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DOI: 10.1039/C3RP00034F (Paper) Chem. Educ. Res. Pract., 2013, 14, 555-565

Relational analysis of college chemistry-major students' conceptions of and approaches to learning chemistry

Wei-Ting Lia, Jyh-Chong Liang*b and Chin-Chung Tsaic
aGraduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taipei, Taiwan
bGraduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei, Taiwan. E-mail: aljc@mail.ntust.edu.tw
cGraduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taipei, Taiwan

Received 28th February 2013 , Accepted 12th July 2013

First published on 29th August 2013

Abstract

The purpose of this research was to examine the relationships between conceptions of learning and approaches to learning in chemistry. Two questionnaires, conceptions of learning chemistry (COLC) and approaches to learning chemistry (ALC), were developed to identify 369 college chemistry-major students' (220 males and 149 females) conceptions of and approaches to learning chemistry. First, it was found that students in higher grade levels (juniors and seniors) tended to express more agreement with higher-level COLC, such as learning chemistry by transforming, than those in lower grades (freshmen and sophomores). The regression analyses, in general, revealed that the students who expressed lower-level COLC, such as learning chemistry by memorizing and preparing for tests, tended to use surface approaches to learning chemistry, whereas those students possessing higher-level COLC, that is, learning chemistry by transforming, tended to use deep approaches to learning chemistry. However, inconsistent with theoretical perspectives, this study revealed that learning chemistry by memorizing could positively predict a deep motive for learning chemistry, while learning chemistry by transforming was associated with a surface motive for learning chemistry. The special features of learning chemistry which might account for these relationships are discussed.

Introduction

In recent years, research interest in conceptions of learning has increased, prompting education and psychology researchers to explore students' conceptions of and approaches to learning. Previous studies have also revealed that students' conceptions of learning are related to their approaches to learning, which then influence their learning outcomes (Tsai, 2004).

In general, conceptions of learning represent a coherent knowledge scheme, learning beliefs, and perceptions of associated phenomena about learning, such as ideas about learning values, learning goals, learning activities and methods, perceived features of learning tasks, and also the tasks which are handled among teachers and students in the learning process (Vermunt and Vermetten, 2004). Moreover, Reid et al. (2005) identified the three aspects of students' conceptions of learning: intention, method, and outcome. The intention aspect means that students refer to future plans or targets (such as learning as passing the subject or course); the method aspect includes students' learning methods or specific details of what they do (such as learning as focusing on course requirements and expectations); and the outcome aspect indicates students' developed procedural skills and conceptual skills, or attitudes (such as learning as understanding the subject).

Tsai (2004) has suggested that students' conceptions of learning should be viewed as academic epistemic beliefs in school, as they are related to students' beliefs about the nature of school knowledge and learning in class.

Students' approaches to learning indicate their ways of academic learning (Biggs, 1994). That is, approaches to learning also have been defined as the ways in which students process their academic tasks and thus are assumed to influence their learning outcomes (Marton and Säljö, 1976). Students who have more sophisticated epistemic beliefs may tend to employ deeper or more meaningful learning approaches such as trying to integrate the acquired knowledge. The studies by Lin et al. (2012a) and Liang et al. (2010) have supported this view. Moreover, the interplay between students' conceptions of learning and learning approaches have also been studied. For example, Dart et al. (2000) found that students who adopted “qualitative” conceptions of learning, such as learning as seeking for in-depth understanding, tended to use deep approaches to learning. On the other hand, students who held “quantitative” conceptions of learning, such as memorizing, were likely to use surface approaches to learning.

Furthermore, Tsai's (2004) study revealed that students' learning outcomes and academic performance may be affected by numerous factors, and two of these factors, students' conceptions of learning and approaches to learning, are considered to be particularly important. Thus, in this study, we argue that for chemistry educators, it is important to investigate students' conceptions of learning and approaches to learning, particularly in chemistry. The purpose of the present research was therefore to identify Taiwanese college students' conceptions of and approaches to learning chemistry through a quantitative methodology. The relationships between the conceptions and approaches were also investigated. This study also aimed to examine the grade differences in students' conceptions of and approaches to learning chemistry.

Research on conceptions of learning

As previously mentioned, conceptions of learning refer to learners' beliefs about or interpretations of learning and learning experience in school. The first research on conceptions of learning may have been undertaken by Säljö (1979). By interviewing 90 college students about their learning experiences and their conceptualizations of learning, he distinguished five different categories of conceptions of learning: (1) increase of knowledge, (2) memorizing, (3) acquisition of facts or principles, (4) abstraction of meaning, and (5) interpretive process aimed at understanding reality. These conceptions are in a hierarchical order. Many studies subsequently followed that of Säljö to investigate conceptions of learning. For example, Marton et al. (1993) in Sweden identified a sixth category, “changing as a person,” and argued that these six categories could represent most people's conceptions of learning. In the UK, Marshall et al. (1999) studied engineering background university students' conceptions of general learning. By using a qualitative method, they concluded somewhat different categories, namely (1) learning as memorizing definitions, equations and procedures, (2) learning as applying equations and procedures, (3) learning as making sense of physical concepts and procedures, (4) learning as seeing phenomena in the world in a new way, and (5) learning as a change as a person. They also argued that the difference in these categories of conceptions is not surprising given the fact that different educational backgrounds can lead to a variation in the conceptions of learning clarified by the different groups of students.

