| Title: | Loads and Formats the Open University Learning Analytics Dataset for Data Analysis |
|---|---|
| Description: | The Open University Learning Analytics Dataset (OULAD) is available from Kuzilek et al. (2017) <doi:10.1038/sdata.2017.171>. The 'ouladFormat' package loads, cleans and formats the OULAD for data analysis (each row of the returned data set is an individual student). The package’s main function, combined_dataset(), allows the user to choose whether the returned data set includes assessment, demographics, virtual learning environment (VLE), or registration variables etc. |
| Authors: | Emma Howard [aut, cre] |
| Maintainer: | Emma Howard <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.1.2 |
| Built: | 2025-03-01 06:07:41 UTC |
| Source: | https://github.com/ehoward1/ouladformat |
Combines multiple OULAD .csv files into one tibble that is formatted for data analysis and where each row represents a unique student.
combined_dataset( module = c("AAA", "BBB", "CCC", "DDD", "EEE", "FFF", "GGG"), presentation = c("2013J", "2014J", "2013B", "2014B", "All"), repeat_students = c("remove", "keep"), withdrawn_students = c("remove", "keep"), demographics = FALSE, registration = FALSE, VLE = c("omit", "daily", "weekly", "activity", "FSLM", "FSLSM", "OLS", "VARK"), VLE_clicks = c("total", "binary", "standardise1", "standardise2", "logarithmic"), week_begin = -4, week_end = 39, assessment = FALSE, na.rm = FALSE, example_data = FALSE )combined_dataset( module = c("AAA", "BBB", "CCC", "DDD", "EEE", "FFF", "GGG"), presentation = c("2013J", "2014J", "2013B", "2014B", "All"), repeat_students = c("remove", "keep"), withdrawn_students = c("remove", "keep"), demographics = FALSE, registration = FALSE, VLE = c("omit", "daily", "weekly", "activity", "FSLM", "FSLSM", "OLS", "VARK"), VLE_clicks = c("total", "binary", "standardise1", "standardise2", "logarithmic"), week_begin = -4, week_end = 39, assessment = FALSE, na.rm = FALSE, example_data = FALSE )
module |
name of the module to be included, either |
presentation |
name of the semester of the module to be included, either |
repeat_students |
indicator of whether students who had previous attempts at the module should be removed, either |
withdrawn_students |
indicator of whether students who had withdrawn from the module should be removed, either |
demographics |
logical. Indicates whether demographic data is included in the outputted data set. |
registration |
logical. Indicates whether registration data is included in the outputted data set. |
VLE |
indicates whether VLE data is included in the outputted data set
(default is |
VLE_clicks |
indicates the format that the VLE data should be returned as, either |
week_begin |
the first semester week of VLE and assessment data to be included in formatted data. Depending on the module presentation, students started to view activities four weeks prior to the initial module start date. Weeks prior to the initial module start are indicated by a negative integer. |
week_end |
the last semester week of VLE and assessment data to be included in the formatted data. Week 39 is the last week material was viewed (and earlier in some module presentations). This parameter is also used to remove withdrawn students. |
assessment |
logical. Indicates whether assessment data is included in the outputted data set. |
na.rm |
logical. Indicates whether NAs should be omitted from the average continuous assessment calculations
or treated as zeroes (default). This calculation only includes continuous assessment that
was due between the period set by |
example_data |
logical. Indicates whether to run a subset of the VLE data as an example. |
Returns the inputs specified for whether assessment, demographics, registration and VLE variables are to be included,
as well as the other inputs. Also, one tibble (object of class tbl_df) is returned, dataset_combined.
A tibble where each row represents a unique student. Depending on the inputs specified,
the tibble includes assessment, demographics, registration and VLE data for each student.
Kuzilek, J., Hlosta, M., & Zdrahal, Z. (2017). Open university learning analytics dataset. Scientific Data volume 4 , (pp. 1–8). https://doi.org/10.1038/sdata.2017.171.
For more information on different inputs and variables in the dataset_combined tibble, see:
dataset_assessment() for information on the assessment performance data,
dataset_demographics() for information on the demographics data,
dataset_registration() for information on the registration data,
dataset_VLE_time() for information on the VLE daily or weekly data,
dataset_VLE_activity() for information on the VLE activity data,
VLE_learning_classification() for information on the VLE activities classified under a learning model, and
convert_VLE() for information on transforming the data type of the VLE data.
