What are they trying to tell me? Large-scale viability of the Start, Stop, Continue teaching evaluation method

References

• Alhija, F. N. A., & Fresko, B. (2009). Student evaluation of instruction: What can be learned from students’ written comments? Studies in Educational Evaluation, 35(1), 37–44. https://doi.org/https://doi.org/10.1016/j.stueduc.2009.01.002
   Google Scholar
• Angelo, T. A., & Cross, K. P. (1993). Classroom assessment techniques: A handbook for college teachers. Jossey-Bass. http://www.tap.pna.ps/pdf/ucc/ReadingAssignment2_Angelo,%20Cross,%20Classroom%20Assessment%20Techniques_1_(2).pdf
   Google Scholar
• Boysen, G. A. (2016). Using student evaluations to improve teaching: Evidence-based recommendations. Scholarship of Teaching and Learning in Psychology, 2(4), 273. https://doi.org/https://doi.org/10.1037/stl0000069
   Google Scholar
• Brockx, B., Van Roy, K., & Mortelmans, D. (2012). The student as a commentator: Students’ comments in student evaluations of teaching. Procedia, Social and Behavioral Sciences, 69(1), 1122–1133. https://doi.org/https://doi.org/10.1016/j.sbspro.2012.12.042
   Google Scholar
• Cashin, W. E. (1999). Student ratings of teaching: Uses and misuses. In P. Seldin (Ed.), Changing practices in evaluating teaching: A practical guide to improved faculty performance and promotion/tenure decisions (pp. 25–44). Anker.
   Google Scholar
• Chism, N. V. (2007). Peer review of teaching: A sourcebook. Anker.
   Google Scholar
• Cousins, J. B., & Earl, L. M. (1992). The case for participatory evaluation. Educational Evaluation and Policy Analysis, 14(4), 397–418. https://doi.org/https://doi.org/10.3102/01623737014004397
   Web of Science ®Google Scholar
• Foddy, W. H. (1994). Constructing questions for interviews and questionnaires: Theory and practice in social research. Cambridge University Press.
   Google Scholar
• Franklin, J. (2001). Interpreting the numbers: Using a narrative to help others read student evaluations of your teaching accurately. New Directions for Teaching and Learning, 87, 85–100. https://doi.org/https://doi.org/10.1002/tl.10001
   Google Scholar
• Hoon, A., Oliver, E., Szpakowska, K., & Newton, P. (2015). Use of the ‘Stop, Start, Continue’ method is associated with the production of constructive qualitative feedback by students in higher education. Assessment & Evaluation in Higher Education, 40(5), 755–767. https://doi.org/https://doi.org/10.1080/02602938.2014.956282
   Web of Science ®Google Scholar
• Hutto, C. J., & Gilbert, E. E. (2014, June). VADER: A parsimonious rule-based model for sentiment analysis of social media text [Paper presented]. Eighth International Conference on Weblogs and Social Media (ICWSM-14), Ann Arbor.
   Google Scholar
• Jordan, D. W. (2011). Re-thinking student written comments in course evaluations: Text mining unstructured data for program and institutional assessment [Unpublished doctoral dissertation]. California State University.
   Google Scholar
• Lewis, K. G. (2001). Making sense of student written comments. New Directions for Teaching and Learning, (2001(87), 25–32. https://doi.org/https://doi.org/10.1002/tl.25
   Google Scholar
• Marincovich, M. (1999). Using student feedback to improve teaching. In P. Seldin (Ed.), Changing practices in evaluating teaching: A practical guide to improved faculty performance and promotion/tenure decisions (pp. 45–69). Anker.
   Google Scholar
• Marsh, H. W. (2007). Students’ evaluations of university teaching: A multidimensional perspective. In P. Perry & J. C. Smart (Eds.), The scholarship of teaching and learning in higher education: An evidence-based perspective (pp. 319–384). Springer.
   Google Scholar
• Newman, H., & Joyner, D. A. (2018). Sentiment analysis of student evaluations of teaching. In Proceedings of the 19th International Conference on Artificial Intelligence in Education. Springer.
   Google Scholar
• Nielsen, F. Å. (2011, May). A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. In Proceedings of the ESWC2011 Workshop on ‘Making Sense of Microposts’: Big things come in small packages (pp. 93–98).
   Google Scholar
• Palmer, S., & Campbell, M. (2013, May). Practically and productively analysing Course Experience Questionnaire student comment data. In AAEE 2013: Proceedings of the 24th Australasian Association for Engineering Education Conference, Griffith School of Engineering, Griffith University.
   Google Scholar
• Seldin, P. (1999). Changing practices in evaluating teaching: A practical guide to improved faculty performance and promotion/tenure decisions. Anker.
   Google Scholar
• Silge, J., & Robinson, D. (2016). Tidytext: Text mining and analysis using tidy data principles in R. The Journal of Open Source Software, 1(3), 37. https://doi.org/http://dx.doi.org/10.21105/joss.00037
   Google Scholar
• Silge, J., & Robinson, D. (2017). Text mining with R: A tidy approach. O’Reilly.
   Google Scholar
• Spinelli, S., Dinnella, C., Masi, C., Zoboli, G. P., Prescott, J., & Monteleone, E. (2017). Investigating preferred coffee consumption contexts using open-ended questions. Food Quality and Preference, 61, 63–73. https://doi.org/https://doi.org/10.1016/j.foodqual.2017.05.003
   Web of Science ®Google Scholar
• Wasserman, L. (2004). All of statistics: A concise course in statistical inference. Springer.
   Google Scholar