The aforementioned studies were undertaken to investigate conceptions of learning in general. However, there have also been a number of studies particularly focused on science. For instance, Tsai (2004) in Taiwan interviewed 120 high school students and proposed a framework for the conceptions of learning science (COLS) consisting of seven categories: (1) learning science as memorizing, (2) learning science as preparing for tests, (3) learning science as calculating and practicing tutorial problems, (4) learning science as an increase of knowledge, (5) learning science as applying, (6) learning science as understanding, and (7) learning science as seeing in a new way. Tsai's (2004) and a number of following studies (Tsai et al., 2011; Lin et al., 2012b) also suggested that these conceptions could be viewed as having a hierarchical order, from lower-level to higher-level where the first three, “Memorizing,” “Testing,” and “Calculating and Practicing,” can be viewed as the lower-level conceptions, while the others are the higher-level conceptions, including “Increase of knowledge,” “Applying,” “Understanding,” and “Seeing in a new way.”

According to Chiou et al. (2012), to gain a deeper understanding of how students learn in various scientific domains, future research should move forward to explore conceptions of learning in more specific domains. Chemistry is one of the important domains in science. Hence, in this study, we intended to investigate college students' conceptions of learning chemistry (COLC).

Research on approaches to learning

Approaches to learning have been defined as the ways in which learners process their academic tasks and influence their learning outcomes. In general, two modes of approaches to learning are predominant in the educational literature: a deep approach and a surface approach (e.g., Marton and Säljö, 1997; Trigwell et al., 1999; Chin and Brown, 2000; Cano, 2005). Generally, students who adopt a deep approach to learning aim at achieving a better personal understanding of new ideas and information, and are more likely to value the pedagogical intentions underlying the learning task. In contrast, students using a surface approach usually focus on the completion of their most obvious task requirements, and often distort the intent of the task in order to achieve their extrinsic goals (Bliuc et al., 2011).

According to Yang and Tsai (2010), approaches to learning have been extensively investigated in various domains and for different academic tasks. For example, approaches to problem solving in engineering (Marshall et al., 1999), approaches to learning pharmacy (Smith et al., 2010), approaches to learning through discussion (Ellis et al., 2008), and approaches to learning mathematics (Cano and Berben, 2009) have all been studied. Specifically in science, research has been undertaken on approaches to learning biology by Chiou et al. (2012).

The essence of the deep and surface approaches to learning varies widely across different domains (Ramsden, 1992). For example, in mathematics, the surface approaches to learning may refer to the processes of repeatedly calculating and following an algorithm, while in biology, they may refer to the processes of matching the name of a specific species with its distinct features. Although some researchers in the field of science education have started to tackle this issue (e.g., Lee et al., 2008; Liang and Tsai, 2010), most of them have concentrated only on students' approaches to learning in a broad content domain, such as science. Given the nature of different domains in the field of science, such as physics, chemistry and biology (Tsai, 2006), there would be merit in exploring students' approaches to learning in the different scientific domains. Based on this premise, this research planned to focus specifically on students' approaches to learning chemistry (ALC).

Grade level development in learning

Research has also extended to the exploration of the role of grade differences in students' conceptions of and approaches to learning. Some studies have examined whether they differ across grade levels. For example, Sadler-Smith (1996) found that more mature students, that is, older students, indicated a deeper approach than younger students. Similarly, Zeegers' (2001) finding supported that more experienced students tended to use deep strategies to process their learning tasks in science. However, an opposite result that students at higher grade levels (juniors and seniors) are more likely to use surface approaches rather than deep approaches has also been revealed by Kember (2000). The present research also attempted to investigate the role of grade level in students' conceptions of and approaches to learning chemistry. In this study, undergraduate juniors and seniors were viewed as higher grade, and freshmen and sophomores were regarded as lower grade, the same categorization adopted by Lin et al. (2012a).

Relationships between conceptions of learning and approaches to learning

Since the 1980s, the research interest in relationships between students' conceptions of learning and their approaches has been shared by many educational researchers. In van Rossum and Schenk's (1984) study, they first used an open-ended questionnaire to investigate 69 undergraduate psychology students' conceptions of learning; first they asked them to read an article, and then they asked them to report on how they approached the reading task. They indicated that the students' lower-level conceptions of learning were closely linked to their surface approaches to learning. They found that students' learning outcomes of relatively high quality must be especially associated with their deep approaches to learning. There are similar findings in other research (e.g., Dart et al., 2000; Edmunds and Richardson, 2009) that students holding more sophisticated, higher-level, conceptions of learning such as learning as applying, understanding and seeing in a new way, tend to employ deep approaches such as deep motive and deep strategies to learn.

