| Title: | GIFT Questions Format Generator from Dataframes |
|---|---|
| Description: | A framework and functions to create 'MOODLE' quizzes. 'GIFTr' takes dataframe of questions of four types: multiple choices, numerical, true or false and short answer questions, and exports a text file formatted in 'MOODLE' GIFT format. You can prepare a spreadsheet in any software and import it into R to generate any number of questions with 'HTML', 'markdown' and 'LaTeX' support. |
| Authors: | Omar I. Elashkar |
| Maintainer: | Omar I. Elashkar <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2024-11-11 03:44:19 UTC |
| Source: | https://github.com/omarashkar/giftr |
GIFTr function is the main function of 'GIFTr' package.
You provide it dataframe and select question_type column
where a more specialized functions can process the file. See Details and Examples
GIFTr(data, output, questions, answers, question_type, categories = NULL, question_names = NULL, mcq_answer_column = FALSE, verbose = TRUE)GIFTr(data, output, questions, answers, question_type, categories = NULL, question_names = NULL, mcq_answer_column = FALSE, verbose = TRUE)
data |
dataframe or tibble of the questions data |
output |
directory of .txt file name the questions will be exported to. |
questions |
name(string) or index(integer) of the questions column |
answers |
a vector of names(strings) or indices of answers column(s) |
question_type |
name(string) or index(integer) of the questions type column. |
categories |
name(string) or index(integer) of categories column if available, Default: NULL |
question_names |
name(string) or index(integer) of the questions names column. If NULL, it will be the first 40 letters of the question title, Default: NULL |
mcq_answer_column |
If TRUE, the first column of answers columns will be set as the right answer, Default: FALSE |
verbose |
If TRUE, the functions will print to the console the statistics of writing the output, Default: TRUE |
'GIFTr' package is intended to reduce the time a course creator would
take to make input question on 'MOODLE' without the pain of writing markup.
'GIFTr' package is build on 'MOODLE' guidelines. The idea is simple, you create a spreadsheet in a special format, call GIFTr function, get a GIFT formatted file that can be imported by 'MOODLE' and other LMS systems. GIFTr function is unique in that it gives you detailed statistics and can work with the 4 question types supported by 'GIFTr' package. question_type argument is unique for GIFTr function in this package and must be passed to map the questions. The current supported question types are mcq{multiple choices question}, num_q{numeric entry}, tf_q{true or talse}, and short_ans{short answer}questions.You can find more details on the individual functions details.
'GIFTr' supports basic markdown and GIFT syntax. See the vignette and sections below for complete documentation of formatting your data.
None
A guideline for creating you questions data can be found below. Check the data GIFTrData and GIFTrData_2 as example for formatted questions.
'MOODLE' itself supports basic markdown and HTML for questions formatting. So when formatting your data you can use HTML tags like <sub> and <sup>. Also you can use markdown **bold** or __bold__ and *italic* or _italic_ ...etc. However it is better a better practice to avoid using asterisk to avoid confusion with MCQ format. For more about the supported markdown see 'MOODLE' documentation.
'MOODLE' also supports inline and block LATEX equations through mathjax, however you have to be careful with the special characters like curly brackets // and equal sign = , so you have to use back slash before those to ensure you can import correctly.
Note that if you see thee data in console, you will find it with 2 backslash \, however that's the escaping of the backslash in R and you write with with single slash normally. Check GIFTrData GIFTrData[11,3]for an example. For further details on LATEX, check 'MOODLE' docs.
You can easily choose to enter a feedback by using #sign after the answer you want to specify a feedback on. Check GIFTrData for examples.
The data passed would differ slightly according to question type, however generally you need to have:
a column contains question. This cannot contain empty values
answer(s) column(s). This may be multiple columns if you have multiple answers. If you mix single and multiple answer it is better to write the single answer in the first column. If you have only single answer MCQ and NO multiple answer MCQ, you can set the answers without asterisk in the first column of the answers columns. More details in the vignette and below.
a column specifying the type of question. The current supported questions are multiple choices, numerical entry, true or false and short answer questions. The should be named 'mcq' , 'num_q' , 'tf_q' and 'short_ans' respectively.
a column specifying categories and subcategories in the 'MOODLE' categories are important when you are preparing a quiz as you may want to specify certain proportions of inclusions. Categories and subcategories are spaced by forward slash like Categ1/subcateg1/subsubcateg1 ...etc.
a column specifying question names. So you can easily enter a certain question names by like certain ID or keyword to make the questions easily on the system. however you don't need to worry about that as automatically if not set, the first 40 letter are set a question name.
