| 1 | # -*- coding: utf-8; mode: tcl; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- vim:fenc=utf-8:ft=tcl:et:sw=4:ts=4:sts=4 |
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| 2 | # $Id$ |
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| 3 | |
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| 4 | PortSystem 1.0 |
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| 5 | PortGroup github 1.0 |
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| 6 | PortGroup python 1.0 |
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| 7 | |
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| 8 | github.setup tazzben WW 1.1 |
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| 9 | |
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| 10 | categories science education |
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| 11 | platforms darwin |
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| 12 | maintainers unomaha.edu:bosmith |
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| 13 | license MIT |
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| 14 | supported_archs noarch |
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| 15 | |
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| 16 | description A command line tool to disaggregate pre and post test responses into Walstad and Wagner learning types |
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| 17 | |
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| 18 | long_description In the Spring of 2016, Walstad and Wagner released a paper suggesting that the pretest/posttest delta is insufficient in assessing learning outcomes. However, performing such a disaggregation is time intensive, especially if the questions appear in a different location (or order) on the pre and post test. WW_out is a command line tool that makes this disaggregation easy. It uses four CSV files to generate outcomes by question and student. |
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| 19 | |
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| 20 | checksums rmd160 241bd5236a9d31e4cffd7066e341b4294051ba7c \ |
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| 21 | sha256 f6ef7d0f4f185bf9da203cf3f24260e3af1abc672a4791b8163834036824bf94 |
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| 22 | |
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| 23 | python.default_version 27 |
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| 24 | |
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| 25 | depends_build port:py${python.version}-setuptools |
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| 26 | depends_lib-append port:py${python.version}-pandas |
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