Examining and Extending the ‘Robert and Susan Problem’: Exploring the Associations of Deep Approaches to Learning, Level of Student Activity and Use of Formative Feedback within HE Enterprise and Entrepreneurship Education
Creators: name: Curtis, Vic
Publisher: name: College of Arts, Humanities and Education, University of Derby
Year: 2024
Subjects: approaches to learning
deep learning
active learning
formative feedback
enterprise education
entrepreneurship education

description: The principle of constructive alignment [CA] is heavily underpinned by the approaches two hypothetical student personas, ‘Academic Susan’ and ‘Nonacademic Robert’, take to their learning. The seminal work of Biggs and Tang (2011) suggests Susan spontaneously adopts a deep approach to learning whilst Robert does not and needs help to reach similar levels. Actively engaging with constructively aligned curricula is the proposed solution to encourage Robert to learn more in the manner of Susan and to enhance the outcomes for the increasing number of Roberts in today’s higher education [HE] classrooms. However, little evidence supports the relationships Biggs and Tang (2011) suggest in a hypothetical graph of this ‘Robert and Susan Problem’. Neither is there evidence to support their assertion that feedback during learning, used constructively, is the “most powerful enhancement to learning” (Biggs and Tang, 2011, p.64) for Robert or Susan. This study aims to explore these claims through examining the associations of deep approaches to learning with level of student activity for both hypothetical students. It then extends the ‘Robert and Susan problem’ to explore similar relationships with use of formative feedback to provide guidance to practitioners around the importance of each variable in predicting deep learning. 

The study was conducted within two constructively aligned HE enterprise and entrepreneurship education [EEE] modules which employ best EEE teaching practice of active learning and use of feedback. Student data from the Revised Study Process Questionnaire was aligned to Susan and Robert across the students’ own most liked and disliked modules, mid EEE module and end of EEE module through an original allocation procedure and then analysed through linear and multiple regression. 

Strongly significant, positive linear relationships were shown to exist between deep learning and both level of student activity and use of formative feedback for both student personas, with Robert’s deep learning starting from a lower point but increasing more rapidly than Susan’s. This partially corroborates Biggs and Tang’s (2011) hypothetical graph although the study also reveals greater nuance and complexity than they originally suggested. Both hypothetical students appear to exhibit a range of deep learning rather than a singular adopted approach with Susan more influenced by context and interest and Robert more impacted by activity and formative feedback. 

Furthermore, the study reveals that level of student activity is more important and predicts more variance in deep learning than use of formative feedback earlier in the module. However, later in the module, use of formative feedback becomes the more important predictor. This suggests that students’ ongoing approaches to learning are a response to the interaction between their own interest and the teaching context, and the shifting importance of activity and formative feedback throughout the module. Therefore, to engage both Susan and Robert and to maximise deep learning, the overall implication for EEE and wider practice is to create constructively aligned interest through activity at the start of teaching and progress to greater use of formative feedback towards the end.