Instr Sci (2012) 40:241–256 DOI 10.1007/s11251-011-9176-3

Factors affecting bioscience students’ academic achievement Henna Rytko¨nen • Anna Parpala • Sari Lindblom-Yla¨nne Viivi Virtanen • Liisa Postareff

•

Received: 24 August 2010 / Accepted: 23 June 2011 / Published online: 2 July 2011 Ó Springer Science+Business Media B.V. 2011

Abstract The examination of academic progression has become an essential tool for measuring the effectiveness of educational systems. Research concerning the relationship between student learning and how they progress in their studies, however remains scarce. The aim of this study is two-fold: Firstly, the study aims to analyse first-year bioscience students’ perceptions of their teaching–learning environment and their approaches to learning as well as the relationship of these to academic achievement as measured by students’ progression in studies and how they succeed. Secondly, the present study explores factors students feel either enhance or impede their studying as well as the relationship of those factors with their approaches to learning and academic achievement. The data consist of responses from 188 first-year students who began their studies in the fall 2007 and 2008. The data were collected in a Finnish context with a modified and shortened version of the Experiences in Teaching and Learning Questionnaire (ETLQ). The analyses were carried out using factor analysis, one-way ANOVA and structural equation modeling. According to the results, organised studying was related to both academic progression and study success. In addition, academic progression was positively related to peer support. Furthermore, most of the students found that problems in time

H. Rytko¨nen (&)
A. Parpala
S. Lindblom-Yla¨nne
L. Postareff Faculty of Behavioural Sciences, University of Helsinki, Siltavuorenpenger 5A, PL 9, 00014 Helsinki, Finland e-mail: [email protected] A. Parpala e-mail: [email protected] S. Lindblom-Yla¨nne e-mail: [email protected] L. Postareff e-mail: [email protected] V. Virtanen Faculty of Biological and Environmental Sciences, University of Helsinki, Viikinkaari 9, PL 56, 00014 Helsinki, Finland e-mail: [email protected]

123

242

H. Rytko¨nen et al.

management impeded their studies and that pre-set schedules enhanced them. Results indicate that social support and self-regulation skills are important for academic achievement. Keywords Approaches to learning
Teaching–learning environment
Academic achievement
Academic progression
Higher education

Introduction Universities are facing the challenge of providing evidence of their effectiveness to government, society and the international higher education area. Across many countries, student graduation rates, for example, have served as an indicator of effectiveness (Organisation for Economic Co-operation and Development (OECD), 2009). In order to graduate students must study effectively and pass their exams. Thus, academic progression has become an important factor in measuring academic achievement and there is a need for examination of the factors that affect that (Constantini and Vitale 2011). In earlier studies academic progression has been measured by accumulation of passed courses or credits (Duff 2004). A number of studies have explored the connection between approaches to learning, perceptions of the teaching learning environment and academic achievement (e.g. Entwistle and Ramsen 1983; Prosser and Trigwell 1999), but research concerning students’ progression in their studies is scarce. Failing to earn enough credits has been found to be a problem especially among firstyear students (Constantini and Vitale 2011). In addition, slow progression in studies has been a problem in Finland (KOTA database). Studying at the university is free of change and students graduate and proceed to working life late compared to other European countries (OECD 2002). In the past decade in the field of science, for example, the average completion time for both a bachelor’s and master’s degree in the past decade has been seven years instead of the pre-scheduled five years (3 ? 2) in the University of Helsinki. This average includes both full-time and part-time students. Consequently, after the curriculum reform caused by the Bologna process (Bologna Declaration 1999), the University of Helsinki implemented a system to monitor academic progression by assessing the accumulation of credits at certain ‘checkpoints’. The goal of this monitoring system was to ensure that students progress in their studies, and the system aimed to recognise students with difficulties in their studies in order to offer them counselling (Aronen 2005). The three-year bachelor programme (180 ECTS) has three checkpoints and the two-year master programme (120 ECTS) has two checkpoints. If a student has not earned enough credits at the checkpoints, he or she is obligated to write a personal study plan in which there needs to be a description of how to proceed in studies. If a student fails to do this, the right to study can be restricted. The academic year has a total of four periods, in which the first-year bioscience students are expected to gain *60 credits. In order to earn these credits, students need to pass *20 mostly compulsory courses. The first checkpoint comes at the end of the third period of the first year, in March with the completion of 25 credits. The students will gain the 25 credits when passing *7 courses each ranging from two to five credits (ECTS). In the Faculty of Biological and Environmental Sciences, 23% of first-year students failed to earn enough credits to pass the first checkpoint in 2008. This failure thus raised the question of factors influencing student academic progression.

