PBL에서 심층 및 표면학습: 문헌 고찰(Adv in Health Sci Educ, 2016)

PBL에서 심층 및 표면학습: 문헌 고찰(Adv in Health Sci Educ, 2016)

Deep and surface learning in problem-based learning: a review of the literature
Diana H. J. M. Dolmans1 • Sofie M. M. Loyens2,6 • He´le`ne Marcq4 • David Gijbels5

 

 

 

 

도입 Introduction

문제 해결과 비판적 사고와 같은 평생학습 능력의 육성과 발전을 촉진하는 것이 21세기 고등교육의 결정적 목표가 되었다. 볼로냐 선언에 따르면, 고등교육에서의 성공적인 학습과 공부에는 학생들이 딥러닝에 참여해야 한다(2014 아시카이넨). 최근 문헌 리뷰에서, Dinsmore와 Alexander(2012)는 학생들의 학습에 대한 연구가 실제와 조금이라도 관련이 있다면, 중요한 논의가 필요한 한 영역은 딥 러닝과 표면 학습에 대한 조사라고 말한다. 그들의 리뷰에서, Dinsmore와 Alexander(2012)는 심층 및 표면 학습에 대한 연구의 결과가 종종 모호하고 일관되지 않은 발견을 초래하는 이유를 확인했다. 
Fostering and stimulating the development of lifelong learning skills such as problem solving and critical thinking has become a crucial goal of higher education in the twentyfirst century. According to the Bologna declaration, successful learning and studying in higher education should involve students in deep learning (Asikainen 2014). In a recent literature review, Dinsmore and Alexander (2012) state that if research on students’ learning is going to have any bearing on practice, one area in need of critical discussion is the investigation of deep and surface learning. From their review, Dinsmore and Alexander (2012) identified why the results of studies on deep and surface learning often result in ambiguous and inconsistent findings.

그 이유 중 하나는 딥 러닝과 표면 학습의 개념화가 이러한 개념을 측정하는 방법뿐만 아니라 연구 전반에 걸쳐 다르기 때문이다. 종종 딥 러닝을 측정하는 데 사용되는 도구의 유효성에 대한 증거가 부족하다. 또 다른 이유는 딥 러닝은 맥락과 학문 영역에 따라 다를 수 있는 반면, 연구가 수행되는 컨텍스트는 종종 다양하기 때문이다. 그 결과, Dinsmore와 Alexander(2012)는 리뷰를 통해 향후 연구에 중요한 것이 무엇인지를 강조했습니다.

• (a) 명확한 이론적 프레임워크에서 시작하여 딥 러닝이 의미하는 바를 명확히 정의한다.
  
• (b) 학습 환경의 컨텍스트가 딥 러닝에 영향을 미칠 수 있으므로 특정 학습 컨텍스트 내에서 딥 러닝을 조사한다.
  
• (c) 유효한 도구를 사용하여 딥 러닝을 측정한다.

One of the reasons is that the conceptualization of deep and surface learning differs across studies as well as the way in which these concepts are measured. Often evidence of the validity of the instruments used to measure deep learning is lacking. Another reason is that the contexts in which the studies are conducted often vary, whereas deep learning might differ across contexts and academic domains. As a consequence, Dinsmore and Alexander (2012) emphasized from their review that it is important in future research to

• (a) clearly define what is meant by deep learning, starting from a clear theoretical framework,
• (b) investigate deep learning within a specific learning context, since the context of the learning environment may influence deep learning, and
• (c) measure deep learning by means of valid tools. 
  

 

이론적 프레임워크로서 학습에 대한 학생의 접근 방식
Students’ approaches to learning as a theoretical framework

이론적으로, 우리는 학생들의 학습 접근 방식(SAL)의 프레임워크를 기반으로 한다. 학문에 대한 깊은 접근의 개념은 마르톤과 슐예(1976년)의 연구에서 비롯되었다. 그들은 학생들이 특정 과제에 접근할 때(즉, 나중에 사용하기 위해 텍스트를 공부하는 것) 다른 의도를 가지고 있다는 것을 발견했다. 어떤 학생들은 본문의 의미를 이해하려고 의도한 반면, 다른 학생들은 본문에 대한 질문을 받았을 때 그들이 읽은 것을 재현할 수 있기를 원했다.

• 독서에서 의미를 추출하려는 의도를 가진 학생들은 정보를 사전 지식과 연관시키고, 아이디어를 이해할 수 있는 전체로 구성하며, 본문에 제시된 지식과 결론을 비판적으로 평가하려고 시도할 가능성이 있었다.
• 본문을 암기하는 과제를 스스로 떠맡은 학생들은 암기 학습과 같은 처리 전략을 구사할 가능성이 높았다.

의도와 처리 전략의 전자의 조합은 학습에 대한 [깊은 접근]으로 알려졌고 후자는 [표면 접근]으로 알려지게 되었다. 
Theoretically, we build on the framework of students’ approaches to learning (SAL). The concept of a deep approach to learning originated in the work of Marton and Säljö (1976). They discovered that students had different intentions when approaching a particular task (i.e., studying a text for later use). Some students intended to understand the meaning of the text, while others primarily wanted to be able to reproduce what they had read when questioned on it.

• Students with an intention to extract meaning from their readings were likely to try to relate information to prior knowledge, to structure ideas into comprehensible wholes, and to critically evaluate knowledge and conclusions presented in the text.
• Students who took upon themselves the task of committing text to memory were likely to use processing strategies such as rote learning.

The former combination of intentions and processing strategies became known as a deep approach to learning and the latter as a surface approach.

• 트리그웰 외 (2005) 학습에 대한 깊은 접근 방식을 가진 학생들이 본질적으로 흥미를 가지고 있으며 무엇을 공부하는지 이해하려고 노력한다고 주장한다. 
• 표면 접근법을 채택하는 학생들은 주로 암기 학습에 초점을 맞추고 주로 시험에 합격하기 위해 공부한다. 

• Trigwell et al. (2005) argue that students with a deep approach to learning are intrinsically interested and try to understand what they study.
• Students adopting a surface approach mainly focus on rote learning and primarily study to pass the test.

학습에 대한 심층 및 표면적 접근은 학생들의 의도(또는 동기)와 그에 수반되는 학습 활동의 결합으로 간주된다. 

• 학습에 대한 [표면적 접근]은 일반적으로 암기 학습과 암기로 특징지어지는 학습 과정과 함께 콘텐츠를 재현하려는 의도로 정의되었다. 
• 학습에 대한 [깊은 접근]은 기본 원리를 찾고, 관련 증거를 평가하고, 지식을 비판적으로 평가하는 과정과 함께 내용을 이해하려는 의도로 설명되었다. 

Deep and surface approaches to learning are seen as a combination of students’ intentions (or motives) and the accompanying learning activities.

