Teaching and Learning Forum 99 [ Contents ]

The effects of learning tendencies, learning approaches and learning habits on academic achievement: Evidence from Taiwanese college accounting students

Tungshan F. Chou, Dennis W. Taylor, Hiewu Su
School of Accounting
Curtin University of Technology
This study examines student study behaviours via three major frameworks: Kolb's (1985) Experiential Learning model, Entwistle and Tate's (1994) Approach to Learning model, and Dunn and Dunn's (1993) Learning Style Model. Four learning tendencies (concrete learning, reflective observation, abstract conceptualisation, and active experimentation) were based on Kolb's model. Seven approaches to learning constructs were taken from Entwistle and Tate's Revised Approach to Learning Inventory: deep processing, shallow processing, operation learning, organised methods, strategic approach, motivation, and fear of failure. Learning habit variables were based on the four study factors used by Dunn and Dunn in their analyses of students learning styles: environmental factor, emotional factor, sociological factor and physiological factor. A self-reported survey which incorporates all these elements was administered to a total of 844 accounting major students across six colleges and universities in Taiwan.

Using self-reported class rank score as the academic achievement indicator (dependent variable) and the three sets of study behaviour variables as independent variables, a series of regression analyses were conducted. Regression models were first constructed separately for tendency, approach, and habit sets. For learning tendency variables, all but concrete experience was significantly positively related to achievement. For learning approach variables, deep/shallow processing, organised methods, motivation, and fear of failure were found to be significant. For learning habit variables, studying alone, comfortable study room, pleasant study mood were found to have favourable effect on achievement, whereas listening to music while studying tend to have a negative impact. Finally, all study behaviour variables were entered and a stepwise regression analysis was performed to assess the importance ranking of these variables in relation to their impact on achievement. The results of such analyses and their implications for future accounting education practices were discussed.

Introduction and motivation

Student study behaviours have been investigated extensively in general education research. Such behaviours have been conceptualised and measured in various ways. However, most of the measures of study behaviours found in the general education literature are captured by one of the following three constructs: learning tendencies, learning approaches, or learning habits. In this study the learning tendency construct is taken from studies about learning styles such as those proposed by Kolb (1985) and Honey & Mumford (1992). It describes several dimensions of cognitive qualities relating to the strengths/weaknesses an individual has in learning new tasks, and is generally perceived as innate and spontaneous. The analyses of these learning tendencies have been picked up in a large way by researchers in accounting education (eg, Baker et al,1986. 1987; Baldwin & Reckers, 1984; Hutchinson & Gul, 1997; Desai & Taylor, 1998; Wolk & Nikolai, 1997).

In contrast to the widely cited learning tendency construct in accounting education literature, not so much research has been conducted in the areas of learning approaches and learning habits of accounting students. The learning approaches view of study behaviours is that students display differential propensities depending on how they perceive the surrounding learning environment. Learning approaches are also known in the general education literature as learning orientations, preferences, orchestrations, and processes (eg, Biggs, 1993; Entwistle and Waterson, 1988; Marton and Saljo, 1976a,1976b; Meyer et al., 1990; Richardson, 1990, 1994; Schmeck et al., 1977). Such a construct is usually task-oriented. An individual may display quite different learning approaches in differently perceived learning environments. In the paucity of research on learning approaches in accounting education, Lukas (1996) called on accounting educators for more research into this area.

Learning habits, on the other hand, is a construct which describes how an individual's learning habitually takes place environmentally, emotionally, sociologically, and physiologically. In the general education literature a substantial amount of research has focused on the ways teaching methods can cater for students' specific learning habits in order to produce favourable learning outcomes across a wide rage of disciplines (see Dunn et al, 1995 for a review). However, little has been done with the learning habits of accounting students.

