Working With Canvas Data • nemogb

library(nemogb)

Canvas is a popular classroom management tool that is commonly used to grade assignments and collect student grades throughout colleges and universities. This article describes how to use nemogb with Canvas data.

Advanced users wishing to gain a deep understanding of how Canvas data is structured can also read the Detailed Canvas Export Info vignette.

Getting the Data

This article details nemogb workflows for users who use Canvas to collect student assignment grades. To use nemogb, users must first source the necessary csv files containing student assignment data from Canvas.

The primary csv file needed from Canvas is the grades file, which contains the assignment grades for each student. To source this file, navigate to the “Grades” tab on Canvas. On the grades page, click “Export” and then “Export Entire Gradebook”.

The grades csv is the only file that is needed to generate final grades with nemogb. Read below for more information on other data that can be exported from Canvas.

Data Format

The internal standard for nemogb data is for assignment data to be formatted like Gradescope data, which is another platform for collecting student grades. nemogb functionality converts the Canvas assignment data into our internal standards for seamless use of Canvas data.

To use grade data from Canvas, simply use read_files(); this process is not unique to Canvas data. nemogb assesses the source of the file (e.g. Gradescope, Canvas, or other) and load in file in appropriately. To force the file to be read in as a Canvas sourced file, users can pass the argument source = "Canvas" into the read_files() function. nemogb will track where the data originated and prevent potentially dangerous operations during grading.

After reading in the data, the data can be used to calculate grades as in the case study vignettes.

Here is an example of the transformation nemogb applies to Canvas grade data.

Below is assignment grade data as exported from Canvas.

#> Rows: 11 Columns: 38
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (27): Student, SIS Login ID, Section, Assignments Current Score, Assignm...
#> dbl (11): ID, SIS User ID, HW 1 (8595677), HW 2 (8595678), HW 3 (8595679), H...
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Student	ID	SIS User ID	SIS Login ID	Section	HW 1 (8595677)	HW 2 (8595678)	HW 3 (8595679)	HW 4 (8595680)	Q1 (8671819)	Q2 (8671820)	MT1 (8671822)	MT2 (8671823)	final exam (8677368)	Assignments Current Score	Assignments Unposted Current Score	Assignments Final Score	Assignments Unposted Final Score	Quizzes Current Score	Quizzes Unposted Current Score	Quizzes Final Score	Quizzes Unposted Final Score	Midterms Current Score	Midterms Unposted Current Score	Midterms Final Score	Midterms Unposted Final Score	final exam Current Score	final exam Unposted Current Score	final exam Final Score	final exam Unposted Final Score	Current Score	Unposted Current Score	Final Score	Unposted Final Score	Current Grade	Unposted Current Grade	Final Grade	Unposted Final Grade
Points Possible	NA	NA	NA	NA	1	1	1	1	30.0	30.0	50	50	100.00	(read only)	(read only)	(read only)	(read only)	(read only)	(read only)	(read only)	(read only)	(read only)	(read only)	(read only)	(read only)	(read only)	(read only)	(read only)	(read only)	(read only)	(read only)	(read only)	(read only)	(read only)	(read only)	(read only)	(read only)
Hendricks, Jeff	5620182	1033455469	1756724	Your Class Discussion 1	1	1	0	1	19.0	0.0	23	10	0.00	44.44	44.44	44.44	44.44	31.67	31.67	31.67	31.67	33.00	33.00	33.00	33.00	0.00	0.00	0.00	0.00	22.44	22.44	22.44	22.44	F	F	F	F
Smith, Karen	5734457	1034536709	1833456	Your Class Discussion 1	1	1	1	0	0.0	0.0	7	0	0.00	33.33	33.33	33.33	33.33	0.00	0.00	0.00	0.00	7.00	7.00	7.00	7.00	0.00	0.00	0.00	0.00	9.53	9.53	9.53	9.53	F	F	F	F
Berger, Henry	5721938	1033320625	1886709	Your Class Discussion 1	1	1	1	0	17.0	20.5	31	21	45.25	100.00	100.00	100.00	100.00	62.50	62.50	62.50	62.50	52.00	52.00	52.00	52.00	45.25	45.25	45.25	45.25	59.04	59.04	59.04	59.04	B+	B+	B+	B+
Whitley, Alan	5571234	1035823612	1723974	Your Class Discussion 1	0	1	1	1	11.0	10.0	11	10	35.38	77.78	77.78	77.78	77.78	35.00	35.00	35.00	35.00	21.00	21.00	21.00	21.00	35.38	35.38	35.38	35.38	39.71	39.71	39.71	39.71	C+	C+	C+	C+
Jackson, Keith	5354659	1032235053	3416951	Your Class Discussion 1	1	1	1	1	26.0	7.0	22	3	26.00	77.78	77.78	77.78	77.78	55.00	55.00	55.00	55.00	25.00	25.00	25.00	25.00	26.00	26.00	26.00	26.00	37.36	37.36	37.36	37.36	C+	C+	C+	C+
Harris, Miranda	5621984	1033457242	1769716	Your Class Discussion 1	0	1	0	0	15.5	1.0	10	4	20.50	88.89	88.89	88.89	88.89	27.50	27.50	27.50	27.50	14.00	14.00	14.00	14.00	20.50	20.50	20.50	20.50	30.47	30.47	30.47	30.47	C-	C-	C-	C-
Tines, Maria	5578591	1034768282	1725568	Your Class Discussion 1	0	1	0	0	18.5	11.0	12	12	18.75	55.56	55.56	55.56	55.56	49.17	49.17	49.17	49.17	24.00	24.00	24.00	24.00	18.75	18.75	18.75	18.75	30.35	30.35	30.35	30.35	C-	C-	C-	C-
Rizzo, Teoscar	5654659	1045099144	1440632	Your Class Discussion 1	1	1	1	1	19.5	11.0	34	7	11.50	88.89	88.89	88.89	88.89	50.83	50.83	50.83	50.83	41.00	41.00	41.00	41.00	11.50	11.50	11.50	11.50	39.47	39.47	39.47	39.47	C+	C+	C+	C+
Craig, Linda	5523085	1030100042	1724586	Your Class Discussion 1	0	0	0	0	18.0	0.0	4	0	0.00	0.00	0.00	0.00	0.00	30.00	30.00	30.00	30.00	4.00	4.00	4.00	4.00	0.00	0.00	0.00	0.00	5.38	5.38	5.38	5.38	F	F	F	F
Student, Test	5743823	NA	fee755ff9407d959738822f95f5e2613d9b7688b	Your Class Discussion 1	0	0	0	0	0.0	0.0	0	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	F	F	F	F