• A surface approach to learning has typically been defined as an intention to reproduce content, with learning processes characterized by rote learning and memorization.
• A deep approach to learning has been described as a student’s intention to understand content together with the processes of relating and structuring ideas, looking for underlying principles, weighing relevant evidence, and critically evaluating knowledge (Biggs et al. 2001; Entwistle and McCune 2004; Lonka and Lindblom-Ylänne 1996; Loyens et al. 2013).

학습에 대한 접근 방식은 [학습 환경의 인식된 요구와 관련이 있다]고 가정되며, 순수하게 개인적 특성으로 간주되지 않는다(Biggs and Tang 2007; Nijhuis et al. 2005). 학생들이 학습에 접근하는 방법은 변화무쌍하고, 다음과 같은 특성에 의해 영향을 받는다(Gijbels et al. 2014)

• 학습 환경의 요인,
• 이러한 요인에 대한 학생들의 인식
• 학습 중인 주제에 대한 사전 지식.

Approaches to learning are assumed to be related to the perceived demands of the learning environment and are not seen as purely personal characteristics (Biggs and Tang 2007; Nijhuis et al. 2005). How students approach their learning is viewed as changeable and influenced by

• factors in the learning environment,
• students’ perceptions of these factors and
• student characteristics such as their prior knowledge on the topic under study (Gijbels et al. 2014).

이것이 [학습 접근 방식]의 개념이 (모든 학습자가 학습에 맞춰야 하는 개인적이고 안정적인 학습 스타일을 가지고 있다고 주장하는) [학습 스타일]의 개념과 다른 점이다.

This is where the concept of approaches to learning differs from the concept of learning styles in which all learners are claimed to have their own personal and stable learning style that should be aligned to instruction. 

학습 스타일 분야는 안정적인 개별 특성으로서 학습 스타일이 실제로 존재한다는 확실한 증거가 없기 때문에 최근 많은 비판을 받고 있다. Kirschner와 Van Merrinboer 2013). 그러나, 대학교육에서 학생들이 사용하는 학습 접근법의 종류를 조사한 연구는 모순된 결과를 가져왔다. Baeten 외 연구진(2010)은 학생 중심 학습 환경에서 [학습에 대한 깊은 접근을 장려하거나 저해하는 요소]를 감지하기 위해 25개의 연구를 검토했다. 그들의 리뷰는 교육방식의 특징, 학생들이 어떻게 교육맥락을 인지하는지, 그리고 학생요소가 역할을 한다는 것을 보여주었다. Baeten 외 연구진(2010)은 이러한 요인의 많은 부분이 서로 얽혀 있으며, 서로 어떻게 관련이 있고 학생 중심의 다른 학습 환경에서 어떻게 다른지 아직 거의 알려져 있지 않다고 결론지었다. 

The field of learning styles has recently been heavily critiqued because of the lack of solid evidence that learning styles—as stable individual characteristics—actually exists (see e.g. Kirschner and Van Merriënboer 2013). However, research that investigated the kind of learning approaches that are used by students in university education has led to contradictory results (see e.g., Gijbels et al. 2009; Struyven et al. 2006; Wilson and Fowler 2005). Baeten et al. (2010) reviewed 25 studies to detect which factors encourage or discourage a deep approach to learning in student-centered learning environments in general. Their review demonstrated that characteristics of the teaching method, how students perceive the teaching context, and student factors play a role. Baeten et al. (2010) concluded that many of these factors are intertwined and that still little is known about how they relate to each other and differ across different student-centered learning environments.

문제 기반 학습
Problem-based learning

문제 기반 학습(PBL)은 전 세계 많은 대학에서 구현되는 학생 중심 교육 접근법이다. PBL 학생들은 소규모 그룹에서 전문적으로 관련된 문제를 토론한다.

• 이 문제들은 학생들의 사전 지식을 활성화하기 위해 어떤 준비나 자습이 일어나기 전에 먼저 논의된다.
• 학생들의 사전 지식이 부족하여 문제를 완전히 이해할 수 없기 때문에, 그룹 내 학생들에 의해 추가적인 개별적 자율 학습을 위한 질문(즉, learning issue)가 생성된다formulated.
• 이 개별적 자율학습 기간(즉, 2, 3일 후)이 끝나면 학생들은 다시 모여 배운 내용을 토론하고 공식화된 학습 문제에 대한 답을 찾는다.

Problem-based learning (PBL) is a student-centered instructional approach that is implemented at many universities worldwide. PBL students discuss professionally relevant problems in small groups. The problems are first discussed before any preparation or self-study has taken place to activate students’ prior knowledge. Because students’ prior knowledge is insufficient to fully understand the problem, questions (i.e., learning issues) are formulated for further individual self-study by the students in the group. After this individual self-study period (i.e., 2 or 3 days later), students gather again and discuss what they have learned and come to an answer to the formulated learning issues.

• 그룹 토론은 교사(즉, 소위 튜터)에 의해 촉진되며, 지식을 습득하고, 문제를 더 잘 이해하며, 문제를 해결하는 기술을 습득하는 것을 목표로 한다(Barrows 1996).
• 한편, PBL은 학생들이 그들 자신의 학습에 적극적으로 참여하도록 하고, 다른 한편으로 [세심하게 설계된 문제]들과 [지도교사에 의해 촉진된 그룹 토론]과 같이, [학생 학습을 강화하기 위한 많은 스캐폴드]를 포함한다.
• PBL 프로세스의 설계(즉, 사전 지식을 활성화하고 학습 문제를 공식화하기 위한 문제의 사전 논의, 개별 자체 학습 시간, 서로 다른 문헌 발견이 논의되고 통합되는 보고 단계)는 [단편화를 피하고 지식, 기술 및 태도의 통합을 장려하기 위해 전체 문제를 통해 학습하는 것]의 중요성을 강조하는 현재의 교육 설계 접근법과 잘 일치한다. (Merrill 2012; Van Merrienboer 및 Kirschner 2013).
• The group discussion is facilitated by a teacher (i.e., so-called tutor) and is aimed to acquire knowledge, to better understand the problem, and to acquire skills to solve the problem (Barrows 1996).
• PBL does, on the one hand, actively engage students in their own learning and, on the other hand, includes many scaffolds to enhance student learning such as carefully designed problems and a group discussion facilitated by a tutor.
• The design of the PBL process (i.e., a pre-discussion of the problem to activate prior knowledge and formulate learning issues, an individual self-study time period, and a reporting phase in which different literature findings are discussed and integrated) is well aligned with current instructional design approaches that emphasize the importance of learning by means of whole problems in order to avoid fragmentation and encourage integration of knowledge, skills, and attitudes (Merrill 2012; Van Merrienboer and Kirschner 2013).

