Tag: framework

Week 15 and 16 Annotation – It’s not like a calculator, so what is the relationship between learners and generative artificial intelligence?

A.M. Ternullo

Lodge, J. M., Yang, S., Furze, L., & Dawson, P. (2023). It’s not like a calculator, so what is the relationship between learners and generative artificial intelligence? Learning: Research and Practice, 9(2), 117-124. https://doi.org/10.1080/23735082.2023.2261106

            Lodge et al. (2023) set out to establish a frame for discussions about generative AI technologies in education by offering typologies for AI. They begin by dispelling the common analogy of generative AI technology to the calculator. Lodge et al. hold that comparing generative AI to a calculator assumes that generative AI technologies will be able to do tasks to arrive at a correct answer, but really generative AI functions are more complex than that oversimplified analogy of the calculator. Lodge et al. view generative AI as an infrastructure, rather than a singular tool. Next, Lodge et al. provide an overview of human-generative AI interaction. They contextualize this interaction in the more firmly established human computer interactions that  take in to account social and cognitive processes involved in learning. Computers were used to offload tasks – like complex addition problems to a calculator, for example. However generative AI is not about offloading a task to take the data and move forward. Lodge et al. introduced a four-quadrant typology for human and machine interactions for education. The vertical axis represents how AI can free up humans from boring tasks so they can engage in higher-level thinking or extend human thought capabilities. The horizontal axis then illustrates the way the human-machine relationship functions, individually or in collaboration. For example, using a calculator is an individual use and is an example of cognitive offloading. Cognitive offloading occurs people shift part of their cognitive tasks elsewhere – for example, a Google calendar keeps track of appointments, cell phones hold phone numbers, journals hold notes, etc. – but technology does not necessarily have to be involved. Cognitive offloading can damage learning if too much information or cognitive work is left to other devices. But cognitive offloading can also free up thinking space to allow people to engage in higher-order thinking processes. The extended mind theory holds that technology is used to expand human capabilities in complex tasks and thought processes. Generative AI could be an extension of the mind. Next. AI can also be used as a collaborative tool that assists in the co-regulation of learning. While generative AI cannot regulate human learning, the outputs of generative AI as it monitors human learning can help humans reflect on and monitor their learning in relationship to their goals. AI can “coach” humans here. Finally, ther eis hybrid learning. AI tools can help humans learn because it provides real-time feedback that is adaptive and personalized. AI can guide learners to grow and develop through opportunities for reflection.

            Lodge et al. provide a very clear description of their typologies for generative AI use and human interaction in education. Their writing is clear and concise and cites relevant resources. This article does provide a framework for discussing generative-AI human interaction without reducing it down to an overly simplified statement of: “It’s just like a calculator.” Not only does that phrase oversimplify, but it also discredits those who have legitimate concerns about the integration and implementation of generative AI in the classroom. The four typologies are discussed in a way that connects each one to the next. Lodge et al. start out with generative-AI human interaction, then discuss cognitive offloading, then expanded mind theory, then co-regulated learning and hybrid learning. This process allows the framework to develop from more simple to more complex, and as the paper moves forward the seeds for discussion about generative AI grow more complex, leaving the reader to seek out more complex connections.

            I am interested in this as a doctoral student because I am extremely interested in the ways generative AI, cognitive offloading, knowledge acquisition, and transactive memory partnerships work. When I was earning my Ed.S. degree, I focused my work on the ways Google was shifting knowledge acquisition and learning as a possible transactive memory partner in the context of classroom discussions. But as my work was ending on that degree ChatGPT emerged, and my interested shifted to generative AI. As Lodge et al. showed, there are many ways that generative AI will reshape learning and knowledge work – and don’t know all of those ways yet. So this is something that is very in line with my research interests.

Also, reading this made me cringe because I have been using the calculator analogy in almost every discussion I have had about generative AI. And when I read how the analogy was broken down to illustrate the ways generative AI is more complex than a calculator, I realized I had inadvertently been dismissing some very legitimate concerns about the inclusion of generative AI in the classroom. This was a good reminder to slow down and think through something before just embracing it.

APA Citations for Additional Sources

References

Chauncey, S. A., & McKenna, H. P. (2023). A framework and exemplars for ethical and responsible use of AI chatbot technology to support teaching and learning. Computers and Education: Artificial Intelligence, 5, 100182. https://doi.org/10.1016/j.caeai.2023.100182

Chen, B., Zhu, X., & Díaz del Castillo H, F. (2023). Integrating generative AI in knowledge building. Computers and Education: Artificial Intelligence, 5, 100184. https://doi.org/10.1016/j.caeai.2023.100184

Markauskaite, L., Marrone, R., Poquet, O., Knight, S., Martinez-Maldonado, R., Howard, S., Tondeur, J., De Laat, M., Buckingham Shum, S., Gašević, D., & Siemens, G. (2022). Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with ai? Computers and Education: Artificial Intelligence, 3, 100056. https://doi.org/10.1016/j.caeai.2022.100056

Vinchon, F., Lubart, T., Bartolotta, S., Gironnay, V., Botella, M., Bourgeois, S., Burkhardt, J.-M., Bonnardel, N., Corazza, G. E., Glaveanu, V., Hanson, M. H., Ivcevic, Z., Karwowski, M., Kaufman, J. C., Okada, T., Reiter-Palmon, R., & Gaggioli, A. (2023). Artificial intelligence & Creativity: A manifesto for collaboration. Pre-Print. https://doi.org/10.31234/osf.io/ukqc9

Week 6 Annotation 1 – It is not television anymore: Designing digital video for learning and assessment.

