This Friday SLO Talk examines how behaviorism provides a clear, evidence-based framework for instruction and assessment built on one central idea: learning is observable behavior. If students haven’t learned, the educator hasn’t yet found the right way to teach. This philosophy shifts accountability from students to teachers, viewing instructors as detectives who design environments where success is inevitable.

The discussion centers on explicit and systematic instruction—the “I Do, We Do, You Do” model—where skills are modeled, practiced with feedback, and mastered through independent performance. When paired with a clear scope and sequence, this approach ensures that prerequisite skills are established before introducing more complex tasks, minimizing student frustration and maximizing success.

Participants also explore data-driven assessment methods like Curriculum-Based Measurement (CBM) and Precision Teaching, which track performance frequently and objectively. These approaches focus on what students can do, not what they say they understand. Accuracy and speed together define fluency—the strongest indicator that a skill will endure and transfer beyond the classroom.

The episode revisits Project Follow Through, the largest educational experiment ever conducted, which found that Direct Instruction—a model grounded in behaviorism—produced the highest achievement across academic and affective outcomes. Despite its success, this model was often dismissed for being too structured, revealing how educational culture sometimes resists accountability to measurable results.

Listeners will learn how the principles of applied behavior analysis can enhance today’s classrooms by defining learning objectives in observable terms, using continuous assessment to guide instruction, and replacing judgment with data-informed action. Rather than labeling students as unmotivated or unprepared, the behaviorist framework asks what can be changed in teaching conditions to produce learning.

Featuring Dr. Nancy Marchand-Martella, Board-Certified Behavior Analyst (BCBA-D) and national leader in instructional design, the session highlights practical strategies for improving instruction through precise measurement and feedback. Her experience—spanning more than 190 publications and four decades of research—shows how effective teaching is not about intuition, but about design, monitoring, and systematic improvement.

This conversation reframes instruction as a scientific endeavor:

• Accountability: Learning outcomes reflect instructional quality.
• Design: Skills develop through a structured, cumulative sequence.
• Measurement: Data, not opinion, drives decision-making.
• Fluency: Mastery means accurate and efficient performance.

This Friday SLO Talk with Drs. Ronald and Nancy Martella challenges common misconceptions about behaviorism and reintroduces it as a precise, ethical, and evidence-based framework for understanding learning and motivation. The discussion emphasizes a simple but powerful truth: behavior is shaped by the environment, not by invisible mental states. When teachers design conditions that promote success, learning becomes predictable, measurable, and replicable.

Behaviorism views learners as active participants whose actions are selected by consequences. The three-term contingency—Stimulus → Response → Stimulus (S-R-S)—explains how antecedent cues prompt behavior and how reinforcing outcomes make that behavior more likely to reoccur. Reinforcement increases behavior; punishment decreases it. “Positive” means adding a stimulus; “negative” means removing one. What matters is not intent but effect on future behavior.

The Martellas dismantled persistent myths:

• Myth 1: Behaviorism is simplistic “S-R psychology.” Operant conditioning studies voluntary behavior shaped by consequences, far beyond reflexive responses.
• Myth 2: Behaviorism relies on punishment. Ethical practice requires reinforcement first and limits punishment to rare, last-resort use. Skinner’s work sought humane alternatives.
• Myth 3: Skinner’s “daughter in a box.” False. The “air-crib” was a safe, climate-controlled baby bed; his daughter later described a happy childhood.
• Myth 4: Behaviorism ignores motivation. Motivation is explained through environmental variables such as the Premack Principle, Response Deprivation, and Motivating Operations, which alter the value of reinforcers and the probability of behavior.
• Myth 5: Behaviorism only applies to animals. In reality, it underlies effective human interventions—from autism therapy and literacy instruction to Positive Behavior Supports and MTSS.
• Myth 6: Behaviorism dismisses the mind. Internal events are acknowledged as behaviors influenced by environment, not mysterious causes. This perspective leads to concrete instructional fixes instead of speculation.

The Martellas contrasted this model with cognitive and developmental theories that often rely on labels and circular logic (“She can’t read because she has a reading disability—she has a reading disability because she can’t read”). Behaviorism avoids such traps by identifying functional relationships between environment and action and then changing those conditions to improve performance.

They noted three forces shaping behavior—physiology, culture, and environment—and stressed that only the immediate environment is under a teacher’s control. Thus, effective education depends on designing reinforcement systems and clear contingencies that support desired academic and social behaviors.