PBL은 작은 부분을 하나하나 배우는 대신 지식과 기술의 통합을 강조한다. 예를 들어, 그룹 내에서 문헌 조사 결과를 논의하면 개별 자율 학습 기간 동안 학생들은 어느 정도, 문제의 주제 범위 내에서 스스로 문헌 자원을 선택하고 연구할 자유가 있기 때문에 학습 문제에 대한 해답이 다른 각도에서 조명되도록 한다. 보고 단계에서 콘텐츠 지식을 배우는 것 외에도, 학생들은 당면한 문제의 근본적인 메커니즘을 이해하는 방법을 배우고, 따라서 그들의 문제 해결 기술은 동시에 훈련을 받는다. 즉, 학생들이 개념과 원칙 사이의 관계를 논의하고, 서로 다른 문헌 자원을 통합하고, 그룹에서 논의되는 문제에 이러한 개념과 원칙을 적용하고, 지식과 기술을 통합하기 때문에, PBL은 [학습에 대한 깊은 접근]을 장려하는 것으로 가정된다.

Instead of learning small parts piece by piece, PBL emphasizes the integration of knowledge and skills. For example, discussing literature findings within a group makes that the answers to the learning issues become illuminated from different angles, since during the individual self-study period, students have—to a certain extent and within the boundaries of the problem’s topic—freedom to select and study their own literature resources. Besides learning content knowledge during the reporting phase, students also learn how to understand the underlying mechanisms of the problem at hand and hence, their problem-solving skills get trained at the same time. In other words, since students discuss relationships between concepts and principles, integrate different literature resources, apply these concepts and principles to the problems that are discussed in the group, and integrate knowledge and skills, PBL is assumed to encourage a deep approach to learning.

현재 연구
The present study

본 검토 연구는 PBL이 심층 및 표면 학습에 미치는 영향을 조사하기 위한 것이다.

• 우리는 딥 러닝을 [의도나 동기 및 실제 전략을 모두 반영하여, 학습에 대한 학생 접근 방식]으로 정의한다. 우리는 딥 러닝 접근법을 [내재적 관심이 있고, 학습 중인 것을 이해하려고 노력하는 것을 목표함]으로 간주한다.
• 표면적 접근은 주로 시험 통과를 위한 암기 학습과 학습을 목표로 하는 의도 및 전략으로 정의된다.

The present review study is aimed at investigating the effects of PBL on deep and surface learning.

• We define deep learning in terms of students approaches to learning, reflecting both intentions or motives and actual strategies. We consider a deep learning approach as being intrinsically interested and aimed at trying to understand what is being studied.
• A surface approach is defined as an intention and strategy that is mainly aimed at rote learning and studying to pass the test.

따라서, 이 검토는 딥 러닝 접근법과 표면 학습 접근법이 학습 환경과 관련이 있다고 가정되고 순수한 개인적 특성으로 보기보다는, [학생들의 의도와 그에 수반되는 학습 활동의 결합]으로 보는 이론적 프레임워크에서 출발한다. 이 검토에서 우리는 문제 기반 학습 환경에서 수행된 연구에 초점을 맞춘다. PBL의 영향에 대한 이전의 검토(예: Dochy et al. 2003)에 따라 우리는 커리큘럼 전체 또는 단일 과정 PBL 구현을 구별한다. 

So, this review does start from a theoretical framework in which deep and surface learning approaches are seen as a combination of students’ intentions and accompanying learning activities, which are assumed to be related to the learning environment and are not seen as purely personal characteristics. In this review we focus on studies conducted in a problem-based learning environment. In line with earlier reviews on the effects of PBL (e.g., Dochy et al. 2003) we distinguish between either a curriculum wide or single course PBL implementation. 

1. 문제 기반 학습이 학생들의 학습에 대한 깊고 표면적인 접근에 미치는 영향은 무엇인가?
2. (a) 학습 환경의 맥락(단일 과정 대 PBL의 커리큘럼 광범위한 구현) 및 (b) 학습 품질(방법론적으로 높은 수준의 연구 대 저/중간 품질 연구)에 따라 영향이 다른가?

1. What are the effects of problem-based learning on students’ deep and surface approaches to learning?
2. Do the effects differ across (a) the context of the learning environment (single course vs. curriculum wide implementations of PBL), and (b) study quality (methodologically high level quality of studies vs. low/medium quality studies)?

방법
Methods

캠벨 협업에 따르면, 문헌의 체계적인 검토에는 (1) 포함에 대한 명확한 기준, (2) 명확한 검색 전략 (3) 체계적 코딩 및 (4) 메타 추출 기법을 사용한 포함된 연구에 대한 체계적인 분석이 포함되어야 한다. 

According to the Campbell Collaboration, a systematic review of the literature should include

• (1) clear criteria for inclusion,
  
• (2) a clear search strategy
  
• (3) systematic coding and
  
• (4) a systematic analyses of the included studies, using meta-analyses techniques were appropriate (www.campbellcollaboration.org). 

포함기준
Criteria for inclusion

우리의 검토에 연구를 포함시키기 위한 몇 가지 기준이 정의되었다. 

• 첫째, 연구는 (a) 소규모 그룹의 학습, (b) 그룹 학습을 촉진하는 교사/강사, (c) 문제에 의해 학습 과정이 시작되고 (d) 자율 학습을 통해 새로운 정보를 습득하는 것으로 특징지어지는 문제 기반 학습 환경에서 수행되어야 한다(Barrows 1996). 
• 둘째, 각 연구는 학습에 대한 심층 또는 표면 접근 방식을 다루는 경험적 데이터를 포함해야 한다. 우리는 PBL 커리큘럼이 다른 커리큘럼과 비교되는 연구로 연구를 제한하지 않았고 정량적 연구로 제한하지도 않았다. 우리는 다른 방법론을 사용한 연구를 포함했다.

Several criteria were defined for inclusion of studies in our review.

• First, the study should be conducted in a problem-based learning environment that is characterized by: (a) learning in small groups, (b) a teacher/tutor facilitating group learning, (c) the learning process is initiated by problems, and (d) new information is acquired through self-study (Barrows 1996).
• Second, each study should contain empirical data dealing with a deep or surface approach to learning. We did not restrict our studies to studies in which PBL curricula were compared with other curricula, nor did we restrict to quantitative studies; we included studies using different methodologies.

문헌검색
Literature search

 

연구 특성 코딩
Coding study characteristics

 

연구합성
Synthesizing research

 

결과 Results

PBL이 학습에 대한 심층 및 표면 접근법에 미치는 영향은 무엇인가?
What are the effects of PBL on deep and surface approaches to learning?

표 1은 21개 연구에 대한 개요를 제공합니다. 이 표 1의 마지막 행에서 볼 수 있듯이, 17개의 연구가 전체 PBL 환경에서 수행되었고, 3개는 하이브리드 PBL 환경, 1개는 PBL 컴퓨터 환경에서 수행되었다. 또한 커리큘럼 전체 PBL 구현에서 12개의 연구가 수행되었고 단일 과정 PBL 환경에서 9개의 연구가 수행되었다. 표 1은 또한 PBL이 11개 연구에서 학습에 대한 심층 접근법을 강화한다는 것을 보여준다. PBL은 네 가지 연구에서 심층 접근법의 감소를 초래하며 다른 여섯 가지 연구에서 심층 접근법에는 영향을 미치지 않는다. 또한, 표 1에 PBL이 11개 연구에서 학습에 대한 표면 접근에 영향을 미치지 않는 것으로 나타났다. PBL은 6개의 연구에서 표면 학습의 감소와 4개의 연구의 증가로 이어진다.

