Identifying Problematic Study Progression and “At-Risk” Students in Higher Education in Finland

Abstract

Finland offers a specific example of a country with a broad enrolment in higher education, where the educational starting age is relatively high and where studies may last considerably longer than in most other European countries. This study attempted to identify at-risk students in Finnish universities with the greatest probability of non-completion of studies. This probability is by far the highest in the fields of information technology and information sciences and, in specific cases, in the fields of mathematics and science and economics and management. The situation of at-risk students is best described in terms of the scant use of time for present studies, uncertainty regarding their study field choices and their general intention to withdraw from their studies. An analysis of qualitative case study interviews revealed six general student types, each representing different meanings of studying for slowly advancing at-risk students.

Dropping out from Higher Education in Germany an Empirical Evaluation of Determinants for Bachelor Students

“…The same observations are made by Heublein et al (2017), who also reveal the highest dropout rates in Germany to be in Engineering, Mathematics and Natural Sciences. This result is confirmed by Lassibille and Navarro G´omez (2008) for Spain and Korhonen and Rautopuro (2019) for Finland. Moreover, there are some important determinants related to study conditions that affect students' decision to drop out.…”

Dropping out of university: a literature review

“…On the institutional level, the type of higher education institution (e.g., Heublein et al , 2017), the study field (e.g., Lassibille & Navarro Gómez, 2008; Korhonen & Rautopuro, 2018), teaching quality and learning environment (e.g., Georg, 2009; Hovdhaugen & Aamodt, 2009), class size (e.g., Montmarquette et al , 2001), and the relationship between students and teachers (e.g., Johnes & McNabb, 2004; Ghignoni, 2017) seem to have an impact on the probability of withdrawal.…”

“…Besides these pre‐study determinants, several study‐related individual aspects affect students’ risk of dropping out. These are, for instance, study motivation (e.g., Heublein et al , 2017), especially intrinsic motivation, and study satisfaction (e.g., Suhre et al , 2007), person‒environment fit (e.g., Suhlmann et al , 2018), learning strategy and students’ study organisation (e.g., Schiefele et al , 2007), class attendance (e.g., Korhonen & Rautopuro, 2018; Nordmann et al , 2019), as well as working while studying (e.g., Brandstätter & Farthofer, 2003; Beerkens et al , 2011; Hovdhaugen, 2015).…”

“…For Spain, Lassibille and Navarro Gómez (2008) analysed 7,000 students from the University of Malaga and observed high dropout rates, especially in subjects such as Engineering, Science and Law. Korhonen and Rautopuro (2018) used data on more than 20,000 students from four Finnish universities and found the highest dropout rates in the fields of Information Sciences, Information Technology, Mathematics and Economics. There are large between‐subject differences in the time students spend weekly on studying.…”

“…Nordmann et al (2019) found that class attendance and the use of recorded lectures at the University of Aberdeen in the UK has a positive influence on study performance for the analysed 347 first‐year students. Korhonen and Rautopuro (2018) observed that at‐risk students spend less time on the study course and are often uncertain about their choice of study or subject.…”