Kember et al. (2004) claimed that students might perform differently in each learning domain. Students may use deep approaches to learning science but use surface approaches to learning in other domains. Tsai (2004) also suggested that conceptions of learning are domain specific.

Therefore, it has become an important issue for science educators to investigate the interplay between students' conceptions of learning science and their approaches to learning science (e.g., Chin and Brown, 2000; Tsai, 2004; Tsai and Kuo, 2008). However, few studies have addressed this interplay, particularly in the specific area of learning chemistry.

This research, by using a stepwise regression model, was conducted to explore the relationships between college chemistry-major students' conceptions of learning chemistry and their approaches to learning chemistry.

Research questions

While some existing questionnaires have been established to investigate students' conceptions of learning and approaches to learning (e.g., Biggs et al., 2001; Lee et al., 2008; Chiou et al., 2012; Chiu, 2012), there have been almost no specifically designed questionnaires to measure college chemistry-major students' COLC and ALC. Therefore, in this research, the first step was to validate two instruments modified from previous studies (Lee et al., 2008; Liang et al., 2010) to assess college chemistry-major students' COLC and ALC. This research then tested whether grade difference existed in the students' COLC and in their ALC. Through the correlation analysis and stepwise regression method, this study then examined the relationship between the students' COLC and ALC.

First, using exploratory factor analysis to examine chemistry-major students' COLC and ALC questionnaire items, this study was undertaken to investigate the following questions:

(1) Do the chemistry-major students display grade differences in their COLC and ALC?

(2) Are the chemistry-major students' COLC related to their ALC?

(3) Can the chemistry-major students' COLC predict their ALC by stepwise regression models?

Method

Participants

The participants in this research included 369 college students in Taiwan, of which 220 were males and 149 were females. They were from 6 different universities in Taiwan.

All of the students were chemistry-related majors and had taken a series of chemistry-related courses before participating in this study. Their ages ranged from 18 to 25, with an average age of 20.2; 152 were lower grade (freshman and sophomore) students (41.2%), and 217 were higher grade (junior and senior) students (58.8%).

Measure

To investigate the students' COLC, the researchers in this study developed a survey which was a modification of the questionnaire used by Lee et al. (2008) and Liang and Tsai (2010).

To develop the questionnaire about conceptions of learning chemistry, two experts in science education and chemistry examined the content of all the questionnaire items, providing expert validity for the survey. Following the procedure above, the questionnaire was slightly modified. The six factors in the final COLC are described below, with a sample item provided:

1. Memorizing: learning chemistry through memorizing definitions, formulae, laws, regulations and special terms.

˙ Item: when learning chemistry, I usually memorize the special terms found in the chemistry textbook that can help me to answer the teacher's questions.

2. Testing: learning chemistry is conceptualized to get higher scores to pass the chemistry exam.

˙ Item: if there were no exams, I would not learn chemistry.

3. Calculating and practising: learning chemistry means calculating, practising tutorials and manipulation of formulae and numbers.

˙ Item: to learn chemistry well, I need to do a series of calculations and practise answering questions.

4. Increasing one's knowledge: learning chemistry is viewed as increasing chemistry knowledge for the learner.

˙ Item: learning chemistry means acquiring knowledge that I did not know before.

5. Application: the application of receiving the chemistry knowledge is considered the main feature of learning chemistry.

˙ Item: learning chemistry is to acquire some knowledge and skills to solve real-life problems.

6. Understanding and seeing in a new way: learning chemistry is conceptualized as achieving true understanding and getting a new perspective.

˙ Item: learning chemistry means understanding more natural phenomena and knowledge.

˙ Item: learning chemistry means using a new way to view chemical phenomena or topics related to chemistry.

These factors represent students' COLC in a hierarchical framework. The first three factors are lower-level while the others are higher-level (Liang and Tsai, 2010).

The second questionnaire, ALC, was based on another questionnaire named “Approaches to Learning Science” (ALS) with four factors developed by Lee et al. (2008).

It was designed for Taiwanese high school students, and Liang et al. (2010) modified it to survey university science-major students, a similar target sample to this study. Again, two experts in science education and chemistry revised the content of all the questionnaire items. Based on the above procedure, the questionnaire was modified to fit the needs of this study. These four factors in the final ALC are described below:

1. Deep motive: this factor concerns whether the student has more intrinsic motive (e.g., feeling happy and satisfied) for learning chemistry.

˙ Item: I always feel interested in learning chemistry.

2. Deep strategy: this factor refers to the student using deep strategies (e.g., exploring the relationships between science concepts or other ideas) to learn chemistry.

˙ Item: when I learn new contexts about chemistry, I am trying to explore the relationships with other contexts I learned before.

3. Surface motive: this factor concerns the student's extrinsic motive (e.g., fear of failure or someone's expectations) for learning chemistry.

˙ Item: although I prepared well for the chemistry exam, I still fear I could not get a great score.

4. Surface strategy: this factor refers to the degree to which the student uses surface strategies (e.g., narrow target, rote learning) to learn chemistry.

˙ Item: I find the best way to get high scores in chemistry exams is to remember the answers to likely questions.