# Uses subset of the VLE data set for example combined_dataset(module = "AAA", presentation = "2013J", repeat_students = "remove", withdrawn_students = "remove", demographics = TRUE, registration = TRUE, assessment = TRUE, na.rm = FALSE, VLE = "weekly", VLE_clicks = "total", example_data = TRUE) # Slow to run as it loads the full VLE data set combined_dataset(module = "BBB", presentation = "2013J", repeat_students = "remove", withdrawn_students = "remove", demographics = TRUE, registration = FALSE, assessment = TRUE, na.rm = FALSE, VLE = "activity", VLE_clicks = "standardise1", week_begin = -4, week_end = 14, example_data = FALSE)# Uses subset of the VLE data set for example combined_dataset(module = "AAA", presentation = "2013J", repeat_students = "remove", withdrawn_students = "remove", demographics = TRUE, registration = TRUE, assessment = TRUE, na.rm = FALSE, VLE = "weekly", VLE_clicks = "total", example_data = TRUE) # Slow to run as it loads the full VLE data set combined_dataset(module = "BBB", presentation = "2013J", repeat_students = "remove", withdrawn_students = "remove", demographics = TRUE, registration = FALSE, assessment = TRUE, na.rm = FALSE, VLE = "activity", VLE_clicks = "standardise1", week_begin = -4, week_end = 14, example_data = FALSE)
Converts the data format of the Virtual Learning Environment (VLE) data set from total view counts to binary, standardised or logarithmic view count data.
convert_VLE( data, conversion = c("binary", "standardise1", "standardise2", "logarithmic") )convert_VLE( data, conversion = c("binary", "standardise1", "standardise2", "logarithmic") )
data |
VLE data set to be converted. The VLE data set should consist of student_id as the first column followed by total view counts per time period/activity in each of the remaining columns. |
conversion |
type of conversion to implement, either |
Two tibbles are returned: 1) original_data, and 2) converted_data.
A tibble of the original data inputted into the function.
A tibble where all columns, except the first column, have been changed from total view counts to either binary, standardised, globally standardised or logarithmic view count data.
dataset_VLE_time() or dataset_VLE_activity() for obtaining the VLE data set needed for the input data.
VLE_data = dataset_VLE_activity(example_data = TRUE)$resource_data convert_VLE(VLE_data, conversion = "standardise1") VLE_data = dataset_VLE_time(example_data = TRUE)$weekly_data convert_VLE(VLE_data, conversion = "logarithmic")VLE_data = dataset_VLE_activity(example_data = TRUE)$resource_data convert_VLE(VLE_data, conversion = "standardise1") VLE_data = dataset_VLE_time(example_data = TRUE)$weekly_data convert_VLE(VLE_data, conversion = "logarithmic")
Load, combines and formats the assessment and student assessment data sets from the OULAD for data analysis.
dataset_assessment( module = c("All", "AAA", "BBB", "CCC", "DDD", "EEE", "FFF", "GGG"), presentation = c("2013B", "2014B", "2013J", "2014J", "All", "Summer", "Winter"), repeat_students = c("remove", "keep"), week_begin = 1, week_end = 39, na.rm = FALSE )dataset_assessment( module = c("All", "AAA", "BBB", "CCC", "DDD", "EEE", "FFF", "GGG"), presentation = c("2013B", "2014B", "2013J", "2014J", "All", "Summer", "Winter"), repeat_students = c("remove", "keep"), week_begin = 1, week_end = 39, na.rm = FALSE )
module |
name of the module to be included, either |
presentation |
name of the semester of the module to be included, either |
repeat_students |
indicator of whether students who had previous attempts at the module should be removed, either |
week_begin |
the first semester week of the assessment data to be included in formatted data. |
week_end |
the last semester week of the assessment data to be included in the formatted data. To ensure all continuous assessment is included regardless of the module presentation, set this to 39, the last week of data in the OULAD. |
na.rm |
logical. Indicates whether NAs should be omitted from the average continuous assessment calculations
or treated as zeroes (default). This calculation only includes continuous assessment that
were due between the period set by |
Returns four tibbles (objects of class tbl_df) based on the OULAD studentAssessment.csv and assessments.csv files
and the specified inputs (module, presentation, and repeat_students).
The four tibbles are: 1) assessment_data, 2) assessments, 3) assessment_performance, and 4) assessment_reactivity.
