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Impact of Prior Exposure to the PLP Instruction Set Architecture in a Computer Architecture Course

Published:08 March 2017Publication History

ABSTRACT

This paper presents an initial investigation on the effect of non-pre-exposure to an instruction set architecture (ISA). In particular, a specialized ISA based on the Progressive Learning Platform (PLP) is implemented in the computer architecture course. Prior research has demonstrated the benefits of using PLP in the computer engineering curriculum. However, it is possible that the PLP ISA could hinder learning by requiring extra work for students to master it (extraneous load), if they have not had prior exposure to it. To investigate this, the current study implemented a quasi-experimental design with two groups (students knowledgeable with PLP from a previous course, and new users) and a pretest to determine differences in students' familiarity with the common terms in computer engineering, pre-requisite knowledge for a computer architecture course, and course knowledge. Both sets of students implemented the PLP CPU in behavioral Verilog in the computer architecture course. Results of the evaluations revealed significant learning from pretest to posttest by students in both groups on all measures. Moreover, no group differences were seen, indicating that pre exposure to an ISA (specifically PLP ISA) might not be necessary for successful course implementation. This is promising, considering that many students at 4-year colleges in the USA transfer from other institutions, and may have exposure to different instruction set architectures in their prerequisite courses. The sample size for this study is too small to draw a firm conclusion, but these preliminary findings merit further exploration of this topic.

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                cover image ACM Conferences
                SIGCSE '17: Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education
                March 2017
                838 pages
                ISBN:9781450346986
                DOI:10.1145/3017680

                Copyright © 2017 ACM

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                • Published: 8 March 2017

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