From 6441562e65d1654daaad97ecde92c67abfa05952 Mon Sep 17 00:00:00 2001 From: Greg Heileman Date: Wed, 9 Jan 2019 08:50:20 -0500 Subject: [PATCH] Attempted fix of figures in documentation --- docs/src/BW_plan.jl | 18 ------------------ docs/src/metrics.md | 10 +++++----- docs/src/visualize.md | 1 + 3 files changed, 6 insertions(+), 23 deletions(-) delete mode 100644 docs/src/BW_plan.jl diff --git a/docs/src/BW_plan.jl b/docs/src/BW_plan.jl deleted file mode 100644 index 8454ed08..00000000 --- a/docs/src/BW_plan.jl +++ /dev/null @@ -1,18 +0,0 @@ -using CurricularAnalytics - -A = Course("Introduction to Baskets", 3) -B = Course("Introduction to Baskets Lab", 1) -C = Course("Basic Basket Forms", 3) -D = Course("Advanced Basketry", 3) - -add_requisite!(A, B, strict_co) -add_requisite!(A, C, pre) -add_requisite!(C, D, co) - -curric = Curriculum("Basket Weaving", [A,B,C,D]) - -terms = Array{Term}(undef, 2) -terms[1] = Term([A,B]) -terms[2] = Term([C,D]) - -dp = DegreePlan("2-Term Plan", curric, terms) \ No newline at end of file diff --git a/docs/src/metrics.md b/docs/src/metrics.md index af9c57f2..061d75c3 100644 --- a/docs/src/metrics.md +++ b/docs/src/metrics.md @@ -12,9 +12,9 @@ CurricularAnalytics The blocking factor is an important curriculum-based metric because it measures the extent to which one course blocks the ability to take other courses in the curriculum. That is, a course with a high blocking factor acts as a gateway to many other courses in the curriculum. Students who are unable to pass the gateway course will be blocked from taking many other courses in the curriculum. -We define the blocking factor of a course ``v_i`` as the number of courses in the graph that are reachable from ``v_i``. As examples of the blocking factor metric, conisder the two four-course curricula, with courses ``v_1, v_2, v_3`` and ``v_4``, shown below. In part (a) of this figure, ``v_1`` is a prerequisite for courses ``v_2`` and ``v_3``, and ``v_2`` is a prerequisite for course ``v_4``, while in part (b), courses ``v_1`` and ``v_2`` are prerequisites for course ``v_3``, and ``v_3`` is a prerequisite for course ``v_4``. The blocking factor of each course are shown inside of the course vertices in this figure. +We define the blocking factor of a course ``v_i`` as the number of courses in the graph that are reachable from ``v_i``. As examples of the blocking factor metric, conisder the two four-course curricula, with courses ``v_1, v_2, v_3`` and ``v_4``, shown below. In part (a) of this figure, ``v_1`` is a prerequisite for courses ``v_2`` and ``v_3``, and ``v_2`` is a prerequisite for course ``v_4``, while in part (b), courses ``v_1`` and ``v_2`` are prerequisites for course ``v_3``, and ``v_3`` is a prerequisite for course ``v_4``. The blocking factor of each course are shown inside of the course vertices in this figure. -![blocking factor example](./blocking_ex.png) +![blocking factor example](./blocking-ex.png) ```@docs blocking_factor @@ -26,7 +26,7 @@ Many curricula, particularly those in science, technology engineering and math ( We define the delay factor of course vertex ``v_i`` to be the length of the longest path that contains ``v_i``. As an example of the delay factor metric, consider the same four-course curricula shown above. The delay factor of each course are shown inside of the course vertices in the figure below. -![delay factor example](./delay_ex.png) +![delay factor example](./delay-ex.png) ```@docs delay_factor @@ -38,7 +38,7 @@ A course can be thought of as central to a curriculum if it requires a number of We define the centrality of source and sink vertices to be 0. For all other course vertices, consider all of the long paths (i.e., unique paths from a source to a sink) containing course vertex ``v_i``. The centrality of ``v_i`` is given by the sum of these path lengths. As an example of the centrality metric, consider the same four-course curricula shown above. The centrality factor of each course are shown inside of the vertices in the figure below. -![centrality factor example](./centrality_ex.png) +![centrality example](./centrality-ex.png) In the case of the curriculum in part (a), there is one long path of length three that includes course ``v_2``, hence its centrality is 3, while in part (b), there are two long paths of length three that include course ``v_2``, hence its centrality is 6. @@ -52,7 +52,7 @@ The curricular complexity of a course is meant to capture the impact of curricul As an example of the structural complexity metric, consider the same four-course curricula shown above. The compleixty factor of each course, which is simply the sum of the course's delay and blocking factors, are shown inside of the course vertices in this figure. -![complexity factor example](./complexity_ex.png) +![complexity example](./complexity-ex.png) ```@docs complexity diff --git a/docs/src/visualize.md b/docs/src/visualize.md index cf69613c..ca5a361f 100644 --- a/docs/src/visualize.md +++ b/docs/src/visualize.md @@ -31,5 +31,6 @@ This should produce the following window: In order to visualize the various metrics associated with this degree plan, simply hover your mouse over a course, as shown below: ![UK EE degree plan metrics](./UK-EE-metrics.png) + To learn more about the metrics displayed in this visualization, see the [Metrics](@ref) section.