All of the items in the two questionnaires were presented with a 1–7 Likert scale, from “strongly disagree” to “strongly agree.”

Data collection and analysis

The questionnaire was distributed to the chemistry-major students with the permission of the 6 universities in Taiwan. The students in this study volunteered to respond to the aforementioned two questionnaires. These students answered the two questionnaires at the same time anonymously.

Utilizing the 369 students' responses to the final-version questionnaires, further analyses were conducted to explore the relationships between their conceptions of and approaches to learning chemistry.

This research first used exploratory factor analysis to examine the factor structure of the COLC and the ALC questionnaires. The correlations within and between the COLC and ALC factors were analyzed. Then, to examine the lower grade and higher grade students' differences in the COLC and ALC, t-tests were conducted. Finally, the regression analyses between the factors of the COLC and those of the ALC were analyzed. The COLC factors were considered as predictor variables, whereas the ALC factors were processed as outcome variables.

Results and discussion

Before carrying out the factor analysis of the COLC and the ALC questionnaires, KMO statistics were undertaken to determine the suitability of the factor analyses. All the variables in the overall KMO value must be greater than 0.50, and a value close to 1 means that the correlation patterns are quite compact, and so can give reliable factors as a result of the factor analysis (Field, 2000). In addition, a non-normal distribution can be determined by the skewness and kurtosis values of each questionnaire item. Kline (1998) suggested that skewness absolute values of less than 3 and kurtosis values less than 10 should be regarded as normal states. Furthermore, Noar (2003) also suggested that the skewness absolute values should not be greater than 1 and the kurtosis absolute values should not be greater than 2.

As shown in Table 1, the KMO values are 0.95 and 0.92 for the COLC and the ALC questionnaire respectively, and the skewness and kurtosis values for each item of both questionnaires are also shown to fall within acceptable ranges, indicating a normal distribution. This means that these two questionnaire items are quite suitable for further factor analysis.

Table 1 Results of KMO statistics, skewness and kurtosis values for COLC and ALC
  COLC ALC
KMO statistics 0.95 0.92
Skewness value for each item −0.23 to 0.17 −0.16 to 0.20
Kurtosis value for each item −1.23 to −0.44 −1.17 to −0.29

Factor analysis of COLC

This study utilized exploratory factor analysis to validate the factors of the COLC survey. According to the previous study (Lee et al., 2008), an oblimin rotation was performed because the factors of the COLS appeared to be correlated. To validate the COLC questionnaire, this research adopted the principal component analysis and the oblimin rotation method to clarify the factors of the items. The factor loading of each item weighed greater than 0.4 on the relevant factor and less than 0.4 on the non-relevant factors in the COLC. The results of applying the exploratory factor analysis method revealed four factors with a total of 26 items of the COLC (the full list of items is given in Appendix 1) and were grouped into the following four factors: “Memorizing,” “Testing,” “Calculating and Practising,” and one mixed factor for higher-level conceptions. That is, the higher-level conceptions of learning chemistry were inseparable, being merged into a single factor, “Transforming” (shown in Table 2). The meaning of “Transforming” indicates a relatively considerable change in somebody or something; for this study, it is a reflection of learning as a process of active, personal construction of meaning (Brownlee et al., 2003). The Cronbach's alpha coefficients for the four factors were 0.87, 0.84, 0.83, and 0.97, and the overall alpha was 0.91, suggesting that these factors have high reliabilities in assessing students' conceptions of learning chemistry. In addition, the total variance explained in the COLC is 72.02%. Table 2 also shows the factor means and the standard deviations of the COLC. As shown in Table 2, the students scored highly on the “Testing” factor (an average of 4.23 per item), and the “Calculating and practising” factor (an average of 4.23 per item). Their scores on the “Transforming” factor (an average of 3.78 per item) were relatively lower than the other three factors on the COLC.
Table 2 Rotated factor loadings, Cronbach's alpha values, means and standard deviations for the six factors of the COLC questionnaire
  Factor 1: memorizing Factor 2: testing Factor 3: CP Factor 4: transforming
Note: Overall alpha: 0.91; and total variance explained: 72.02%.
Factor 1: memorizing, α = 0.87, mean = 4.10, S.D. = 1.21
Memorizing 1 0.78      
Memorizing 2 0.85      
Memorizing 3 0.84      
Memorizing 4 0.79      
Memorizing 5 0.68      
 
Factor 2: testing, α = 0.84, mean = 4.23, S.D. = 1.20
Testing 1   0.74    
Testing 2   0.69    
Testing 3   0.59    
Testing 4   0.84    
Testing 5   0.79    
 
Factor 3: calculating and practicing (CP), α = 0.83, mean = 4.23, S.D. = 1.17
CP 1     0.71  
CP 2     0.83  
CP 3     0.80  
CP 4     0.82  
 