A tibble based on the combined OULAD files of studentAssessment.csv and assessments.csv,
and the inputs. The tibble consists of (Kuzilek et al., 2017):
id_student - unique student identification number.
code_module - module identification code.
code_presentation - module presentation identification code.
id_assessment - assessment identification number.
assessment_type - type of assessment.
date - information about the final submission date of the assessment calculated as the number of days since the start of the module-presentation.
weight - weight of the assessment in %.
date_submitted - day of the assessment submission by student.
is_banked - logical indicator whether assessment result has been transferred from a previous presentation.
score - student’s score for the specific assessment (range 0-100).
reactivity - calculated using the date variable minus date_submitted variable.
A tibble of seven columns which details for each assessment item for a module presentation,
the type of assessment it is, the due date and week of the assessment,
and the weight of the assessment. The assessment types are Tutor Marked Assessment (TMA), Computer Marked Assessment (CMA)
and Final Exam (Exam). Exams are generally treated separately to other assessments
and have weight equal to 100%. The sum of all other assessments is also 100% (Kuzilek et al., 2017).
A tibble where each row represents a unique student and their scores in the range of 0-100
for different assessment items. When a specific module (not 'All') and presentation (not 'All' or 'Winter' or 'Summer') is selected,
the final column states each student's average continuous assessment score based on their continuous assessment
scores and the assessments weights. For this calculation, the NAs may be not included or replaced by 0 (default; see na.rm argument).
The average assessment score will not be calculated if the only variable outputted is exam score.
Not all module presentations have exam scores available.
Details of the assessment items can be seen in the assessments tibble.
A tibble where each row represents a unique student and their reactivity for
different assessment items. Treuillier and Boyer (2021) define reactivity for the OULAD as the delay between
the date the assessment is returned and the deadline (in days). Negative numbers indicate overdue assessments.
Details of the assessment items can be seen in the assessments tibble.
Treuillier, C., & Boyer, A. (2021). Identification of class-representative learner personas. In LA4SLE 2021 - Learning Analytics for Smart Learning Environments (pp. 38-45). Bolzano, Italy.
Kuzilek, J., Hlosta, M., & Zdrahal, Z. (2017). Open university learning analytics dataset. Scientific Data volume 4, (pp. 1–8). https://doi.org/10.1038/sdata.2017.171.
dataset_assessment(module = "BBB", presentation = "2013J", repeat_students = "remove", week_begin = 1, week_end=39, na.rm = FALSE)dataset_assessment(module = "BBB", presentation = "2013J", repeat_students = "remove", week_begin = 1, week_end=39, na.rm = FALSE)
Load and formats the student demographic data set from the OULAD for data analysis.
dataset_demographics( module = c("All", "AAA", "BBB", "CCC", "DDD", "EEE", "FFF", "GGG"), presentation = c("2013B", "2014B", "2013J", "2014J", "All", "Summer", "Winter"), repeat_students = c("remove", "keep") )dataset_demographics( module = c("All", "AAA", "BBB", "CCC", "DDD", "EEE", "FFF", "GGG"), presentation = c("2013B", "2014B", "2013J", "2014J", "All", "Summer", "Winter"), repeat_students = c("remove", "keep") )
module |
name of the module to be included, either |
presentation |
name of the semester of the module to be included, either |
repeat_students |
indicator of whether students who had previous attempts at the module should be removed, either |
Returns one tibble (object of class tbl_df), called 'studentInfo', based on the OULAD studentInfo.csv file
and the specified inputs (module, presentation, and repeat_students).
The tibble consists of 12 columns (Kuzilek et al., 2017):
code_module - module identification code.
code_presentation - module presentation identification code.
id_student - the unique student identification number.
gender - student’s gender, either Male or Female.
region - the geographic region where the student lived while taking the module-presentation.
highest_education - the highest student education level on entry to the module presentation.
imd_band - the index of multiple deprivation band of the place where the student lived during the module-presentation.
age_band - a band of student’s age.
num_of_prev_attempts - the number of times the student has attempted this module previously.
studied_credits - the total number of credits for the modules the student is currently studying.
disability - indicates whether the student has declared a disability.
final_result - student’s final result in the module-presentation.