You can specify answers simply using asterisk in a the start on the answer. If you choose more than one answer, the function will generate a multiple answers mcq with every answer holds partial even credit. See GIFTrData for this usage.
If you intend to use single answer only, you might specify 'make_answer' or 'mcq_answer_column' to TRUE, this will consider the first answer column to be the answer. For example, if the answers are in columns c(5,9), the answers will be listed in the first column, 5. See GIFTrData_2 for this usage.
Numeric Answer can be in single column or multiple columns. You can also format it as range or interval limit for the answer and partial credit. For further illustration, check the vignette.
If you want the answer to be between 1 and 2, you can enter you data as '1..2'. If you want to set an acceptance limit, for example your answer is '158' and you want to set limit plus or minus 2, you can write it as '158:2'. Check the GIFTrData for examples.
You enter multiple numeric answers with the same concepts above, but if you did not enter the credits for an answer, 'MOODLE' will consider it '100%'. So you have to specify the credit at the start of an answer. For example '122' and '%50%122:5'.
True or False answers is to be set in 1 column with letter 'T' or 'F' insensitive to the case.
For further illustration, check GIFTrData.
Short answer question answers can be in single column or multiple columns. If an answer has not credit it will be given 100% credit automatically. For example if the answer is 'statistics', it will be equivalent to '%100%statistics'. While '%80%Data Science' answer will take 80% of the credit
For further illustration, check GIFTrData.
#' load Data and Check structure data(GIFTrData) str(GIFTrData) GIFTr::GIFTr(data = GIFTrData, questions = 3, answers = c(4:8), categories = 1, question_type = 9, output = file.path(tempdir(), "quiz.txt")) #write file"quiz.txt" in tempdir() GIFTr::GIFTr(data = GIFTrData, question_names = 2, questions = 3, answers = c(4:8), categories = 1, question_type = 9, output = file.path(tempdir(), "quiz2.txt")) #write file"quiz2.txt" in tempdir()#' load Data and Check structure data(GIFTrData) str(GIFTrData) GIFTr::GIFTr(data = GIFTrData, questions = 3, answers = c(4:8), categories = 1, question_type = 9, output = file.path(tempdir(), "quiz.txt")) #write file"quiz.txt" in tempdir() GIFTr::GIFTr(data = GIFTrData, question_names = 2, questions = 3, answers = c(4:8), categories = 1, question_type = 9, output = file.path(tempdir(), "quiz2.txt")) #write file"quiz2.txt" in tempdir()
DATASET_DESCRIPTION
data(GIFTrData)data(GIFTrData)
A data frame with 10 rows and 9 variables:
categoriescharacter categories of questions to be imported to 'MOODLE' to hierarchical order
namescharacter names of the questions
questioncharacter question text
Acharacter first answer option
Bcharacter second answer option
Ccharacter third answer option
Dcharacter fourth answer option
Echaracter fifth answer option
question_typecharacter question type value should be either 'mcq', 'num_q', 'tf_q', 'short_ans'
This data is a demo on a typical structure of data passed in 'GIFTr' package. This data mcq question contains asterisk in the first 2 questions, one with single answer and the other is multiple answers question.
Markdown is supported in GIFT format and example for it's usage in first and second question text.
Simple HTML syntax is supported as in question 10
LATEX is supported in GIFT but you have to escape any curly brackets as in question 11
Partial Credits for a question can be specified if like in question 9, you could also use them in short answer question. If you have a short answer question or numeric question with no specified credits, this answer will take "100%" credit
Every answer can by accompanied with feedback using # as in questions 1 and 9
This data is a manipulated copy of GIFTrData data with removal of the multiple answers mcq and removal of asterisk from the single answer mcq.
data(GIFTrData_2)data(GIFTrData_2)
A data frame with 10 rows and 9 variables:
categoriescharacter categories of questions to be imported to 'MOODLE' to hierarchical order
namescharacter names of the questions
questioncharacter question text
Acharacter first answer option, this column must be the answer column for mcq
Bcharacter second answer option
Ccharacter third answer option
Dcharacter fourth answer option
Echaracter fifth answer option
question_typecharacter question type value should be either 'mcq', 'num_q', 'tf_q', 'short_ans'
This data is an altered form of GIFTrData. Check it usage in mcq. For further details on the data structure, check GIFTr documentation.