Factor 4: transforming, α = 0.97, mean = 3.78, S.D. = 1.47
Transforming 1       0.84
Transforming 2       0.87
Transforming 3       0.84
Transforming 4       0.78
Transforming 5       0.80
Transforming 6       0.91
Transforming 7       0.89
Transforming 8       0.92
Transforming 9       0.89
Transforming 10       0.88
Transforming 11       0.90
Transforming 12       0.86

Factor analysis of ALC

Table 3 shows the exploratory factor analysis results for the ALC questionnaire. According to the previous study (Lee et al., 2008), an oblimin rotation was performed because the factors of the ALS appeared to be correlated. This study also utilized the principal component analysis and the oblimin rotation method to determine the factors of the ALC questionnaire items. The ALC used a factor loading greater than 0.4 for retaining the items. Thus, 18 items were kept in the final version of the ALC (the full list of items is in Appendix 2). The Cronbach's alpha coefficients for the four factors were 0.90, 0.88, 0.92 and 0.74, which indicates a satisfactory level of internal consistency. In addition, the total variance explained in the ALC is 70.62%. Table 3 also shows the factor means and the standard deviations of the ALC. As shown in Table 3, the “Surface Strategy” factor was scored highly by students (an average of 4.15 per item).
Table 3 Rotated factor loadings, Cronbach's alpha values, means and standard deviations for the four factors of the ALC questionnaire
  Factor 1: deep motive Factor 2: deep strategy Factor 3: surface motive Factor 4: surface strategy
Note: Overall alpha: 0.90; and total variance explained: 70.62%.
Factor 1: deep motive, α = 0.90, mean = 3.79, S.D. = 1.27
Deep motive 1 0.81      
Deep motive 2 0.88      
Deep motive 3 0.86      
Deep motive 4 0.87      
 
Factor 2: deep strategy, α = 0.88, mean = 3.81, S.D. = 1.28
Deep strategy 1   0.79    
Deep strategy 2   0.84    
Deep strategy 3   0.76    
Deep strategy 4   0.67    
Deep Strategy 5   0.71    
 
Factor 3: surface motive, α = 0.92, mean = 3.87, S.D. = 1.49
Surface motive 1     0.78  
Surface motive 2     0.79  
Surface motive 3     0.82  
Surface motive 4     0.93  
Surface motive 5     0.88  
 
Factor 4: surface strategy, α = 0.74, mean = 4.15, S.D. = 1.10
Surface strategy 1       0.73
Surface strategy 2       0.84
Surface strategy 3       0.81
Surface strategy 4       0.59

The correlation between COLC and ALC

In order to explore the relationships between students' COLC and their ALC, Pearson's correlation coefficients were calculated between the responses of the COLC factors and those of the ALC factors. Table 4 presents the results of the correlation analysis between COLC and ALC.
Table 4 The correlations among the factors between the COLC and ALC
  Memorizing Testing CP Transforming
***p < 0.001; **p < 0.01. Notes: CP: calculating and practicing.
Deep motive 0.26*** −0.30*** 0.09 0.58***
Deep strategy 0.23*** −0.24*** 0.05 0.80***
Surface motive 0.37*** −0.12 0.13 0.78***
Surface strategy 0.32*** 0.44*** 0.31*** 0.08

There are statistically significant positive correlations between “Memorizing,” and “Transforming” of the COLC and two factors of the ALC, “Deep Motive” (r = 0.26 and 0.58, p < 0.001), and “Deep Strategy” (r = 0.23 and 0.80, p < 0.001), and negative correlation was identified between “Testing” and two deep approach factors (r = −0.30 and −0.24, p < 0.001 for “Deep Motive” and “Deep Strategy” respectively).

This positive relationship between the lower-level COLC, “Memorizing” and the deep approaches deserves more discussion, as it contradicts a common result regarding the negative relationship between lower-level conceptions of learning and deep strategies of learning (e.g., Zeegers, 2001; Lee et al., 2008; Chiou et al., 2012). Chemical representations, such as formulae, symbols, equations, and structures, are widely seen in professional journals and routinely used to describe and explain chemical reactions and phenomena in textbooks (Black and Deci, 2000). Because of this feature of chemistry, no matter how much experience students accumulate or how they meaningfully process the chemistry learning tasks, they still have to use a certain degree of memorization to learn chemistry.

Moreover, positive associations were found between “Memorizing,” and “Transforming” of the COLC and the factor of the ALC, “Surface Motive” (r = 0.37 and 0.78, p < 0.001). The correlation between “Memorizing,” “Testing,” and “Calculating and Practicing,” of the COLC and the ALC factor, “Surface Strategy,” are statistically positive, with coefficients ranging from 0.31 to 0.44 (p < 0.001).