In this paper, we endeavour to bring these three learning constructs together in one study for the purpose of obtaining a more integrated understanding about the study behaviours of college accounting students. Specifically, we seek to explore the association, if any, between these three learning constructs and their effects on the academic achievement of accounting students. Although this study was conducted in a Taiwanese higher education setting, our instrument should be readily applicable amongst accounting students in other countries. The following two specific research questions are addressed in this study:

Question One:What is the profile of Taiwanese college accounting students' study behaviours in terms of their learning tendencies, learning approaches, and learning habits?
Question Two:Are there statistically significant and practically important relationships between academic performance and the variables within the three constructs of study behaviours?

Instrumentation and research method

The dimensions and items adopted in this study to measure learning tendencies, learning approaches, and learning habits are given in Figure 1, which was used as our composite study behaviours instrument. Our instrument was first compiled in English jointly by the authors and was then translated into Chinese by two of the authors. The learning tendency, learning approach, and learning habit items were presented in the same format as three parts in the same questionnaire, and the items within each part were in random order. We adopted a six-point Likert Scale format for presenting the items. Respondents were asked to rate each item according to how it related to their personal preferences in learning based on the following scale: 1=Rarely, 2=infrequently, 3= less often, 4=more often, 5=frequently, and 6=almost always their preference for learning. The score of each subscale was obtained as the average score of its corresponding items. The average score was taken over the sum score for the sake of easy reference to our six-point Likert scale. We also included three sets of check items in the questionnaire, two of which were not used in the computation of scale scores, but served to detect unusual patterns of responses. These three sets of items were worded to elicit a reverse direction response. For example, two questions included are "When I learn, I seek to discover new dimensions of my learning tasks" and "When I learn, I limit myself to only what's been presented to me." If the absolute value of the difference within each pair was less than two (indicating anomaly), the flag was then set on.

Figure 1: Dimensions and items adapted for the learning tendencies,
learning approaches, and learning habits constructs


Concrete Experience
When I learn, I like to work through given examples.
I learn best when working with concrete concepts that I can relate to in real life.
When I learn, I tend to trust my personal feelings to direct my next step of learning.

Reflective Observation
When I learn, I watch and look for related events around me carefully.
I listen carefully to what the instructor and my fellow classmates have to say about the materials to be learned.
I tend to take time before I respond to a specific question/problem.

Abstract Conceptualisation
Upon learning new concepts, I set up hypothesis and check if my understanding is correct.
I tend to analyse the logical relationships among all the elements I learn.
When I am learning, I evaluate many similar ideas about a concept to be learned.

Active Experimentation
Upon learning a concept, I tend to generate lots of spontaneous ideas.
When I learn, I seek new dimensions by risking the possibility of no gains.
I have an urge to apply the ideas I learn.


Deep Processing
I read the books/articles carefully to examine evidence supporting their conclusions.
I seek thorough understanding of the assignments I am asked to do.
I try to relate the ideas learned in this class to other classes I'm taking or have taken.

Surface Learning
I do a lot of cramming before tests.
I put a lot of emphasis on memorising contents of my learning.
For my assignments, I copy materials from books or related sources.
I read only what's required of me to pass the course.

Operation Learning
For my assignments, I need precise steps to follow.
In my reading, I purposely look for important facts to memorise.
I practice examinations by looking for old examination items.
I proceed with my class project in a step-by-step fashion.

Organised Methods
I arrange all my class-related activities efficiently.
Despite the loads of this class, I also have ample time for leisure.

Strategic Approach
When studying, I assign differential weights to the elements of study.
I have the habit of taking notes in class time.
For completing an assignment, I look for easy ways.

I have the desire to do better than other people in this class.
I know what I want to get out of this course.
The materials learned are very important for my future career development.

Fear of Failure
I feel stressed in the face of tests.
I will worry if the materials are perceived as hard.
I panic if I fall behind in the class.


Environmental Factors
I listen to music when I study.
I study in an ambience with some noises.
I study in a quiet place.
I study under dim light.
I study under bright light.
I study in a cool place.
I study in a warm place.
I study in a room with comfortable seat and table/desk.

Emotional Factors
I study under pleasant mood.
I study under gloomy mood.
I study in a pre-planned time frame.

