Proceedings of the 8th International Academic Conference on Education
Year: 2024
DOI:
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“Wait, What?” Combining Physical Activity and Data Science for a Higher Education Interdisciplinary Course
Shawn R. Cradit, Ed.D., MS, MA, ATC, C-EP
ABSTRACT:
Interdisciplinary courses are becoming more popular with higher education students, especially those that involve computer and data analytics. Research has proven that the more physically active we are, the better longevity we will have. At North Carolina State University there is a physical activity requirement for each student to successfully pass two credit hours in physical activity courses. In an attempt to develop a course that would marry physical activity and data science a special topics course was created by myself and a colleague. This course involves creating a live data set by student participation in in-class and out-of-class activities. This course is housed in the Data Science Academy department and I co-teach this course with a colleague. The first 3 classes involve discussions on data, data science, and how data will be collected throughout the course. Student participation is crucial in creating the data set. Since this course is a Data Science course, this is a project-based course. No tests or quizzes, instead worksheets, Daily Google sheets (reflection and journal)- only the days that the class meets, a tri-semester measurements Google sheet, and giving the students guidelines on which statistical analysis will be needed in their final project. In the final project they are assigned a small subset of the data to analyze and if they choose, they can analyze the whole class. The last two – three weeks is when the students are instructed on the statistics required and designing their PowerPoint in a group. They work on designing a PowerPoint presentation with their peers on their interpretation of the data set. They are also required to include one slide comparing their data to the entire classroom data set means for all categories. Introduction: After an exhaustive search for any studies involving Data Science and Physical Activity courses turned up empty, I decided to do a research study on my special topics course which marries these two disciplines. This study compared all graded worksheet assignments, and assignments associated with the project-based assignment (hypothesis, individual slide with assigned components of the group project, and oral presentations of the group project). Students can enroll in this elective course which has a General Education Program, Health and Exercise Studies option, or students enroll under the Data Science course option. The students are tasked with creating a live data set by their participation in the physical activity and other parameters included in the data set which is recorded into a Google Form which is connected to a Google Sheet. The students fill out the Google Form only on activity days. The class is taught two days a week for 50 minutes. On the third day of class when baseline measurements are performed, the students are anonymized by being given a number, which they are instructed not to share with their peers. This is their student identifier in the Google Form. At the end of the course, all gradebook data was anonymized (any identifying information was removed), placed into a separate data sheet (Google sheet or Excel document) then analyzed. This course has now been taught twice and had eighteen participants, seventeen of which completed the entire course and one who completed all components except the final project. Purpose: To create an interdisciplinary course that requires physical activity participation and recording results from the participation into a live data set incorporating data science with fitness. Subjects: Three males and three females who registered for this elective course and completed the course. Literature review: The Student Outcomes Survey will be utilized for this study. Using a validated peer reviewed student satisfaction survey is critical for this study “Questions (numbered 1 to 6) correlate to the teaching block, those (numbered 7 to 11) correlating with the assessment block, and those (numbered 12 to 19) correlating with the generic skills and learning experience block of questions” (Fieger, 2012). The generic skills block of questions may be slightly modified or not included due to the nature of these being specifically for vocational education students. This information is important because most studies evaluating course content success exclude a student satisfaction survey. Due to the limited time to teach activity and allow for students to obtain blood pressure and temperature measurements, the lecture component is brief and mostly covered in online PowerPoints which “helps instructors and students with technology to enable time, privacy, and independent study” (Al-Kahtani, 2022). The students have the choice to either look at the PowerPoints or use other tools to complete their worksheets which is “suggested to increase intrinsic motivation by providing the learner a sense of control, thus promoting engagement and improving learning gains” (Feldman-Maggor et al., 2022). Educators today after the Covid-19 pandemic changed educational delivery recognize that learners overwhelmingly prefer and expect the use of electronic presentation software in their courses (Bolkan, 2019; Hill et al., 2012). Learners identify that electronic software presentations aid in attentiveness and individual comprehension of information (Apperson et al., 2008; Hill et al., 2012) if there is a verbal component to PowerPoints. It has been noted that many instructors add salacious details to the voiceover PowerPoints which have no bearing on the PowerPoint content (Sperring, 2023), however, our PowerPoints have no voiceover element. Due to limited class time, the online PowerPoint emphasizes the important aspects that the wellness worksheet assignments cover. This aspect of the course is required to meet the GEP HES characteristic of this course. Methods: Students register for an elective course under the data science special topics that has the Health and Exercise Studies General Education Program requirement. This is a 16 week course that is taught for 50 minutes twice a week. Before and data is collected or reported, the students are given a random number, picked by a free number wheel that chooses a number, once a number is used, it disappears from the wheel. This anonymizes the student, the students are instructed to not tell anyone what their number is. The students are tasked with recording and submitting their daily results in a Google form, which starts with a pull-down of their student identifier. They are asked to record their meals using the MyPlate.Gov status that best matches how they ate (Rookie through Allstar), or none if they didn’t eat anything. Their water intake in ounces, sleep in hours and minutes, as well as the quality of their sleep according to the student (Good, Moderate, or Restless), the type of exercise that was performed in class, the resting heart rate and Blood Pressure (both given on the same wrist cuff device), body temperature (forehead thermometer), Peak heart rate (taken 15-20 minutes into the routine), recovery heart rate (taken after the cool down and /or stretching), as quickly after class as the student can (urinating on) a urinalysis strip analysis, and finally a stress rating 1-5 based on how the student ranks their overall stress level for the day. The students are also asked to fill out a daily reflection if they choose to and/or to tell why they may have had irregular numbers during the workout or explain their stress rating or anything they may want to add. Prior to the activity beginning, baseline measurements are taken, Functional Movement Screening, Circumference measurements, and two fitness tests (timed plank and wall sit). After the 8th workout, the students get a midpoint measurement as well as after the 16th workout, this gives the students three reference points, the most crucial measurements are the baseline and final ones. After the activity concludes, students work in a traditional classroom setting individually and in their groups, they are given one day of instruction on what statistics to include and a rubric and other helpful materials to design their PowerPoint. The students work for two weeks, then present their findings to the class as a group. They also were tasked to design a slide on their individual results compared to the whole class, which is not required in the group presentations. Results: The students all improved with their measurements from baseline to final. One student had a severe shoulder injury that they had not properly rehabilitated, their measurements stayed the same for the shoulder mobility FMS measurement but felt that their flexibility improved, as stated in their daily reflection, from the baseline measurements. All students except one improved from the baseline measurements, I believe this is due to what they wore to the final measurement class, a business pant suit which hindered their mobility. The students scored in the B through A+ ranges. There were no significant p-values or t-test scores when comparing project scores or measurements. Conclusions: Creating a course that combines physical activity and data science is a daunting task, but is a valuable experience for both students and instructors. Students who would not have otherwise taken a data science course are introduced to the field of data science and discover that they use data science every day. This marriage of Health and Exercise Studies physical activity along with data science is a rewarding for all. Many students complain that the outside of class time activities take more than three hours to complete, so making this a two credit hour class has been a request from all of the students as stated in their class evaluations. Unfortunately, this course cannot be a two credit hour course, as one of the stipulations of the Data Science Academy is that all courses must be one credit hour. This course allows students to see results based data and perform physical activity at the same time. The students realize they can continue their own version to track their individual progress in a similar manner once the course ends.
keywords: Physical activity, Data Science, Interdisciplinary, Project-based