It is interesting that both “Memorizing” and “Transforming” have positive relationships with “Surface Motive.” Most Western researchers seem to believe that many South-East Asian learners display a special feature of learning, that is, memorizing with understanding, and this may be rooted in an orthodox philosophy, namely the Confucian heritage (Au and Entwistle, 1999). Besides, Hong and Peng (2008) have revealed that Chinese students will work hard for good scores on exams, no matter what motivational and metacognitive strategies they are using. Furthermore, Tsai (2004) has asserted that students' learning is associated with the exam-driven sociocultural environment in Taiwan, where standard test scores and nationwide examinations are highly emphasized by parents and teachers across different levels of education. When facing these exams, they need to rely on the memorization of some chemistry knowledge, but at the same time, a good transformation of instructional materials into meaningful representations is also necessary. That is, even though students simply want to get good grades in chemistry exams, they may still possess higher-level conceptions of learning chemistry, such as applying chemistry knowledge to problem-solving. This may explain why students' “Memorizing” and “Transforming” have positive correlations with the “Surface Motive” approach to learning chemistry at the same time.

Grade differences in COLC and ALC

This study also found some grade differences in undergraduate students' COLC and ALC by t-test. These differences imply learning progress from novice to expert.

Table 5 shows the lower grade and the higher grade students' mean scores for the factors in the COLC and the ALC. The t-test results indicated that higher grade students had a higher “Transforming” score (t = −3.43, p < 0.01) than the lower grade students in COLC. After examining the grade differences by t-test, effect sizes were also calculated to examine the significance of factor-score differences between the lower and higher grade students. The effect size for t-test is often described as a Cohen's d value (Cohen, 1998), where d = 0.2 is considered as a small effect size, while d = 0.5 is deemed as a medium effect size, and d = 0.8 is a large effect size. The results presented in Table 5 show that the differences between the two groups (lower and higher grade students) were not only statistically significant, but the effect of the size of the ‘Transforming’ score (d = −0.36) shows that the difference was, to some extent, still substantial in this study, at least a small to medium effect size. Besides the “Transformation” factor, other COLC factors did not show any statistical difference between the two different grades (“Memorizing,” t = −0.40, p > 0.05, d = −0.04; “Testing,” t = 0.30, p > 0.05, d = 0.03; “Calculating and practicing,” t = −0.76, p > 0.05, d = −0.08).

Table 5 The scores of the factors of COLC and ALC for the lower and higher grade students
  Memorizing Testing CP Transforming DM DS SM SS
***p < 0.001; **p < 0.01. CP: calculating and practicing, DM: deep motive, DS: deep strategy, SM: surface motive, SS: suface strategy.
Lower grade (n = 152) 4.11 (1.08) 4.26 (1.25) 4.17 (1.07) 3.49 (1.43) 3.67 (1.22) 3.58 (1.26) 3.51 (1.49) 4.14 (1.04)
Higher grade (n = 217) 4.16 (1.22) 4.22 (1.18) 4.26 (1.16) 4.00 (1.31) 3.87 (1.29) 3.98 (1.27) 4.12 (1.45) 4.16 (1.14)
t-Test −0.40 0.30 −0.76 −3.43** −1.53 −2.96** −3.91*** −0.23
Cohen's d −0.04 0.03 −0.08 −0.36 −0.16 −0.31 −0.41 −0.02

This result reveals that the higher grade students tend to have more high-level COLC than the lower grade students. The higher grade students spent more time on learning the chemistry knowledge and accumulated more experience in chemistry than the lower grade students. It is plausible that the higher grade students would tend to apply chemistry knowledge in daily life, and make more radical changes when conceptualizing learning chemistry.

Moreover, higher grade students also had significantly higher scores for two ALC factors, “Deep Strategy” (t = −2.96, p < 0.01, d = −0.31) and “Surface Motive” (t = −3.91, p < 0.001, d = −0.41) with small to medium effect sizes, than those in the lower grades. There was no significant difference shown between the lower grade and the higher grade students' scores on the “Deep Motive” (t = −1.53, p > 0.05, d = −0.16) and the “Surface strategy” (t = −0.23, p > 0.05, d = −0.02) factors.

This result is consistent with the finding confirmed in Zeegers' (2001) study that more experienced students tend to use deep strategies to process their learning tasks in science. Nevertheless, according to Tsai (1998), there are more job opportunities for students with a major in science than for other majors in Taiwan. Therefore, students in the later stage of undergraduate study may be gradually led to the motive of learning science as a means of obtaining a better economic situation in life. This may go some way to explaining the higher-grade students' surface motive for learning chemistry.

Stepwise regression analysis for predicting students' ALC

Previous studies (e.g., Marton and Säljö, 2005) have suggested that students' conceptions of learning may be related to or guide their approaches to learning. According to a similar theoretical perspective, this research viewed students' COLC as predictors to explain the variations in ALC when conducting step-wise regression analyses, which was applied to explain the students' ALC with their COLC.

Table 6 shows the regression analysis results. The COLC factors, “Transforming,” “Testing,” and “Memorizing,” could make significant predictions for the students' ALC factor, “Deep Motive.” “Transforming” and “Memorizing” made positive predictions (t = 9.88 and t = 3.93, p < 0.001), while “Testing” could negatively explain “Deep Motive” (t = −5.11, p < 0.001). “Transforming” (t = 23.88, p < 0.001) was the only predictor to explain the “Deep Strategy” of the ALC, and was a powerful predicting factor (R2 = 0.64).