Finally, they linked behaviorism to Skinner’s concept of selection by consequences, analogous to natural selection. Just as adaptive traits survive through reinforcement, effective behaviors are “selected” and strengthened. When reinforcement is consistent, students build repertoires of successful behavior; when it is absent or inconsistent, learning stalls.

The message for educators is clear: learning is observable change, not an internal mystery. By focusing on measurable performance, continuous feedback, and well-designed environments, teachers can replace blame with accountability and speculation with evidence. Behaviorism, properly understood, is not mechanical—it is humane, pragmatic, and relentlessly focused on helping every student succeed.

In this episode, educators and innovators explore how generative AI can transform classrooms into continuous experiments in growth. Drawing from the October 10, 2025 session Empowering Inquiry: AI and the Scientific Method in Practice, the discussion reveals how AI can support every stage of scientific inquiry—from background research and hypothesis formation to data analysis and communication.

Adult educators Lizelena Iglesias, Vi Hawes, Dr. Jarek Janio, and Enrique Jauregui unpack how AI tools can boost problem-solving, data interpretation, and critical thinking while helping teachers model evidence-based teaching. The conversation reframes prompt engineering as a new literacy—an experimental skill that mirrors how scientists refine hypotheses.

Yet, the panel also wrestles with a vital question: how do we preserve independent thought in the age of intelligent tools? Their answer—treat AI as a partner, not a crutch. Listeners will hear practical classroom strategies for balancing AI assistance with authentic student inquiry and learn how the scientific method can guide not only student learning but also teaching itself.

In this session, Dr. Pavithra Suresh and Dr. Sabrina Altema of Howard University share how the Doctor of Physical Therapy (DPT) program has built a student-centered, equity-driven model of assessment that goes far beyond the traditional transcript. Their approach focuses on preparing graduates who are not only clinically competent but also culturally sensitive and deeply committed to serving under-resourced communities.

The presentation highlights Howard’s university-wide framework, the Howard Annual Assessment Process (HAP), and its six guiding pillars: centering students and equity, honoring community expertise, prioritizing quality over compliance, fostering collaboration, ensuring transparency, and cultivating lifelong learning.

The DPT program illustrates these principles in action. With a 94% licensure pass rate in 2023–24, the program emphasizes training underrepresented physical therapists and embedding community service into the student experience. Assessment is designed “with the end in mind,” developing confident and competent practitioners through scaffolded practical exams, formative feedback, Bloom’s Taxonomy made transparent to students, and authentic clinical experiences supported by standardized evaluation tools and trained preceptors.

Evaluation is holistic, capturing cognitive knowledge, psychomotor skills, and affective growth, while also addressing the “hidden curriculum” of professional norms and communication. Faculty monitor progress at both individual and program levels through weekly meetings, developmental teams, comprehensive exams, and curricular mapping aligned with evolving accreditation standards.

Key takeaways include the importance of faculty and student buy-in, the value of empowering learners with self-assessment tools, and the role of transparency in deepening engagement. The Howard DPT program demonstrates how assessment can drive both student success and continuous program improvement, ensuring graduates leave with the competence, confidence, and commitment to serve where they are most needed.

Dr. Brian Beatty from San Francisco State University discussing "Addressing the Challenges of Assessment in HyFlex Courses Using Custom GPTs." Dr. Beatty, a professor of instructional design, introduces the concept of HyFlex learning, which blends face-to-face, synchronous, and asynchronous online instruction to offer students flexible participation choices, initially developed to address enrollment issues. A significant portion of the discussion focuses on how he leverages generative AI—specifically custom GPTs built on platforms like ChatGPT—to support both student self-assessment and faculty course design in these complex, multi-modal environments. He details various student-facing GPT tools he created for engagement and formative assessment, such as "QuizMe" and "Breakout for 3," and explains the simple process of building these custom tools. The presentation also addresses assessment challenges in HyFlex, emphasizing the need for equivalent learning outcomes across all modes and the importance of authentic, flexible, and self-assessment strategies.

In this Friday SLO Talk, Jarek Janio from Santa Ana College and Enrique Jauregui from Fresno City College host a dynamic session highlighting an innovative nursing course development project from the University of Texas at Arlington (UTA). Dr. Leslie Jennings, Missina Minter, Megan Zara, and Dr. Stacy Greathouse, an interdisciplinary team nicknamed the "Motley Crew"—share their collaboration model for building a high-impact, competency-based perioperative nursing course.