Sociological Factors
I study with friends.
I study by myself.
I study in the presence of someone who can provide guidance for me.
I study in library.

Physiological Factors
I drink water/beverages when I study.
I munch snacks when I study.
I study in the morning.
I study in the afternoon.
I study in the evening.
I study in midnight.
I read out what I study to help me remember.
I try to form visual images of what I read.
I have many body movements when I study.
I continuously tap the table/desk with my fingers or tap the floor with my feet.

In additional to the study behaviour variables, self-rated academic performance, perceived course benefit, and aptitude variables were also included in the background section of the questionnaire. Each was assessed with two items also on 1-6 Likert scale of agreement and the mean of these two items was taken as the indicator for that construct. The items addressing academic performance were: "In this course I am likely to get a satisfactory score" and "I have achieved satisfactory grades in most of the courses of the accounting program I've taken to date". The items addressing perceived course benefit were: "As far as knowledge acquisition is concerned, I have benefited much from this course" and "The instructor has been helpful to me in this course". The items addressing aptitude were: "Overall, I have a strong ability to learn the knowledge required by the accounting profession" and "I am suitable for a career in the accounting professions".

The questionnaire was administered to 844 students majored in accounting while they were taking one of the following courses: introductory accounting, intermediate accounting, advance acocunting, and auditing. The student sample was drawn from seven major colleges/universities in Taiwan (four public and three private). Of the 844 responses collected, 31 were returned as almost blank sheets and 58 cases were found to be with unusual response patterns as detected with at least one flag by our check item sets. We looked through all the flagged respondents and deleted 38 of them as appearing to lack of credibility. Twenty-eight of these records had consistent zigzag response patterns and the rest appeared like random responses. The other 20 cases were mostly detected with only one flag, and their responses appeared to be within tolerance, so we decided to keep them in the data and ended up with a total sample of 775 cases (241 males and 532 females, two with missing gender data) for our investigation purpose.

We felt reasonably convinced about the statistical stability of our aggregate statistics due to the large sample size involved in our study. Research question one was answered by providing the summary statistics for all the study behaviour dimensions. Research question two was approached in two stages. The first stage involved setting up three multiple regression equations, one for each major study behaviour construct. For each equation, the relevant dimensions of each construct were the determinants of academic achievement. The magnitude of the R-square value for each equation was compared. The second stage involved a single regression equation in which those variables found to be significant in the first stage were entered in a stepwise fashion as determinants of academic achievement.


The descriptive statistics and the internal consistency checks for the dimensions of learning tendencies and learning approaches were obtained via MICROCAT test analysis software (Assessment Ststems Corporation, 1988). The rest of the analyses were performed using SAS 6.07 PC version. The alpha coefficients of CE (0.43) and RO (0.32) are quite low, consistent with those reported in the LSI manual and by Fung et al (1993). Albeit the low internal consistency for CE and RO, AC and AE displayed high internal consistency properties (0.70 and 0.82 respectively) consistent with those reported by Geiger et al (1993). For the learning approach subscales, the alpha coefficients were consistent with most previous studies. No alpha coefficients were given for learning habit items because each item was used an a stand-alone indicator (no subscale scores were used). Since our 1-6 Likert scale was constructed with references to the frequency of event occurrence, the numerical values of these statistics can be readily interpreted.

For the learning tendency aspect, Taiwanese accounting students appear to display substantial concrete experience tendency, moderate reflective observation and abstract conceptualisation tendencies, and less in active experimentation tendency. In terms of learning approach aspect, Taiwanese accounting students tend to display moderate uses of deep processing and organised methods, show slightly more signs of surface processing, achievement motivation and fear of failure. More often than not, the most prominent features of Taiwanese accounting students' learning approach is described by operation learning and strategic approach. It is worth pointing out that both operation learning and strategic approach have been found to be associated with characteristics of a surface learning approach in a factor analysis of the original 15 ASI scales. The most mentioned learning habits are studying in quiet and alone, under bright light and cool temperature in a comfortable room, studying in pleasant mood, drinking beverages, and studying in evenings.

