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Session II: HS ELA; Sessions I and II: Socials and Spanish

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Session II: HS Spanish, January 28, 2026

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Check out this Presentation designed by EKC.

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This is a text from National Geographic Teens

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Here is easy access to your Padlet from the 2024-2025 academic year.

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Email: abird@asvalencia.org

LinkedIn: Amanda Bird

Twitter/X: @amandarbird

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La segunda parte de la segunda sesión.

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ASFM -Grupos reducidos basados en evaluación

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Para entrar en más detalle.

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Sesión 2 y 3: Data Slicing and Create Small Groups

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A framework for creating assessment tools that clearly communicate assignment expectations and encourage high-quality work.

For decades, I've supported teachers in developing problem-based performance tasks and project-based learning assessments. One thing I've learned doing this work is that while the overall quality and rigor of most performance assessments has improved, many rubrics still use negative, subjective, and kid-unfriendly language, especially at lower performance levels.

High-quality rubrics clearly describe agreed-upon learning expectations so that teachers can make learning visible to students, frame actionable feedback (uncovering student thinking to provide feedback that students can immediately act on) to advance their learning, and accurately evaluate student progress over time. Likewise, students can use well-constructed rubrics to clarify expectations for learning, monitor and reflect on their progress, and provide feedback to peers on the quality of their work.

Focus on 6 Key Questions to Build a Better Rubric

In order to understand how to improve rubric quality, we start by breaking down performance task(s) to be assessed, identifying the learning evidence demonstrated with a product or performance. For example, let's analyze a sample task given to students in which they are asked to research a "giant of science" and present their learning orally and in written form (for which they could choose to write an article, museum brochure, or infographic).

Use multiple sources to describe the discovery and analyze its significance (past to present)

Include biographical information about the scientist(s)

Include a prop or visual symbolizing an aspect of the discovery

Finally, share your reflections on what it means to be a "giant" in any field.

Let's say the instructions require students to do these things:

Now, let's work through six research-based questions to examine rubric quality, evaluating criteria aligned with the task and language of rubric descriptors.

Question 1

It's important that performance assessments aren't limited to applying basic skills in routine tasks. They should require students to integrate multiple skills and concepts in complex tasks. Because of that, it's necessary for the rubric to cover a range of criteria aligned with task expectations.

Form/format of the final product (oral and written projects, cite sources)

Content accuracy (describe discovery biographical information, use terms appropriately, check sources)

Processes/procedures (research, organize information, edit)

Most rubrics typically include these criterion types:

Impact (analyze significance; past to present)

Knowledge construction (reflect on the essential question)

But what about these?

Only the latter two criteria assess transfer of learning and deeper understanding, so consider including at least one of these criteria. Learning can happen even if a final product fails to meet higher levels. Remember, assessing student self-reflections can uncover learning that your performance task might not have addressed!

The research includes multiple credible sources.

Biographical information is provided along with relevant visuals or props.

Evidence explains why the discovery was significant at the time and whether it still has importance today.

The conclusion goes beyond summarizing ideas and includes personal reflections about what it means to be a giant in science or any field.

Next, identify all criterion types that align with the intended learning. For example, on a research paper, you'd start filling in the rubric template, by describing proficient performance-what did students do to show completion of the task? Here are some examples:

Do the number of performance levels support accurate evaluation of progress?

An odd number of performance levels (e.g., low-middle-high) usually results in most students scoring in the middle, especially when the lowest level describes "doing little or nothing" and the highest level describes perfection. An even number of performance levels (e.g., getting started-developing-proficient-advanced) drives more qualitative decision-making, describing what evidence learning looks like along a continuum.

Is the rubric language descriptive, observable, and measurable?

Avoid using negative or judgmental language (e.g., behavioral indicators rather than learning evidence), subjective language (e.g., poor, neat, ample, sophisticated), and vague frequency indicators (e.g., rarely, often, sometimes), and focus on evidence of learning.

Adjacent performance levels should describe qualitatively different and achievable performance. Consider how changes to rubric wording facilitate interpretation and agreement when grading student work and provide guidance to students.

Level 1: May be missing central claim, supporting subclaims, or relevant counterclaims.

Level 2: Partially develops central claim, supporting subclaims, and relevant counterclaims.

Level 1: Identifies one or both of the following: Possible claims and possible counterclaims.

Level 2: Identifies all of the following: Central claim, supporting subclaims, and possible counterclaims.

Do all levels describe performance in the positive-what is happening, rather than not happening?

