Blog Post 3: Perspective On Learning Styles And Quantum Computing

Written: June 2nd 2024

What Are Learning Styles? What Is My Perspective On Them?

Clark (2016; as cited by Power, 2023) describes learning styles as an oversimplification of more complex theories. It is inherently a cognitivist belief about how the brain and humans learn and interact with their environments. For its time, the justification for this perspective was understandable. This was a period when the first complex computers were created and utilized, and we continued to look to complex machinery to learn more about our own internal wiring. During this period, we continued to believe that humans were binary, and the belief in being "trainable" persisted. The concept of some people being "visual" learners and not "verbal" learners, and that these deal in absolutes, was prevalent. Just like computers and binary systems, it was seen as either a 0 or a 1, with no conditions. Over time, we recognized that people could be both "visual" and "logical" learners. In relation to binary computers, this means we could be 0101101 or 1001001.

I agree with Power (2023) that we are more complex than being strictly "aural" and/or "social" and/or "solitary" learners. The research against the validity of strict learning styles is strong. Arguing that these learning styles could be scalar, or that their effectiveness depends on the content being learned, may bring more rigor to the original concept. This can compare us more to quantum computers and concepts such as the superposition of qubits and their continuous spectrum of states.

Figure 1: This image is to represent the different learning styles that people may possess.
Learning Styles Online. (n.d.). Overview of learning styles. https://www.learning-styles-online.com/overview/

Quantum Computing? Where Are You Going With This?

Quantum computing introduces concepts that provide a compelling analogy for understanding the complexity of human learning. One of the foundational principles of quantum computing is the superposition of qubits, represented by the equation:

∣ψ⟩=α∣0⟩+β∣1⟩

In this equation, α and β are complex numbers that describe the probability amplitudes for the qubit being in states ∣0⟩ and ∣1⟩, respectively. The condition ∣α∣ ^2 + ∣β∣ ^2 =1 ensures that the total probability is conserved (Quantiki, n.d.).

This concept of superposition illustrates that a qubit can exist in multiple states simultaneously, rather than being restricted to a single state at any given time. Similarly, human learning cannot be neatly categorized into distinct, binary styles like "visual" or "verbal" learners. Instead, individuals often exhibit a blend of learning preferences that can change depending on the context and content being learned.

Just as qubits have a continuous spectrum of states, learners demonstrate a spectrum of abilities and preferences that cannot be confined to rigid categories. The complexity of human cognition and learning is better represented by the fluid and dynamic nature of qubits in superposition. This perspective acknowledges that learning is influenced by a multitude of factors, including prior knowledge, motivation, context, and the nature of the material being studied.

By embracing the analogy of quantum computing, we can move beyond the outdated, binary views of learning styles. Recognizing the continuous and multifaceted nature of learning allows educators to design more effective and inclusive instructional strategies that cater to the diverse needs of all learners. This approach aligns with contemporary research that emphasizes the importance of adaptive, evidence-based teaching methods over rigid adherence to debunked theories of learning styles.

In conclusion, the principles of quantum computing, particularly the superposition of qubits, provide a powerful framework for understanding the true complexity of human learning. Just as qubits can exist in a superposition of states, learners can exhibit a combination of preferences and abilities, highlighting the need for a more nuanced and flexible approach to education.

Figure 2: This image is to represent the complexities of the human brain.
Hesman Saey, T. (2023). Universal process that wires the brain is consistent across species. Live Science. https://www.livescience.com/health/neuroscience/universal-process-that-wires-the-brain-is-consistent-across-species

Where Can We Go With This?

I genuinely believe learning needs to be adaptive and dynamic. Constant questioning, looking in the past to create a better future, and not following strict ways of thinking. My experiences with the schooling system was genuinely mentally incapacitating. Everyday felt the same, and felt discipline specific. The sooner we can avoid picking one individual lens to observe the world and attempt to make cross-disciplinery connections and truly innovate multiple fields at once. I am no computer scientist or engineer, but I challenge myself every day to learn the theories behind technology and neural networks.

I will leave this question as a cliff hanger for next week and I intend on dicussing leveraging AI, RLOs and how this all connects to my research in the Technology & Accessibility class.