123

Factors affecting bioscience students’ academic achievement

243

Approaches to learning A rich body of research differentiates two qualitatively different ways students learn (Biggs 2003; Marton et al. 1997; Marton and Sa¨ljo¨ 1976; Prosser and Trigwell 1999). Students applying a surface approach to learning concentrate on the text itself and try to memorise it whereas students applying a deep approach to learning attempt to understand what the author intends to say, relate the ideas and make sense of the learning material by linking it to prior knowledge (Biggs 2003; Marton and Sa¨ljo¨ 1997). The deep approach to learning is considered a combination of the intention to understand and the deep learning process (Entwistle and Ramsen 1983) Furthermore, a third, strategic (Entwistle and Ramsen 1983) or achieving (Biggs 1987) approach to learning has also been identified, which refers to how students organised their studies according to the assessment in the course. Currently, research on approaches to learning refers mainly to organised studying or organised effort in studying, which emphasises good time management, self regulation and effort in studying rather than of the motivation to achieve (Entwistle and McCune 2004; Entwistle and Peterson 2005). The teaching–learning environment Approaches to learning depend greatly on the context (Marton and Sa¨ljo¨ 1976; Prosser and Trigwell 1999). According to previous studies, positive perceptions of the teaching– learning environment are related to a deep approach to learning and, respectively, negative perceptions to a surface approach to learning (Diseth 2007a; Parpala et al. 2010; Richardson 2005). Furthermore, assessment significantly impacts how students engage in learning (Reid et al. 2007). Students’ perceptions of a heavy workload and inappropriate assessment seem to be related to a surface approach to learning (Biggs 2003; Lizzio et al. 2002). According to Biggs (2003), teaching should be based on constructive alignment, which means that the learning objectives, teaching methods and assessment should be in line with each other in order to promote a deep approach to learning. In addition, students’ perceptions of alignment in teaching have been found to be related to a deep approach to learning (Parpala et al. 2010) Students’ approaches to learning and their perceptions of the teaching–learning environment have been found to be related in various educational contexts and subject areas have been found (Entwistle et al. 2000; McCune 2004). Parpala et al. (2010) explored students’ approaches to learning and their perceptions of the teaching–learning environment by using latent profile analysis in ten faculties at the University of Helsinki (n = 2509). They found four student groups representing different combinations of approaches to learning: (1) organised students, (2) students applying a deep approach, (3) students applying a surface approach and (4) unorganised students applying a deep approach. The results also showed that two major student groups are represented in the Faculty of Biological and Environmental Sciences: organised students and unorganised students applying a deep approach (Parpala et al. 2010). Organised students comprised students who scored highly on items measuring organised studying and achieved close-toaverage scores on items measuring a deep approach to learning. Unorganised students applying a deep approach scored high on items measuring a deep approach to learning, but low on items measuring organised studying.

123

244

H. Rytko¨nen et al.

Study success and academic progression Several studies have shown a relationship between approaches to learning and study success (Diseth 2003; Watters and Watters 2007). Previous studies indicate that the deep approach to learning is positively related to academic achievement (e.g. Amirali et al. 2004; Roma´n et al. 2008), and the surface approach negatively related (Diseth and Martinsen 2003). Recent research has shown a positive relationship between strategic approach and academic achievement (Diseth 2003, 2007b). However, contradictory results have also been found. Lizzio et al. (2002) studied 646 undergraduate commerce, humanities and science students in Australia and found a positive relationship between a surface approach to learning and academic achievement. Research concerning the relationship between student learning and academic progression has been scarce. Lindblom-Yla¨nne and Lonka (1999) and Duff (2004) have found a connection between a deep approach to learning and a faster study pace measured with accumulation of credits. According to Parpala et al. (2009), students who had earned the most credits (ECTS) scored higher on scales measuring deep approach to learning and organised studying than did the students who had earned the fewest earned credits. Furthermore, Ruohoniemi et al. (2010) examined the relationship between veterinary students’ approaches to learning and their study success and found that students applying a deep approach achieved higher GPAs and progressed faster in their studies than did other student groups. Duff (2004) has found similar results. These studies suggest a relationship between academic progression and study success. Aims of the study The aim of the present study is two-fold. Firstly, the aim is to explore the relationship between first-year students’ experiences of their teaching–learning environment, their approaches to learning, their and study success at the Faculty of Biological and Environmental Sciences. Secondly, the aim is to include students’ own perspectives in the analyses by exploring what factors the students themselves feel impede and enhance their studying, and how these factors are related to the students’ approaches to learning and their study success.

Method Participants The data were collected with an online questionnaire in 2008 and 2009 just before the first checkpoint in March at the Faculty of Biological and Environmental Sciences at the University of Helsinki. First-year students from the Bachelor programmes in biology, biochemistry, limnology and fishery science participated in the study during 2008 and 2009. In addition, students studying environmental ecology and environmental protection science participated in 2009. All participants have had the same entrance examination and their first year of studies were quite similar. Thus, the students comprise a fairly homogenous group. Ten of the responses (5%) were removed from the analysis because they provided no identifying information. A total of 188 responses were obtained, comprising 93 first-year students who began their studies in 2008 and 95 students who began their studies in the study in 2009. The total