Below is the dataframe after being transformed by nemogb.

#> Rows: 11 Columns: 38
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (27): Student, SIS Login ID, Section, Assignments Current Score, Assignm...
#> dbl (11): ID, SIS User ID, HW 1 (8595677), HW 2 (8595678), HW 3 (8595679), H...
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

First Name	Last Name	SID	Sections	HW 1 (8595677)	HW 1 (8595677) - Max Points	HW 1 (8595677) - Submission Time	HW 1 (8595677) - Lateness (H:M:S)	HW 2 (8595678)	HW 2 (8595678) - Max Points	HW 2 (8595678) - Submission Time	HW 2 (8595678) - Lateness (H:M:S)	HW 3 (8595679)	HW 3 (8595679) - Max Points	HW 3 (8595679) - Submission Time	HW 3 (8595679) - Lateness (H:M:S)	HW 4 (8595680)	HW 4 (8595680) - Max Points	HW 4 (8595680) - Submission Time	HW 4 (8595680) - Lateness (H:M:S)	Q1 (8671819)	Q1 (8671819) - Max Points	Q1 (8671819) - Submission Time	Q1 (8671819) - Lateness (H:M:S)	Q2 (8671820)	Q2 (8671820) - Max Points	Q2 (8671820) - Submission Time	Q2 (8671820) - Lateness (H:M:S)	MT1 (8671822)	MT1 (8671822) - Max Points	MT1 (8671822) - Submission Time	MT1 (8671822) - Lateness (H:M:S)	MT2 (8671823)	MT2 (8671823) - Max Points	MT2 (8671823) - Submission Time	MT2 (8671823) - Lateness (H:M:S)	final exam (8677368)	final exam (8677368) - Max Points	final exam (8677368) - Submission Time	final exam (8677368) - Lateness (H:M:S)
Jeff	Hendricks	1033455469	Your Class Discussion 1	1	1	NA	00:00:00	1	1	NA	00:00:00	0	1	NA	00:00:00	1	1	NA	00:00:00	19.0	30	NA	00:00:00	0.0	30	NA	00:00:00	23	50	NA	00:00:00	10	50	NA	00:00:00	0.00	100	NA	00:00:00
Karen	Smith	1034536709	Your Class Discussion 1	1	1	NA	00:00:00	1	1	NA	00:00:00	1	1	NA	00:00:00	0	1	NA	00:00:00	0.0	30	NA	00:00:00	0.0	30	NA	00:00:00	7	50	NA	00:00:00	0	50	NA	00:00:00	0.00	100	NA	00:00:00
Henry	Berger	1033320625	Your Class Discussion 1	1	1	NA	00:00:00	1	1	NA	00:00:00	1	1	NA	00:00:00	0	1	NA	00:00:00	17.0	30	NA	00:00:00	20.5	30	NA	00:00:00	31	50	NA	00:00:00	21	50	NA	00:00:00	45.25	100	NA	00:00:00
Alan	Whitley	1035823612	Your Class Discussion 1	0	1	NA	00:00:00	1	1	NA	00:00:00	1	1	NA	00:00:00	1	1	NA	00:00:00	11.0	30	NA	00:00:00	10.0	30	NA	00:00:00	11	50	NA	00:00:00	10	50	NA	00:00:00	35.38	100	NA	00:00:00
Keith	Jackson	1032235053	Your Class Discussion 1	1	1	NA	00:00:00	1	1	NA	00:00:00	1	1	NA	00:00:00	1	1	NA	00:00:00	26.0	30	NA	00:00:00	7.0	30	NA	00:00:00	22	50	NA	00:00:00	3	50	NA	00:00:00	26.00	100	NA	00:00:00
Miranda	Harris	1033457242	Your Class Discussion 1	0	1	NA	00:00:00	1	1	NA	00:00:00	0	1	NA	00:00:00	0	1	NA	00:00:00	15.5	30	NA	00:00:00	1.0	30	NA	00:00:00	10	50	NA	00:00:00	4	50	NA	00:00:00	20.50	100	NA	00:00:00
