dataset_assessment {ouladFormat}R Documentation

Returns the formatted assessment data sets

Description

Load, combines and formats the assessment and student assessment data sets from the OULAD for data analysis.

Usage

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
)

Arguments

module

name of the module to be included, either "All", "AAA", "BBB", "CCC", "DDD", "EEE", "FFF" or "GGG".

presentation

name of the semester of the module to be included, either "2013B", "2014B", "2013J", "2014J", "All", "Summer" or "Winter". "B" indicates a February start time whereas "J" indicates an October start time. "All" indicates that all presentations of the module will be included in the returned data. Where possible, "Summer" returns both "2013B" and "2014B", and "Winter" returns both "2013J" and "2014J".

repeat_students

indicator of whether students who had previous attempts at the module should be removed, either "remove" or "keep".

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 "week_begin" and "week_end" inclusive, and only occurs when a specific module presentation is requested (e.g., 'BBB 2013J').

Value

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.

assessment_data tibble

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):

assessments tibble

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).

assessment_performance tibble

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.

assessment_reactivity 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.

References

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.

See Also

combined_dataset()

Examples

dataset_assessment(module = "BBB", presentation = "2013J",
repeat_students = "remove", week_begin = 1, week_end=39, na.rm = FALSE)

[Package ouladFormat version 1.1.2 Index]