In regard to the association between study behaviour variables and academic achievement, all variables except concrete experience gave a significant positive effect on academic achievement (as indicated by positive regression coefficient estimates at p<0.001). Secondly, in relation to learning approaches, achievement motivation displayed the most significant positive result as a determinant of academic achievement (regression coefficient estimate=0.372, p<0.000). Organised methods also substantially affected the positive outcome of academic performance (regression coefficient estimate=0.154, p<0.000). Surface learning and fear of failure, however, showed negative effects on academic performance (regression coefficient estimates are -0.183 and -0.137 both at p<0.000 respectively). Only four learning habit variables gave statistical significance in their relations to academic performance at 0.05 level. Listening to music when studying showed a slight negative effect (b=-0.055, p<0.043), whereas studying in a comfortable room and in a pleasant mood showed slight positive effects (b=0.068 and 0.083 at p<0.029 and 0.049 respectively).

The second stage of the regression analyses was conducted by entering the eleven study behaviour variables as identified in the previous stage of analyses into a single regression scenario in a stepwise fashion. The Statistical Analysis System (SAS) provides three options for selecting variables in the PROC STEPWISE procedure: forward selection, backward selection, and stepwise selection (allow previously deleted variables to renter the selection process at a later stage). All three options yielded identical interpretations of our results using 0.10 as the significance level for entry into the model. Of the 11 study behaviour variables entered, seven were retained at the final step of selection. None of the variables relating to learning tendencies and learning habits met the criterion of selection into the final model. We also added certain product terms into the final model to test for the possible existence of interaction effects among our study behaviour variables. Only one of the interaction terms tested showed noticeable significance - fear * strategy. The major determinants of academic performance in order of descending importance are: aptitude, perceived benefit, organised study methods, and surface learning. The interaction term changed only the interpretation of the strategic approach effect. The interaction effect was further examined by collapsing it into low, medium, and high categories for fear of failure and strategic approach. The interaction appears to be of ordinal nature for both variables. The high use of a strategic approach for low fear of failure students showed the highest level of academic achievement.

Judging from the results of our regression analyses, it is concluded that the learning approaches aspect of study behaviours has the most impact on the academic outcome of learning. The adjusted R-square of learning approaches for predicting performance was 0.317, compared with 0.117 and 0.088 for learning tendencies and learning habits respectively. The effects of learning tendencies variables have been absorbed by the more powerful effect of learning approaches variables. Furthermore, when both learning tendencies and learning approaches are taken into account, the learning habits variables have been found to have no significant influence on academic achievement.

Conclusions and discussions

The results of the current study provide us with a better understanding of Taiwanese accounting students' study behaviours. The results also yield insights into the relative influence on perceived academic achievement of the three aspects of study behaviours, namely learning tendencies, learning approaches, and learning habits. While our findings will need replication in other countries before they can be generalised, they do suggest that future accounting education research should focus more on the learning approaches aspect of students' study behaviours. The learning approaches of deep processing and sound strategies are consistent with goals of higher education, whereas the learning approach of surface processing is deemed to be undesirable. Even though deep processing was not detected as significantly related to academic outcome in our study, this result should not undermine the desirability of deep processing in accounting education. The reason for its lack of significance in the model is likely to be due to the strong opposing influence from surface learning. In fact, removing the surface learning element from the model has indeed shown a strong positive effect of deep learning on academic achievement. Since surface approach carries more weight in determining the learning outcomes than deep learning, working to reduce reliance on surface learning may prove to be the most effective way of promoting deep learning. This way of thinking may indeed provide accounting educators a good starting point in their efforts of improving students' learning.