Create GIFT file with single & multiple answers MCQs from a spreadsheet to be exported to LMS.
mcq(data, questions, answers, categories = NULL, question_names = NULL, output, make_answer = FALSE, verbose = TRUE)mcq(data, questions, answers, categories = NULL, question_names = NULL, output, make_answer = FALSE, verbose = TRUE)
data |
dataframe or tibble of mcq questions data |
questions |
name(string) or index(integer) of the questions column |
answers |
a vector of names(strings) or indices of answers column(s) |
categories |
name(string) or index(integer) of categories column if available, Default: NULL |
question_names |
name(string) or index(integer) of the questions names column. If NULL, it will be the first 40 letters of the question title, Default: NULL |
output |
string of .txt file name and path where the questions will be exported to. |
make_answer |
If TRUE, the first column of answers columns will be set as the right answer, Default: FALSE. |
verbose |
If TRUE, the functions will print to the console the statistics of writing the output, Default: TRUE |
mcq function takes a dataframe with multiple choices questions(mcq) and export a text file in 'MOODLE' GIFT format. The function automatically makes an mcq a single answer or multiple answers depends on asterisks present in the answers column. If you have additional column of question_type set to 'mcq' you can also use GIFTr function which wraps all question generating functions.
See Vignette and GIFTrData for demos.
None
You can specify answers simply using asterisk in a the start on the answer. If you choose more than one answer, the function will generate a multiple answers mcq with every answer holds partial even credit. See GIFTrData for this usage.
If you intend to use single answer only, you might specify 'make_answer' or 'mcq_answer_column' to TRUE, this will consider the first answer column to be the answer. For example, if the answers are in columns c(5,9), the answers will be listed in the first column, 5. See GIFTrData_2 for this usage.
A guideline for creating you questions data can be found below. Check the data GIFTrData and GIFTrData_2 as example for formatted questions.
'MOODLE' itself supports basic markdown and HTML for questions formatting. So when formatting your data you can use HTML tags like <sub> and <sup>. Also you can use markdown **bold** or __bold__ and *italic* or _italic_ ...etc. However it is better a better practice to avoid using asterisk to avoid confusion with MCQ format. For more about the supported markdown see 'MOODLE' documentation.
'MOODLE' also supports inline and block LATEX equations through mathjax, however you have to be careful with the special characters like curly brackets // and equal sign = , so you have to use back slash before those to ensure you can import correctly.
Note that if you see thee data in console, you will find it with 2 backslash \, however that's the escaping of the backslash in R and you write with with single slash normally. Check GIFTrData GIFTrData[11,3]for an example. For further details on LATEX, check 'MOODLE' docs.
You can easily choose to enter a feedback by using #sign after the answer you want to specify a feedback on. Check GIFTrData for examples.
The data passed would differ slightly according to question type, however generally you need to have:
a column contains question. This cannot contain empty values
answer(s) column(s). This may be multiple columns if you have multiple answers. If you mix single and multiple answer it is better to write the single answer in the first column. If you have only single answer MCQ and NO multiple answer MCQ, you can set the answers without asterisk in the first column of the answers columns. More details in the vignette and below.
a column specifying the type of question. The current supported questions are multiple choices, numerical entry, true or false and short answer questions. The should be named 'mcq' , 'num_q' , 'tf_q' and 'short_ans' respectively.
a column specifying categories and subcategories in the 'MOODLE' categories are important when you are preparing a quiz as you may want to specify certain proportions of inclusions. Categories and subcategories are spaced by forward slash like Categ1/subcateg1/subsubcateg1 ...etc.
a column specifying question names. So you can easily enter a certain question names by like certain ID or keyword to make the questions easily on the system. however you don't need to worry about that as automatically if not set, the first 40 letter are set a question name.
