This session was held on Friday, November 6th at noon.
This week’s Lunch & Learn Session will be on the Student Course Time Estimator Tool by Michelle Lamberson, Director, Flexible Learning Special Projects, and Daulton Baird, both of UBC Okanagan’s Office of the Provost and Vice President (Academic).
The session will introduce the planning tool that enables instructors to estimate their student’s time commitment in a course based on the assigned learning activities. The tool is designed to be used for courses that represent the blended learning spectrum from face-to-face to fully online teaching. Bring your course syllabi and work through the examples with us!
Background on the Student Course Time Estimator Tool:
The Student Course Time Estimator Tool calculates the total time commitment expected and allocates activities into scheduled (set by the institution, typically live meetings) and independent (at the discretion of the student within the parameters set by course deadlines) activities. This tool was developed over the summer by a project team from UBC Okanagan and TRU, and adapted from an open source tool created by Barre, Brown and Esarey (see below)
Background and tool preview: https://ubcoapps.elearning.ubc.ca/
Original tool: Barre, B., Brown, A., & Esarey, J. (n.d.). Workload Estimator 2.0. Retrieved June 30, 2020, from https://cat.wfu.edu/resources/tools/estimator2/
Post Event Notes:
The Student Course Time Estimator Tool developed by Michelle Lamberson, Daulton Baird and Heather Berringer from UBC Okanagan in association with Mateen Shaikh and Brian Lamb from Thompson Rivers University to assist instructors plan their online curriculum keeping the weekly student workload in mind. The course time estimator tool breaks down the time estimates in “scheduled” and “independent” to reflect what amount of time would students spend on scheduled activities like labs and lectures and what independently on their own time, such as assignments, projects and preparations for labs or exams.
The tool was developed on Shiny Apps using R. Two references were used for developing this tool:
Barre, B., Brown, A., Esarey, J., Workload Estimator 2.0 (The reference tool has been provided here: https://cat.wfu.edu/resources/tools/estimator2/)
Beer, Nicola. (2019) Estimating student Workload During the Learning Design of Online Courses
The reference tool was developed keeping Course Information, Writing Assessments, Discussion Posts, Exams, and Other Assignments as the major categories for estimating the workload, however it did not include science-based aspects of courses such as labs and other technical assessments. The team at UBCO and TRU came together to create a version of the course workload estimator tool that would be more flexible and adaptable for science-based courses that would need estimators for other course modules.
The 1st and 2nd Adaptation of UBCO’s Student Workload Estimator Tool:
The first adaptation of the tool was designed on the same interface as the reference tool (Barre, Brown and Esarey) with additions to the tool focussing on adding more functionalities for science-based courses, such as labs, preparation time for labs, projects, etc. This ensured that it was a more diverse tool instead of an Arts-focused tool. However, the User Interface for the tool was not very simple after the addition of the extra functionalities that led to Adaptation 2 which let the instructors see all activities on a single screen. These adaptations also allowed instructors to manually enter estimates for hours if they felt that the time estimates for certain activities did not reflect what students would invest in certain projects from the instructors’ past experience teaching a course.
Each of these adaptations allowed the instructors to add individual course activities and enter the values for hours per week and number of weeks that the course would run to provide a scheduled and independent time workload for students per week. According to the research done by Michelle’s team, the ideal course workload for a 3-hr-per-week course would be 8-10 hours in total.
How the Tool works:
Instructors can use this tool to plan their online courses before the semester begins by entering their planned activities for the course. These activities can be marked as scheduled (marked (S); taking place during assigned course lecture or lab hours) or independent (marked (I); taking place on the student’s own time). These activities are then broken down by this tool into an average weekly workload. For a course with 3 hours worth of scheduled activities, about 8-10 hours of total course load is considered to be ideal based on research.
Each course activity has been outlined in this tool to add a set number of hours to the student workload based on the parameters set by the instructors. These activities include lectures (S), labs (S), tutorials (S), assignments of all types (I), projects (I), prep time for labs (I), prep time for exams (I), assigned readings (I), amongst others. The tool includes parameters like word limits for longer written assignments and number of pages for reading assignments, and automatically calculated the hourly workload for the instructors. These calculations are based on the reference research mentioned in the background for the tool. For instructors who feel that the hourly estimates are less than they expect to see, they can manually enter the hourly data for most activities.
Once all the data has been entered, the instructors can then evaluate whether the total course load exceeds their expectations and make alterations to their planned curriculum. The instructors may use this data to set expectations for the students to help them prepare better for the courses. If used in tandem with other courses, the students can get a realistic idea of how heavy their course load is. The project team hopes that the academic leadership can also use this tool to set targets for combined student course loads, especially in an online academic environment.
Some of the questions raised on the reference tool were regarding the values used for calculating the hourly workload for reading assignments which were considered to be quite high in terms of pages/hour. Another question raised was whether the factors affecting current students with respect to the environment of their education was considered as a student studying in a dorm room would have significantly lesser distractions as compared to someone taking the course online in their homes. These were resolved with the adaptations created by the UBCO team as instructors could then manually adjust the values to ensure that it reflects the time estimates for assignments that they knew from their experience.
The results of this tool will be seen after more research is done by instructors on the effect of this tool has had on their students’ performance.