123

Factors affecting bioscience students’ academic achievement

245

response rate was 74% comprising the response rates of in 2008 (82.3%) and 2009 (67.9%). Ages ranged from 18 to 55 years (M = 21.3, SD = 4.2). The participants consisted of 142 female (76%) and 46 male students (24%). In 2008, the percentage of male students studying at the Faculty of Biological and Environmental Sciences was 32%. Thus, the percentage of male students in the present study was slightly lower than the proportion of male students studying at the Faculty. According to Virtanen and Lindblom-Yla¨nne (2009), bioscience students’ and teachers’ conceptions of learning and teaching at the University of Helsinki differed. Students viewed learning mostly as the transmission of information, the collection of facts and the practical use of knowledge, whereas teachers’ conceptions emphasised critical thinking, problem solving and independence in learning. Furthermore, teaching at the faculty is based on high-level research in the biological and environmental sciences. The instruction includes mostly lectures and practical laboratory work, but also field courses, seminars and web-based teaching. During the first study year, lecturing is clearly the most common teaching method (about 40%), and a written examination is used as an assessment method in 80% of the courses. In addition, students are required to write an essay and give an oral presentation in two courses. Furthermore, students can choose their studies from a wide range of optional courses. Biology students, for example, can choose their learning paths from among 19 possibilities. However, the first year follows a pre-set programme, the purpose of which is to familiarise students with different areas of biology. Students are expected to choose a main subject in the second year of their studies and then the optionality of the courses increases. Research instruments The questionnaire was a modified version of the Experience of Teaching and Learning Questionnaire (ETLQ) used in the ETL project (Entwistle et al. 2003). The original ETLQ measures students’ perceptions of the teaching–learning environment and their approaches to learning after a specific course unit or module, whereas the modified Finnish version focused on students’ major subject (for more, Parpala et al. 2011). The questionnaire used in the present study was shortened from the modified ETLQ on the basis of factor loadings, communalities and reliabilities based on earlier analyses (Parpala 2010; Parpala et al. 2010, 2011). The modified ETLQ has been used to measure students’ approaches to learning and perceptions of the teaching–learning environment in a typical learning situation in different disciplines at the University of Helsinki (Parpala 2010; Parpala et al. 2010, 2011; HaaralaMuhonen et al., in press a; Ruohoniemi et al. 2010). The original questionnaire consisted of 40 items enquiring about students’ perceptions of the teaching–learning environment and 18 items enquiring about students’ approaches to learning. The shortened version used in the present study consisted of 11 items enquiring about students’ approaches to learning and 21 items enquiring about perceptions of the teaching–learning environment. Students were also asked to choose three of the most important factors enhancing or impeding their studies from among pre-selected factors (16 impeding and 19 enhancing). These factors were found in a previous study (Myllyla¨ et al. 2007) on the basis of a qualitative content analysis of students’ answers to open-ended questions. The alternatives consisted of factors related to faculty-level procedures, teaching practices and students’ own activities and personal life. Furthermore, study success was measured by calculating a grade point average (GPA) and academic progression was measured with the number of earned credits (ECTS) within the first three periods. Of the whole cohort, 15 students (14.5%) lacked the 25 credits required to pass the checkpoint.

123

246

H. Rytko¨nen et al.

Analyses Before we carried out the main analyses, we analysed the similarities between two student cohorts (2008, 2009) with cross-tabulation and Chi square. The analyses of the variables describing students’ approaches to learning, perceptions of their teaching learning environment, the impeding and enhancing factors and achievement as well as background variables showed that there were no statistically significant differences between the groups. Consequently, we analysed the data as one sample. We also conducted a missing value analysis even though the number of missing cases was very low (0.2%). We conducted exploratory factor analysis (principal axis factoring) with direct oblimin rotation on the items describing approaches to learning and perceptions of the teaching– learning environment. According to previous studies, it is justifiable to assume that items describing approaches to learning and perceptions of the teaching–learning environment correlate with each other (Parpala et al., in press; Richardson 2006). The relationship between approaches to learning, perceptions of the teaching–learning environment, academic progression and study success was first examined with Pearson’s correlation. The relationship between enhancing and impeding factors, approaches to learning and academic achievement as well as the differences between students who had passed or failed the first checkpoint were analysed with univariate analysis of variance (ANOVA). The final analysis was conducted with structural equation modeling (SEM) using Amos 7.0. A statistical technique for testing and estimating causal relationships using a combination of statistical data and qualitative causal assumptions, SEM is used to test theoretical models and how well the model fits the data (Munro 1997). The analysis was conducted with SEM instead of regression analysis because SEM provides a clearer picture of the relationships between the factors.

Results Factors enhancing and impeding studying First, we aimed to explore factors that students felt enhanced and impeded their studying. The most common impeding factor was difficulty in time management. In addition, almost half of the students felt that course overlap, inappropriate course schedules and lack of motivation also impeded their studying. Factors concerning the students’ personal lives, such as family, friends or work, were seldom cited as impeding factors. Over two-thirds of the students identified pre-set programme, inspiring teaching and peer support as factors enhancing studying. In addition, more than half of the students felt that self-help, diligence, flexibility enhanced their studies (see Table 1). Dimensions of the teaching–learning environment Analysis of the items measuring student perceptions of the teaching–learning environment revealed that variables 2 (We were allowed some choice over what aspects of the subjects to concentrate on) and 16 (This course unit provided plenty of opportunities for me to discuss important ideas) had very low communalities and no sizeable loadings. The two variables were removed from the final analysis, and a four-factor solution was selected: relevance and evoking interest (F1), constructive feedback (F2), peer support (F3) and alignment (F4). The Cronbach Alphas ranged between 0.73 and 0.84. This solution

123

Factors affecting bioscience students’ academic achievement Table 1 First-year students’ perceptions of factors impeding and enhancing progression in studies

247

Impeding factors

Percent (%)

Enhancing factors

Percent (%)