Table 6 Stepwise regression model of predicting students' learning approaches
Approaches B Standard Error β T R2
**p < 0.01; ***p < 0.001. Notes: DM: deep motive; DS: deep strategy; SM: surface motive; SS: surface strategy.
DM
  Transforming 0.41 0.04 0.48 9.88*** 0.42
  Testing −0.27 0.05 −0.26 −5.11***  
  Memorizing 0.20 0.05 0.20 3.93***  
  Constant 2.54 0.27   9.26***  
             
DS
  Transforming 0.70 0.03 0.80 23.88*** 0.64
  Constant 1.15 0.12   9.89***  
             
SM
  Transforming 0.74 0.04 0.73 20.59*** 0.62
  Memorizing 0.20 0.04 0.17 4.65***  
  Constant 0.21 0.20   1.09  
             
SS
  Testing 0.34 0.05 0.37 7.13*** 0.22
  Memorizing 0.16 0.05 0.18 3.43**  
  Constant 2.02 0.23   8.91***  

That is, for the deep approaches to learning chemistry, the COLC factor, “Transforming,” likely plays a dominant role. This result suggests that the higher-level conceptions of learning chemistry constitute the foundation for all the students' deep approaches to learning chemistry.

Moreover, the lower-level conception of learning chemistry, “Testing,” is a negative predictor of “Deep Motive” to learn chemistry, a result similar to those concluded by the aforementioned research (e.g., Lee et al., 2008; Edmunds and Richardson, 2009; Chiou et al., 2012). However, it was interesting to find that “Memorizing” could positively predict “Deep Motive” to learn chemistry; that is, because of the features of chemistry, such as numerous formulae, equations or symbols for explaining chemical reactions, memorizing always plays an important role, no matter which motivation students hold in learning chemistry.

For the ALC factor “Surface Motive,” both COLC factors “Transforming” and “Memorizing” were significantly positive predictors (t = 20.59 and t = 4.65, p < 0.001) with high explained variation (R2 = 0.62). Finally, two COLC factors, “Testing” and “Memorizing,” could positively explain students' responses for the “Surface Strategy” factor (t = 7.13, p < 0.001 and t = 3.43, p < 0.01).

According to the results, for the surface approaches to learning chemistry, first, “Memorizing” is a main factor to predict surface approaches. Second, another COLC, “Testing,” was also a positive predictor of “Surface Strategy” of learning chemistry. These conclusions are also in line with the aforementioned past research on learning science (e.g., Lee et al., 2008; Tsai and Kuo, 2008; Chiou et al., 2012).

However, it was surprising to find that the higher-level conceptions of learning chemistry, “Transforming,” could be a positive predictor of “Surface Motive”; that is, as the students who have the higher-level COLC tend to adopt the surface motive to learn chemistry, they might study chemistry just to fulfill a course requirement and want to have great performance so as to get ideal jobs in the future.

To sum up, the regression results suggest that these college chemistry-major students' COLC play an important role in their ALC. In general, students who held lower-level COLC, such as “Memorizing” and “Testing,” tended to use surface approaches to learn chemistry; however, their surface motive can also be positively predicted by the higher-level COLC, “Transforming.”

At the same time, the students who have the higher-level conceptions of learning chemistry tend to adopt deep strategies and deep motives for learning chemistry, and they might view learning chemistry as arousing their intrinsic interest. It should be noted that “Transforming” can explain a large extent of the variation in the deep approach factors, especially for “Deep Strategy.” To possess higher-level COLC is quite important for the potential utilization of deep strategies. But surprisingly, “Memorizing” is also a positive predictor of possessing deep motive to learn chemistry.

Implications and conclusions

The two questionnaires developed in this study, the COLC and the ALC, have been shown to be able to adequately investigate students' conceptions of and approaches to learning chemistry, respectively. The COLC was modified from the COLS questionnaires. With respect to the COLC, the four extracted factors are different from those obtained by Lee et al. (2008) and Liang and Tsai (2010). As shown in Table 2, the first three factors, lower-level conceptions of learning chemistry, “Memorizing,” “Testing,” and “Calculating and Practising” are the same as COLS, but the other three factors, the higher-level conceptions of learning chemistry, “Increasing one's knowledge,” “Application,” and “Understanding and seeing a new way” originally, were inseparable in COLC. That is, there is only one single factor accounting for the higher-level COLC. All of the higher-level conceptions of learning chemistry were merged into a single factor. “Transforming” means a relatively considerable change in somebody or something. That is, when learning chemistry is conceptualized as a process of transforming, it means that an active transformation of external information into meaningful, understandable, and applicable knowledge takes place.

According to the curriculum design for the department of chemistry in Taiwan, the core ability for students is not only to understand the theoretical chemistry knowledge but also to apply the theories to the experimental operations or more real-life problems. Undergraduate chemistry majors may have to increase their knowledge and then transform the knowledge in practical applications or in explaining chemistry phenomena. That is, for chemistry-major college students, their higher-level conceptions of learning chemistry may occur at the same time.