#data with asterisk and multiple answer mcq(Q2 question) data(GIFTrData) mcqdata <- GIFTrData[which(GIFTrData$question_type == "mcq"),] mcq(data = mcqdata, questions = 3, answers = c(4:8), categories = 1, question_names = 2, output = file.path(tempdir(), "mcq.txt")) #No make_answer argument, question_name specified #write file "mcq.txt"in tempdir() #data with no atrisk and no multiple answer mcq mcqdata_2 <- GIFTrData_2[which(GIFTrData_2$question_type == "mcq"),] mcq(data = mcqdata_2, questions = 3, answers = c(4:8), categories = 1, question_names = 2, make_answer = TRUE, output = file.path(tempdir(), "mcq_1.txt")) #Answer is in column 4 #write file "mcq_1.txt"in tempdir()#data with asterisk and multiple answer mcq(Q2 question) data(GIFTrData) mcqdata <- GIFTrData[which(GIFTrData$question_type == "mcq"),] mcq(data = mcqdata, questions = 3, answers = c(4:8), categories = 1, question_names = 2, output = file.path(tempdir(), "mcq.txt")) #No make_answer argument, question_name specified #write file "mcq.txt"in tempdir() #data with no atrisk and no multiple answer mcq mcqdata_2 <- GIFTrData_2[which(GIFTrData_2$question_type == "mcq"),] mcq(data = mcqdata_2, questions = 3, answers = c(4:8), categories = 1, question_names = 2, make_answer = TRUE, output = file.path(tempdir(), "mcq_1.txt")) #Answer is in column 4 #write file "mcq_1.txt"in tempdir()
Create GIFT file with numeric entry questions from a spreadsheet to be exported to LMS.
num_q(data, questions, answers, categories, question_names = NULL, output, verbose = TRUE)num_q(data, questions, answers, categories, question_names = NULL, output, verbose = TRUE)
data |
dataframe or tibble of numerical entry questions data |
questions |
name(string) or index(integer) of the questions column |
answers |
a vector of names(strings) or indices of answers column(s) |
categories |
name(string) or index(integer) of categories column if available, Default: NULL |
question_names |
name(string) or index(integer) of the questions names column. If NULL, it will be the first 40 letters of the question title, Default: NULL |
output |
string of .txt file name and path where the questions will be exported to. |
verbose |
If TRUE, the functions will print to the console the statistics of writing the output, Default: TRUE |
num_q function takes a dataframe with numeric entry questions and export a text file in 'MOODLE' GIFT format. The function automatically makes an numeric entry question either single or multiple according to your data format(check numeric questions formatting below). If you have additional column of question_type set to 'num_q' you can also use GIFTr function which wraps all question generating functions.
See Vignette and GIFTrData for demos.
None
Numeric Answer can be in single column or multiple columns. You can also format it as range or interval limit for the answer and partial credit. For further illustration, check the vignette.
If you want the answer to be between 1 and 2, you can enter you data as '1..2'. If you want to set an acceptance limit, for example your answer is '158' and you want to set limit plus or minus 2, you can write it as '158:2'. Check the GIFTrData for examples.
You enter multiple numeric answers with the same concepts above, but if you did not enter the credits for an answer, 'MOODLE' will consider it '100%'. So you have to specify the credit at the start of an answer. For example '122' and '%50%122:5'.
A guideline for creating you questions data can be found below. Check the data GIFTrData and GIFTrData_2 as example for formatted questions.
'MOODLE' itself supports basic markdown and HTML for questions formatting. So when formatting your data you can use HTML tags like <sub> and <sup>. Also you can use markdown **bold** or __bold__ and *italic* or _italic_ ...etc. However it is better a better practice to avoid using asterisk to avoid confusion with MCQ format. For more about the supported markdown see 'MOODLE' documentation.
'MOODLE' also supports inline and block LATEX equations through mathjax, however you have to be careful with the special characters like curly brackets // and equal sign = , so you have to use back slash before those to ensure you can import correctly.
Note that if you see thee data in console, you will find it with 2 backslash \, however that's the escaping of the backslash in R and you write with with single slash normally. Check GIFTrData GIFTrData[11,3]for an example. For further details on LATEX, check 'MOODLE' docs.
You can easily choose to enter a feedback by using #sign after the answer you want to specify a feedback on. Check GIFTrData for examples.