A.M. Ternullo

Schwartz, D. L., & Hartman, K. (2007). It is not television anymore: Designing digital video for learning and assessment. In Goldman, R., Pea, R., Barron, B., & Derry, S.J. (Eds.), Video research in learning science (pp. 349-366). Mahwah, NJ: Lawrance Erlbaum Associates.

Schwartz et al. (2007) establish a framework specifically for those new to the learning sciences for how to use video to observe and identify learning outcomes and to strategically implement videos into the classroom learning space. This framework is situated in the new world of YouTube and streaming video, where students at the time had access to more information, but were limited by broadband access (because streaming video was spotty in 2005). They also contextualize their framework in the current research of the day, giving an overview of the minimal research available on the topic in 2007. Schwarts et al. give an overview of four common learning outcome: seeing, engaging, doing, and saying. Within each of these four common leaning outcomes is a variety of criteria that are observable when learners are engaging with video, and might direct what video and when video is selected to be used in a learning situation. Seeing videos are videos that help learners visualize and experience things they have not or cannot experience. Seeing videos can be categorized as tour videos (e.g. travel videos, historical re-enactments, nature videos), point of view videos (e.g. from a character’s point of view), simulated experiences (e.g. first person video of a sky dive). The associated assessable criteria are: recognition, noticing, discernment, and familiarity. Engagement videos are designed to keep people engaged in a topic. These videos develop interest and contextualize information. The associated assessable actions are assessing preferences for learning and measuring future learning. Doing videos present human behavior or processes – there are attitude and skill distinctions. In order to do an action, the viewer needs to see the action. Videos that shape attitudes ask viewers to identify the behavior and demonstrate the behavior – either globally or in step-by-step fashion. To assess the effectiveness of the video, a viewer would be asked do the behavior they learned from watching the video. If there is an action that is unable to replicated, then the viewer should be able to explain the action in detail. Saying videos are videos that lead to knowledge acquisition of facts and retaining the facts. Things like news broadcasts, fall into this category. Features of analogy, commentary, and explosion can be used. To assess success of saying videos, viewers should be asked to recall facts they acquired from watching the video. Overall, video works within a larger context. They also provided an extended example of pre-service teachers applying the framework in a course.

Schwartz et al. (2007) did an excellent job of establishing the framework. The framework was clearly and explicitly explained. There was a clear visual representation of the framework. The tenants of the framework were explained, supported with evidence from the literature, and then clear and specific examples were given that a reader could apply to their own situation or research. Additionally, they provided an extended example of how this process could be applied in a learning context. Schwartz et al. also provided appropriate critique and contextualization for the framework. This framework is deceptively simple, as it easy to apply to a condition, but has a lot of room for growth and assessment in application.

As a doctoral student, this framework provides a way to view the application of video usage in a classroom. It was interesting to see the development of a framework for studying something that was so new. This framework emerged alongside the technology. The way the framework was explained and presented in the article was also of great value. Thinking forward to explaining my own conceptual or theoretical framework in my dissertation, I also want to be as clear in my writing. I also appreciate that the framework was so explicit. I feel as though I could pick this framework up and apply it to a scenario. As an administrator who works with faculty, I could direct faculty to this framework to help them assess their use of video in their classes, as this could be part of the evaluation process. Since this is easily accessible, I feel like it’s something that could be seen as value-added right away, especially since it looks a lot like the Bloom’s Taxonomy wheels that many faculty are already familiar with and use. They know it’s easy to apply Bloom’s and would likely assume this framework is just as easy to apply since it can be visually represented in the same way.

Annotation – “The Triple-S framework: ensuring scalable, sustainable, and serviceable practices in educational technology”

A.M. Ternullo

Moro et al. (2023) present a new research-based framework, Triple-S Framework, for educators and institutions to consider before electing to adopt and adapt educational technology into learning spaces. The research-based framework was built in the context of every-evolving technology and the push and drive of institutions and educators to adopt the latest technology to remain relevant, the financial and practical cost of technology implementation, and student desire to see more consistent technology implementation. The Triple-S Framework guides institutions and educators to evaluate the scalability (continued growth of use) , sustainability (long-term implementation viability) , and serviceability (access to skills, tools, and resources to maintain use of technology)of educational technologies that are implemented into the schools. Moro et al. provide an overview of common and trendy educational technologies from most scalable, sustainable, and serviceable (digital text texts and images) to least scalable, sustainable and serviceable (VR technology) to illustrate application of the model.

Moro et al. (2023) provide a clear presentation of the need for a framework that takes into account not just learning out comes, but long-term viability of educational technology intervention in classrooms and institutions. The examinations of common, widely used educational technology such as digital texts and images, audio, slideshow presentations, and video allow for newcomers to the framework to bring their practical experience to bear on the benefits and pitfalls of technology implementation. Progressing to apps, which are accessible to use, but not necessarily to create then extends the application of the framework to less common technologies to show how the Triple-S framework is practical and accessible to researchers, educators and decision makers. Moro et al. also use very easy-to-grasp common language when explaining their framework. An college professor with no formal educational training can pick this up and implement the steps without having to do much work to make it happen. It’s very practical.

As a college administrator, I really love the practical examples and explanations that are provided and grounded in research. I can see that there are clear steps, questions, and processes to follow. This would be very easy to use as a jumping off point for discussions with faculty about technologies they would like me to purchase from my budget to use in their classroom. As a doctoral student, I can only hope to strive for the level of clarity of explanation, clear connection to the literature, and clear and concise applications to make the research I do practical for the practitioner and administrators who support practitioners.

Moro, C., Mills, K. A., Phelps, C., & Birt, J. (2023). The triple-s framework: Ensuring scalable, sustainable, and serviceable practices in educational technology. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-022-00378-y