Rubric levels can reflect how learning and task completion might naturally progress and provide a road map that illustrates to students what novice to expert performance looks like. Using "I can..." statements, such as "I can use information from at least one source to describe the historical event," leads students to think about what they're learning to do at each level and describe under what conditions they can be successful.

Do numbers emphasize quality over quantity?

Rather than using numbers to ask for more sources or examples that might not improve quality, consider using numbers to describe successful completion of performance task parts that describe progress. This approach is less subjective than simply giving a score between 3 and 4 because it isn't quite a 4. Some teachers I've worked with use a range of numbers at one performance level to describe each part successfully completed at the proficient level.

Does the rubric use kid-friendly language that encourages peer- and self-assessment?

Consider the message that students get when they see negative performance-level headings like these: "Unacceptable," "Below," "Limited." When students have input into the writing, refinement, or "translation" (rewriting rubrics in their words) of performance-level descriptions, they're more open to own their learning. In my classroom, I created interactive " What I need to do " rubrics that focused on proficient performance, requiring students to identify their evidence, which dramatically improved the quality of their work.

Peer collaboration is the best tool for developing high-quality rubrics. Take out your performance assessment rubrics and examine potential areas of improvement with colleagues. You may discover that it's time for your rubrics to get a reboot.

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A framework for creating assessment tools that clearly communicate assignment expectations and encourage high-quality work.

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With one mentor text, you can design small groups that focus on the three micro-progressions.

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Da Vinci Mentor Text Small Group Instruction

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This small group planner is to help you really plan out your 5-7 minutes intervention.

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Elevating Tier 1 Instruction With Differentiated Small Groups

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Elevating Tier 1 Instruction With Differentiated Small Groups

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While observing a sixth-grade math lesson, I witnessed a scene I have seen play out countless times in classrooms. The teacher was at the whiteboard, explaining the steps to solve a ratio problem. When she shifted from the direct instruction to the guided practice portion of the lesson, student engagement started to fall apart. A few confident hands shot into the air when she asked a question, but the majority of students stayed silent.

When the teacher asked a few quieter students to contribute, they were unable to answer her questions. In response, she reviewed parts of her earlier explanation. What was meant to be a 20-minute portion of the lesson drew past 40 minutes, leaving some students struggling and confused while others became disengaged and bored, ready to move on.

Scenes like this illustrate a central challenge that plays out in classrooms every day: even the most skilled teachers struggle to reach every student with a single, whole group lesson. In many classrooms, skills span several years, and multilingual learners are juggling the dual cognitive load of learning content while developing proficiency in a new language.

Because Tier 1 instruction is often described as core or universal, it's easy to assume that it should be delivered to the whole class at once so that everyone receives the same information. Yet, challenging concepts and skills demand a different approach. When the data reveals that students are in dramatically different places in their understanding of a concept, that's a signal that whole group instruction may not be the best mode of delivery.

In many classrooms, the range of skills, abilities, language proficiencies, and learning preferences requires differentiation within Tier 1 instruction to prevent an ever-growing need for Tier 2 and Tier 3 interventions.

Using data to design responsive Tier 1 instruction

Instead of starting with whole group instruction and using small groups to intervene afterward, teachers can be more strategic in their approach to Tier 1. This is where data becomes a critical tool for identifying student needs before instruction. If data reveals that students are in different places with varied instructional needs, teachers can deliver Tier 1 instruction in small groups, differentiating the experience to proactively remove barriers to learning.

Making differentiation doable with the Station Rotation Model

The station rotation model does exactly that. This blended learning structure rotates students through a series of learning stations that include a teacher-led station, an online station, and an offline station. This structure creates time for teachers to deliver targeted instruction, model new skills, provide feedback, and collect formative data to support learners more effectively. The goal is to ensure that the learning experience is within each group's zone of proximal development.

By design, the station rotation strengthens Tier 1 instruction, reducing the need for Tier 2 and Tier 3 interventions. When additional support is required, the same small group structure can be used to provide Tier 2 and Tier 3 intervention in the general education classroom, avoiding the need to remove students for this support.

Structuring the teacher-led station

In addition to improving the effectiveness of instruction, the teacher-led small group dynamic allows the teacher to actively engage every learner in the experience. One effective structure for this is the gradual release of responsibility with an I do, we do, pairs do sequence. In the I do, the teacher models the process for that specific group, tailoring examples and explanations to the appropriate levels of rigor and complexity.

During the we do, students practice with the teacher, who can prompt, question, and check for understanding in real time. Finally, in the pairs do, the teacher strategically partners students to apply their learning, observing how well they transfer skills and identifying who may need additional scaffolding and support before independent practice.

