Enhancing Digital Simulated Laboratory Assessments: a Test of Pre-Laboratory Activities with the Learning Error and Formative Feedback Model
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Digitally simulated laboratory assessments (DSLAs) may be used to measure competencies such as problem solving and scientific inquiry because they provide an environment that allows the process of learning to be captured. These assessments provide many benefits that are superior to traditional hands-on laboratory tasks; as such, it is important to investigate different ways to maximize the potential of DSLAs in increasing student learning. This study investigated two enhancements—a pre-laboratory activity (PLA) and a learning error intervention (LEI)—that are hypothesized to enhance the use of DSLAs as an educational tool. The results indicate students who were administered the PLA reported statistically lower levels of test anxiety when compared to their peers who did not receive the activity. Furthermore, students who received the LEI scored statistically higher scores on the more difficult problems administered during and after the DSLA. These findings provide preliminary evidence that both a PLA and LEI may be beneficial in improving students’ performance on a DSLA. Understanding the benefits of these enhancements may help educators better utilize DSLAs in the classroom to improve student science achievement.
KeywordsDigitally simulated laboratory assessment Pre-laboratory activity Learning error and formative feedback (LEAFF) model
This study was funded by Social Sciences and Humanities Research Council (grant number 435-2016-0114) to Dr. Jacqueline Leighton.
Compliance with Ethical Standards
Conflict of Interest
Dr. Man-Wai Chu declares that she has no conflict of interest. Dr. Jacqueline P. Leighton declares that she has no conflict of interest.
Informed consent was obtained from all individual participants’ parents/guardians included in the study. Student assent was also obtained from all individual participants included in the study.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
The ethics board approvals from the institution where the corresponding author worked (No. Pro00040790) and school district in which students were enrolled were obtained.
- American Educational Research Association [AERA], American Psychological Association [APA], & National Council on Measurement in Education [NCME]. (2014). Standards for educational and psychological testing. Washington, DC: Author.Google Scholar
- Bennett, R.E., Persky, H., Weiss, A.R., & Jenkins, F. (2007). Problem solving in technology-rich environments: A report from the NAEP technology-based assessment project (NCES 2007–466). U.S. Department of Education. Washington, DC: National Center for Education Statistics Retrieved from http://nces.ed.gov/nationsreportcard/pubs/studies/2007466.asp
- Chu, M-W. (2017). Using computer simulated science laboratories: A test of pre-laboratory activities with the learning error and formative feedback model. Edmonton: Unpublished doctoral dissertation, University of Alberta.Google Scholar
- Chu, M-W., & Leighton, J. P. (2016). Using errors to enhance learning and feedback in computer programming. In S. Tettegah & M. P. McCreery (Eds.), Emotions, technology, and learning (pp. 89-117). London Wall, London: Elsevier Incorporated.Google Scholar
- Chu, M-W., Guo, Q., & Leighton, J. P. (2013). Students’ interpersonal trust and attitudes towards standardized tests: Exploring affective variables related to student assessment. Assessment in Education: Principles, Policy & Practice, 21(2), 167-192. https://doi.org/10.1080/0969594X.2013.844094.
- Duncan, T. G. & McKeachie, W. J. (2005). The making of the motivated strategies for learning questionnaire. Educational Psychologist, 40(2), 117–128. https://doi.org/10.1207/s15326985ep4002_6.
