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Ohio 8 Framework


Standard Description
CS.D.8.a Evaluate the advantages and limitations of existing computing devices to recommend design improvements based on analysis of how users interact with the device. Lessons
CS.HS.8.a Design projects that combine hardware and software components that could complete a task. Lessons
CS.T.8.a Use a systematic process to identify and evaluate the source of a routine computing problem. Select the best solution to solve the computing problem and communicate the solution to others. Lessons
NI.N.8.a Model the role of hardware components to diagram the infrastructure of networks and the internet (including cloud servers). Lessons
NI.N.8.b Model protocols (i.e., rules) and explain why they are used to transmit data across networks and the internet. Lessons
NI.N.8.c Explain how a system responds when information is lost to understand the effect it has on the transferred information. Lessons
NI.C.8.a Explain how physical and digital security measures are used to protect electronic information. Lessons
NI.C.8.b Compare and contrast the effects of different types of malware to determine strategies for how to protect devices. Lessons
NI.C.8.c Compare and contrast examples of various threat actors, such as nation-states, cyber terrorist groups, organized crime or hacktivists. Lessons
NI.C.8.d Explore and differentiate examples of complex encryption methods, e.g., Vigenère, Bacon’s cipher and Enigma. Lessons
NI.IOT.8.a Explore career pathways related to IoT to identify careers associated with the computer science field. Lessons
NI.IOT.8.b Model the lifecycle of information in the IoT including data gathering, transmission, reception and analysis to recreate a realworld scenario. Lessons
DA.DCS.8.a Interpret digital data collection tools to manage information effectively. Lessons
DA.DCS.8.b Identify data storage systems to define how data is stored and accessed. Lessons
DA.DCS.8.c Create a logical file structure to organize data in different storage systems to support individual and collaborative work. Lessons
DA.VC.8.a Evaluate data to construct a model or representation. Lessons
DA.VC.8.b Create a spreadsheet utilizing formulas, functions and graphs to represent and analyze data. Lessons
DA.IM.8.a Create and analyze models and simulations to accurately hypothesize a real-world situation. Lessons
ATP.A.8.a Create multiple pseudocode to solve a multi-step process and justify the most efficient solution. Lessons
ATP.VDR.8.a Analyze test cases and determine the range of valid solutions. Lessons
ATP.VDR.8.b Use a data structure to represent a collection. Lessons
ATP.CS.8.a Use and apply decisions and loops in a program to solve a problem. Lessons
ATP.M.8.a Decompose problems and subproblems into parts to facilitate the design, implementation and review of complex programs. Lessons
ATP.PD.8.a Write code that utilizes algorithms, variables and control structures to solve problems or as a creative expression. Lessons
ATP.PD.8.b Systematically test and refine programs using a range of test cases. Lessons
ATP.PD.8.c Use procedures that utilize parameters to pass values. Lessons
AI.P.8.a Explain how sounds and images are represented digitally in a computer to explain how sensor data is stored in a computer. Lessons
AI.P.8.b Describe how a vision system might exhibit cultural bias if it lacked knowledge of objects not found in the culture of the people who created it to create inclusive and equitable data sets. Lessons
AI.P.8.c Illustrate how sequences of words can be recognized as phrases, even if some of the words are unclear, by looking at how the words fit together to create a text recognition program. Lessons
AI.RR.8.a Model the process of solving a graph-search problem using breadth-first search to draw a search tree. Lessons
AI.ML.8.a Explain the difference between training and using a reasoning model to identify how a machine learns. Lessons
AI.ML.8.b Illustrate how objects in an image can be segmented and labeled to construct a training set for object recognition. Lessons
AI.ML.8.c Explain how the choice of training data shapes the behavior of the classifier to identify how bias can be introduced if the training set is not properly balanced. Lessons
AI.NI.8.a Create a program, individually and collaboratively, that implements a language processing algorithm to create a functional chatbot. Lessons
AI.NI.8.b Critically analyze and discuss features that make an entity “intelligent,” including discussing differences between human, animal and machine intelligence to identify how machine intelligence varies from natural intelligence. Lessons
AI.SI.8.a Identify and explain how the composition of training data affects the outcome of a supervised artificial intelligence system to identify bias in data sets. Lessons
AI.SI.8.b Identify bias potential in the design of artificial intelligence systems and describe how to utilize inclusive AI design to prevent algorithmic bias. Lessons
IC.Cu.8.a Compare current technologies and how they affect the current economy. Lessons
IC.Cu.8.b Propose potential guidelines/standards/criteria to positively impact bias and accessibility in the design of future technologies. Lessons
IC.Cu.8.c Identify and explore careers related to the field of computer science. Lessons
IC.Cu.8.d Explain how computing impacts innovation in other fields. Lessons
IC.SI.8.a Evaluate the impacts of electronic communication on personal relationships to be able to evaluate differences between face-to-face and electronic communication. Lessons
IC.SLE.8.a Explain user privacy concerns related to the collection and generation of data that may not be evident through automated processes. Lessons
IC.SLE.8.b Describe the social and economic implications of privacy in the context of safety, law or ethics to be global digital citizens. Lessons
IC.SLE.8.c Identify ethical and legal security measures used to protect electronic information. Lessons
IC.SLE.8.d Provide appropriate credit when using resources or artifacts that are not our own. Lessons