The data passed would differ slightly according to question type, however generally you need to have:
a column contains question. This cannot contain empty values
answer(s) column(s). This may be multiple columns if you have multiple answers. If you mix single and multiple answer it is better to write the single answer in the first column. If you have only single answer MCQ and NO multiple answer MCQ, you can set the answers without asterisk in the first column of the answers columns. More details in the vignette and below.
a column specifying the type of question. The current supported questions are multiple choices, numerical entry, true or false and short answer questions. The should be named 'mcq' , 'num_q' , 'tf_q' and 'short_ans' respectively.
a column specifying categories and subcategories in the 'MOODLE' categories are important when you are preparing a quiz as you may want to specify certain proportions of inclusions. Categories and subcategories are spaced by forward slash like Categ1/subcateg1/subsubcateg1 ...etc.
a column specifying question names. So you can easily enter a certain question names by like certain ID or keyword to make the questions easily on the system. however you don't need to worry about that as automatically if not set, the first 40 letter are set a question name.
data(GIFTrData) #data with numeric entry questions numq_data <- GIFTrData[which(GIFTrData$question_type == "num_q"),] num_q(data = numq_data, questions = 3, answers = c(4:8), categories = 1, question_names = 2, output = file.path(tempdir(), "numq.txt")) #write file "numq.txt" in tempdir()data(GIFTrData) #data with numeric entry questions numq_data <- GIFTrData[which(GIFTrData$question_type == "num_q"),] num_q(data = numq_data, questions = 3, answers = c(4:8), categories = 1, question_names = 2, output = file.path(tempdir(), "numq.txt")) #write file "numq.txt" in tempdir()
Create 'GIFT' file with short answer questions from a spreadsheet to be exported to LMS.
short_ans(data, questions, answers, categories, question_names = NULL, output, verbose = TRUE)short_ans(data, questions, answers, categories, question_names = NULL, output, verbose = TRUE)
data |
dataframe or tibble of short answer questions data |
questions |
name(string) or index(integer) of the questions column |
answers |
a vector of names(strings) or indices of answers column(s) |
categories |
name(string) or index(integer) of categories column if available, Default: NULL |
question_names |
name(string) or index(integer) of the questions names column. If NULL, it will be the first 40 letters of the question title, Default: NULL |
output |
string of .txt file name and path where the questions will be exported to. |
verbose |
If TRUE, the functions will print to the console the statistics of writing the output, Default: TRUE |
short_ans function takes a dataframe with short answer questions and export a text file in 'MOODLE' GIFT format. The function automatically makes a short answer question either single or multiple with or without different credit weight according to your data format(check short answer questions formatting below). If you have additional column of question_type set to 'short_ans' you can also use GIFTr function which wraps all question generating functions.
See Vignette and GIFTrData for demos.
None
Short answer question answers can be in single column or multiple columns. If an answer has not credit it will be given 100% credit automatically. For example if the answer is 'statistics', it will be equivalent to '%100%statistics'. While '%80%Data Science' answer will take 80% of the credit
For further illustration, check GIFTrData.
A guideline for creating you questions data can be found below. Check the data GIFTrData and GIFTrData_2 as example for formatted questions.
'MOODLE' itself supports basic markdown and HTML for questions formatting. So when formatting your data you can use HTML tags like <sub> and <sup>. Also you can use markdown **bold** or __bold__ and *italic* or _italic_ ...etc. However it is better a better practice to avoid using asterisk to avoid confusion with MCQ format. For more about the supported markdown see 'MOODLE' documentation.
'MOODLE' also supports inline and block LATEX equations through mathjax, however you have to be careful with the special characters like curly brackets // and equal sign = , so you have to use back slash before those to ensure you can import correctly.
Note that if you see thee data in console, you will find it with 2 backslash \, however that's the escaping of the backslash in R and you write with with single slash normally. Check GIFTrData GIFTrData[11,3]for an example. For further details on LATEX, check 'MOODLE' docs.
You can easily choose to enter a feedback by using #sign after the answer you want to specify a feedback on. Check GIFTrData for examples.
