Exercise Science Software

Purpose: to help other instructors teaching the same course

Common Course ID: KINE 2400
CSU Instructor Open Textbook Adoption Portrait

Abstract: The open educational resources (OER) are being utilized in a Kinesiology course for undergraduate students by Dr. April Karlinsky at California State University, San Bernardino. The OER utilized include publicly available software and associated instructional materials along with open data sets. The main motivation to adopt OER was to reduce student costs and also to curate and personalize course content. Most students access the OER through the course’s online learning management system, Canvas, where the instructor provides weblinks and uploads materials. 

Exercise Science Software KINE 2400
Brief Description of course highlights: 
Examination and application of technology in exercise science, physical activity, and heath. Content is consistent with skills necessary to prepare students for professions in the field of kinesiology. Formerly known as KINE 240, students may not receive credit for both. Materials fee required. Course description in the catalog.

Student population: 
The student population is comprised of undergraduate Kinesiology students with diverse educational backgrounds. This is an elective course for Kinesiology majors (particularly geared towards those students in the Exercise Science concentration) and there are no prerequisites prior to enrolling in the course. Most students are junior or senior level students.

Learning or student outcomes: Student Learning Outcomes: Upon successful completion of this course, students will be able to:

1. Describe and discuss equipment, technology, and software applications used in the field of Kinesiology
2. Input, organize, and analyze data in Excel
3. Graphically represent data output in Excel and PowerPoint
4. Calculate basic statistics in JASP
5. Teach the class how to use a new software program/app using PowerPoint
6. Ethically utilize ChatGPT
7. Explore emerging software and applications

Key challenges faced and how resolved:  
The key challenges faced were related to locating appropriate no-cost course materials. As one strategy, I designed the course to leverage the Student Software freely available to CSUSB students via the university’s Information Technology Services. For example, I utilized the Microsoft Office 365 suite for activities using Excel and supplemented these with activities using GoogleSheets, which students would retain free access to following their graduation. 

Textbook or OER/Low cost Title: 
Various no-cost resources were utilized, including the following:

• Microsoft Excel
• Google Sheets
• Kaggle
• JASP
• ChatGPT

Brief Description:  Various no-cost resources were utilized, including the following:

• Microsoft Excel: Excel is a spreadsheet software program, which also features data visualization and analysis tools. Although Excel is not free, CSUSB students have free access to Microsoft Office 365 (which includes Excel) through the university’s Information Technology Services. 
• Google Sheets: Similar to Excel, Google Sheets is a (free) spreadsheet software program, which also includes data visualization and analysis features. 
• Kaggle: Kaggle is a data science community with thousands of open datasets available to be freely downloaded. I designed activities and assignments wherein students could practice using Microsoft Excel, Google Sheets, and JASP based on these datasets. 
• JASP: JASP is an open-source analysis program, which offers standard analysis procedures in both their classical and Bayesian form. 
• ChatGPT: ChatGPT is a form of generative AI, which answers questions in comprehensive way and allows users to engage in human-like conservations. 

Student access:  
Students had access to links for all course materials within the course management system (Canvas). 

Supplemental resources: Sample student activities/assignments:
Excel: Olympics Data
ChatGPT: Elevator Pitch

Provide the cost savings from that of a traditional textbook.
a. Individual student savings: $79
b.  Class savings (for 10 students): $799.90

OER/Low Cost Adoption Process

Provide an explanation or what motivated you to use this textbook or OER/Low Cost option.   Recognizing that many students have limited financial resources, a major motivation for opting into the IA program for this course was to save students money. 

How did you find and select the open textbook for this course? To find open resources for this course, I reviewed the student software available to students through the university, browed open data websites, and used free statistical software recommended by colleagues.

Sharing Best Practices: As noted below, the biggest challenge I faced using OER for this course was the time commitment, and I think more strategically using generative AI (e.g., ChatGPT) could have facilitated my experience with this course. I only started using ChatGPT near the end of the semester when I was preparing ChatGPT-related instructional materials and assignments for the students, and I think it would have been very helpful earlier on when preparing assignments (and associated rubrics) for lessons involving other forms of software, such as Excel. Indeed, ChatGPT proved to be a big time-saver when it came to brainstorming potential assignments, which could be particularly helpful to counterbalance some of the additional time invested in researching OER or Low-Cost options.

Describe any key challenges you experienced, how they were resolved  and lessons learned. Identifying the OER may be time consuming! The initial preparation may be more time consuming than relying on a traditional textbook, as the material may not be discipline specific or entirely relevant to the learning objectives. The free software and open source data sets I chose to use did not include any form of instructor slides, assignment suggestions, or test question bank, so additional instructor time was required to prepare these resources. 

Instructor Name - April Karlinsky
I am an Assistant Professor in the Department of Kinesiology at the California State University, San Bernardino. https://www.csusb.edu/profile/april.karlinsky 

Please describe the courses you teach I regularly teach the following undergraduate courses within the Department of Kinesiology:

• KINE 3700: Statistics in Kinesiology
• KINE 4100: Motor Learning and Control
• KINE 4800: Biomechanics

Describe your teaching philosophy and any research interests related to your discipline or teaching.  My evolving teaching philosophy and strategies have been informed and refined by my teaching activities to-date and by my own experiences as a learner. I have recognized that well-organized and knowledgeable instructors who presented their teaching material in a clear and engaging manner and who appreciated and encouraged intellectual diversity in the classroom, research labs, and beyond have heavily and positively influenced me. I have also valued those instructors who were not threatened by challenging questions, divergent opinions, or constructive feedback, and who were committed and devoted to their respective academic interests. My best teachers also modeled their own intellectual curiosity and critical thinking as well as a commitment to self-improvement and life-long learning. My own approach to teaching – whether in-person or online – respectfully attempts to abide by such positive examples and has also been influenced by the pedagogical literature around evidence-based teaching and the latest examples in educational technology. As a brief summary, my teaching philosophy can be reflected in the three key principles that follow: Know Your Material, Know Your Students, and Commit to Becoming a Better Teacher.

As a brief overview of my research program, my main research interests are focused on motor performance and learning in a social context. This area is an emerging and exciting field of research because, to-date, the majority of motor behavior research has been conducted on individuals performing and learning alone in isolated settings. Although this previous research has been clearly valuable, people typically act and learn in social settings with other co-actors and co-learners and there are a multitude of other yet to be discovered factors that influence performance and learning in these more complex environments. To date, my research has employed behavioral measures of action (such as movement times and error scores) and self-report questionnaires to gain insight into individuals’ motor skill acquisition, practice-related decisions, and psychological constructs (e.g., motivation, perceptions of competence). An additional important component of my research-related activities at CSUSB is a serious effort to encourage and mentor student participation.