Kuzilek, J., Hlosta, M., & Zdrahal, Z. (2017). Open university learning analytics dataset. Scientific Data volume 4 , (pp. 1–8). https://doi.org/10.1038/sdata.2017.171.
dataset_demographics(module = "BBB", presentation = "2013J", repeat_students = "remove")dataset_demographics(module = "BBB", presentation = "2013J", repeat_students = "remove")
Load and formats the student registration data set from the OULAD for data analysis.
dataset_registration( module = c("All", "AAA", "BBB", "CCC", "DDD", "EEE", "FFF", "GGG"), presentation = c("2013B", "2014B", "2013J", "2014J", "All", "Summer", "Winter"), repeat_students = c("remove", "keep") )dataset_registration( module = c("All", "AAA", "BBB", "CCC", "DDD", "EEE", "FFF", "GGG"), presentation = c("2013B", "2014B", "2013J", "2014J", "All", "Summer", "Winter"), repeat_students = c("remove", "keep") )
module |
name of the module to be included, either |
presentation |
name of the semester of the module to be included, either |
repeat_students |
indicator of whether students who had previous attempts at the module should be removed, either |
Returns one tibble (object of class tbl_df), called 'studentRegistration', based on the OULAD studentRegistration.csv file
and the specified inputs (module, presentation, and repeat_students).
The tibble consists of five columns (Kuzilek et al., 2017):
id_student - the unique student identification number.
code_module - module identification code.
code_presentation - module presentation identification code.
date_registration - the day of student’s registration for the module presentation. Modules start on day 0.
date_unregistration - the day of student unregistration from the module presentation. This is NA if the student completed the module presentation.
Kuzilek, J., Hlosta, M., & Zdrahal, Z. (2017). Open university learning analytics data set. Scientific Data volume 4 , (pp. 1–8). https://doi.org/10.1038/sdata.2017.171.
dataset_registration(module = "BBB", presentation = "2013J", repeat_students = "remove")dataset_registration(module = "BBB", presentation = "2013J", repeat_students = "remove")
Load and formats the student Virtual Learning Environment (VLE) data set from the OULAD for data analysis.
dataset_VLE_activity( module = c("AAA", "BBB", "CCC", "DDD", "EEE", "FFF", "GGG"), presentation = c("2013B", "2014B", "2013J", "2014J", "All"), repeat_students = c("remove", "keep"), week_begin = -4, week_end = 39, example_data = FALSE )dataset_VLE_activity( module = c("AAA", "BBB", "CCC", "DDD", "EEE", "FFF", "GGG"), presentation = c("2013B", "2014B", "2013J", "2014J", "All"), repeat_students = c("remove", "keep"), week_begin = -4, week_end = 39, example_data = FALSE )
module |
name of the module to be included, either |
presentation |
name of the semester of the module to be included, either |
repeat_students |
indicator of whether students who had previous attempts at the module should be removed, either |
week_begin |
the first semester week of VLE data to be included in the formatted data. Depending on the module presentation, students started to view activities up to four weeks prior to the initial module start date. Weeks prior to the initial module start are indicated by a negative integer. |
week_end |
the last semester week of VLE data to be included in the outputted data. Week 39 is the last week material was viewed (and earlier in some module presentations). |
example_data |
logical. Indicates whether to run a subset of the data as an example. |
Returns two tibbles (objects of class tbl_df) based on the OULAD studentVle.csv and vle.csv files,
the specified inputs (module, presentation, and repeat_students), and
the range of the weeks included in the tibbles. week_begin and week_end
indicates the first and last semester week respectively that is included in
the output tibbles. These may be different to the corresponding input parameters.
Weeks prior to the initial module start day are indicated by a negative integer.
The two tibbles returned are: 1) filtered_data, and 2) resource_data.
A tibble based on the combined oulad files of studentVLE.csv and vle.csv,
and the inputs. The tibble consists of (Kuzilek et al., 2017):
code_module - the module identification code.
code_presentation - the presentation identification code.
id_site - the VLE material identification number.
activity_type - the role associated with the module material.
week_from - the week from which the material is planned to be used.
week_to - the week until which the material is planned to be used.
id_student - the unique student identification number.
date - the day of student’s interaction with the material (module starts on day 0).
sum_click - the number of times the student interacted with the material for the date.
A tibble where each row represents a unique student and each column their number of interactions with
different VLE activity types for the weeks and module presentation inputted.