Moreover, as shown in Table 2, while the students scored higher on the first three factors, “Memorizing,” “Testing,” and “Calculating and practising,” they scored lowest on the final factor, “Transforming.” This result reveals that the students prefer to view learning chemistry from a lower-level rather than a higher-level perspective. However, the research by Chiou et al. (2012) has indicated that undergraduate biology-major students prefer to view learning biology from a higher-level rather than a lower-level perspective. Each domain in science has different features, which might influence students' conceptions of learning and approaches to learning. Undergraduate chemistry educators in Taiwan may modify the curricula to help students focus more on the transformation of learning chemistry such as learning for in-depth understanding or new perspectives of thinking, rather than simply focusing on preparing for tests or calculations.

In terms of the practical implications, the two questionnaires developed in this study have high reliability. Chemistry educators could therefore use these questionnaires to explore undergraduate students' conceptions of and approaches to learning chemistry. Also, more attempts should be made to foster students' conceptions of learning chemistry. For example, chemistry teachers may highlight the idea that there are other things more important than getting good grades in examinations, such as expanding their knowledge and experience, and applying chemistry knowledge to solve problems in daily life.

This study used quantitative measures to explore college chemistry-major students' conceptions of learning chemistry and approaches to learning chemistry. More in-depth qualitative research is recommended to further explore these issues.

Appendix 1: the questionnaire items in the COLC

Memorizing

1. When learning chemistry, I usually memorize a lot of formulae, definitions, and laws in textbooks.

2. When learning chemistry, I usually memorize the important concepts found in the chemistry textbook.

3. When learning chemistry, I usually memorize the proper nouns found in the chemistry textbook as that can help me to answer the teacher's questions.

4. When learning chemistry, I usually remember what the teacher talked about in chemistry class.

5. When learning chemistry, I usually memorize chemistry symbols, chemistry concepts, and facts.

Testing

1. If there were no tests, I would not learn chemistry.

2. There are no benefits to learning chemistry other than getting great scores on the exams. I can live well without knowing the chemical facts.

3. The major purpose of learning chemistry is to get more familiar with all the questions which may appear in the exams.

4. Because of the exams, I would learn chemistry.

5. There is a close relationship between learning chemistry and taking exams.

Calculating and practising

1. Learning chemistry means constantly practising doing calculations and answering questions.

2. When I can do calculations and answer questions well it means I have great performance in learning chemistry.

3. Learning chemistry means knowing how to use the correct formulae when answering questions.

4. To learn chemistry well, I need to do a series of calculations, and practise answering questions.

Transforming

1. Learning chemistry means acquiring chemical knowledge.

2. Learning chemistry means acquiring knowledge that I did not know before.

3. The purpose of learning chemistry is to know the facts about nature.

4. Learning chemistry is to acquire some knowledge and skills to solve real-life problems.

5. Learning chemistry means how to apply knowledge and skills I knew in problem-solving.

6. Learning chemistry is for solving the problems and phenomena which were unknown before.

7. Learning chemistry is to understand the problems and chemical phenomena which could not be solved before.

8. Learning chemistry means expanding my knowledge and experience.

9. Learning chemistry means understanding more natural phenomena and knowledge.

10. Learning chemistry means changing my way of viewing chemical phenomena and topics related to chemistry.

11. I can learn more ways of thinking about chemical phenomena or topics related to chemistry by learning chemistry.

12. Learning chemistry is to use more reasonable ways to explain events in daily life.

Appendix 2: the questionnaire items in the ALC

Deep motive

1. I find that at times studying chemistry makes me feel really happy and satisfied.

2. I always feel interested in learning chemistry.

3. I work hard at studying chemistry because I am interested in it.

4. I always look forward to going to chemistry class.

Deep strategy

1. I like to create a theory to combine the separate contents when I learn chemistry.

2. I will try to find out the relationships with the contents of chemistry when I learn chemistry.

3. When I learn new contexts about chemistry, I try to explore the relationships with other contexts I learned before.

4. I try to understand the meaning of the contents I have read in chemistry textbooks.

5. I try to understand the meaning of the contents when I learn chemistry.

Surface motive

1. Although I prepared well for the chemistry exam, I still fear that I could not get a great score.

2. I will worry that I couldn't fulfill the teacher's expectations in the chemistry class.

3. No matter whether I like it or not, I know that getting good achievement in chemistry could help me to get an ideal job in the future.

4. I want to get good achievement in chemistry so that I can get a better job in the future.

5. I would like to get good achievement in chemistry to make my family and teacher happy.

Surface strategy

1. As long as I feel I am preparing well enough to pass the exam, I reduce the time I spend on studying chemistry. There are many more interesting things to do with my time.

2. I find the best way to get high scores in chemistry exams is to remember the answers to likely questions.

3. I find that memorizing the most important contents of chemistry makes me get high scores in chemistry exams instead of understanding it.

4. I will notice and memorize the parts that will appear in the exams when I learn chemistry.

Acknowledgements

Funding of this research work was supported by the National Science Council, Taiwan, under grant numbers NSC 100-2511-S-011-004-MY3 and NSC 101-2628-S-011-001-MY3.

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