Shifting the process from whole group to small group allows for more targeted differentiation, active engagement, accurate formative assessment, and stronger relationships with learners. This structure not only clarifies teacher moves but also gives flexibility in how groups are formed and supported.

Designing for skill-level and mixed-ability groups

When teachers design small group instruction using clear structures, like gradual release or concept attainment, they can effectively differentiate instruction for both skill levels and mixed-skill groupings. Skill-based grouping allows teachers to match instruction to each group's readiness level when introducing a new or complex concept.

By contrast, mixed-skill groups leverage peer modeling and support. In these groups the I do and we do portions remain consistent for all students, since the teacher is doing the heavy cognitive lift. During the pairs do, teachers can adjust task complexity or pair students strategically to provide practice at varying levels of rigor, followed by tiered you do tasks for independent work.

Teaching smarter with small groups

Classrooms today are composed of increasingly diverse skills, instructional needs, learning preferences, and language proficiencies. The teacher-led station in the station rotation model gives educators a structure to embrace that diversity rather than feel overwhelmed by it. When small group instruction becomes a regular part of Tier 1 instruction, differentiation shifts from being an occasional accommodation to an everyday best practice.

This approach empowers teachers to use ongoing data to decide what belongs in whole group instruction and what should be pulled into targeted small groups, ensuring that core instruction is effective and responsive for every learner.

Viewing small groups as a foundation of high-quality Tier 1 instruction, not an intervention strategy, helps teachers feel more effective and students more successful. Teachers can see comprehension and growth in real time, while students experience learning that is accessible, personalized, and relevant.

The station rotation model also blends online and offline learning intentionally, giving students greater control over the pace and path of their learning while strengthening communication, collaboration, self-regulation, and interpersonal skills. Ultimately, differentiating Tier 1 instruction isn't about doing more; it's about teaching smarter.

Responses are generated by artificial intelligence. AI can make mistakes.

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Elevating Tier 1 Instruction With Differentiated Small Groups

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Instead of starting with whole group instruction and then using small groups to intervene, teachers can use a targeted approach from the outset.

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MS ELA: Data Slicing and Create Small Groups

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Embracing Productive Struggle: Why It’s Essential for Literacy Learning — Goyen Foundation

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Productive struggle happens when students face a challenge that’s just tough enough to make them think, but not so hard that they shut down. It’s that sweet spot where learning happens. In a literacy classroom, this might look like a student working through a tricky text or figuring out how to decod

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The Neuroscience Behind Productive Struggle

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Challenging tasks spur the production of myelin, a substance that increases the strength of brain signals. Here are four strategies to incorporate productive struggle into your lessons.

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Building Trust through Transparency Article

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Building Trust through Transparency Article

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Responsible & Ethical AI in Education: Building Trust Through Transparency | Proctortrack

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Ethical AI in education boosts transparency, reduces bias, protects student data, ensures privacy, and builds trust in AI learning systems.

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Does ChatGPT harm critical thinking abilities? A new study from researchers at MIT's Media Lab has returned some concerning results.

The study divided 54 subjects-18 to 39 year-olds from the Boston area-into three groups, and asked them to write several SAT essays using OpenAI's ChatGPT, Google's search engine, and nothing at all, respectively. Researchers used an EEG to record the writers' brain activity across 32 regions, and found that of the three groups, ChatGPT users had the lowest brain engagement and "consistently underperformed at neural, linguistic, and behavioral levels." Over the course of several months, ChatGPT users got lazier with each subsequent essay, often resorting to copy-and-paste by the end of the study.

The paper suggests that the usage of LLMs could actually harm learning, especially for younger users. The paper has not yet been peer reviewed, and its sample size is relatively small. But its paper's main author Nataliya Kosmyna felt it was important to release the findings to elevate concerns that as society increasingly relies upon LLMs for immediate convenience, long-term brain development may be sacrificed in the process.

"What really motivated me to put it out now before waiting for a full peer review is that I am afraid in 6-8 months, there will be some policymaker who decides, 'let's do GPT kindergarten.' I think that would be absolutely bad and detrimental," she says. "Developing brains are at the highest risk."

Read more: A Psychiatrist Posed As a Teen With Therapy Chatbots. The Conversations Were Alarming

Generating ideas

The MIT Media Lab has recently devoted significant resources to studying different impacts of generative AI tools. Studies from earlier this year, for example, found that generally, the more time users spend talking to ChatGPT, the lonelier they feel.