Kuzilek, J., Hlosta, M., & Zdrahal, Z. (2017). Open university learning analytics dataset. Scientific Data volume 4 , (pp. 1–8). https://doi.org/10.1038/sdata.2017.171.
convert_VLE(), dataset_VLE_time(), VLE_learning_classification() and combined_dataset()
# Uses subset of the VLE data set for example dataset_VLE_activity(example_data = TRUE) # Slow to run as it loads the full VLE data set dataset_VLE_activity(module = "BBB", presentation = "2013J", repeat_students = "remove", week_begin = 1, week_end = 13, example_data=FALSE)# Uses subset of the VLE data set for example dataset_VLE_activity(example_data = TRUE) # Slow to run as it loads the full VLE data set dataset_VLE_activity(module = "BBB", presentation = "2013J", repeat_students = "remove", week_begin = 1, week_end = 13, example_data=FALSE)
Load and formats the student Virtual Learning Environment (VLE) data set from the OULAD for data analysis.
dataset_VLE_time( module = c("AAA", "BBB", "CCC", "DDD", "EEE", "FFF", "GGG"), presentation = c("2013B", "2014B", "2013J", "2014J", "All"), repeat_students = c("remove", "keep"), week_begin = -4, week_end = 39, example_data = FALSE )dataset_VLE_time( module = c("AAA", "BBB", "CCC", "DDD", "EEE", "FFF", "GGG"), presentation = c("2013B", "2014B", "2013J", "2014J", "All"), repeat_students = c("remove", "keep"), week_begin = -4, week_end = 39, example_data = FALSE )
module |
name of the module to be included, either |
presentation |
name of the semester of the module to be included, either |
repeat_students |
indicator of whether students who had previous attempts at the module should be removed, either |
week_begin |
the first semester week of VLE data to be included in formatted data. Depending on the module presentation, students started to view activities four weeks prior to the initial module start date. Weeks prior to the initial module start are indicated by a negative integer. |
week_end |
the last semester week of VLE data to be included in the formatted data. Week 39 is the last week material was viewed (and earlier in some module presentations). |
example_data |
logical. Indicates whether to run a subset of the data as an example. |
Returns three tibbles (objects of class tbl_df) based on the OULAD studentVle.csv file,
the specified inputs (module, presentation, and repeat_students), and
the range of the weeks included in the tibbles. week_begin and week_end
indicates the first and last semester week respectively that is included in
the output tibbles. These may be different to the input parameters of week_begin and week_end.
Weeks prior to the initial module start day are indicated by a negative integer.
The three tibbles returned are: 1) filtered_data, 2) daily_data, and 3) weekly_data.
A tibble based on the students' VLE interactions and the inputs. The tibble consists of (Kuzilek et al., 2017):
id_student - the unique student identification number.
code_module - the module identification code.
code_presentation - the presentation identification code.
id_site - the VLE material identification number.
date - the day of the student’s interaction with the material.
sum_click - the number of times the student interacted with the material on a specific day.
A tibble where each row represents a unique student, and each column their number of interactions with
the VLE for different days of the module. The module starts on day 0. Days prior to the initial start day, are indicated by negative integers.
A tibble where each row represents a unique student, and each column their number of interactions with
the VLE for different weeks of the module. Weeks prior to the initial start week (week 1) are indicated by the term 'pre' in the week name.
Kuzilek, J., Hlosta, M., & Zdrahal, Z. (2017). Open university learning analytics dataset. Scientific Data volume 4 , (pp. 1–8). https://doi.org/10.1038/sdata.2017.171.
convert_VLE(), dataset_VLE_activity(), and combined_dataset()
# Uses subset of the VLE data set for example dataset_VLE_time(example_data = TRUE) # Slow to run as it loads the full VLE data set dataset_VLE_time(module = "BBB", presentation = "2013J", repeat_students = "remove", week_begin = 1, week_end = 13, example_data=FALSE)# Uses subset of the VLE data set for example dataset_VLE_time(example_data = TRUE) # Slow to run as it loads the full VLE data set dataset_VLE_time(module = "BBB", presentation = "2013J", repeat_students = "remove", week_begin = 1, week_end = 13, example_data=FALSE)
Loads .RData files directly from a GitHub repository. The original Rfssa function (Haghbin et al., 2022) is based on the Stack Overflow post (Hartman, 2016): https://stackoverflow.com/questions/24846120/importing-data-into-r-rdata-from-github, and loads the .RData to the global environment. This function allows the user to choose the environment which the .RData files are loaded to.
load_github_modified(github_data_url, env)load_github_modified(github_data_url, env)
github_data_url |
The GitHub url of the .RData data set to be loaded. |
env |
Environment where the data is to be loaded into. |
Loads the .RData into the local environment.
