Assessment in Higher Education
Li-Ting Chen, PhD
Associate Professor
University of Nevada Reno, United States
Li-Ting Chen, PhD
Associate Professor
University of Nevada Reno, United States
Location: Room 301
Abstract Information: In education and many other fields, statistics courses are required for completing a degree program. However, many students have anxiety toward learning statistics, let alone individual exams required in most of the statistics courses. The following quotes are mentioned frequently when students are asked to express what they feel in a statistics course: “I am not a math person and I feel that this course is challenging.” “I feel statistics is intimidating.” Students enrolled in statistics courses may have had negative learning experience in statistics related subjects, which may prevent them from enjoying learning. Advances in technology allow instructors to utilize alternative evaluation methods other than traditional individual exams to assess student learning. These alternative methods may also alleviate statistics anxiety and help students enjoy learning. To this end, alternative evaluation methods are reviewed and summarized. Based on literature review and my own teaching experience, I summarize four alternative evaluation methods: (a) online discussion activities, (b) research projects, (c) article analysis papers, and (d) individual homework assignments with opportunities for resubmission after receiving instructor’s feedback. An example of an online discussion activity is that an instructor provides a discussion prompt and students are required to post their responses to the prompt as well as to reply to at least two of their peers’ posts. In comparison to in-class discussion activities, online discussions allow students to spend more time and dive deeper into the learning materials. Discussion activities also provide opportunities for instructors to facilitate knowledge construction, collaborative learning, and cognitive engagement. Research project assignments can be used to assess whether students can apply the statistic methods to real-world problems. Instructors may provide datasets for students to use and/or allow students to use their own data to complete the project. If a learning management system (e.g., Canvas) is used, instructors can ask students to upload their written research projects to the learning management system and require peer feedback. Similar to research project assignments, article analysis papers facilitate student understanding of application of statistics methods in real-word problems. Instructors may ask students to locate a published peer-reviewed article in their field of interest or provide a list of articles for students to choose from for their analysis paper. Students from diverse backgrounds are typical in a statistics classroom. When an instructor is able to provide constructive and timely feedback to students as well as allow students to use feedback for resubmission, students feel they are supported and are not afraid of making mistakes. In 2011, Dr. George Cobb suggested three aspects for evaluating student learning in statistics courses: (1) assessing a student’s work in the context of that student’s own background, interests, goals, and leaning styles, (2) emphasizing effort and accomplishments, and (3) making it safe to mess up. With today’s technology, instructors can do more to embrace these three aspects for enhancing student learning. I hope this proposal can invite more discussions on alternative evaluation of student learning in statistics courses.
Relevance Statement: Learning activities, textbooks, teaching strategies, and methods and results for evaluation of student learning can all together affect student motivation and confidence in learning statistics. On top of statistics anxiety, students may also have test anxiety that may result in obtaining lower scores that don’t accurately reflect their understanding of course content and material. As an instructor who teaches and evaluates students at the same time, the instructor needs to clearly express the expectations and evaluation standards to students, regardless of evaluation methods. The reviewed evaluation methods are based on literature review and the author’s 15+ years of teaching experience. These evaluation methods have not only shown positive results in literature but agree with the American Evaluation Association’s Guiding Principles. The American Evaluation Association’s Guiding Principles D and E read as follow, D: Respect for People: Evaluators honor the dignity, well-being, and self-worth of individuals and acknowledge the influence of culture within and across groups. E: Common Good and Equity: Evaluators strive to contribute to the common good and advancement of an equitable and just society. With the diverse background of student population in a statistics course, the alternative evaluation methods, namely online discussion activities, research projects, article analysis papers, and individual homework assignments with opportunities for resubmission after receiving instructor’s feedback, allow students to work on open-ended assignments and to exercise application of statistics methods in real-world problems. These alternative evaluation methods may be more useful for students and promote active learning than traditional individual exams. Advances in technology allow instructors to utilize alternative evaluation methods other than traditional individual exams to assess student learning. These alternative evaluation methods can be used in face-to-face, hybrid, and online statistics classrooms. There should be other alternative evaluation methods that can enhance student learning. I hope the proposal can facilitate more discussions on alternative evaluations of student learning in statistics courses.
References
GAISE College Report ASA Revision Committee (2016). Guidelines for assessment and instruction in statistics education college report 2016.
Garfield, J., Zieffler, A., Kaplan, D., Cobb, G. W., Chance, B. L., & Holcomb, J. P. (2011). Rethinking assessment of student learning in statistics curses. The American Statistician, 65(1), 1-10. https://doi.org/10.1198/tast.2011.08241
Hayat, M. J. (2014). Guidelines for Assessment and Instruction in Statistics Education (GAISE): Extending GAISE into nursing education. Journal of Nursing Education, 53(4), 192-198. https://doi.org/10.3928/01484834-20140325-01
Mills, J. D., & Raju, D. (2011). Teaching statistics online: A decade's review of the literature about what works. Journal of Statistics Education, 19(2). https://doi.org/10.1080/10691898.2011.11889613
Ritzhaupt, A. D., Valle, N., & Sommer, M. (2020). Design, development, and evaluation of an online statistics course for educational technology doctoral students: A design and development case. Journal of Formative Design in Learning, 4(2), 119–135. https://doi.org/10.1007/s41686-020-00051-5