References:

Power, R. (2023). The zombie of instructional design: Learning styles. In Everyday instructional design. Pressbooks. https://pressbooks.pub/everydayid/chapter/the-zombie-of-instructional-design-learning-styles/

Quantiki. (n.d.). Qubit. https://www.quantiki.org/wiki/qubit

Bibliography:

Clark, D. (2016). 7 reasons why teachers believe, wrongly, in ‘Learning Styles’. Donald Clark Plan B. https://donaldclarkplanb.blogspot.com/2016/10/7-reasons-why-teachers-believe-wrongly.html

Learning Styles Online. (n.d.). Overview of learning styles. https://www.learning-styles-online.com/overview/

Blog Post 4: Thinking About Adaptive Testing

Written: June 10th 2024

The Problem With Current Testing Methods

Current testing methods in education, such as traditional exams and standardized tests, are increasingly recognized as problematic due to their inherent limitations and potential negative impacts on students. One of the primary concerns is the significant stress and anxiety these high-stakes assessments induce. The pressure to perform well can lead to test anxiety, which often skews the results and fails to reflect a student's true understanding or capabilities. This stress not only affects academic performance but also detracts from overall well-being, creating an environment where learning is driven by fear rather than curiosity and engagement.

Traditional tests tend to focus heavily on memorization and rote learning, neglecting the development of critical thinking, problem-solving, and practical application of knowledge. This narrow scope means that while students may excel at recalling information, they are not necessarily prepared to apply concepts in real-world scenarios. Moreover, these assessments often employ a one-size-fits-all approach, failing to account for the aforementioned diverse learning preferences, backgrounds, and abilities of students. This is particularly disadvantageous for those with disabilities, language barriers, or differing cultural backgrounds, further entrenching educational inequities.

Additionally, the prevalent culture of "teaching to the test" undermines a more holistic and engaging approach to education. Teachers, constrained by the need to prepare students for standardized tests, may limit the curriculum to what is testable, thus stifling creativity and critical thinking. This focus on test preparation can also reduce intrinsic motivation, as students become more concerned with achieving high scores rather than understanding and enjoying the material. The inequities are further exacerbated by disparities in access to test preparation resources, tutoring, and other supports, often reflecting socioeconomic status more than academic ability.

Moreover, traditional exams provide limited feedback, focusing primarily on grades rather than offering detailed insights into areas needing improvement. This can hinder the learning process, as students are not given the tools to understand their mistakes or how to address them effectively. The slow adaptation of these testing methods to modern educational needs and technological advancements also poses a significant issue. They fail to incorporate interactive, project-based, or collaborative approaches that better prepare students for future challenges and the dynamic nature of the modern workplace.

Figure 1: This graph represents the changes in students' cortisol levels, a stress hormone, during low-stakes (internal school tests) and high-stakes (statewide standardized tests) testing weeks relative to non-testing weeks. The probability density on the y-axis shows the distribution of these cortisol changes among students. The data indicate that cortisol levels significantly increase during high-stakes tests compared to low-stakes tests, highlighting the greater stress induced by high-stakes testing. This information is derived from a study involving students from a charter school network in New Orleans.

Heissel, J. A., Adam, E. K., Doleac, J. L., Figlio, D. N., & Meer, J. (2019). Test-related stress and student scores on high-stakes exams. NBER Digest. https://www.nber.org/digest/mar19/test-related-stress-and-student-scores-high-stakes-exams

The Necessity of Computerized Adaptive Testing (CAT)

I am a firm believer that Computerized Adaptive Testing (CAT) is the way forward to start deconstructing and redesigning what knowledge and learning is. I would like to express that I do still prefer authentic assessments such as essays, presentations and other forms of multi-modial assignments. However, it is crucial to have a tool-box of assessment methods to have opportunities for options.

CAT is a computer-based exam that uses special algorithms to tailor test question difficulty to each individual test taker. According to Dr. David Foster, a CAT adapts in real time to the test taker’s ability level and provides questions accordingly. This form of secure exam design protects test content from being exposed and prevents cheating, while allowing tests to be administered more quickly, with fewer items, and increased security.

An adaptive test adapts or tailors exam questions in real time to the ability of each test taker, resulting in a different set of questions for each person based on their performance. Correct answers lead to more difficult questions, while incorrect answers result in easier questions, enabling the test to efficiently determine a test taker's ability level with fewer questions than traditional tests. One of the earliest known adaptive tests was the Stanford-Binet Intelligence Scale given at the beginning of the 20th century.

In contrast to adaptive testing, CAT makes the adaptive test computerized rather than paper-based. The shift to computer-based testing has proven significant for the industry, allowing for faster scoring, greater accessibility, increased fairness, easier administration, and enhanced security.

Figure 2: This infographic illustrates the process and benefits of Computerized Adaptive Testing (CAT). Specifically highlighting the improved testing accuracy, targeted measurement of relevant areas, tailored questions to the test-taker's ability level, and time-saving due to the adaptive nature of the test.

Libre. (2016). How does a computerized adaptive test work? View our new Libre infographic! Boston University. https://sites.bu.edu/libre/2016/06/09/how-does-a-computerized-adaptive-test-work-view-our-new-libre-infographic/

What Do I See In CAT?

