Learning and teaching theories focused on approaches to learning consider the link between the way learners approach learning and their level of understanding. Developed from research originally undertaken by Marton and Saljo (mid 70's), and further developed by Entwistle (early 80's), Biggs (later 80's) and Ramsden (early 90's).
- Learning to specifically meet course requirements
- Studying unrelated bits of knowledge
- Memorising facts and figures to repeat
- No linking or connection of learning
The surface approach to learning comes from “the intention to get the task out of the way with minimum trouble while appearing to meet course requirements” (Biggs, 2003, p14). This often includes rote learning content, filling an essay with detail rather than discussion and list points rather than providing background or context to the work.
- Learning that seeks to understand and connect the concepts
- Relates ideas to previous knowledge and experience
- Explores links between evidence and conclusions
- Critiques arguments and examines rationale
The deep approach comes “from a felt need to engage the task appropriately and meaningfully, so the student tries to use the most appropriate cognitive activities for handling it” (Biggs, 2003, p16). Using this approach students make a real effort to connect with and understand what they are learning. This requires a strong base knowledge for students to then build on seeking both detailed information and trying to understand the bigger picture.
- Learning to achieve highest possible grades in a course
- Focused on assessment requirements and criteria
- Effort to understand knowledge to demonstrate learning
- Focused on perceived preferences of lecturer
Strategic learning, can be considered to be a balance between the two approaches.
Some may place a negative connotation on surface learning whilst viewing deep learning in a more positive light but there is a place for surface learning to lay a base knowledge or terminology for deep learning to build on.
How do you view the approaches to learning in your own context?
Can you think of examples of where surface, deep and strategic learning occurs in your own context ?
Further Reading and Links
Approaches to Study “Deep” and “Surface” - an easy to read site described by the author, James Atherton as a "quick and dirty" overview exploring deep and surface approaches to learning
Deep and Surface Approaches to Learning - a page from within The Higher Education Academy's UK website that provides another perspective and more information although it does take the crude viewpoint that "deep is good, surface is bad, and we should teach in a way that encourages students to adopt a deep approach; although achieving this is not so easy".
Biggs, J. (2003). Teaching for quality learning at University (2nd ed.). London: The Society for Research into Higher Education & Open University Press.
This review was aimed at investigating the effects of PBL on deep and surface approaches to learning. The studies included were all conducted within the specific context of PBL and most of the studies used Biggs’ theoretical framework to measure deep and surface processing. Dinsmore and Alexander (2012) made a plea to study deep learning approaches from a clear theoretical framework and within a specific context; a specific learning environment. We addressed these points in this review. The review demonstrated that eleven of the 21 the studies give indications that PBL does encourage a deep approach to learning and in eleven of the 21 studies measuring surface learning, PBL had no effect on a surface approach. As also indicated by the effect sizes, PBL does seem to enhance deep learning to some extent (ES = .11) and has less effect on surface learning (ES = .08). Furthermore, this review demonstrated that differences in effects between the studies could be partly explained by differential characteristics of the environment in which the PBL studies were conducted (a curriculum wide implementation has a more positive impact on students’ deep approach (ES = .18) compared to a single course (ES = −.05) implementation), but not by study quality.
The mechanisms through which PBL is assumed to enhance deep learning are active and self-directed learning. PBL is considered an active form of learning, since students need to analyze, compare, contrast, and explain information (Serife 2011). They are actively involved in their learning process because they themselves need to develop and explain hypotheses for the problem at hand and search for evidence for these explanations and hypotheses, using various literature and other learning resources (Gurpinar et al. 2013). Self-directed learning comes into play in PBL since students take responsibility over their own learning. They have, to a certain degree and within the boundaries of the problem, the freedom to select their own resources to answer the learning issues, which gives them ownership over their learning. Eleven out of the 21 studies included in this review demonstrate that PBL does foster deep learning (ES = .11). This effect is possibly mediated through intrinsic motivation. A recent PBL study in which having the freedom to choose literature resources (i.e., self-directed condition) from a set was compared to a condition in which two literature resources were given to students, indeed demonstrated that students in the self-directed condition scored higher on autonomous motivation (Wijnia et al. 2015), giving evidence for the relationship between self-directed learning and autonomous/intrinsic motivation.
The findings of this review also indicate that PBL has little effect on surface learning in eleven out of 18 studies (ES = .08) measuring surface learning. Is this good news or not? It could be argued that this finding is in a way a positive effect too. Nevertheless we should also take into account that in some situations a surface approach or perhaps better a combination of a deep and surface approach should best be used to learn effectively (Dinsmore and Alexander 2012). A high perceived workload will more likely result in surface approaches to studying and might be detrimental for deep learning. Students who perceive the workload as high in their learning environment are more likely to display a lack of interest in their studies as well as exhaustion. This is particularly true for beginning PBL students (Litmanen et al. 2014). Another factor that can lead to more surface learning is the assessment methods used. If the assessment is perceived as not rewarding deep learning, students will rely on surface learning. Therefore, the role of assessment is important to take into account in studies on SAL. Entwistle et al. (2003, p. 90) state in this respect that research findings vary “due to differences in the extent to which understanding is explicitly rewarded in the assessment procedure”. A qualitative study by Al Kadri et al. (2009) under PBL medical students confirmed indeed that students adapt their approaches to studying to the assessment demands (i.e. type of assessment and weight accorded to it). Scouller (1998) and Jensen et al. (2014) demonstrated that students were more likely to employ a deep approach when studying for assignment essays, which they perceived as measuring higher levels of cognitive processing, compared to a multiple choice assessment.
Although most studies demonstrate that PBL does enhance deep learning and has no effect on surface learning, this review also shows that studies often result in ambiguous and inconsistent findings as is also concluded by Dinsmore and Alexander (2012). One reason is that only three studies out of 21 studies reported about the validity of the data and eight about the reliability of the data. Often evidence of validity was lacking as concluded before by Dinsmore and Alexander (2012). Within this review we investigated deep learning within a specific context, being PBL. Although the studies demonstrated a trend towards a positive effect on deep learning and no effect on surface learning, findings differed across studies which could indicate that PBL is applied differently across the different studies, even although we included only studies in this review that met our definition of PBL. In addition, in one study it was argued that students already displayed high scores on deep learning due to which it might be difficult to further improve deep learning (Reid et al. 2005).
This review has several limitations. First of all, the studies included in this review only made use of self-report data; actual student behaviors were not measured and could differ from students’ self-perceptions. Next, the relationship with academic achievement was not considered in this review. Further, the number of longitudinal studies and qualitative studies was limited and some studies included only one group (i.e., no control group) or only post-test data (i.e., no pre-test data) due to which no clear comparisons could be made. As mentioned, not all studies included reported data about the validity and reliability of the instruments used to measure deep and surface processing, although the majority of the studies used previously validated instruments. Not all the studies included in the review reported the necessary information to calculate effect sizes. Hence, effect sizes of only 16 studies were included and aggregated across different study designs. For future research, more longitudinal studies are needed to determine the long terms effects of PBL on deep and surface learning, as well as experimental studies with a control group and pre- and post measurements that can give better insight in the actual changes in students’ deep and surface processing. Longitudinal studies provide opportunities to measure how approaches to learning might differ over time, although it should be taken into account that characteristics of the learning environment may also vary over time. Qualitative studies are needed as well since they could give us better insight in why and how PBL does or does not enhance deep and surface processing. Finally, future studies should report validity and reliability data of the instruments used to measure deep and surface processing.