The panel describes how the course originated in response to the national nursing shortage, aiming to prepare students for perioperative roles in operating rooms. Dr. Jennings, a perioperative nurse with over 30 years of experience, led the design effort with the support of an instructional designer, librarians, and an OER specialist. Together, they developed an accelerated "Maymester" course, compressing a full semester’s clinical and didactic content into an intensive two-and-a-half-week experience.

Stacy Greathouse introduces the "Dark Classroom" and "Motley Crew" frameworks, emphasizing collaboration, transparency, and the breakdown of traditional course design silos. Each member of the team fulfilled clearly defined roles—content specialist, learning architect, instructional technologist, accessibility specialist, and OER librarian—to ensure smooth workflow, full accessibility, and rigorous alignment with student learning outcomes.

A key focus was meticulous alignment: the team employed a master course map, “Holy Hail” language (ensuring consistent terminology across outcomes, content, and assessments), and Quality Matters (QM) standards. They also integrated Open Educational Resources (OER) and accessibility reviews, while using Rice University’s Workload Estimator to help minimize hidden time constraints for students.

Jennings and Greathouse explain how an international travel theme unified the course design. Students "traveled" through different stages of perioperative care, earning "passport stamps" as they progressed, with every assignment explicitly tied to course outcomes.

The discussion highlights the importance of formative assessments and structured "pause points" to help students reflect and maintain mental wellness during the compressed course. Flipped classroom strategies shifted the responsibility for preparation onto students, promoting agency and ownership of learning.

Attendees then engaged in breakout rooms with the "Motley Crew" members to dive deeper into key practices:

• OER sourcing and licensing
• Accessibility auditing and integration
• Instructional design strategies for accelerated learning
• Building student-centered activities with tools like H5P

The session concluded with a reflection on the cultural changes needed in higher education to support collaborative course development and the critical role of advocating for institutional resources to make sustainable, high-quality programs possible. Attendees left inspired by the team's commitment to authentic assessment, transparency, and student-centered design.

Key Takeaways:

• High-stakes, accelerated nursing education requires intentional design, transparency, and teamwork.
• True accessibility and OER integration enhance quality and equitable access for all students.
• A "Motley Crew" team structure—built on mutual accountability and clear roles—produces stronger, more resilient learning environments.
• Flipped classrooms and competency-based education support student agency while reducing faculty workload.

Special Thanks: Thank you to Dr. Leslie Jennings, Missina Minter, Megan Zara, and Stacy Greathouse for sharing their remarkable journey, and to the Friday SLO Talks community for supporting these groundbreaking conversations on student learning outcomes.

Podcast Episode: Assessment of Student Learning – The Case for Observable Behavior with Dr. Jarek Janio

In this episode of Friday SLO Talks, organized by the California Outcomes Assessment Hub (COACHES), Dr. Jarek Janio from Santa Ana College led a compelling discussion on the role of observable behavior in assessing student learning. The session challenged traditional metrics of success such as grades, graduation rates, and course completion by questioning whether they truly reflect learning or simply institutional priorities.

The Gap Between Institutional and Student Success Metrics

Dr. Janio identified a disconnect between what students seek practical skills and competencies and how institutions evaluate success. He argued that while colleges track graduation and employment rates, these indicators do not necessarily capture learning outcomes. He invited participants to consider alternative assessment strategies that focus on what students can do rather than just what they remember or complete.

The Role of Learning Theories in Assessment

The discussion transitioned to educational theories, including behaviorism, constructivism, and transformative learning. Dr. Janio highlighted that these frameworks influence how faculty teach and assess learning, yet they often fail to translate into meaningful, skill-based assessment practices. He proposed flipping Bloom’s Taxonomy on its head starting with application and problem-solving before introducing memorization so that students first engage with real-world challenges and later refine their foundational knowledge in context.

Observable Behavior as the Key to Effective Assessment

Dr. Janio argued that the only valid measure of learning is observable behavior. He criticized traditional assessments like standardized tests and essays, noting that they often fail to capture real competencies. Instead, he advocated for:

• Performance-Based Assessments: Evaluating students based on their ability to demonstrate skills in real-world scenarios.
• Portfolios and Projects: Allowing students to compile evidence of their learning over time.
• Self-Assessment and Peer Feedback: Encouraging students to reflect on their own progress and engage in meaningful discussions about their learning.
• AI-Assisted Assessment: Leveraging generative AI tools to analyze student responses, create adaptive learning paths, and enhance formative assessment strategies.