From my perspective, Computer Adaptive Testing (CAT) holds great promise for transforming the educational assessment landscape. Here is what I see in CAT:

Increasing Equity:

Tailored Difficulty: CAT adjusts the difficulty of questions based on the test-taker's performance, offering a personalized assessment experience that can level the playing field. This is particularly beneficial for students from diverse educational backgrounds and abilities.
Focused Assessment: By honing in on each student's unique ability level, CAT minimizes the disadvantages faced by those who might struggle with standardized, one-size-fits-all testing formats.

Deeper Understanding:

Dynamic Insights: With each question, CAT gathers more nuanced data about the test-taker’s knowledge and skills. This ongoing adjustment provides a more detailed picture of their understanding and capabilities.
Iterative Learning: CAT’s immediate adaptation to student responses creates opportunities for real-time learning, as students can receive feedback and adapt their thinking throughout the test.

Integration with Accessibility Tools:

Assistive Technologies: CAT can be integrated with various tools to support individuals with disabilities. For example, question and answer option readers can assist those who have difficulty reading.
Multimodal Question Presentation: Offering questions through different modalities (text, audio, video, images) can cater to various learning styles and needs. This flexibility ensures that all students, regardless of their abilities, can engage with the content effectively.

Enhanced Visualization and Expression:

Illustrative Questions: Similar to how a speller in a spelling bee can request the word in a sentence for better understanding, CAT can present questions in multiple formats. This might include images, videos, or real-life scenarios to help students grasp the context and content more thoroughly.
Interactive Learning: Such multimodal integration does not just test knowledge but also teaches, reinforcing learning through varied expressions of the same question. This approach can help students learn and recall information better by engaging with it in different ways.

Maintaining Rigour While Innovating:

Content Fidelity: Despite the varied presentations and adaptive nature, the core content and rigor of the test remain intact. CAT ensures that the assessment is still challenging and aligned with learning objectives.
Learning on the Spot: Allowing students to interact with and explore the content during the test can lead to immediate learning. Whether they recall studied content or deduce answers through provided resources, the learning process itself becomes more dynamic and engaging.

Overcoming Limitations of Traditional Tests:

Fluid and Accessible Testing: Unlike traditional paper-based tests, CAT offers a flexible and adaptive approach that can accommodate various needs and preferences. This fluidity helps create a more inclusive and effective assessment environment.

In conclusion, CAT represents a significant advancement in educational assessment. By promoting equity, enhancing understanding, integrating accessibility tools, and maintaining rigorous standards, CAT can offer a more personalized, engaging, and fair testing experience for all students.

References:

Foster, D. (2024). What is a computer adaptive test (CAT)? Caveon. https://blog.caveon.com/computer-adaptive-test-meaning

Bibliography:

Blog Post 5: Bringing These Ideas Home In Relation To Course Content

Written: June 14th 2024

CAT as an RLO: Redefining the Testing Space

A few years ago, the concept of Computer Adaptive Testing (CAT) seemed like a distant fable to me. However, with advancements in Artificial Intelligence and the involvement of dedicated experts, this "fable" is becoming a reality. AI's ability to verbally and visually interact with individuals, comprehend their unique intersectionalities, and apply appropriate strategies has revolutionized testing. The pandemic forced most testing online, focusing on the Substitution level of the SAMR model (Substitution, Augmentation, Modification, Redefinition). Yet, the widespread availability and usage of AI present new opportunities. My vision for it follows the Redefinition of testing as a whole.

With the forthcoming release of ChatGPT Edu to universities and colleges, higher education will have greater access to GPT-4 and future models, which can be tailored for CAT purposes. The significance of CAT lies in its continuous update capability, making it a Reusable Learning Object (RLO) (Polsani, 2002; as cited by Power, 2015). Once a high-quality algorithm is developed, the question bank can be easily adjusted by instructors or testers, facilitating a more efficient and adaptive testing process.

Figure 1: The SAMR Model, created by Dr. Ruben R. Puentedura, is illustrated in a step-like diagram. It comprises four levels that describe the integration of technology into teaching and learning processes. The model is divided into two main sections: Enhancement and Transformation.

Kendon, T., & Anselmo, L. (n.d.). The SAMR model. Taylor Institute for Teaching and Learning, University of Calgary. https://taylorinstitute.ucalgary.ca/resources/SAMR-TPACK

References:

Kendon, T., & Anselmo, L. (n.d.). The SAMR model. Taylor Institute for Teaching and Learning, University of Calgary. https://taylorinstitute.ucalgary.ca/resources/SAMR-TPACK

Polsani, P. R. (2003). Use and abuse of reusable learning objects. Journal of Digital Information, 3(4). https://jodi-ojs-tdl.tdl.org/jodi/article/view/jodi-105

Power, R. (2015). A framework for promoting teacher self-efficacy with mobile reusable learning objects (Doctoral dissertation, Athabasca University). http://hdl.handle.net/10791/63