This paper investigates the viability of a machine learning model based on similarities in text structure compared to one based on statistical properties in the text to detect cheating in digital examinations. The machine learning model comparing similarity in text structure used Levenstein distance and the one comparing statistical text properties compared cosine distance between word vectors. The paper also investigates whether security has been a driving force impacting the industrial dynamics of the digitalization of examinations in Sweden. This is done using the multi-level perspective framework and interviewing users of a digital examination platform. The results show that the machine learning model based on statistical text properties has a higher accuracy, recall, precision and F-score. Nothing is concluded from this, however, due to discussion of validity of the results from the machine learning model based on the similarities in text structure. The analysis of the industrial dynamics shows that security has been a driving force towards digitalization.

The Corona pandemic and Covid-19 has affected the entire globe where we had to adapt to a new way of living and a new standard of doing things. For the universities in Sweden and around the world this generally meant that the universities had to switch to online teaching and digital examinations to the largest extent possible. Even though we have lived in this new kind of every day we know barely anything about how this has affected the grades of the students, and what we can learn about it to do things more effectively in the future. In this paper, I examine the possible effects on grades for students at the university level by using newly collected data from Linnaeus University’s department of economics and statistics. Results show that the grades after the decision to switch to online teaching and digital exams in March of 2020, overall became higher for both males and females but no significant difference between the genders were found.