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There is high demand for qualified Information and Communication Technology (ICT) practitioners in the European labor market. In Estonia, the problem is not a low number of ICT students but a high dropout rate. The aim of this study is to find how it is possible to predict first-year dropout in higher education ICT studies and possibly to engage methods to decrease dropout rate. Data was collected from 301 first-year ICT students in Estonia who filled in a questionnaire at the beginning of the first semester and after the first semester. Additionally, some information was collected electronically during the admission process. The results showed that on average, 32.2% of the ICT students in Estonia dropped out during the first study-year. It was found that students who dropped out had lower scores in the state mathematics exam. This means that the score of the mathematics exam is one characteristic that can predict dropout during the first study-year. At the beginning of the studies there were not many differences in students' perception of their interest and how well the studies met their expectations. However, the answers received after the first semester showed some statistically significant differences between the students who dropped out during the first study-year and those who did not. Differences occurred, e.g., in the case of the following questions: how big their interest in ICT was, how well the studies met their expectations, how pleasant studying was for them, and how high they felt was the probability of them finishing their studies. It can be concluded that asking questions after the first semester gives information to universities as to who are about to drop out. Based on the information universities can support their students to retain them. The results support some factors that were found in literature to be important for avoiding dropout (e.g., motivation, earned credit points, prior studies, expectations), but in some cases the results of this study are different than the literature suggests (e.g., age, gender, working during studies, number of friends in the ICT field). It could be that these factors are not that important in influencing first-year dropout in ICT studies.

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Abstract-There is a high demand for qualified Information and Communication Technology (ICT) practitioners in the European labor market. However, a high dropout rate in higher education among ICT students is a big problem. One reason why students drop out is low study motivation, which in turn can influence the learning outcomes. Prior experience, such as learning ICT at the general education level and working in ICT field may influence their study motivation and learning outcomes at the higher education level and for this reason, the relationship between the above indicated variables was investigated in the current study. Data were collected in three higher education institutions in Estonia from 301 first-year ICT students. After the first semester, students filled in a questionnaire which contained the Academic Motivation Scale (AMS-C 28) College (CEGEP) version and questions about prior and current experience in ICT. The results show that the students who had learned programming before entering university had higher weighted average grades in the first semester and the students who started studying programming for the first time at university had more external regulation (a subcategory of extrinsic motivation) influencing their studies than other students. Working students had less motivation and lower results regarding the two subcategories of extrinsic motivation (introjected and external regulation). The findings show that learning programming before starting ICT studies gives an advantage in studies and working during studies is related to lower extrinsic motivation. This suggests improving students' opportunities for gaining experience in programming (studying or working) prior to university studies in order to support their future studies in the field of ICT.

The main aim of the present study is to assess whether the open learner model (OLM) is capable of promoting students' active thinking by enhancing their self‐regulation in online higher education learning environments. To this aim, we systematically reviewed the literature of the last three decades and found 67 articles, of which only a sample of 15 were considered. Based on the findings, we performed a narrative analysis of the studies concerning technological features of OLMs that cater to the three main aspects concerning self‐regulated learning, namely, cognition, metacognition and motivation. Our analysis of the literature confirmed that these three aspects are all subject to some measure of influence. In mutually interacting, these three components support learners to reach a better understanding of their learning process. Specifically, it seems that mostly all three type of OLMs, inspectable, negotiable, and co‐operative, with simple and complex graphical presentation of their learner models and capacity to colour‐code and compare—alike appear optimal for augmenting cognition, metacognition, and motivation. They seemingly do so through offering a wealth of techniques pertaining to knowledge, difficulties, and misconception visualization. The results presented suggest that OLMs have a positive impact on learners' active thinking regarding their learning process. Practitioner NotesWhat is already known about this topic Several educational environments have been enhanced with open learner models (OLMs) with the aim of supporting learners. OLM seems to be a potential avenue for improving higher education by supporting learners' active thinking about their learning process by enhancing self‐regulated learning. Despite growing interest in OLMs, there has yet to be any effort to systematically review current research studies investigating their potential to promote self‐regulated learning. What this paper adds All type of OLMs, with their varied visualization features, to some extent are useful for promoting cognitive, metacognitive, and motivational components of self‐regulated learning. In mutually interacting, the three components of self‐regulated learning support learners to reach a better understanding of their learning process, and thereby promoting active thinking. Implications for practice and/or policy We suggest that practitioners must first grasp the possibilities for the cultivation of self‐regulated learning contained in the particular features of each OLM, and then add this insight to their own expertise in order to best integrate them for supporting students' active thinking. While a number of specific features of OLMs seem pertinent to cultivating one particular element of self‐regulated learning, there remains the chance that, through the interaction of cognitive, metacognitive, and motivational tactics, these features could in fact make a more holistic and general contribution. This supports learners to move from active‐in‐behaviour to active‐in‐thinking activities in digital learning environments.

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Low retention rates in higher education Information Technology (IT) studies have led to an unmet demand for IT specialists. Therefore, universities need to apply interventions to increase retention rates and provide the labor market with more IT graduates. However, students with different characteristics may need different types of interventions. The current study applies a person-oriented approach and identifies the profiles of first-year IT students in order to design group-specific support. Tinto's [13, 14] integration model was used as a framework to analyze questionnaire data from 509 first-year IT students in Estonia. The students’ response profiles were distinguished through latent profile analysis, and the students were divided into four profiles based on their responses to questions about academic integration, professional integration, and graduation-related self-efficacy. The difference in academic integration was smaller among the profiles than the difference in professional integration. Knowing these profiles helps universities to design interventions for each student group and apply the interventions to increase the number of IT graduates.