The Role of AI in Assessment

Dr. Janio explored how artificial intelligence can support assessment by automating rubric development, generating feedback, and helping faculty analyze student progress over time. However, he cautioned against over-reliance on AI-generated responses, emphasizing that faculty must take responsibility for interpreting and applying AI insights in a meaningful way.

Redefining Student Engagement and Success

A significant portion of the discussion focused on student engagement. Dr. Janio questioned whether traditional engagement metrics such as class participation, assignment completion, and time spent in tutoring centers actually indicate learning. He proposed that engagement should instead be measured by students’ ability to articulate and apply what they have learned in meaningful ways.

The episode concluded with a call to action: Educators must rethink assessment practices to ensure they capture real learning, rather than relying on outdated proxies like grades and course completion. By shifting toward skill- and competency-based assessment, faculty can provide students with a more meaningful and equitable learning experience.

Here’s a more detailed description of the episode:

Podcast Episode: GenAI – Your Supercharged Assessment Assistant with Dr. Gavin Henning

In this episode of Friday SLO Talks, organized by the California Outcomes Assessment Hub (COACHES), Dr. Gavin Henning, professor of higher education at New England College, provided an in-depth look at how Generative AI (GenAI) is revolutionizing assessment in higher education. With over 25 years of experience in student learning assessment and institutional research, Dr. Henning explored the practical applications of AI in streamlining assessment processes, reducing faculty workload, and improving the quality of learning outcomes.

Moderated by Dr. Jarek Janio from Santa Ana College and Enrique Jauregui from Fresno City College, the session opened with a discussion on the rapid evolution of AI, highlighting how tools such as ChatGPT, NotebookLM, and Gamma are reshaping assessment strategies. Dr. Henning acknowledged the common anxieties faculty have about AI—particularly concerns about academic integrity and student cheating—but emphasized the immense potential of AI to enhance, rather than undermine, meaningful assessment.

Throughout the presentation, Dr. Henning demonstrated multiple AI-driven applications for assessment, including:

• Developing and Refining Learning Outcomes – AI can generate student learning outcomes based on course descriptions, revise them for clarity, and align them with different learning taxonomies such as Bloom’s Taxonomy or Fink’s Significant Learning.
• Rubric Creation and Evaluation – AI tools can draft assessment rubrics, refine them based on institutional needs, and help faculty evaluate the quality of their rubrics to ensure clear, measurable learning criteria.
• Survey and Interview Protocols – AI can assist in designing assessment instruments, such as survey questions and focus group protocols, reducing the time needed to create structured assessment tools.
• Qualitative and Quantitative Data Analysis – While Dr. Henning cautioned against relying too heavily on AI for statistical analyses, he demonstrated how AI can identify themes in qualitative data, synthesize feedback, and even generate reports summarizing key findings.
• Program Review and Accreditation Support – AI can streamline the program review process by helping faculty organize assessment data, generate accreditation reports, and compare institutional assessment strategies.

Dr. Henning also showcased how AI-generated reports and summaries can be transformed into interactive learning materials, including AI-generated podcasts. He demonstrated Google’s NotebookLM, a tool that allows users to create AI-powered podcasts from assessment reports, complete with interactive question-and-answer capabilities. This innovative application of AI, he suggested, could make assessment findings more engaging and accessible to broader audiences.

The discussion also touched on the ethical implications of AI in assessment, including concerns about bias, data privacy, and the environmental cost of AI technology. Dr. Henning encouraged faculty to approach AI as a tool for efficiency and equity while remaining vigilant about its limitations. He also highlighted the importance of maintaining faculty agency in assessment design, ensuring that AI complements, rather than replaces, human expertise.

Dr. Henning’s message was clear: AI is moving at the speed of the Road Runner, and while it presents challenges, it also offers unprecedented opportunities to improve assessment, streamline faculty workload, and enhance student learning outcomes.

1. The Evolving Role of Assessment Professionals: As AI automates routine tasks, assessment professionals must focus on higher-order skills like identifying key data, designing effective data collection strategies, and interpreting AI-generated insights. "These tools are going to allow us to collect a lot more data about students… giving us deeper insights into what’s working and what’s not in our programs."

2. Agent-Based AI Models: The transition from dialogue-based tools (e.g., ChatGPT) to autonomous agent-based models introduces new considerations for task delegation and ethical use. "When we have models that we can delegate tasks to, it becomes critical to be thoughtful about what tasks are delegated."

