest and most cited definitions (Sacket et al, 1996) describes evidence-based practice as ‘integrating individual clinical expertise with the best available external evidence from systematic research.’

Evidence-based teaching similarly involves more than the implementation of practices that have been shown to be effective in controlled research studies. As in medicine, evidence-based practice depends on the integration of reliable, local, practitioner-collected evidence with evidence from systematic, external research. Policies and discussions of ‘evidence-based teaching’ sometimes overlook the importance of this broader, more integrated understanding of the role of evidence in teaching and learning.

Evidence to identify starting points for teaching and learning

A first, essential form of evidence for teaching is information about the points individual learners have reached in their learning. This usually means establishing what they know, understand and can do as starting points for teaching and to ensure that individuals are provided with well-targeted learning opportunities and appropriately challenging learning goals. The parallel in medical practice is diagnosing the state of a patient’s health to guide appropriate treatment. Understanding where learners are in their learning is as essential to clinical teaching practice as understanding a patient’s symptoms and health is to effective medical practice.

The process of establishing where students are in their learning may involve the review of available historical evidence – for example, evidence from a previous teacher or evidence from past assessments. It may also involve administering tests or other assessments to identify appropriate starting points.

One view of teaching – now largely outmoded – sees it merely as the delivery of the appropriate year-level curriculum to all students. Under this view, the role of teachers is to deliver the relevant curriculum; the job of students is to learn what teachers teach; and the role of assessment is to establish how well students have learned what teachers have taught and to grade them accordingly. In contrast, ‘evidence-based’ teaching uses evidence about where students are in their learning to guide and personalize teaching. The objective is to develop a good understanding of where each student is in their learning so that they can be provided with appropriately targeted teaching and learning opportunities.

Evidence-based teaching of this kind depends on a frame of reference against which learning can be monitored – a ‘roadmap’ that describes and illustrates what it means to grow and become more proficient in a learning area. Learning is depicted as an ongoing process through which students develop progressively higher levels of knowledge, understanding and skill over extended periods of time.

In evidence-based teaching, assessments are undertaken to gather evidence and draw conclusions about where students are in their learning. The objective is to use observations of students’ performances and work to draw inferences about their current levels of attainment. A thorough understanding of where a student is in their learning may require a detailed diagnostic investigation of the errors they are making or the misunderstandings they have developed – often essential evidence for addressing obstacles to further progress and a key element of clinical teaching practice. Reports of student attainment are then expressed not as percentages or grades, but as the points individuals have reached, interpreted by reference to what they know, understand and can do.

Evidence to inform teaching strategies and interventions

A second, powerful form of evidence for promoting student learning is evidence from research into effective teaching strategies and interventions. Knowing where students are in their learning provides a starting point; However, the crucial next question is how to promote further learning. Which interventions are likely to improve students’ levels of understanding and skill? What teaching strategies have been shown to work in practice? For which learners? Under what conditions? Answers to questions of this kind are derived from rigorous, systematic research and professional teaching experience.

As a general principle, effective teaching builds on and extends learners’ existing knowledge, skills and understanding. Teachers need to know how to do this, which in turn depends on a deep understanding of the learning domain itself and, in particular, typical paths and sequences of student learning. How does learning build on prior learning and lay the foundations for further learning? How does prerequisite knowledge influence future learning success? What are the foundational, enabling skills that students must develop before they can progress to higher levels of attainment? Learning research has a crucial role to play in answering these questions, elucidating the nature of learning, in particular learning domains, and generating research evidence to inform teaching practice.

Research also has an important role to play in uncovering the kinds of misunderstandings and alternative conceptions that students commonly develop. Such research adds to an understanding of how learning occurs within a particular learning domain. As well as recognizing typical and logical sequences of development, teachers require an appreciation of the side-tracks that some students go down and how these impede learning progress. Research that provides evidence in the form of insights into common errors and misconceptions assists teachers in diagnosing and addressing the difficulties that individuals experience.

Importantly, research evidence of this kind is domain specific. Because teachers teach subjects, they generally benefit from research into how students learn those subjects. For example, the evidence likely to be most useful to teachers of reading is evidence about how students learn to read, including the role of pre-reading and early reading skills in establishing the foundations for subsequent reading development. The evidence likely to be most useful to teachers of science is evidence about how students progressively learn science, including evidence relating to the development of deeper understandings of scientific concepts and principles, and the kinds of misunderstandings that students commonly develop.

‘Evidence-based’ educational practices sometimes take the form of general solutions such as ‘individualized learning’, ‘early years intervention’, ‘metacognition’, ‘homework’, ‘peer tutoring’ and ‘feedback’. However, general solutions of these kinds must be interpreted and implemented in the contexts of the subjects teachers teach. What kind of homework? For whom? Feedback of what kind? When? In general, teachers require evidence about the best ways to implement effective teaching strategies and interventions in subject-specific contexts.

Evidence to evaluate student progress and teaching effectiveness

A third form of evidence for teaching is information about the progress students make in their learning over time. This is important information for evaluating learning success and for making judgments about the effectiveness of teaching strategies and interventions.

A traditional approach to evaluating learning is to compare students’ performances with expectations based on their age or year level. For example, a Year 5 student’s learning success is commonly assessed and graded against Year 5 performance expectations. However, this approach takes no account of where students are in their long-term learning at the beginning of a school year and so does not reflect the progress (or growth) they have made. Under this approach, two students may achieve the same grade, one having made significant progress during the year, the other having made very little.