Table 1 provides an overview of the 21 studies. As can be seen in the final row of this Table 1, 17 studies were conducted in a full PBL environment, 3 in a hybrid PBL environment and 1 in a PBL computer environment. Furthermore, 12 studies were done in a curriculum wide PBL implementation and nine in a single course PBL environment. Table 1 also demonstrates that PBL does enhance a deep approach to learning in eleven studies. PBL does lead to a decrease in deep approach in four studies and has no effect on deep approach in another six studies. Furthermore, it is shown in Table 1 that PBL has no effect on a surface approach to learning in eleven studies. PBL does lead to a decrease in surface learning in six studies and an increase in four studies.

 

 

표 2는 PBL이 학습에 대한 심층 및 표면 접근에 미치는 주효과와 관련된 첫 번째 연구 질문에 대한 답변을 제공하기 위해 개표, 부호 테스트 및 효과 크기의 결과를 제시한다(Cooper 등). 2009). 표 2의 개표는 PBL이 딥 러닝에 미치는 영향에 대한 긍정적인 경향을 보여주며, 21 중 11개의 연구가 부정적인 효과를 산출하는 것에 비해, 4개에서는 긍정적인 효과를 산출한다. 그러나 학습에 대한 심층 접근법을 장려하고 낮추는 연구의 차이는 통계적으로 유의하지 않았다는 점을 언급해야 한다. [0.11의 평균 효과 크기]는 PBL의 [심층 접근법에 대한 작은 양의 효과]로 향한다.

Table 2 presents the result of the vote-counts, sign test and effect sizes in order to give an answer on our first research question related to the main effects of PBL on deep and surface approaches to learning (Cooper et al. 2009). The vote count in Table 2 shows a positive tendency for the effects of PBL on deep learning with eleven studies of the 21 yielding a positive effect (i.e., a higher score or increase in the learning approach which we will label in the remaining of the text as ‘increase’) compared to four studies yielding a negative effect (i.e., a lower score or decrease in the learning approach which we will label in the remaining of the text as ‘decrease’). It should be mentioned, however, that this difference between studies fostering and lowering a deep approach to learning, was not statistically significant. The average effect size of .11 points towards a small positive effect of PBL on the deep approach.

 

 

PBL이 표면 학습 접근법에 미치는 영향에 대해서는 11개 연구가 표면 학습에 영향을 미치지 않으며, 6개 연구는 점수 또는 감소, 4개 연구는 표면 학습의 증가를 보여준다. 다시, 양면 부호 검정은 학습에 대한 표면 접근 방식을 감소 및 증가시키는 연구 수에 유의하지 않았으며, 또한 평균 효과 크기 0.08은 PBL이 표면 접근 방식에 거의 영향을 미치지 않음을 나타낸다.

As for the effects of PBL on surface learning approaches, eleven studies show no effect on surface learning, six studies show a lower score or decrease and four studies an increase in surface learning. Again, the two-sided sign-test was not significant for the number of studies decreasing and increasing a surface approach to learning and also the average effect size of .08 indicates that PBL has little effect on the surface approach.

 

맥락이나 공부의 질이 딥 러닝에 영향을 미치는가?
Does context or study quality impact deep learning?

표 3에서 커리큘럼 전체 PBL 구현(n = 12) 및 단일 과정 PBL 구현(n = 9)에서 수행된 연구에 대한 심층 및 표면 접근에 대한 긍정적, 부정적 또는 영향을 보고한 연구가 미치는 영향을 보고한다. 개표 결과 [교육과정 전반에 걸친 PBL]의 경우 7개 연구가 딥 러닝의 증가를, 3개 연구는 딥 러닝의 감소를, 2개 연구는 효과가 없는 것으로 나타났다. 0.18의 효과 크기는 커리큘럼 전체 구현에서 PBL이 학생들의 심층 접근 방식에 미치는 작은 영향을 나타낸다. 4개의 연구는 표면 학습의 감소를 나타낸 반면, 3개의 연구는 표면 접근법의 증가를 보여주었고 다른 5개의 연구는 효과가 없었다. 그러나 양면 부호 테스트는 딥 및 표면 학습 접근법에 대한 영향 모두에 대해 유의하지 않았으며, 이는 딥 및 표면 학습 접근법의 증감을 보고하는 연구 수의 차이가 통계적으로 유의하지 않음을 의미한다. 또한 0.08의 효과 크기는 교육과정 전반의 PBL 구현이 표면 접근법에 거의 영향을 미치지 않음을 나타낸다.

In Table 3 the effects of studies reporting positive, negative or no effects on deep and surface approaches to learning across studies conducted in a curriculum wide PBL implementation (n = 12) and a single course PBL implementation (n = 9) are reported. The vote count indicated that for curriculum wide PBL implementations, seven studies showed an increase in deep learning, three studies led to a decrease in deep learning and two studies showed no effect. The effect size of .18 indicates a small effect of PBL in a curriculum wide implementation on students’ deep approach. Four studies indicated a decrease in surface learning, whereas three studies showed an increase in surface approach and another five studies no effect. However, the two-sided sign-tests were not significant for both the effects on deep and surface approaches to learning, meaning that the difference in number of studies reporting an increase and decrease in both deep and surface approaches to learning, is not statistically significant. Also the effect size of .08 gives an indication that a curriculum wide implementation of PBL has little effect on the surface approach.

 

표 3의 하단 부분에서는 단일 PBL 과정(n = 9)에서 수행된 연구에 대한 심층 및 표면 접근법에 대한 영향을 보고한다. 개표 결과 4개 연구는 딥러닝 증가, 1개 연구는 딥러닝 감소, 4개 연구는 효과가 없는 것으로 나타났다. 표 3은 또한 두 개의 연구가 표면 학습의 감소를 보여주었고, 한 연구는 표면 접근의 증가의 증거를 제시했으며, 다른 여섯 개의 연구는 아무런 영향도 보이지 않았다. 커리큘럼 전체 구현 결과와 유사하게, 양면 부호 시험은 학습에 대한 심층 및 표면 접근에 대한 효과 모두에 대해 유의하지 않았다. 따라서, 학습에 대한 심층 및 표면 접근 방식을 장려하거나 방해하는 연구 수의 차이는 통계적으로 유의하지 않았다. 그러나 단일 과정 PBL 구현의 경우 대부분의 연구는 학습에 대한 표면 접근법에 영향을 미치지 않았다. 심층(-0.05) 및 표면(.07) 접근법의 효과 크기는 PBL의 단일 코스 구현에 대해 0에 가깝다.
The bottom part of Table 3 reports the effects on deep and surface approaches to learning across studies conducted in a single PBL course (n = 9). The vote count showed that four studies showed an increase in deep learning, one study led to a decrease on deep learning and four studies showed no effect. Table 3 also demonstrates that two studies showed a decrease in surface learning, one study gave evidence of an increase in surface approach and another six studies showed no effects. Similarly to the results for curriculum wide implementations, the two-sided sign-tests were not significant for both the effects on deep and surface approaches to learning. Hence, the difference in number of studies fostering or hindering both deep and surface approaches to learning was not statistically significant. However, for single course PBL implementations, the majority of the studies did not show an effect on surface approaches to learning. The effect sizes for both the deep (−0.05) and the surface (.07) approach are close to zero for the single course implementation of PBL.