The data passed would differ slightly according to question type, however generally you need to have:
a column contains question. This cannot contain empty values
answer(s) column(s). This may be multiple columns if you have multiple answers. If you mix single and multiple answer it is better to write the single answer in the first column. If you have only single answer MCQ and NO multiple answer MCQ, you can set the answers without asterisk in the first column of the answers columns. More details in the vignette and below.
a column specifying the type of question. The current supported questions are multiple choices, numerical entry, true or false and short answer questions. The should be named 'mcq' , 'num_q' , 'tf_q' and 'short_ans' respectively.
a column specifying categories and subcategories in the 'MOODLE' categories are important when you are preparing a quiz as you may want to specify certain proportions of inclusions. Categories and subcategories are spaced by forward slash like Categ1/subcateg1/subsubcateg1 ...etc.
a column specifying question names. So you can easily enter a certain question names by like certain ID or keyword to make the questions easily on the system. however you don't need to worry about that as automatically if not set, the first 40 letter are set a question name.
data(GIFTrData) #data with short answer question type shortans_data <- GIFTrData[which(GIFTrData$question_type == "short_ans"),] short_ans(data = shortans_data, questions = 3, answers = c(4:8), categories = 1, question_names = 2, output = file.path(tempdir(), "shortq.txt")) #write file "shortq.txt" in tempdir()data(GIFTrData) #data with short answer question type shortans_data <- GIFTrData[which(GIFTrData$question_type == "short_ans"),] short_ans(data = shortans_data, questions = 3, answers = c(4:8), categories = 1, question_names = 2, output = file.path(tempdir(), "shortq.txt")) #write file "shortq.txt" in tempdir()
Create GIFT file with true or false questions from a spreadsheet to be exported to LMS.
tf_q(data, questions, answers, categories, question_names = NULL, output, verbose = TRUE)tf_q(data, questions, answers, categories, question_names = NULL, output, verbose = TRUE)
data |
dataframe or tibble of true or false questions data |
questions |
name(string) or index(integer) of the questions column |
answers |
a vector of names(strings) or indices of answers column(s) |
categories |
name(string) or index(integer) of categories column if available, Default: NULL |
question_names |
name(string) or index(integer) of the questions names column. If NULL, it will be the first 40 letters of the question title, Default: NULL |
output |
string of .txt file name and path where the questions will be exported to. |
verbose |
If TRUE, the functions will print to the console the statistics of writing the output, Default: TRUE |
tf_q function takes a dataframe with true or false questions and export a text file in 'MOODLE' GIFT format. The function automatically makes a true or false when it detect ‘T' or ’F' in the answers vector regardless of case. If you have additional column of question_type set to 'tf_q' you can also use GIFTr function which wraps all question generating functions.
See Vignette and GIFTrData for demos.
None
True or False answers is to be set in 1 column with letter 'T' or 'F' insensitive to the case.
For further illustration, check GIFTrData.
A guideline for creating you questions data can be found below. Check the data GIFTrData and GIFTrData_2 as example for formatted questions.
'MOODLE' itself supports basic markdown and HTML for questions formatting. So when formatting your data you can use HTML tags like <sub> and <sup>. Also you can use markdown **bold** or __bold__ and *italic* or _italic_ ...etc. However it is better a better practice to avoid using asterisk to avoid confusion with MCQ format. For more about the supported markdown see 'MOODLE' documentation.
'MOODLE' also supports inline and block LATEX equations through mathjax, however you have to be careful with the special characters like curly brackets // and equal sign = , so you have to use back slash before those to ensure you can import correctly.
Note that if you see thee data in console, you will find it with 2 backslash \, however that's the escaping of the backslash in R and you write with with single slash normally. Check GIFTrData GIFTrData[11,3]for an example. For further details on LATEX, check 'MOODLE' docs.
You can easily choose to enter a feedback by using #sign after the answer you want to specify a feedback on. Check GIFTrData for examples.
The data passed would differ slightly according to question type, however generally you need to have:
a column contains question. This cannot contain empty values
answer(s) column(s). This may be multiple columns if you have multiple answers. If you mix single and multiple answer it is better to write the single answer in the first column. If you have only single answer MCQ and NO multiple answer MCQ, you can set the answers without asterisk in the first column of the answers columns. More details in the vignette and below.
a column specifying the type of question. The current supported questions are multiple choices, numerical entry, true or false and short answer questions. The should be named 'mcq' , 'num_q' , 'tf_q' and 'short_ans' respectively.
a column specifying categories and subcategories in the 'MOODLE' categories are important when you are preparing a quiz as you may want to specify certain proportions of inclusions. Categories and subcategories are spaced by forward slash like Categ1/subcateg1/subsubcateg1 ...etc.
a column specifying question names. So you can easily enter a certain question names by like certain ID or keyword to make the questions easily on the system. however you don't need to worry about that as automatically if not set, the first 40 letter are set a question name.