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Visualizations are proved to be beneficial in facilitating learning. Various parameters are used for classifying different types of visualizations. Interactivity level in visualization, as one of the parameters, encourages experimental mode of learning; especially useful in engineering education. This paper details the work-in-progress that offers guidelines for selecting an appropriate visualization based on its interactivity level for a given learning objective. The authors propose `Visualization Selection Matrix' a tool for selecting visualization, that maps cognitive levels and content types of a learning objective to interactivity level of visualization. The justification for the matrix is being offered in the context of a course on `Signals and Systems'. The paper concludes with a brief outline of experimentation planned for validating `Visualization Selection Matrix'.