표 4에는 연구의 방법론적 품질이 요약되어 있다. 이 표에서 볼 수 있듯이, 대부분의 연구는 정량적 연구(n = 18)였고, 소수의 연구는 혼합적 연구(n = 3)였다. 연구 설계의 측면에서, 11개의 연구는 실험 통제 그룹 연구를 포함했고 10개의 연구는 하나의 그룹 설계를 사용했다. 총 14개의 사전-사후 테스트 연구 설계가 사용되었으며 7개의 사후 테스트 전용 설계가 사용되었습니다. 단 하나의 연구만이 측정 모멘트가 3개인 종방향 연구였다. 표본 크기는 21개 연구 중 16개 연구 중 40개를 분명히 웃돌았다. 연구의 대부분은 John Biggs와 동료들에 의해 개발된 Study Process Questinate(11개 연구)를 이용했다. 사용된 계측기의 유효성에 대한 연구는 3건, 데이터의 신뢰성에 대한 연구는 7건에 불과했습니다. 총 8개의 연구가 전체 연구 품질 점수(5, 6 또는 7점)가 높았다.

In Table 4 the methodological quality of the studies is summarized. As can be seen from this table, the majority of the studies were quantitative studies (n = 18) and a minority were mixed-methods studies (n = 3). In terms of study designs, eleven studies involved experimental control group studies and ten studies used a one group design. In total, 14 pre-post test study designs were used and seven post-test only designs. Only one study was a longitudinal study with three measurement moments. The sample size was clearly above 40 in 16 out of 21 studies. The majority of the studies did make use of the Study Process Questionnaire developed by John Biggs and colleagues (11 studies). Only three studies reported about the validity of the instrument used and seven studies about the reliability of the data. In total, eight studies had a high overall study quality score (a score of 5, 6 or 7).

 

 

학습 품질에 따라 PBL이 딥 러닝과 표면 학습에 미치는 영향은 표 5에 언급되어 있다. 

The effects of PBL on deep and surface learning depending on study quality are mentioned in Table 5. 

수준 높은 연구(n = 8)의 경우, 개표 결과 3개의 연구가 딥 러닝의 증가를 보였고, 1개의 연구는 딥 러닝의 감소를 이끌었으며, 4개의 연구는 아무런 효과를 보이지 않았다. 작은 양의 효과로 향하는 .13점의 효과 크기입니다. 표면 학습과 관련하여, 두 가지 연구는 감소했고, 한 연구는 증가했고, 다섯 가지 효과는 없었다. 효과 크기는 -.01이 0에 가까웠습니다. PBL 구현 규모에 관한 결과와 유사하게, 양면 부호 테스트는 학습에 대한 심층 및 표면 접근에 대한 효과 모두에 대해 유의하지 않았다.

For high-quality studies (n = 8), the vote count showed that three studies showed an increase in deep learning, one study led to a decrease on deep learning and four studies showed no effect. The effect size of .13 points towards a small positive effect. With respect to surface learning, two studies showed a decrease, one study an increase and five no effect. The effect size was with −.01 close to zero. Similar to the results regarding the scale of PBL implementation, the two-sided sign-tests were not significant for both the effects on deep and surface approaches to learning.

 

표 5의 하단에는 저품질 및 중품질 연구에 대한 결과가 나와 있습니다(n = 13). 7개 연구는 딥 러닝의 증가의 증거를 제시했고, 4개 연구는 딥 러닝의 감소를 이끌었으며, 2개 연구는 아무런 효과를 보이지 않았다. 효과 크기가 0.07이면 효과가 없거나 매우 작음을 나타냅니다. 학습에 대한 표면 접근법의 경우, 6개의 연구는 감소했고, 1개의 연구는 증가했으며 6개의 연구는 아무런 영향도 보이지 않았다. 중저품질 연구의 경우, 연구의 거의 절반이 학습에 대한 표면 접근에 영향을 미치지 않았지만, 효과 크기는 의미 있는 효과를 향해 .17 포인트이다.
In the bottom part of Table 5, results are mentioned for low and medium quality studies (n = 13). Seven studies gave evidence of an increase in deep learning, four studies led to a decrease on deep learning and two studies showed no effect. The effect size of .07 indicates there is no or a very small effect. For surface approaches to learning, six studies showed a decrease, one study showed an increase and six studies showed no effects. For medium–low quality studies, although almost half of the studies found no effect on surface approaches to learning, the effect size of .17 points towards a meaningful effect.

다시, 양면 부호 테스트는 딥 및 표면 학습 접근법에 대한 효과 모두에 대해 유의하지 않았으며, 이는 [심층 또는 표면 학습 접근법에 대한 증가 또는 감소]를 보여주는 연구 숫자에 [차이를 찾을 수 없음]을 의미한다.

Again, the two-sided sign-tests were not significant for both the effects on deep and surface approaches to learning, meaning no differences could be found in the number of studies showing an increase versus a decrease for both deep and surface approaches to learning.

결론 및 토론
Conclusion and discussion

이 검토는 PBL이 학습에 대한 심층 및 표면 접근에 미치는 영향을 조사하기 위한 것이었다. 포함된 연구는 모두 PBL의 특정 컨텍스트 내에서 수행되었으며 대부분의 연구는 Biggs의 이론적 프레임워크를 사용하여 심층 및 표면 처리를 측정하였다. Dinsmore와 Alexander(2012)는 명확한 이론적 프레임워크와 특정 컨텍스트 내에서 딥 러닝 접근 방식을 연구하기 위해 간청했다. 우리는 이 검토에서 이러한 사항들을 다루었다. 

• 리뷰는 21개 연구 중 11개가 PBL이 학습에 대한 깊은 접근을 장려한다는 것을 보여주었고, 21개의 표면 학습을 측정하는 연구 중 11개에서 PBL은 표면 접근에 영향을 미치지 않았다.
  
• 효과 크기에서도 알 수 있듯이, PBL은 딥 러닝을 어느 정도 강화시키는 것으로 보이며(ES = .11) 표면 학습에 미치는 영향은 적다(ES = 0.08).
  