Haghbin, H., Trinka, J., Najibi, S., M., & Maadooliat, M. (2022). Package 'Rfssa'. https://cran.r-project.org/web/packages/Rfssa/Rfssa.pdf.
Hartman, R. (2016). Importing data into R (.rData) from Github. https://stackoverflow.com/questions/24846120/importing-data-into-r-rdata-from-github.
env = environment() load_github_modified("https://github.com/ehoward1/oulad_data/blob/main/vle.RData", env)env = environment() load_github_modified("https://github.com/ehoward1/oulad_data/blob/main/vle.RData", env)
ouladFormat comes bundled with the sample studentVLE data in its inst/extdata
directory (sample_studentVLE.csv). This .csv file is a subset (rows 10,000-14,999) of
the original OULAD studentVLE.csv. This function makes it easy to access.
path_to_file(path = NULL)path_to_file(path = NULL)
path |
Name of file in quotes with extension. If |
No return value, called for side effects.
This function is adapted from readxl::readxl_example().
path_to_file() read.csv(path_to_file("sample_studentVLE.csv"))path_to_file() read.csv(path_to_file("sample_studentVLE.csv"))
Transform Open University VLE activities to classifications under either the:
Filder-Selverman Learning Style Model (FSLM) mapped by Balti at al. (2023),
Felder–Silverman Learning Style Model (FSLSM) (see Felder & Silverman, 1988) mapped by Nazempour and Darabi (2023),
VARK Learning Style Model (see Fleming and Colleen, 1992) mapped by Balti at al. (2023), or
Corresponding to the Online Learning Style Characteristics (OLS) mapped by Yan et al. (2021) and drawing on Li and Yin (2017).
VLE_learning_classification( activity_data, classification = c("FSLM", "FSLSM", "OLS", "VARK") )VLE_learning_classification( activity_data, classification = c("FSLM", "FSLSM", "OLS", "VARK") )
activity_data |
VLE activity data set to be converted. |
classification |
classification system to be used to categorise the different VLE activities under. |
Returns the classification specified, the mapping for the VLE activities to each category in the specified learning model, and one tibble, VLE_classified_data.
A tibble where each row represents a unique student and their number of interactions with different categories of the specified learning model.
Balti, R., Hedhili, A., Chaari, W.L. & Abed, M. (2023). Hybrid analysis of the learner’s online behavior based on learning style. Education and Information Technologies. https://doi.org/10.1007/s10639-023-11595-x
Felder, R. M., & Silverman, L. K. (1988). Learning and Teaching Styles in Engineering Education. Engineering Education, 78(7), 674–681. https://www.engr.ncsu.edu/wp-content/uploads/drive/1QP6kBI1iQmpQbTXL-08HSl0PwJ5BYnZW/1988-LS-plus-note.pdf
Fleming, N. D., & Mills, C. (1992). Not Another Inventory, Rather a Catalyst for Reflection. To Improve the Academy. 246. https://digitalcommons.unl.edu/podimproveacad/246
Kuzilek, J., Hlosta, M., & Zdrahal, Z. (2017). Open university learning analytics dataset. Scientific Data volume 4 , (pp. 1–8). https://doi.org/10.1038/sdata.2017.171.
Li, R., & Yin, C. (2017). Analysis of Online Learning Style Model Based on K-means Algorithm. Proceedings of the 3rd International Conference on Economics, Management, Law and Education (EMLE 2017). (pp. 692-697). Atlantis Press. https://doi.org/10.2991/emle-17.2017.148
Nazempour, R, & Darabi, H. (2023). Personalized Learning in Virtual Learning Environments Using Students’ Behavior Analysis. Education Sciences. 2023; 13(5), 457. https://doi.org/10.3390/educsci13050457
Yan, L., Yin, C., Chen, H., Rong, W., Xiong, Z., & David, B. (2021). Learning Resource Recommendation in E-Learning Systems Based on Online Learning Style. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science, 12817. Springer, Cham. https://doi.org/10.1007/978-3-030-82153-1_31
dataset_VLE_activity() for obtaining the VLE data set needed for input data
VLE_data = dataset_VLE_activity(example_data = TRUE)$resource_data VLE_learning_classification(VLE_data, classification = "FSLSM")VLE_data = dataset_VLE_activity(example_data = TRUE)$resource_data VLE_learning_classification(VLE_data, classification = "FSLSM")