Evaluating and understanding undergraduate students’ basic psychological needs satisfaction and motivation across different learning environments: a mixed-methods study

Self-determination theory posits that learning environments supporting students’ basic psychological needs (autonomy, competence, relatedness) stimulate their autonomous motivation and well-being. This study investigated basic psychological needs satisfaction, motivation and well-being in distinctive learning environments, and explored how learning environment characteristics contributed to basic psychological needs and autonomous motivation. We examined first-year students’ basic psychological needs satisfaction, motivation and well-being across three Bachelor’s programmes (A, B, C) using a survey (N = 202) and focus groups (N = 4, 4, 3) from each programme, respectively. All programmes used a student-centered problem-based learning approach. Programmes A and B offered pre-determined courses, while C allowed course selection in the second semester and included a research project where students chose a topic of interest. Programme A students had a lower score than Programme C’s in autonomy satisfaction (mean = 3.4 vs. 3.7, p = 0.017, ηpart2 = 0.039). They also had lower scores than Programme B’s in autonomous motivation (mean = 5.0 vs. 5.5, p = 0.008, ηpart2 = 0.039) and relative autonomous motivation (mean = 1.3 vs. 3.8, p < 0.001, ηpart2 = 0.070). Learning environments providing freedom and support for choosing topics of interest fostered students’ autonomy satisfaction, while insufficient guidance hindered it. Acquiring knowledge, small-group collaborations, peer connections, and real-life relevant learning activities stimulated autonomous motivation, while assessments not reflecting deep understanding hampered it. Students experienced different degrees of autonomy satisfaction, autonomous motivation and relative autonomous motivation in learning environments of distinctive characteristics. To stimulate students’ autonomous motivation, learning environments should offer choices of students’ interest with guidance, use assessments that reflect deep learning, facilitate group collaboration and connections and provide real-life relevant problems.