• 더욱이, 이 검토는 연구들 사이의 효과의 차이가 부분적으로 PBL 연구가 수행된 환경의 차이에 의해 설명될 수 있지만 연구 품질에 의해 설명될 수 없다는 것을 보여주었다. (교육과정 전반에 걸친 구현은 단일 과정(ES = -.05) 구현에 비해 학생들의 심층적 접근 방식(ES = 0.18)에 더 긍정적인 영향을 미칩니다.)

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.
  

PBL이 딥 러닝을 강화하는 메커니즘으로 가정되는 것은 [능동적이고 자기 주도적인 학습]이다. PBL은 학생들이 정보를 분석, 비교, 대조 및 설명할 필요가 있기 때문에 학습의 능동적인 형태로 간주된다(Serif 2011). 그들은 그들 자신이 당면한 문제에 대한 가설을 개발하고 설명하고 다양한 문헌과 다른 학습 자원을 사용하여 이러한 설명과 가설에 대한 증거를 찾아야 하기 때문에 학습 과정에 적극적으로 참여하고 있다(Gurpinar et al. 2013). 자기 주도 학습은 학생들이 자신의 학습에 대해 책임을 지기 때문에 PBL에서 시행된다. 어느 정도까지는, 그리고 문제의 테두리 안에서, 학습 문제에 답하기 위해 그들 자신의 자원을 선택할 수 있는 자유가 있고, 이것은 그들에게 학습에 대한 소유권ownership을 줍니다. 

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.

본 검토에 포함된 21개 연구 중 11개는 PBL이 딥 러닝을 육성한다는 것을 보여준다(ES = .11). 이 효과는 내재적 동기를 통해 매개될 수 있다. 최근의 PBL 연구에서, 학생들에게 한 세트에서 문헌소스를 제공함 경우와, 두 개의 문헌소스를 주어지는 조건(즉, 자기 주도적 조건)을 비교했다. 실제로 자기 주도적인 상태의 학생들이 자율적인 동기 부여에서 더 높은 점수를 받았다는 것을 입증했고, 이는 자기 주도 학습과 자율/자율적 동기 사이의 관계에 대한 증거를 제시합니다.

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.

또한 이 검토의 결과는 PBL이 표면 학습을 측정하는 18개 연구 중 11개 연구(ES = 0.08)에서 표면 학습에 거의 영향을 미치지 않음을 나타낸다. 좋은 소식인가요, 아닌가요? 이 발견이 어떻게 보면 긍정적인 효과도 있다고 주장할 수 있다. 그럼에도 불구하고 우리는 일부 상황에서는 표면 접근법 또는 심층 및 표면 접근법의 더 나은 조합이 효과적으로 학습하기 위해 사용되어야 한다는 것도 고려해야 한다(Dinsmore 및 Alexander 2012). 높은 인식된 작업량perceived workload은 표면적인 연구 접근으로 이어질 가능성이 높으며 딥 러닝에 해로울 수 있다. 학습 환경에서 업무량이 높다고 인식하는 학생은 학업과 지칠 뿐만 아니라 공부에 대한 관심도 부족함을 드러낼 가능성이 더 높다. 이는 특히 초기 PBL 학생들에게 적용된다(Litmanen et al. 2014). 
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).

더 많은 표면 학습을 유도할 수 있는 또 다른 요인은 [사용된 평가 방법]이다. 평가가 딥 러닝에 대한 보상이 되지 않는 것으로 인식된다면, 학생들은 표면 학습에 의존하게 된다. 따라서 SAL에 대한 연구에서 평가의 역할을 고려하는 것이 중요합니다. 엥트위슬 외. (2003, 페이지 90)은 이러한 측면에서 연구 결과가 "평가 절차에서 [이해가 명시적으로 보상되는 정도의 차이]로 인해" 다양하다고 기술한다. PBL 과정의 의대생들은 학생들이 자신의 접근법을 평가 요구(즉, 그에 따른 평가 유형 및 가중치)에 따라 학습에 적응한다는 것을 실제로 확인했다. 스컬러(1998)와 젠슨 외 연구진(2014)은 학생들이 과제 에세이를 공부할 때 더 깊은 접근법을 채택할 가능성이 높다는 것을 보여주었고, 학생들은 이를 (객관식 시험과 비교하여) 더 높은 수준의 인지 처리를 측정하는 것으로 인식했습니다.

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.

대부분의 연구는 PBL이 딥 러닝을 강화하고 표면 학습에 영향을 미치지 않는다는 것을 보여주지만, 이 리뷰는 또한 Dinsmore와 Alexander(2012)에 의해 결론지어진 것처럼 연구가 종종 모호하고 일관되지 않은 발견을 초래한다는 것을 보여준다. 한 가지 이유는 21개 연구 중 3개 연구만 데이터의 유효성에 대해 보고하고 8개 연구만 데이터의 신뢰성에 대해 보고했기 때문입니다. 종종 Dinsmore와 Alexander (2012)가 이전에 결론지은 것처럼 타당성의 증거가 부족했다. 이 검토에서 우리는 PBL이라는 특정 컨텍스트 내의 딥 러닝을 조사했다. 연구는 딥 러닝에 긍정적인 영향을 주고 표면 학습에 영향을 주지 않는 경향을 보여주었지만, 연구 결과는 PBL에 대한 정의를 충족하는 연구만 이 검토에 포함시켰음에도 불구하고, PBL이 서로 다른 연구에 걸쳐 다르게 적용된다는 것을 나타낼 수 있다. 또한, 한 연구에서 학생들은 딥 러닝을 더 향상시키기가 어려울 수 있기 때문에 이미 딥 러닝에 높은 점수를 보였다고 주장되었다(Reid et al. 2005).
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).

이 검토에는 몇 가지 제한이 있습니다. 우선, 이 검토에 포함된 연구는 자기 보고서 데이터만 활용했다. 실제 학생들의 행동은 측정되지 않았고 학생들의 자기 인식과 다를 수 있다. 다음으로, 학업 성취도와의 관계는 이 검토에서 고려되지 않았다. 또한 종방향 연구와 정성적 연구의 수는 제한되었고 일부 연구는 명확한 비교가 이루어지지 않아 단 하나의 그룹(즉, 대조군 그룹이 없음) 또는 시험 후 데이터(즉, 시험 전 데이터 없음)만을 포함하였다. 언급했듯이, 대부분의 연구가 이전에 검증된 도구를 사용했지만, 모든 연구에 심층 및 표면 processing을 측정하는 데 사용된 도구의 타당성과 신뢰성에 대한 보고된 데이터가 포함되지는 않았다. 검토에 포함된 모든 연구가 효과 크기를 계산하는 데 필요한 정보를 보고하지는 않았습니다. 따라서 16개 연구만 효과 크기가 포함되었고 서로 다른 연구 설계에 걸쳐 통합되었다. 

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.