Of course, there is always a danger of treating learning approaches as if they are independent of learning environments. In fact, numerous researchers have suggested that learning environments do have a profound effect on study behaviours. Therefore, any attempt to modify students' learning approaches should also take into consideration students' perceptions of their learning environment. It may be argued that each course within the accounting program provides a unique learning environment. It is thus quite worthwhile to investigate how each course is perceived by the majority of students in that specific learning environment. A direction for further accounting education research might be the investigation of the match between students' perceptions of the learning environment and their manifest learning approaches. Both students and staff may benefit in a complementary manner, at an individual level, in the light of results from such contextual studies,


Assessment Systems Corporation (1988). MICROCAT Testing System. St. Paul, MN: Assessment Systems Corporation.

Baker, R. E., Simson, J. R., & Bazeli, F. P. (1986). An assessment of the learning style preferences of accounting majors. Issues in Accounting Education, Spring, 1-12.

Baker, R. E., Simon, J. R., & Bazeli, F. P. (1987). Selecting instructional design for introductory accounting based on experiential learning model. Journal of Accounting Education, 5(2), 207-226.

Baldwin, B. A., Reckers, J. M. (1984). Exploring the role of learning style research in accounting education policy. Journal of Accounting Education, 2(2), 1-12.

Biggs, J. (1993). What do inventories of students' learning processes really measure? A theoretical review and clarification. British Journal of Educational Psychology, 63, 3-19.

Dunn, R., Griggs, S. A., Olson, J., Gorman, B., & Beasley, M. (1995). A meta analytic validation of the Dunn and Dunn learning styles model. Journal of Educational Research,88(6), 353-362.

Entwistle, N. J., & Waterson, S. (1988). Approaches to studying and levels of processing in university students. British Journal of Educational Psychology, 58, 258-265.

Fung, Y. H., Ho, S. P., & Kwan, K. P. (1993). Reliability and validity of the learning styles questionnaire. British Journal of Educational Technology, 24(1), 12-21.

Geiger, M. A., Boyle, E. J., Pinto, J. K. (1993). An examination of ipsative and normative versions of Kolb's revised learning style inventory. Educational and Psychological Measurement, 53, 717-726.

Honey, P., & Mumford, A. (1992). The Manual of Learning Styles. Maidenhead, Berkshire: Peter Honey (Revised version).

Hutchinson, M., & Gul, F. (1997). The interactive effects of collectivism/individualism cultural beliefs on student group learning prefernces. Journal of Accounting Education, 15(1), 95-107.

Kolb, D. A. (1985). Learning Styles Inventory: User's Guide. Boston: McBer & Co.

Marton, F., & Saljo, R. (1976a). On the qualitative differences in learning. I. Outcome and process. British Journal of Educational Psychology, 46, 4-11.

Marton, F., & Saljo, R. (1976b). On the qualitative differences in learning. II. Outcome and process. British Journal of Educational Psychology, 46, 115-127.

Meyer, J. H. F., Parsons, P., & Dunne, T. T. (1990). Individual study orchestrations and their association with learning outcomes. Higher Education, 20, 67-89.

Richardson, J. T. E. (1990). Reliability and replicability of the approaches to studying questionnaire. Studies in Higher Education, 15, 155-168.

Richardson, J. T. E. (1994). Mature students in higher education: I. A literature survey on approaches to studying. Studies in Higher Education, 19(3), 309-325.

Schmeck, R. R., Ribich, F. D., and Ramanaih, N. (1977). Development of a self-report inventory for assessing individual differences in learning processes. Applied Psychological Measurement, 1, 413-431.

Wolk, C., & Nikolai, L. A. (1997). Personality types of accounting students and faculty: Comparisons and implications. Journal of Accounting Education, 15(1), 1-17.

Please cite as: Chou, T., Taylor, D. and Su, H. (1999). The effects of learning tendencies, learning approaches and learning habits on academic achievement: Evidence from Taiwanese college accounting students. In K. Martin, N. Stanley and N. Davison (Eds), Teaching in the Disciplines/ Learning in Context, 76-83. Proceedings of the 8th Annual Teaching Learning Forum, The University of Western Australia, February 1999. Perth: UWA. http://lsn.curtin.edu.au/tlf/tlf1999/chou.html

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