Difficulties with time management

59

Pre-set schedules

82

Course overlap

48

Interesting teaching

71

Inappropriate schedules

44

Peer support

67

Lack of motivation

44

Self-help

54

Tight schedule

40

Diligence

53

Concentration problems

39

Flexibility

52

Literature in other languages

28

Good learning experiences

46

presented the clearest matrix and explained 47% of the variance. The factor loadings appear in Table 2. Dimensions of approaches to learning When analysing approaches to learning, a four-factor solution of the 11 items was the clearest and explained 51% of the total variance. The factors were: organised studying (F1), deep approach (F2), intention to understand (F3) and surface approach (F4). The factor loadings appear in Table 3. The Cronbach Alphas ranged between 0.68 and 0.76 except for the surface approach (0.59). Differences in impeding and enhancing factors according to approaches to learning Students’ experiences impeding and enhancing factors were explored in relation to their approaches to learning (see Table 4). The analysis showed that the enhancing and impending factors selected by the students were related to approaches to learning: students who chose interesting teaching, self-help and diligence as enhancing factors scored significantly higher on organised studying than did students who did not find these factors enhancing. In addition, students who felt that self-help enhanced their studies scored higher on the deep approach. Students who found that the pre-set programme enhanced their studies scored significantly lower on deep approach than did those students who did not consider it enhancing. Furthermore, students who found that inspiring teaching and diligence enhanced their studies scored higher on intention to understand. No differences in approaches to learning were found between students who felt peer support enhanced their studies and those who did not feel peer support was an enhancing factor. Students who felt that difficulties with time management, motivation problems and concentration problems impeded their studies, scored significantly lower in organised studying than did students who had no such experiences. In addition, students who felt that lack of motivation impeded their studies scored significantly lower on intention to understand and deep approach. Furthermore, students who had experienced difficulties with time management and concentration problems scored significantly higher on surface approach than did those students who reported no such experiences. Finally, students who found course overlap, inappropriate course schedules and tight schedules to impede their studying showed no differences in their scores on scales measuring approaches to learning from students who found that no such factors impeded their studies.

123

H. Rytko¨nen et al.

248

Table 2 Pattern matrix of the items describing perceptions of the teaching–learning environment Items

Factors Relavance and evoking interest F1

Constructive feedback F2

Peer Support F3

Alignment F4

9. I found most of what I learned in this course unit really interesting

0.716

-0.206

0.077

-0.080

7. This unit encouraged me to relate what I learned to issues in the wider world

0.639

0.097

-0.121

0.051

4. I could see the relevance of most of what we were taught in this unit

0.616

-0.020

0.065

0.107

13. I enjoyed being involved in this course unit

0.596

-0.054

0.192

0.033

14. Staff helped us to see how you are supposed to think and reach conclusions in this subject

0.552

0.125

0.025

-0.029

6. The teaching in this unit helped me to think about the evidence underpinning different views

0.529

0.195

0.014

0.005

5. This unit has given me a sense of what goes on ‘behind the scenes’ in this subject area

0.511

0.034

0.103

-0.140

10. Staff tried to share their enthusiasm about the subject with us

0.469

-0.028

-0.032

0.195

3. What we were taught seemed to match what we were supposed to learn

0.458

0.204

-0.126

0.235

12. Staff were patient in explaining things which seemed difficult to grasp

0.339

0.078

0.072

0.217

19. The feedback given on my work helped me to improve my ways of learning and studying

0.033

0.813

-0.082

0.004

21. The feedback given on my set work helped to clarify things I hadn’t fully understood

-0.081

0.780

0.121

0.020

20. The set work helped me to make connections to my existing knowledge or experience

0.145

0.536

0.080

0.104

-0.077

0.069

0.789

0.095

8. Students supported each other and tried to give help when it was needed

0.128

-0.144

0.708

0.115

11. Talking with other students helped me to develop my understanding

0.139

0.211

0.581

-0.149

-0.143

0.007

0.083

0.851

18. I could see how the set work fitted in with what we were supposed to learn

0.247

0.019

-0.053

0.556

1. It was clear to me what I was supposed to learn in this course unit

0.111

0.144

0.087

0.507

0.323

15. I found I could generally work comfortably with other students on this unit

17. It was clear to me what was expected in the assessed work for this course unit

Factor correlations F1

0.368

0.351

F2

0.155

0.319

F3

0.085

Bold values show the factor loading more clearly in each factor

123

Factors affecting bioscience students’ academic achievement

249

Table 3 Pattern matrix items describing approaches to learning Items

Factors Organised studying 1

Deep approach 2

Surface approach 3

Intention to understand 4

5. On the whole, I’ve been quite systematic and organised in my studying

0.827

-0.008

-0.144

-0.039

3. I have generally put a lot of effort into my studying

0.654

-0.050

0.139

0.246

9. I’ve organised my study time carefully to make the best use of it

0.644

0.117

-0.042

-0.041

7. I’ve looked at evidence carefully to reach my own conclusion about what I’m studying

0.042

0.720

-0.039

0.012

6. Ideas I’ve come across in my academic reading often set me off on long chains of thought

-0.095

0.715

0.049

0.107

8. When I’ve been communicating ideas, I’ve thought over how well I’ve got my points across

0.092

0.614

0.016

-0.164

11. If I’ve not understood things well enough when studying, I’ve tried a different approach

0.067

0.403

-0.137

0.238

1. I’ve often had trouble making sense of the things I have to remember

-0.097

0.111

0.749

0.035

0.076

-0.138

0.576

-0.117

2. I have usually set out to understand for myself the meaning of what we had to learn

-0.022

-0.003

-0.083

0.779

10. In reading for this course unit, I’ve tried to find out for myself exactly what the author means

0.168

0.020

-0.017

0.572

0.233

-0.241

0.348

4. Much of what I’ve learned seems no more than lots of unrelated bits and pieces in my mind

Factor correlations F1 F2 F3

-0.235

0.192 -0.309

Bold values show the factor loading more clearly in each factor

The relationship between approaches to learning, perceptions of the teaching–learning environment and academic achievement Relationships between academic achievement, perceptions of the teaching–learning environment and approaches to learning were first analysed using Pearson’s correlation. All factors describing student perceptions of their teaching–learning environment correlated positively with each other. In addition, the factors describing students’ approaches to learning correlated positively with each other, except for the Surface approach, which was negatively related to the other factors. The relationship between the factors describing approaches to learning and perceptions of the teaching–learning environment was also clear. Positive perceptions correlated positively with deep approach, intention to understand and organised studying and negatively with surface approach. The correlations appear in Table 5. In addition, Table 5 shows that the correlation between study success and was statistically significant. However, different factors were related to them. Intention to understand