향후 연구를 위해서는 PBL이 심층 및 표면 학습에 미치는 장기적인 영향뿐만 아니라 제어 그룹을 통한 실험 연구 및 학생들의 심층 및 표면 처리의 실제 변화에 대한 더 나은 통찰력을 제공할 수 있는 사전 및 사후 측정 연구가 더 많이 필요하다. 종적 연구는 학습 환경의 특성도 시간에 따라 달라질 수 있다는 점을 고려해야 하지만, 시간에 따라 학습에 대한 접근법이 어떻게 다를 수 있는지를 측정할 기회를 제공한다. PBL이 심층 및 표면 처리를 강화하거나 강화하지 않는 이유와 방법에 대한 더 나은 통찰력을 제공할 수 있기 때문에 질적 연구도 필요하다. 마지막으로, 향후 연구는 심층 및 표면 처리를 측정하는 데 사용되는 도구의 타당성과 신뢰성 데이터를 보고해야 한다.

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.

 

 

1*	Serife (2011)	Current issues in education	To investigate the effects of computer supported PBL on students’ approaches to learning	One group pre- and post-test design. PBL implementation during 5 weeks	LAQ	Problem based learning has a significant effect on adopting a predominantly deep approach to learning by students and a negative effect on adopting surface approach to learning
2*	Papinczak et al. (2008)	Advances in Health Sciences Education	To determine the influence of metacognitive activities—self and peer-assessment—within the PBL tutorial environment on the development of deep learning approach, reduction in surface approach, and enhancement of individual learning self-efficacy	Control group pre-test, post-test design was implemented	ASSIST	Over the course of first-year medical studies, students lose self-efficacy and move away from deep-strategic learning approaches towards more surface approaches. The program of metacognitive activities failed to reverse this trend. The substantial swing towards surface learning raises questions about the perceived capacity of PBL curricula to promote deep approaches to learning in dense curricula
3*	Abraham et al. (2008)	Advances in Physiology Education	To study the differences in learning approaches to physiology of undergraduate medical students in a partially PBL and non-PBL oriented curriculum	Control group post-test only design. PBL curriculum from September 2006 admissions onward	SIAL	Scores for deep and strategic approaches of PBL students were found to be significantly higher compared with NPBL students. No difference between PBL and NPBL in surface approach
4*	Wong and Lam (2007)	Research on social work practice	To evaluate the effects of problem-based learning (PBL) in social work education	One group pre-test post-test design. 132s-year social work students who were spread across the 3 academic years of 2000–2001, 2001–2002, and 2002–2003	SPQ + R-SPQ	The results indicated positive learning outcomes, with the most significant gains occurring in knowledge and lesser gains being made in skills and values. No significant positive gain in deep learning and no significant change in surface learning. Surface approach is negatively correlated with learning outcomes. The findings suggest that students with deep learning motives and approaches reap the most benefit from PBL. The switch to the PBL mode increased the students’ workload and did not necessarily result in a deeper learning approach for all of them
5*	Segers et al. (2006)	Studies in Educational Evaluation	To determine if students in a redesigned course, firstly, hold different perceptions of the assessment demands and, secondly, adjusted their learning strategies towards deeper learning	Control group post-test only design—two subsequent cohorts of second-year students	SPQ	Contrary to expectations, the students in the original assignment-based (ABL) course adopted sign. more deep-learning strategies and sign. less surface-learning strategies than the students in the problem-based (PBL) course. Additionally, the results show clearly that the students who express their intentions to employ a certain learning strategy perceive the assessment demands as such and actually employ a related learning strategy
6*	Groves (2005)	Advances in Health Sciences Education	To assess the influence of graduate-entry PBL curriculum on individual learning style and investigate the relationship between learning style, academic achievement and clinical reasoning skill	One group pre-test post-test design	SPQ	Net shift towards a more surface approach over the period of the study (but not significant). Significant decrease in deep-learning scores
7*	Mok et al. (2009)	International journal of speech-language pathology	To better understand the relationship between student learning approaches and academic performance in a problem-based learning (PBL) curriculum	One group pre-test post-test design Cross sectional research design (comparison of 3 cohorts)	R-SPQ-2F	Exposure to PBL led to significant increase in DA (deep approach) and SA (surface approach) to learning during an academic year for students in years 1–3. Students who did well in a PBL examination showed a much stronger DA than SA to learning, while students who performed less well showed a smaller difference between DA and SA to learning
8*	Gurpinar et al. (2013)	Advances in Physiology Education	Determine the satisfaction of medical students with problem-based learning (PBL) and their approaches to learning	Control group post-test only design (three curricula were compared; one full PBL vs. two hybrid) Cross-sectional	R-SPQ-2F	Of the study group, 64.6 % were found to adopt a deep approach to learning, and it is confirmed that these students were reasonably more satisfied with PBL. When the three different curricula were compared in terms of student satisfaction with PBL among surface and deep learners, no significant differences among surface learners in different curricula was found in terms of satisfaction. However, in the full PBL curriculum a higher percentage of students adopted a deep approach and a lower percentage a surface approach as compared to the other curricula (control groups)
9*	Grant et al. (2012)	BMC Research Notes (Biomedicalcentral)	To compare the effect of context on learning at different UK medical schools, schools with conventional and PBL curricula	Control group post-test only design Mixed method—two stages, first qualitative phase and then, quantitative phase where findings from the qualitative phase were tested	ALSI (Entwistle)	Students with PBL curriculum scored significantly higher for reflection in learning, self-efficacy in self-directed leaning and for deep approach to learning. Students surface approach did not differ significantly
10*	Reid et al. (2012)	Medical Education Online	To investigate the hypothesis that the redesigned curriculum was successfully promoting a deep approach to learning and studying and deterring a surface approach in undergraduates during years 1–5 of a medical degree program	One group pre-test post-test design Quantitative Longitudinal	ASSIST	Medical students have high scores for deep and strategic approaches to learning and studying and lower scores for a surface approach, but that, even when efforts were made to promote deep approach, little significant change in these scores occurred during the whole of the medical degree program, apart from some tendency for the surface approach to lessen. Either their approaches are not susceptible to change or else the learning environment may need to alter more drastically than hitherto
11*	McParland et al. (2004)	Medical Education	To measure the effectiveness of a problem-based learning course compared to traditional teaching in undergraduate psychiatry	Control group pre-post test design. A PBL psychiatry course versus a lecture-based psychiatry course	SPQ	The PBL attachment/course resulted in significantly better examination performance than did the traditional teaching course. No differences in surface, deep or strategic learning before and after the course were found. No differences between the two courses. Students were significantly more successful in the examinations if they had received the PBL course, were female, and used deep and strategic learning
12*	Selçuk (2010)	International Journal of the Physical Sciences	To evaluate the effects of (PBL) method on students’ achievement in and approaches and attitudes towards an introductory physics course	Control group pre-test post-test design. One control group (or traditional lecture-based instruction group) and one experimental group (PBL group)	ALS	The results indicated that the problem-based learning method encouraged the students’ deep approach to learning as compared to the control group (sign), and also improved interest (a component of attitude) towards the physics course. The results also signalled that PBL-based physics instruction impacted the students’ achievement in physics positively. No sign. difference between PBL and control group in terms of surface approach were found before and after the course
13*	Kieser et al. (2005)	European Journal of Dental Education	To analyze the influence of context on students’ approaches to learning	One group pre-test post-test design. Low N!	R-SPQ-2F	Those who entered the course with a surface approach (n = 5) left with a deep-learning approach, and quality learning outcomes. There were 7 students who started the course with a deep-learning approach and cohesive conception and had a deep at the end (no change). There were twowho moved from deep to surface. Test results were better for students with a deep approach and worse for students with a surface approach
14*	Schultz and Christensen (2004)	European Journal of Engineering Education	To evaluate the implementation of the highly structured seven-step problem-based learning (PBL) procedure as part of the learning process in a human–computer interaction (HCI) design course	One group post-test only design. Low N! Mixed methods (both qualitative and quantitative methods)	SPQ-modified	The qualitative and quantitative evaluation showed that students took responsibility for their own learning. The quantitative evaluation shows that PBL clearly stimulated the students to take a deep approach to learning and not a surface approach (i.e. the mean scores on items related to deep approach differed from the items dealing with a surface approach; in favour of the deep approach [1.4 difference, scale 1–5)]
15*	Tiwari et al. (2006)	Nurse Education Today	To evaluate the effect of PBL on students’ approaches to learning in clinical nursing education	One group pre-test post-test design	R-SPQ-2F	Study provides empirical support for the suggestion that PBL promotes a deep approach to learning. The R-SPQ-2F scores indicated that for the deep approach to learning, the post-test mean score was significantly higher than at the pre-test. No significance was observed between the pre-test and post-test mean scores for the surface approach to learning
16*	Nijhuis et al. (2005)	Learning Environ-ments Research	To determine if students, firstly, perceived the redesigned course as being more challenging and, secondly, adjusted their learning strategies towards deeper learning	Control group post-test only design. Quantitative methods comparing two groups	SPQ-adapted	The results indicated that the students from the redesigned course showed a higher degree of surface learning and a lower level of deep learning than the students from the assignment-based learning course
17*	Reid et al. (2005)	Medical Teacher	To determine to what extent the early medical course succeeded in promoting a deep approach and deterring a surface approach to learning	Control group pre-test post-test design. Longitudinal study	ASSIST	The results are remarkably consistent from cohort to cohort with relatively high scores for deep (60 out of 80) and strategic approaches and lower for surface (45 out of 80). Disappointingly, the students’ learning approaches did not show any increase in deep approach during year 2, also no change in surface approach was reported
18*	Adiga and Adiga (2010)	Biomedical Research	To study the changing pattern of learning approaches to pharmacology adopting PBL by undergraduate students of an Indian medical school	One group pre-test post-test design. Quantitative method. Mean scores of surface, deep and strategic approaches of students during pre-PBL (end of 2nd block) and post-PBL phase (end of the 4th block) were compared	SIAL	Scores for deep approaches of students in post-PBL phase (3rd and 4th blocks) were found to be significantly higher compared with pre-PBL phase. The score for the surface and strategic approaches did not differ significantly between the two phases even though there was a small change
19*	Newble and Clarke (1986)	Medical Education	To explore the relationship between educational context and approach to learning	Control group (i.e., traditional medical school vs. a PBL medical school) Posttest only	Lancaster Approaches to Studying Inventory	PBL students appear to have an approach to learning which more closely approximates the aims of most medical schools (i.e., high on deep approach and low on surface)
20*	Coles (1985)	Medical Education	To compare PBL and non-PBL students in their approaches to learning	Control group Pre-Posttest design (on entry and after one year)	Short Inventory of Approaches to Studying	The approaches to studying of students at the conventional school appcar to be detrimentally influenced by the experience of the first year Those of PBL students do not, and probably are improved
21*	De Volder and De Grave (1989)	Medical Education	To investigate how the introductory phase of a PBL medical program affects the study methods of students	Pre-posttest design (on the first day of academic year and again after the introductory period, i.e., 6 weeks)	Short inventory of study Approaches (Entwistle 1981)	Results indicate that approaches to learning are made desirable by the training in PBL, but are not desirable on entry

