The first part of the worldwide lockdown starting in March 2020 forced teachers in higher education to implement emergency remote teaching (ERT) in an online learning environment. ERT is a kind of online instruction delivered in pressing circumstances, which contrasts with deliberate and well-planned online learning education (Daniel, 2020; Hodges et al., 2020; Huang, & Wang, 2022; Murphy, 2020). Some students appreciated the autonomy they acquired and the appeal to their self-discipline. Other students, preferring structure and guidelines, perceived these new learning circumstances as ambiguous and unclear. Pressing circumstances, such as a pandemic forcing students into a new learning environment, pose a challenge to their academic motivation. In this study, we used one of the leading theories on motivation, the self-determination theory (SDT; Deci & Ryan, 2000). Deci and Ryan (2000) contributed to the field of motivation theory by making a distinction between two types of motivation regulation, i.e. controlled motivation and autonomous motivation. We used SDT to highlight the processes of motivation within the learning environment (Deemer, & Smith, 2018). Following the self-determination theory, one could promote autonomous motivation by fulfilling the three basic psychological needs of students: the need for autonomy, relatedness and competence. The learning environment is one of the most important factors of learning that affects motivation to learn (Wang, Haertel, & Walberg, 1990). Students are more likely to experience positive outcomes when the learning environment responds to their needs (Gutman & Eccles, 2007). According to Moos (1974, 2002), the learning environment is a psychosocial situation with three dimensions of experience: the relationship dimension, the growth dimension and the change dimension. The dimensional framework of Moos (1974) closely aligns with Basic Psychological Needs Theory (BPNT; Vansteenkiste, Ryan, & Soenens, 2020) (Deemer, & Smith, 2018). During the pandemic, a more autonomously regulated learning environment was introduced, in the form of ERT-learning: students needed to appeal more to their self-discipline to decide when and how to study (autonomy), find new ways to relate to their peers (relatedness) and to feel that they had learned effectively (competence). On that premise, this study suggests that the sudden change of learning environment following ERT has had an impact on the fulfilment of the basic psychological needs of learners and consequently, on their motivation. The level of motivation will steer behavior, hence students’ activities to learn, develop their competences, and succeed in their academic curriculum. In this embedded mixed method study, motivation was measured among students from the Royal Military Academy (RMA), a Belgian university, before the WHO’s declaration of the pandemic (December 2019) and during the pandemic (June 2020). We found that the first college year students’ motivation was the most negatively affected, followed by that of the second college year students. In addition, we found that ERT affected perceived competence suggesting that lower perceived competence contributes to a lower academic motivation. Based on these results, this study underlines the importance of assessing learners’ sense of competence before immersing them into an online learning environment or changing their learning environment in any other way.

In this study, we used an embedded mixed method (Behmanesh, Bakouei, Nikpour, & Parvaneh, 2020). This study comprised two phases. The first phase assessed motivation and the satisfaction of basic needs by a quantitative approach, with only closed questions. The second phase explored the students’ perception and experiences towards new issues that were not captured in the first phase. Here we used quantitative and qualitative approaches, including closed and one open-ended question. We invited the 303 college students of the RMA to participate in a survey regarding their academic motivation once before the WHO’s declaration of the pandemic (T1, December 2019) and once during the pandemic (T2, June 2020). The questionnaire at T1 included the SRQ-L and was implemented in Google Forms and the questionnaire at T2 included the SRQ-L, the BPNSFP, and the open-ended question, and was implemented in the learning management system of the RMA (ILIAS®). In this study, 155 students completed the questionnaires at T1 and T2. First, the properties of the variables were explored. Second, a repeated-measure ANOVA was used to test the hypotheses, as we have two dependent measurements (at T1 and at T2). The independent variables are: a) TIME (T1 vs. T2), and b) Year (BA1 vs. BA2 vs. BA3); the dependent variables are: a) RAI and b) BPNSFP. We controlled for a) Faculty (SMS vs. ENG), b) Language (Dutch speaking vs. French speaking) and c) Gender (male vs. female). To determine which differences were the most relevant, we calculated the effect size using Cohen’s d (Cohen, 1994). Third, to determine the effect of one (or more) explanatory variable(s), such as the need for autonomy, for competence, and for relatedness on a dependent variable such as RAI at T2, we used a regression analysis. A Chi-square test of independence was performed to examine the relation between year and the satisfaction of the need autonomy, the need competence and the need relatedness. For the analysis of the content of the responses to the open-ended question, we tailored our approach on the three steps to O'Cathain & Thomas (2004): 1) reading a sub-set of the comments 2) assigning a coding frame to describe the thematic content of the comments and 3) assigning a selected code to all comments.

We found a drop in motivation from December 2019 to June 2020 possibly due to the sudden introduction of ERT. This drop in motivation was more marked in the first-year college, followed by the second-year college. Students confronted with uncertainty of the first year in higher education, could not compensate their lack of learning skills and probably use an external type of regulation in tackling their studies (Williams, & Hellman, 2004). In addition, we found that ERT did affect perceived competence, more specifically in the first and the second college year. This may suggest that lower perceived competence is associated to a lower academic motivation. During online learning education, higher education should focus extra on transversal competence acquisition for students through exercises, assignments, reflection and digital literacy for teachers (Salas Velasco, 2014) to keep the autonomous motivation as high as possible.

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