An alternative is to define learning success in terms of the progress individuals make. This approach assumes that learning is reflected in, and can be evaluated in terms of, improvements in students’ levels of knowledge, understanding and skill – for example, over the course of a school year.

Evidence about the progress of students makes is crucial information for teaching. It provides a basis for establishing whether, and how effectively, individuals are learning. Low levels of progress may indicate a lack of student effort and/or ineffective teaching, and so warrant closer investigation. Information about progress provides the most direct indicator of teaching effectiveness, as well as being key to the evaluation of educational policies, programs and teaching methods.

References

Sackett, D. L., Rosenberg, W. M., Gray, J. A., Haynes, R. B., & Richardson, W. S. (1996). Evidence based medicine: what it is and what it isn’t. BMJ. 13;312(7023):71-2.

‘The role of evidence in teaching and learning’ was first published in the Research Conference 2018 Proceedings and is available to download from the ACER Research Repository.

Opening the definition dialogue: Personalization, individualization and differentiation

A recent eSchool News contributed article, Differentiation, individualization and personalization: What they mean, and where they’re headedwhich helped define personalized learning in relation to both differentiation and individualization, is a wonderful reminder that while there is broad agreement on many aspects of the definition of personalized learning, there remains an open dialogue on other parts of this complex definition.

The piece cuts right to the heart of the issue by noting something I have often discussed—most of us struggle to clearly delineate differentiation, individualization, and personalization.

This struggle for a definition poses a larger question: If we cannot clearly and successfully define our approaches, what chance do we have for successful implementation?

Much of the article squares with some of the most widely accepted and agreed upon definitions around personalization. I would, however, like to offer some additional points to consider in forming a definition around this complicated topic.

Exploring the Term “Student Agency”

The article does a great job of delineating personalized learning from individualized learning by offering that “in a personalized scenario, the teacher is no longer the sole driver of instruction—each learner now collaborates with the teacher to drive his or her learning, with the students taking a hands-on role in determining their own needs and informing the design of their lessons.”

In breaking down this definition a bit further, I’d recommend applying the term “student agency” to the outcome of students taking ownership of their learning needs. Student agency has become an increasingly popular term and discussion topic during recent education conferences.

Adding to the definition of personalized learning

To augment the discussion, I’d like to point to the US Department of Education’s definition of personalized learning:

Personalized learning refers to instruction in which the pace of learning and the instructional approach are optimized for the needs of each learner. Learning objectives, instructional approaches, and instructional content (and its sequencing) may all vary based on learner needs. In addition, learning activities are meaningful and relevant to learners, driven by their interests, and often self-initiated.

Richard Culatta, CEO of the International Society for Technology in Education, expanded on this definition in his article What are you Talking About?! The Need for Common Language around Personalized Learningexplaining that individualized learning consists of “learning experiences in which the pace of learning is adjusted to meet the needs of individual students, focusing on the ‘when’ of personalized learning,” while differentiated learning is “learning experiences in which the approach or method of learning is adjusted to meet the needs of individual students, focusing on the ‘how’ of personalized learning.”

Personalized learning, then, envelopes both differentiated and individualized learning, and goes ever further with the elements of student agency.

(Next page: Individualization and student pace)

Individualization and students moving at their own pace

“Individualized learning” is an admittedly hard term for which to find a broadly acceptable definition. Culatta asserts that “in individualized learning, all students go through the same experience, but they move at their own pace.” I find that many others do not accept the “go through the same experience” aspect, but what is present here and what must be presented somewhere within the overall dialogue on personalized learning is a discussion of students moving at their own pace.

In my view, the truly new element of personalized learning is individualization, which for our purposes here I will treat as synonymous with competency- or mastery-based learning. Conversations around differentiation have been ongoing for years and although the term “student agency” is relatively new, the overall concept is older and found within other approaches (eg formative assessment). But, in individualization, we see something truly new.

Prior to these discussions, course counts, Carnegie units, graduation requirements, hours of contact and seat time have been at the center of our considerations. These were all remnants of the factory model. Now, conversations are shifting to “mastery,” “outcomes” and “competency-based learning.” School policies mandated seat time, but not mastery.

Adjusting the pace of learning to stress mastery, then, makes tremendous sense—focusing on mastery rather than seat time. But if we accept this, much of the structure of school as we know it unravels. The bell schedules and course counts go out the window and are replaced with mastery models, clear outcomes, flexible schedules and resources and redefined teacher roles.

This element, learning at a flexible pace that is driven by true mastery, must also be a part of our personalized learning conversations. When we get to this point, the depth of personalized learning becomes apparent. This is no minor or even moderate change to the status quo, this is a radical re-thinking of school as we know it.

That all said, despite the root word (“personal”) and references to/requirements of individual plans, we should acknowledge that many lauded examples of personalization also reference extensive group work on broad, multi-disciplinary projects and other cooperative activities. Given this, it is critical to understand that personalizing education certainly does not mean that things have to be completely personalized at every moment.

Additionally, in a school of any significant size, there would surely be groups of students all working on mastering similar skills. So, while the prevalence of lectures and other whole-class activities will decline under personalized learning, a teacher of 125 students would not truly be planning 125 different lessons.

Guided by additional clarity on the multiple elements included within personalized learning, we can begin to turn our attention to the real challenge–implementing at scale.

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