 

 

 

 

 

 

 

---

Adv Health Sci Educ Theory Pract. 2016 Dec;21(5):1087-1112.

 doi: 10.1007/s10459-015-9645-6. Epub 2015 Nov 13.

Deep and surface learning in problem-based learning: a review of the literature

Diana H J M Dolmans 1, Sofie M M Loyens 2 3, Hélène Marcq 4, David Gijbels 5

Affiliations collapse

Affiliations

• 1Department of Educational Development and Research, School of Health Professions Education (SHE), Faculty of Health, Medicine and Life Sciences (FHML), Maastricht University, PO Box 616, 6200 MD, Maastricht, Netherlands. d.dolmans@maastrichtuniversity.nl.
• 2Institute of Psychology, Erasmus University Rotterdam, Rotterdam, Netherlands.
• 3Roosevelt Center for Excellence in Education, University College Roosevelt, Middelburg, Netherlands.
• 4Management of Learning, Maastricht University, Maastricht, Netherlands.
• 5Department of Training and Education, EduBROn research group, Faculty of Social Sciences, University of Antwerp, Antwerp, Belgium.

• PMID: 26563722

 

• PMCID: PMC5119847

 

• DOI: 10.1007/s10459-015-9645-6

Free PMC article

Abstract

In problem-based learning (PBL), implemented worldwide, students learn by discussing professionally relevant problems enhancing application and integration of knowledge, which is assumed to encourage students towards a deep learning approach in which students are intrinsically interested and try to understand what is being studied. This review investigates: (1) the effects of PBL on students' deep and surface approaches to learning, (2) whether and why these effects do differ across (a) the context of the learning environment (single vs. curriculum wide implementation), and (b) study quality. Studies were searched dealing with PBL and students' approaches to learning. Twenty-one studies were included. The results indicate that PBL does enhance deep learning with a small positive average effect size of .11 and a positive effect in eleven of the 21 studies. Four studies show a decrease in deep learning and six studies show no effect. PBL does not seem to have an effect on surface learning as indicated by a very small average effect size (.08) and eleven studies showing no increase in the surface approach. Six studies demonstrate a decrease and four an increase in surface learning. It is concluded that PBL does seem to enhance deep learning and has little effect on surface learning, although more longitudinal research using high quality measurement instruments is needed to support this conclusion with stronger evidence. Differences cannot be explained by the study quality but a curriculum wide implementation of PBL has a more positive impact on the deep approach (effect size .18) compared to an implementation within a single course (effect size of -.05). PBL is assumed to enhance active learning and students' intrinsic motivation, which enhances deep learning. A high perceived workload and assessment that is perceived as not rewarding deep learning are assumed to enhance surface learning.

Keywords: Deep approach; Problem-based learning; Students’ approaches to learning (SAL); Surface approach.