123

H. Rytko¨nen et al.

250

Table 4 Differences between students selected and did not select enhancing and impeding factors according to their approaches to learning Factors

Approach to learning

Selected Mean

Not selected SD

Mean

SD

Enhancing factors Pre-set schedules

Deep approach*

2.94

0.76

3.28

0.80

Interesting teaching

Organised studying*

3.26

0.86

2.99

0.65

Intention to understand**

4.41

0.60

4.05

0.76

Organised studying*

3.32

0.82

3.02

0.78

Deep approach**

3.15

0.70

2.82

0.84

Organised studying***

3.52

0.78

2.80

0.67

Intention to understand*

4.41

0.65

4.20

0.68

Self-help Diligence Impeding factors Difficulties in time management Motivation problems

Concentration problems

Organised studying**

3.02

0.75

3.42

0.85

Surface approach*

2.69

0.83

2.44

0.80

Organised studying***

2.92

0.75

3.39

0.81

Deep approach*

2.86

0.84

3.11

0.71

Intention to understand*

4.17

0.68

4.42

0.65

Organised studying***

2.88

0.77

3.38

0.79

Surface approach***

2.97

0.80

2.34

0.74

Note: p \ 0.05*, p \ 0.01**, p \ 0.001***

Table 5 The relationship between approaches to learning, perceptions of the teaching–learning environment, study success and academic progression Items

Correlations 1

1 Relevance and evoking interest

1

2 Constructive feedback

0.38*

2

3

4

5

6

7

8

9

1

3 Peer support

0.42*

0.24*

4 Alignment

0.45*

0.36*

0.20*

1

5 Intention to understand

0.48*

0.11

0.32*

0.27*

1

-0.08

-0.09

1

6 Surface approach

0.26*

-0.33*

-0.30*

7 Organised studying

0.24*

0.19*

0.21*

0.28*

0.38*

-0.23*

1

8 Deep approach

0.19*

0.17*

0.01

0.23*

0.15*

-0.16*

0.21*

9 Study success

0.20*

0.17*

0.11

0.16*

0.21*

-0.18*

0.38*

0.04

1

10 Academic progression

0.02

0.02

0.20*

0.02

-0.05

0.36*

0.06

0.32*

-0.03

*p \ 0.05, statistically significant correlations shown in bold

123

10

1

1 1

Factors affecting bioscience students’ academic achievement

251

and organised studying as well as relevance and evoking interest, constructive feedback and alignment all correlated positively, and Surface approach negatively, with study success. In other words, peer support and deep approach were the only factors that were unrelated to study success. Academic progression however, correlated positively only with organised studying and peer support. Differences between students who selected and did not select the impeding and enhancing factors were also studied according to students’ academic progression and study success. The results showed that students who found that pre-set schedules, diligence and good learning experiences enhanced their studying had earned both more credits and higher grades (F = 5.4–16.9, p B 0.022). In addition, students who found that concentration problems impede their studies received lower grades than did students who experienced no such problems (F = 4.6, p = 0.033). Students who had experienced motivation problems as impeding their studies had received lower grades than the students who did not experience motivational problems (F = 11.5, p = 0.001). Furthermore, differences between the students who had passed or failed the first checkpoint were also studied. Those students who failed to earn the 25 credits required to pass the first checkpoint scored statistically significantly (F = 15.3, p \ 0.001) lower on organised studying (M = 2.4) than did those students who earned over 25 credits (M = 3.2). Predictors of academic achievement Finally, we tested a SEM model in which approaches to learning served as intervening variables between perceptions of the teaching–learning environment. The model showed clear relationships between approaches to learning and perceptions of the teaching– learning environment. The only factor predicting both study success and academic progression, however, was organised studying. Furthermore, only students’ perceptions of other students’ support predicted progression in studies (see Fig. 1). None of the other factors describing approaches to learning or perceptions of the teaching–learning environment predicted academic progression or study success. The final analysis was therefore carried out with only four factors in the model: peer support, organised studying, study success and progression in studies. The final SEM model fitted the data well (v2 = 0.245, df = 1, p = 0.62, RMSEA = 0.00, CFI = 1). Organised studying was the strongest predictor of academic achievement, predicting both study success and academic progression. Furthermore, peer support was the only item describing perceptions of the teaching–learning environment which predicted students’ progression in studies. Figure 2 shows the final model.