Maria	Tines	1034768282	Your Class Discussion 1	0	1	NA	00:00:00	1	1	NA	00:00:00	0	1	NA	00:00:00	0	1	NA	00:00:00	18.5	30	NA	00:00:00	11.0	30	NA	00:00:00	12	50	NA	00:00:00	12	50	NA	00:00:00	18.75	100	NA	00:00:00
Teoscar	Rizzo	1045099144	Your Class Discussion 1	1	1	NA	00:00:00	1	1	NA	00:00:00	1	1	NA	00:00:00	1	1	NA	00:00:00	19.5	30	NA	00:00:00	11.0	30	NA	00:00:00	34	50	NA	00:00:00	7	50	NA	00:00:00	11.50	100	NA	00:00:00
Linda	Craig	1030100042	Your Class Discussion 1	0	1	NA	00:00:00	0	1	NA	00:00:00	0	1	NA	00:00:00	0	1	NA	00:00:00	18.0	30	NA	00:00:00	0.0	30	NA	00:00:00	4	50	NA	00:00:00	0	50	NA	00:00:00	0.00	100	NA	00:00:00

Limitations

With our current implemention, there are limitations to using assignment data sourced from Canvas, resulting in a reduction of potential grading functionality in nemogb. The primary limitation surrounds the application of lateness policies when using data sourced from Canvas.

The grades csv exported from Canvas does not include information on assignment lateness. As mentioned below, Canvas also can export files detailing late assignments. However, this lateness file omits the time that the assignment was due, making it not possible to accurately calculate how late any late assignment is. As a result, we do not allow lateness policies to be used when the data is sourced from Canvas. Attempts to do so will raise an error.

Currently, our recommendation is for Canvas users to apply any late policies in Canvas, since Canvas can apply simple lateness policies.

Other Data

In addition to the grades file, Canvas also exports information that can be relevant to grade calculation. Examples include the lateness csv file and a roster csv file. Integrating the information from these two files are currently not supported.

The lateness file details which assignments where submitted late by which students. Due to the drawbacks mentioned above, we are currently not using this file to implement lateness penalties in nemogb.

The roster csv file gives all student names and their contact information, which can be relevant if instructors desire to have student contact information available in the exported grades file.

Both of these files are accessed through the “New Analytics” tab available on the right side of the course homepage. To download these files, click Analytics on the home page, navigate to the Reports tab and download the desired report.

Currently, providing files other than the grades file is not supported by nemogb. However, we have built the infrastructure to read in these files for future integration of other data sources (e.g. the lateness or roster csv files from Canvas).

For full information on the formatting of Canvas exported data, see the Detailed Canvas Export Info vignette.