OK google: what’s the answer? characteristics of students who searched the internet during an online chemistry examination

Abstract

Restrictions on movement during the COVID-19 pandemic led to assessment being conducted online, which gave students opportunities to use internet search engines during examinations. We found that in some cases internet searches resulted in characteristic incorrect responses, and around one third of students entered these answers to examination questions in a foundation chemistry unit. Classification of those students allowed us to analyse their performance on other assessment tasks and we found that students who used an internet search and input incorrect results performed worse across all assessment tasks. Use of online proctoring and an academic honesty module in subsequent semesters reduced the percentage of students entering incorrect answers found though internet searches. We explored the relationship between students’ prior knowledge, investment in content and self-efficacy with the use of internet searches during their examination. We found significant correlations with prior knowledge and investment, but not with self-efficacy. Our data calls for better authentication of online assessment, because in most cases internet searches give correct responses and so it is impossible to be sure that a student has generated the response. We propose guidelines to reduce the use of search engines during examinations.

Supplemental data for this article is available online at https://doi.org/10.1080/02602938.2022.2048356 .

Keywords:

• Online examinations
• cheating
• academic dishonesty
• COVID-19
• undergraduate chemistry

Acknowledgements

We are grateful to our students for completing the survey and persisting in spite of unexpected changes during the past two years. We thank Nick Milne who provided the Chegg responses, and the markers of the hand-written questions who alerted us to specific student responses. We acknowledge Anna Miltiadous for helpful discussions about the statistical analyses and Shir Etgar for sharing her motivation coding approach.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Madeleine Schultz is an Honorary Fellow in the School of Life and Environmental Sciences at Deakin University. Her interests in chemistry education research include concept development, assessment and incorporation of sustainability into chemistry curricula.

Kieran F. Lim is Honorary Associate Professor in the School of Life and Environmental Sciences at Deakin University. He has broad research interests in science teaching and learning, particularly in relation to laboratory work.

Yoong Kuan (Andrew) Goh is a staff member at eResearch at Deakin University. His main interests are the application of Python and data analysis.

Damien L. Callahan is Associate Professor in the School of Life and Environmental Sciences at Deakin University. His research spans analytical mass spectrometry, metabolomics, chemical ecology and applied research on metal hyperaccumulating plants and microalgal lipid biochemistry.