Conclusions and discussion The results of the present study showed that students’ perceptions of the teaching–learning environment were clearly associated with their approaches to learning. This replicates the results of previous studies (Kreber 2003; Lawless and Richardson 2002; Parpala et al. 2010, 2011; Richardson 2005; Sadlo and Richardson 2003). Furthermore, both students’ study success and their academic progression correlated most strongly with organised studying. This suggests that students who organise their studies, manage their time well and put effort into their studying earn more credits and receive higher grades. This is partly in line with earlier research which shows a correlation between strategic approach and course grades (Diseth 2003, 2007a, b). Students’ experiences of the enhancing and

123

H. Rytko¨nen et al.

252

Intention to understand

Deep approach

0.39 0.23

Relevance and evoking interest

Organised studying

0.16

0.15

0.33

Academic progression

0.34 0.13

Peer support

0.15

Study success

0.23

Alignment Surface approach

-0.31

Fig. 1 The relationship between perceptions of the teaching–learning environment, approaches to learning, study success and academic progression 0.38

Organised studying

Study success 0.34

0.21 Peer support

0.12

0.21 Academic progress

Fig. 2 The predictors of academic achievement

impeding factors support the preceding results. The impeding factors in particular were related to difficulties in time management and lack of self-regulation, both of which can indicate unorganised studying (Parpala et al. 2010). Interestingly, neither study success nor academic progression was related to the deep approach. The results of the present study differ from those of previous studies. Parpala et al. (2009) studied 2509 university students in ten faculties in University of Helsinki and showed that university students with the most earned credits scored higher on organised studying and deep approach than did students with the fewest credits. In addition, students with the fewest credits scored higher on surface approach to learning than did students with the most credits. Furthermore, first-year law students who scored highly on deep approach passed more courses and earned better grades than did students who scored highly on surface approach (Haarala-Muhonen et al. 2011). The results of the present study can be interpreted in the light that the deep approach to learning is not necessarily encouraged during the first year studies at the Faculty of Biological and Environmental Sciences. This may be due to the pre-set programme in the first

123

Factors affecting bioscience students’ academic achievement

253

year. The students are expected to choose a major subject at the beginning of their second year, and the studies consist mainly of lectures which introduce different subjects to students. Furthermore, the deep approach to learning is found to be less typical among science students than among students studying the social sciences or arts (Entwistle and Ramsen 1983; Parpala et al. 2010). For example, according to Eley (1992) students of English language scored higher on items measuring deep approach to learning than biochemistry or chemistry students. In addition, science teachers are more likely to report more ‘‘teacher-focused’’ approaches to teaching (Lindblom-Yla¨nne et al. 2006) which may not encourage students to adopt a deep approach to learning. On the other hand, the nature of the deep approach to learning may well vary in different faculties, because of the way understanding is developed in each discipline (Entwistle 2009; Entwistle et al. 2001). Different disciplines have different cultures that have different norms, values, aims and problems (Ylijoki 2000) and the role of teaching and learning vary in different academic environments (Entwistle and Tait 1990). The ETL-questionnaire may not necessarily grasp possible differences in meanings that deep learning has in different educational contexts, and that is why the deep approach needs to be refined within each discipline. Finally, grade point average is just one way of measuring academic achievement and reveals little about the quality of the learning outcomes. Amirali et al. (2004), for example, found that deep approach was related to the quality of exam responses but not related to exam grades. Although academic progression and study success correlated with each other, their relationship to each other was less clear. Study success (GPA) correlated positively with all of the factors describing perceptions of the teaching–learning environment, except for peer support, which was the only factor related to progression in studies (ECTS). These results indicate that different factors affect academic progression and study success. However, students’ own experiences of enhancing factors were similar. Students who found that preset schedules, diligence and good learning experiences enhanced their studies not only earned more credits but also received higher grades. This indicates that, more than other students, those students whose GPA is higher and who earn more credits find that similar factors enhance their studies. Our analysis of what enhances or impedes students’ studying supports preceding findings that time management and organised studying are important in first-year studies. Most of the students found that the difficulty with time management impeded their studying. However, those students who selected difficulties with time management were less organised in their studies than were those students who did not select it. Previous results concerning law students suggest that students who earned the most credits organised their studies well, and that students who earned the fewest credits experienced problems in time management (Haarala-Muhonen et al., in press b). Furthermore, the present study showed that students who had failed to earn enough credits to pass the first checkpoint evaluated themselves less organised than the students who had earned enough credits. These findings also emphasise the importance of organised studying in first-year studies. The factors describing students’ personal lives such as work and family were seldom found to impede studies. A previous study found similar results (Haarala-Muhonen et al., in press a; Ruohoniemi and Lindblom-Yla¨nne 2009). Almost half of the students found that overlapping of courses and inappropriate schedules were impeding factors but that these factors were unrelated to approaches to learning. During the Bologna process, the curriculum at the Faculty of Biological and Environmental Sciences underwent massive reform: courses were re-evaluated and re-organised to yield bachelor and master degrees. Results of the present study indicate, however, that the re-evaluation of the degree content may have been inadequate.

