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The main theoretical framework that shapes this research is Constructivism. The lessons and activities that will be discussed will focus on unplugged and hybrid activities to help students understand neural networks. This topic is the Year 2 focus of a three year unit plan which is geared to middle school students but is applicable for other grades as well. The design of the units and lessons fosters students' learning through providing students experiences that help them make connections between what they are learning, what they know, and how to apply their learning. Year 1 (which was discussed at a roundtable presentation at ISTE24) focused on classifiers, Year 2 focuses on neural networks, and Year 3 will focus on deeper machine learning. The lessons path for the Year 2 unit include: Lesson 1 - Sculpting with Clay (unplugged); Lesson 2 - Narrow Down the Card (hybrid); Lesson 3 - Guess Who; Lesson 4 - Guess Space; and Lesson 5 - Guess Your Own Creation. In each of these lessons, students will have hands-on experiences while looking at Computer and Data Science through a lens that is not just coding to gain deeper understanding as they will be able to bring their own experiences into their work. As an example, students will be able to play the Guess Who game in Lesson 3 and begin to think about what questions to ask to get more consistent results. They will be able to eliminate more choices each round by asking consistent questions and will also see what questions are effective and efficient. This learning will take it cue from Lesson 1 where they will get to play with clay and discuss their experiences such as did they do fine detail work first or did they take big chunks of clay away first? This research is also shaped by Cultivation Theory in that the scaffolding of the lessons from Year 1 will extend to the lesson design units and lessons for Year 2 (neural networks) and Year 3 (deep learning). By cultivating confidence in learning, these higher level concepts will become more accessible for students.
The research that was conducted regarding the impact of hybrid and unplugged lessons in computer and data science and the current resources (or lack thereof) that are available for teachers was grounded in literature reviews from the past two years. As with the research from Year 1, resources were scarce and were generally suggested rather than built and provided like we are doing with our research here. As this is Year 2 research in this three year unit plan, we will have further anecdotal evidence and data to lean on through the scaffolding and spiraling of the topics for the teachers and the students. Many of these Year 2 students would have taken part in the Year 1 STEM classes where the unplugged and hybrid lessons were introduced when discussing classifiers and the structure of the lessons will not be new to them. Additionally, there will be a new cohort of students entering the program in Year 1 allowing for larger group analysis. Middle school students (grades 6-8) were targeted for this study as many of the students come in with solid knowledge of Google Workspace due to the 1:1 Chromebook initiative in the district. In middle school, many of these students continue to use Chromebooks in class and come with a solid understanding of Google Workspace but concepts such as understanding components in artificial intelligence can be overwhelming. The lessons and activities provided are designed to tap into prior knowledge with more active and game-based learning approaches without realizing only coding which can give students a deeper understanding of the topic.
Due to the lack of resources provided to teachers, even more so in Year 2 for neural networks than the Year 1 Classifiers, it is hoped that there is more engagement and enthusiasm for both teachers and students to truly begin to understand more difficult concepts regarding artificial intelligence in computer and data science and will help build capacity and resilience of the students so they continue in these areas in their academic careers. The nature of the three year plan also provides for more time and a layering of experiences for students and teachers as they conduct the lessons and implement the unit plan. While many literature reviews may address certain topics in this unit, this research will allow for a more encompassing view. Feedback from students and teachers will also be collected and analyzed as the lessons as the unit plan matures. It is anticipated that both teachers and students will have a positive outlook of the lessons and will produce artifacts that show an extension of their current learning as they complete the five lessons in Unit 2 as they did in Unit 1. It is also anticipated that both teachers and students will have a deeper understanding of what coding is rather than just an understanding of how to write it.
Many students, teachers, and parents/guardians express that there is a retention problem with students studying Computer and Data Science in their academic careers. Many times, this separation happens at the middle school level as students hit a wall with their basic knowledge of programs such as Google Workspace and more challenging concepts such as neural networks. Additionally, many resources are geared to the howto of coding rather than the understanding of it and other times there are lack of resources on the topic in general. Students come from the elementary schools where they use manipulatives, learn different ways to approach a problem, etc. and then find themselves in computer and data science classes that are focused more o n the scripting of the code than anything else. The literature review research in this area provides some examples of studies done in small groups and camps with students using unplugged activities, but more discussion of hybrid activities is needed as many of the lessons morph into this type of lesson. ISTE attendees will find not only the research in this study interesting, but will find the lesson and unit plans that are being provided valuable as these resources are not as readily available. To make the research more approachable for the educators in attendance, the discussion will focus on the construction, uses, and reasons behind the lessons and will make them more user friendly for teachers and coaches, similar to how the lessons themselves are making connections for students.
[1] Lehner, L. (2023). Machine learning unplugged - Training decision trees and artificial neural networks with children [Diploma Thesis, Vienna University of Technology]. reposiTUm. https://doi.org/10.34726/hss.2023.107391
[2] Lim, H., Min, W., Vandenberg, J., Cateté, V., & Mott, B. (2024). Unplugged K-12 AI Learning: Exploring Representation and Reasoning with a Facial Recognition Game. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23285-23293. https://doi.org/10.1609/aaai.v38i21.30376
[3] Lee, I. and Ali, S. 2021. The Contour to Classification Game. Proceedings of the AAAI Conference on Artificial Intelligence. 35, 17 (May 2021), 15583-15590. DOI:https://doi.org/10.1609/aaai.v35i17.17835.
[4] Lindner, A., Seegerer, S., Romeike, R. (2019). Unplugged Activities in the Context of AI. In: Pozdniakov, S., Dagienė, V. (eds) Informatics in Schools. New Ideas in School Informatics. ISSEP 2019. Lecture Notes in Computer Science(), vol 11913. Springer, Cham. https://doi.org/10.1007/978-3-030-33759-9_10
[5] Ossovski, E., Brinkmeier, M. (2019). Machine Learning Unplugged - Development and Evaluation of a Workshop About Machine Learning. In: Pozdniakov, S., Dagienė, V. (eds) Informatics in Schools. New Ideas in School Informatics. ISSEP 2019. Lecture Notes in Computer Science(), vol 11913. Springer, Cham. https://doi.org/10.1007/978-3-030-33759-9_11
[6] Lihui Sun, Junjie Liu, Yunshan Liu, Comparative experiment of the effects of unplugged and plugged-in programming on computational thinking in primary school students: A perspective of multiple influential factors, Thinking Skills and Creativity, Volume 52, 2024, 101542, ISSN 1871-1871, https://doi.org/10.1016/j.tsc.2024.101542.
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