- Dwyer, W. M., & Lopez, V. E. (2001). Simulations in the learning cycle: a case study involving “exploring the Nardoo”. Paper presented at the National Educational Computing Conference, “Building on the Future”, Chicago, IL. Retrieved from http://files.eric.ed.gov/fulltext/ED462932.pdf
- Firestein, S. (2016) Failure: why science is so successful. New York: Oxford University Press.Google Scholar
- Fredericks, J. A., Blumenfeld, P., Friedel, J., & Paris, A. (2005). School engagement. In K. A. Moore & L. Lippman (Eds.), What do children need to flourish?: conceptualizing and measuring indicators of positive development. New York, NY: Springer Science and Business Media.Google Scholar
- Gobert, J., O’Dwyer, L., Horwitz, P., Buckley, B., Levy, S. T., & Wilensky, U. (2011). Examining the relationship between students’ epistemologies of models and conceptual learning in three science domains: biology, physics, & chemistry. International Journal of Science Education, 33(5), 653–684. https://doi.org/10.1080/09500691003720671.CrossRefGoogle Scholar
- Gravetter, F. J. and Wallnau, L. B. (2017). Statistics for the behavioral sciences (10th Ed). Belmont, CA: Wadsworth Cengage Learning.Google Scholar
- Guo, Q., Cui, Y. (2017). LSTM cluster: a novel way to cluster students’ problem solving sequences. Paper session presented at the National Council on Measurement in Education, San Antonio, Texas.Google Scholar
- Kyllonen, P., C. (2017). Socio-emotional and self-management variables in learning and assessment. In A. A. Rupp & J. P. Leighton (Eds.), The handbook of cognition and assessment: frameworks, methodologies, and applications (pp. 174–197). Malden: John Wiley & Sons.Google Scholar
- Leighton, J. P., Chu, M-W., & Seitz, P. (2013). Cognitive diagnostic assessment and the learning errors and formative feedback (LEAFF) model. In R. Lissitz (Ed.), Informing the practice of teaching using formative and interim assessment: A systems approach (pp. 183-207). Information Age Publishing.Google Scholar
- Leighton, J.P., & Bustos Gomez, M. C. (2018). A pedagogical alliance for trust, wellbeing, and the identification of errors for learning and formative assessment. Educational Psychology: An International Journal of Experimental Educational Psychology. https://doi.org/10.1080/01443410.2017.1390073.
- Ma, J., & Nickerson, J. V. (2006). Hands-on, simulated, and remote laboratories: a comparative literature review. ACM Comput Surv, 38(3), Article 7. https://doi.org/10.1145/1132960.1132961.
- Mayrath, M., Clarke-Midura, J., & Robinson, D. (Eds.). (2012). Technology based assessment for 21st century skills: theoretical and practical implications from modern research. New York: Springer-Verlag.Google Scholar
- Midgley, C., Maehr, M. L., Hruda, L. Z., Anderman, E., Anderman, L., Freeman, K. E., … Urdan, T. (2000). Manual for the patterns of adaptive learning scales. Ann Arbor, MI: University of Michigan Press.Google Scholar
- National Assessment of Educational Progress [NAEP]. (2007). NAEP technology-rich environment simulation scenario [Computer software]. Retrieved from https://nces.ed.gov/nationsreportcard/studies/tba/tre/sim-description.aspx
- National Research Council. (2014). Developing assessments for the next generation science standards. Committee on Developing Assessments of Science Proficiency in K-12. Board on Testing and Assessment and Board on Science Education, J. W. Pellegrino, M. R. Wilson, J. A. Koenig, and A. S. Beatty (Eds.), Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press. Retrieved from http://www.nap.edu/catalog.php?record_id=18409
- Next Generation Science Standards [NGSS] Lead States. (2013). Next generation science standards: for states, by states. Washington, DC: The National Academies Press. Retrieved from http://www.nap.edu/catalog.php?record_id=18290
- PhET. (2017). PhET interactive simulations: research. Retrieved from https://phet.colorado.edu/en/research
- Quellmalz, E. S., Timms, M. J., & Buckley, B. C. (2009). Using science simulations to support powerful formative assessments of complex science learning. Retrieved from http://simscientists.org/downloads/Quellmalz_Formative_Assessment.pdf
- Reid, N., & Shah, I. (2007). The role of laboratory work in university chemistry. Chemistry Education Research and Practice, 8(2), 172–185 Retrieved from http://www.rsc.org/images/Reid%20paper%20final_tcm18-85040.pdf.CrossRefGoogle Scholar
- Sahin, S. (2006). Computer simulations in science education: implications for distance education. Turk Online J Dist Educ, 7(4), 132–146 Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.114.8977&rep=rep1&type=pdf.Google Scholar
- Scalise, K., Timms, M., Clark, L., & Moorjani, A. (2009). Student learning in science simulations. What makes a difference? Paper presented at the Session on Conversation, Argumentation, and Engagement in Science Learning during the Annual Conference of the American Educational Research Association. Retrieved from http://works.bepress.com/michael_timms/18/.
- Supasorn, S., Suits, J. P., Jones, L. L., & Vibuljan, S. (2008). Impact of a pre-laboratory computer simulation of organic extraction on comprehension and attitudes of undergraduate chemistry students. Chemistry Education Research and Practice, 9(2), 169–181. https://doi.org/10.1039/b806234j.CrossRefGoogle Scholar
- Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th edition). Boston: Pearson/Allyn & Bacon.Google Scholar