123

254

H. Rytko¨nen et al.

According to Parpala et al. (2010), two major student groups are represented at the Faculty of Biological and Environmental Sciences: organised students and unorganised students applying a deep approach. The results of the present study raise the question of how well unorganised students applying a deep approach achieve in this context, where organised studying is considered crucial. Perhaps students who organise their studies well would succeed better in their studies than students who apply a deep approach to learning but fail to organise their studies. A follow-up study should be conducted to obtain a clearer picture of the relationship between approaches to learning and students’ academic achievement and to explore whether a change has occurred. To sum up, the results indicate that both students’ perceptions of the teaching–learning environment and their approaches to learning should be considered as important factors affecting academic progression. The results also suggest that cooperative learning should be promoted in order to facilitate students’ academic progression. According to Armstrong et al. (2007) cooperative learning strategies, such as group work in lectures, improve students’ knowledge about biological issues better than traditional lecturing and improve student engagement. The present study suggests that cooperative learning strategies should be implemented to lectures from the beginning of the university studies in order to promote students collaboration, engagement and academic progression. In addition, student counselling should emphasise students’ study skills, such as time management and self-regulation skills, as a part of the programme. Furthermore, the possibility of work overload due to schedule problems in the course curriculum should be taken into account. A re-evaluation of the course curriculum would be worthwhile. Furthermore, the absence of the relation between the deep approach to learning and academic progress in the present study raises a question about the nature of the ‘deep learning’ in different faculties. Future research on the nature of deep learning in different faculties is clearly needed. In addition, grade point average and the accumulation of credits are not necessarily best ways to measure students’ learning. Students’ deep learning should rather be explored in relation with qualitative learning outcomes.

References Amirali, M., Huon, G., & Kevin, B. (2004). Approaches to studying and academic performance in shortessay exams. Higher Education, 47, 161–176. Armstrong, N., Chang, S.-M., & Brickman, M. (2007). Cooperative learning in industrial-sized biology classes. GBE–Life Sciences Education, 6, 163–171. Aronen, P. (2005). Bologna process at the University of Helsinki. Policies for the Bachelor’s and Master’s degree programmes. University of Helsinki, Administrative Publications 10/2005. Biggs, J. (1987). Student approaches to learning and studying. Melbourne: Australian council for educational research. Biggs, J. (2003). Teaching for quality learning at university. Buckingham: Open University press. Bologna Declaration, (1999). Joint declaration of the european ministers of education convened in Bologna at the 19th of June 1999. The European Higher Education Area. http://europa.eu.int/comm/ education/bologna_en.html. Constantini, P., & Vitale, M. P. (2011). Analyzing undergraduate student graduation delay: A longitudinal perspective. In M. Attanasio & V. Capursi (Eds.), Statistical methods for the evaluation of university systems (pp. 145–160). Berlin: Springer. ˚ . (2003). Personality and approaches to learning as predictors of academic achievement. European Diseth, A Journal of Personality, 17, 143–155. ˚ . (2007a). Approaches to learning, course experience and examination grade among undergraduate Diseth, A psychology students: Testing of mediator effects and construct validity. Studies in Higher Education, 32, 373–388.

123

Factors affecting bioscience students’ academic achievement

255

˚ . (2007b). Students’ evaluation of teaching, approaches to learning, and academic achievement. Diseth, A Scandinavian Journal of Educational Research, 51, 185–204. ˚ ., & Martinsen, Ø. (2003). Approaches to learning, cognitive style, and motives as predictors of Diseth, A academic achievement. Educational Psychology, 23, 195–230. Duff, A. (2004). Understanding academic performance and progression of first-year accounting and business economics undergraduates: The role of approaches to learning and prior academic achievement. Accounting Education, 13(4), 409–430. Eley, M. (1992). Differential adoption of study approaches within individual students. Higher Education, 23, 231–254. Entwistle, N. J. (2009). Teaching for understanding at university: Deep approaches and distinctive ways of thinking. Basingstoke, Hampshire: Palgrave Macmillan. Entwistle, N., & McCune, V. (2004). The conceptual bases of study strategy inventories. Educational Psychology Review, 16, 245–325. Entwistle, N., McCune, V., & Hounsell, J. (2003). Investigating ways of enhancing university teachinglearning environments: Measuring students’ approaches to studying and perceptions of teaching. In E. De Corte, L. Verschaffel, N. Entwistle, & J. van Merrienboer (Eds.), Unravelling basic components and dimensions of powerful learning environments (pp. 89–107). Oxford: Elsevier Science. Entwistle, N., McCune, V., & Walker, P. (2001). Conceptions, styles and approaches within higher education: Analytic abstractions and everyday experience. In R. J. Sternberg & L.-F. Zhang (Eds.), Perspectives on thinking, learning and cognitive styles (pp. 103–136). London: Lawrence Erlbaum. Entwistle, N., & Peterson, E. (2005). Conceptions of learning and knowledge in higher education: Relationships with study behaviour and influences of learning environments. International Journal of Educational Research, 41, 407–428. Entwistle, N., & Ramsen, P. (1983). Understanding student learning. London: Croom Helm. Entwistle, N., & Tait, H. (1990). Approaches to learning, evaluating of teaching, and preferences for constructing academic environments. Higher Education, 19, 169–194. Entwistle, N., Tait, H., & McCune, V. (2000). Patterns of response to approaches to studying inventory across constructing groups and contexts. European Journal of Psychology of Education, XV(1), 33–48. Haarala-Muhonen, A., Lindblom-Yla¨nne, S., Parpala, A., & Komulainen, E. (2011). High and low achievers’ approaches to learning among first-year law students. Manuscript under review. Haarala-Muhonen, A., Ruohoniemi, M., Katajavuori, N., & Lindblom-Yla¨nne, S. (in press a). Comparison of students’ perceptions of their teaching-learning environments in three professional academic disciplines. A valuable tool for quality enhancement. Learning Environments Research. Haarala-Muhonen, A., Ruohoniemi, M., & Lindblom-Yla¨nne,S. (in press b). Factors affecting the study pace of first-year law students. In search of counselling tools. Studies in Higher Education. KOTA database. Ministry of Education, Finland. http://www.csc.fi/kota/kota.html. Kreber, C. (2003). The relationship between students’ course perception and their approaches to studying in undergraduate science courses: a Canadian experience. Higher Education and Development, 22, 57–70. Lawless, C., & Richardson, J. T. E. (2002). Approaches to studying and perceptions of academic quality in distance education. Higher Education, 44, 257–282. Lindblom-Yla¨nne, S., & Lonka, K. (1999). On the predictive value of entry-level skills for successful studying in medical school. Higher Education, 37, 239–258. Lindblom-Yla¨nne, S., Trigwell, K., Nevgi, A., & Ashwin, P. (2006). How approaches to teaching are affected by discipline and teaching context. Studies in Higher Education, 31, 285–298. Lizzio, A., Wilson, K., & Simons, K. (2002). University students’ perceptions of the learning environment and academic outcomes: Implications for theory and practice. Studies in Higher Education, 27, 27–52. Marton, F., Hounsell, D., & Entwistle, N. (Eds.). (1997). The experience of learning. Edinburgh, UK: Scottish Academic Press. Marton, F., & Sa¨ljo¨, R. (1976). On qualitative differences in learning I—outcome and process. British Journal of Educational Psychology, 46, 4–11. Marton, F., & Sa¨ljo¨, R. (1997). Approaches to learning. In F. Marton, D. Hounsell, & N. Entwistle (Eds.), The experience of learning (Second ed., pp. 166–176). Edinburgh, UK: Scottish Academic Press. McCune, V. (2004). Promoting high-quality learning. Perspectives from the ETL project. UNIPED, 27, 4–25. Munro, B. (1997). Statistical methods for health care research (Third ed.). Lippincott, Philadelphia: Raven Publishers. Myllyla¨, J., Parpala, A., Lindblom-Yla¨nne, S., Korvenranta, S., & Mattila, A. (2007). Ka¨ytta¨ytymistieteellisen tiedekunnan opiskelijoiden kokemuksia oppimisesta ja opetuksesta. [Students’ experiences of learning and teaching in the Faculty of Behavioural Sciences]. Pedagogica, 26, 1–63. The University of Helsinki.