3. Thoughtful AI Adoption: Implementing AI in education requires careful consideration of student needs, data privacy, and equity to ensure ethical and effective use.

Most Important Ideas

• Automation of Routine Tasks: AI streamlines assessment, freeing professionals for strategic roles. For example, Walton’s tool analyzes student artifacts automatically using pre-defined rubrics.
• Prompt Quality Matters: The "garbage in, garbage out" principle applies. Poorly crafted prompts lead to suboptimal AI outputs. "If you give it a dumb prompt, you’ll access the dumb part of that model’s brain."
• Equity Concerns: Wealthier institutions investing heavily in AI may widen gaps between privileged and underserved students. "This is the single biggest equity topic…wealthier schools are throwing money at AI for their students."
• Human Connection is Essential: AI should complement, not replace, human tutors, allowing them to focus on personalized support and emotional needs.
• Communicating AI Benefits: Institutions must clearly convey the advantages of AI-powered tools to students and parents. "How are we going to package this…in a way that helps students understand the benefit?"

Key Quotes

• Devan Walton: "We’re shifting to agent-based tools…thoughtfulness about delegated tasks is crucial."
• Peter Shea: "Wealthier schools are already investing heavily in AI, creating an equity gap."
• Anne Converse Willkomm: "We need to help students understand AI’s benefits."
• Ruth Slotnick: "This is a once-in-a-generation technology…bigger than the Internet."

Call to Action

1. Engage with AI: Assessment professionals should develop a deep understanding of AI’s capabilities and limitations.
2. Create Ethical Frameworks: Institutions must establish guidelines for responsible AI use in education.
3. Foster Collaboration: Open dialogue between educators, students, and technology experts is essential to ensure AI serves all stakeholders effectively.

AI offers transformative potential for higher education assessment, but ethical, equitable, and strategic adoption is critical to harness its benefits fully.

AI and Instructional Design in Higher Education

1. A New Pedagogical Model: AI offers an opportunity to transition from traditional teaching methods to "generativism," a novel approach combining evidence-based learning principles with AI technology. This shift is essential for maximizing AI's potential. "We are largely trying to use AI to drag along an older pedagogical model... rather than build an entirely new model using the unique affordances of AI."

2. Institutional AI Adoption: Colleges and universities vary in their approach to AI adoption, characterized as:

• Ostriches: Ignoring AI.
• Crows: Slowly exploring policies.
• Falcons: Actively experimenting with AI in teaching and learning. "Falcons tend to be those schools... exploring AI for tutoring chatbots, experiential learning, and simulations."

3. Transformative Instructional Design: AI revolutionizes instructional design by enhancing productivity, creating interactive learning tools, and enabling performance-based assessment. "The ability to create learning tools that observe students while they perform tasks is one of the most exciting possibilities."

4. Equity and Accessibility: AI bridges equity gaps by providing consistent, high-quality assessment and feedback. It also supports continuous learning through accessible ecosystems beyond the classroom. "A well-designed AI, guided by a rubric, can generate high-quality feedback quickly. For me as a writing instructor, it's revolutionized student support."

Most Important Ideas

• Generativism: A pedagogical approach leveraging AI's unique capabilities to reimagine teaching and learning.
• Instructional Design Productivity: AI streamlines material creation, like simulations, freeing instructional designers for strategic roles.
• Performance-Based Assessment: AI enables real-time tracking and assessment of student performance, improving personalization and outcomes.
• AI-Powered Ecosystems: AI fosters engaging, continuous learning environments beyond the classroom, enhancing knowledge retention.

Key Takeaways

• Institutions must embrace new models like generativism to harness AI's transformative potential.
• AI enhances instructional design efficiency, personalizes learning, and bridges equity gaps in feedback and assessment.
• Developing AI-powered ecosystems is vital for lifelong learning and extending support beyond traditional classrooms.

Recommendations

1. Adopt Generativism: Incorporate generativism principles into instructional design.
2. Invest in AI Tools: Focus on tools that create interactive and engaging materials.
3. Implement Performance-Based Assessment: Integrate real-time AI-driven feedback into learning experiences.
4. Collaborate: Build partnerships among instructional designers, faculty, and technology experts for effective AI solutions.
5. Professional Development: Stay informed about evolving AI trends in education.

AI offers transformative potential for higher education, requiring institutions to rethink pedagogy, embrace innovation, and prioritize accessibility and equity.