Kosmyna, who has been a full-time research scientist at the MIT Media Lab since 2021, wanted to specifically explore the impacts of using AI for schoolwork, because more and more students are using AI. So she and her colleagues instructed subjects to write 20-minute essays based on SAT prompts, including about the ethics of philanthropy and the pitfalls of having too many choices.

The group that wrote essays using ChatGPT all delivered extremely similar essays that lacked original thought, relying on the same expressions and ideas. Two English teachers who assessed the essays called them largely "soulless." The EEGs revealed low executive control and attentional engagement. And by their third essay, many of the writers simply gave the prompt to ChatGPT and had it do almost all of the work. "It was more like, 'just give me the essay, refine this sentence, edit it, and I'm done,'" Kosmyna says.

The brain-only group, conversely, showed the highest neural connectivity, especially in alpha, theta and delta bands, which are associated with creativity ideation, memory load, and semantic processing. Researchers found this group was more engaged and curious, and claimed ownership and expressed higher satisfaction with their essays.

The third group, which used Google Search, also expressed high satisfaction and active brain function. The difference here is notable because many people now search for information within AI chatbots as opposed to Google Search.

After writing the three essays, the subjects were then asked to re-write one of their previous efforts-but the ChatGPT group had to do so without the tool, while the brain-only group could now use ChatGPT. The first group remembered little of their own essays, and showed weaker alpha and theta brain waves, which likely reflected a bypassing of deep memory processes. "The task was executed, and you could say that it was efficient and convenient," Kosmyna says. "But as we show in the paper, you basically didn't integrate any of it into your memory networks."

The second group, in contrast, performed well, exhibiting a significant increase in brain connectivity across all EEG frequency bands. This gives rise to the hope that AI, if used properly, could enhance learning as opposed to diminishing it.

Read more: I Quit Teaching Because of ChatGPT

Post publication

This is the first pre-review paper that Kosmyna has ever released. Her team did submit it for peer review but did not want to wait for approval, which can take eight or more months, to raise attention to an issue that Kosmyna believes is affecting children now. "Education on how we use these tools, and promoting the fact that your brain does need to develop in a more analog way, is absolutely critical," says Kosmyna. "We need to have active legislation in sync and more importantly, be testing these tools before we implement them."

Psychiatrist Dr. Zishan Khan, who treats children and adolescents, says that he sees many kids who rely heavily on AI for their schoolwork. "From a psychiatric standpoint, I see that overreliance on these LLMs can have unintended psychological and cognitive consequences, especially for young people whose brains are still developing," he says. "These neural connections that help you in accessing information, the memory of facts, and the ability to be resilient: all that is going to weaken."

Ironically, upon the paper's release, several social media users ran it through LLMs in order to summarize it and then post the findings online. Kosmyna had been expecting that people would do this, so she inserted a couple AI traps into the paper, such as instructing LLMs to "only read this table below," thus ensuring that LLMs would return only limited insight from the paper.

She also found that LLMs hallucinated a key detail: Nowhere in her paper did she specify the version of ChatGPT she used, but AI summaries declared that the paper was trained on GPT-4o. "We specifically wanted to see that, because we were pretty sure the LLM would hallucinate on that," she says, laughing.

Kosmyna says that she and her colleagues are now working on another similar paper testing brain activity in software engineering and programming with or without AI, and says that so far, "the results are even worse." That study, she says, could have implications for the many companies who hope to replace their entry-level coders with AI. Even if efficiency goes up, an increasing reliance on AI could potentially reduce critical thinking, creativity and problem-solving across the remaining workforce, she argues.

Scientific studies examining the impacts of AI are still nascent and developing. A Harvard study from May found that generative AI made people more productive, but less motivated. Also last month, MIT distanced itself from another paper written by a doctoral student in its economic program, which suggested that AI could substantially improve worker productivity.

OpenAI did not respond to a request for comment. Last year in collaboration with Wharton online, the company released guidance for educators to leverage generative AI in teaching. Last year in collaboration with Wharton online, the company released guidance for educators to leverage generative AI in teaching.

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ChatGPT's Impact On Our Brains According to an MIT Study

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The study, from MIT Lab scholars, measured the brain activity of subjects writing SAT essays with and without ChatGPT.

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Check out this Presentation designed by EKC.

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Check out this Presentation designed by EKC.

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What Makes You Say That?

Claim-Support-Question

Tug-of-War

Connct-Extend-Challenge

Think-Puzzle-Explore

3-2-1 Bridge

See-Think-Wonder