123

256

H. Rytko¨nen et al.

OECD. (2002). Education at a glance: OECD indicators 2002. Paris: OECD. OECD. (2009). Education at a glance: OECD indicators 2009. Paris: OECD. Parpala, A. (2010). Exploring the experiences and conceptions of good teaching in higher education. Development of a questionnaire for assessing students’ approaches to learning and experiences of the teaching-learning environment. Doctoral Dissertation. University of Helsinki, Institute of Behavioural Sciences. Studies in Educational Sciences 230. Helsinki: Helsinki University Press. Parpala, A., Lindblom-Yla¨nne, S., Komulainen, E., & Entwistle, N. (2011). Assessing students’ experiences of teaching-learning environments and approaches to learning: Validation of a questionnaire used in different countries and varying contexts. Learning Environments Research Accepted for publication. Parpala, A., Lindblom-Yla¨nne, S., Komulainen, E., Hirsto, L., & Litmanen, T. (2009). Students perceptions of the teaching-learning environment, approaches to learning and their relationship among first- and third-year students. Unpublished manuscript. Parpala, A., Lindblom-Yla¨nne, S., Komulainen, E., Litmanen, T., & Hirsto, L. (2010). Students approaches to learning and their experiences of the teaching-learning environment in different disciplines. British Journal of Educational Psychology, 80, 269–282. Prosser, M., & Trigwell, K. (1999). Understanding learning and teaching. the experience in higher education. Philadelphia: The Society for Research into Higher Education & Open University Press. Reid, W. A., Duvall, E., & Evans, P. (2007). Relationship between assessment results and approaches to learning and studying in year two medical students. Medical Education, 41, 754–762. Richardson, J. T. E. (2005). Students’ perceptions of academic quality and approaches to studying in distance education. British Educational Research Journal, 31, 7–27. Richardson, J. T. E. (2006). Investigating the relationship between variations in students‘ perceptions of their academic environment and variations in study behaviour in distance education. British Journal of Educational Psychology, 76, 867–893. Roma´n, S., Cuestas, P., & Fenollar, P. (2008). An examination of the interrelatonships between self-esteem, others’ expectations, family support, learning approaches and academic achievement. Studies in Higher Education, 33, 127–138. Ruohoniemi, M., & Lindblom-Yla¨nne, S. (2009). Students’ experiences concerning course workload and factors enhancing and impeding their learning—a useful resource for quality enhancement in teaching and curriculum planning. International Journal of Academic Development, 14, 69–81. Ruohoniemi, M., Parpala, A., Lindblom-Yla¨nne, S., & Katajavuori, N. (2010). Relationships between students’ approaches to learning, perceptions of the teaching-learning environment, and study success. A case study of third-year veterinary students. Journal of Veterinary Medical Education, 37, 282–288. Sadlo, G., & Richardson, J. T. E. (2003). Approaches to studying and perceptions of the academic environment in students following problem-based and subject-based curricula. Higher Education Research and Development, 22, 253–274. Virtanen, V., & Lindblom-Yla¨nne, S. (2009). University students’ and teachers’ conceptions of teaching and learning in the biosciences. Instructional Science, 38, 355–370. Watters, D., & Watters, J. (2007). Approaches to learning by students in the biological sciences: Implications for teaching. International Journal of Science Education, 29, 19–43. Ylijoki, O. (2000). Disciplinary cultures and the moral order of studying. A case-study of four Finnish university departments. Higher Education, 39, 339–362.

123