Abstract
Different teaching formats are available to teach university level courses like classroom education, distance education or a combination of both. Previous literature has not clearly shown the added value of classroom education compared to distance education. But, based on theoretical models, an added value of additional classroom teaching is expected, although this may differ depending on cognitive dimensions examined. This paper provides data from a setting in which two groups of students take the same distance education course in financial accounting. However, only one of these two groups of students receives additional classroom education. Apart from this difference, these two groups of students are comparable with regard to (a.o.) work experience and prior education. The statistical results for this study are based on open-ended exam questions filled out by both groups of students, while distinguishing between different cognitive dimensions. Empirical results indicate that additional classroom education does not affect overall exam results, but does affect different cognitive dimensions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Krathwohl (2002) distinguishes between different levels of cognitive dimensions, based on the taxonomy of educational objectives by Bloom et al. (1956). This study is partially based on this taxonomy, which consists of six major categories: knowledge, comprehension, application, analysis, synthesis and evaluation.
- 2.
The “deep approach” to learning (Davidson, 2002) entails looking for meaning in the matter being studied and relating it to other experiences and ideas with a critical approach.
- 3.
The term metacognitive skills has been described by Flavell (1979: 907) as the “knowledge or beliefs about what factors or variables act and interact in what ways to affect the course and outcome of cognitive enterprises”.
- 4.
Students are not randomly assigned over the distance learning and blended learning variant. This may possibly induce a self-selection bias in our data, for which we try to control by checking a.o. prior education levels.
- 5.
Enrolling in the Financial Controller programme is considerably more expensive for students than enrolling in the MSc programme of the OUNL. On the other hand, students of the Financial Controller programme are often reimbursed by their employers for the tuition fees.
- 6.
The authors first indexed the exam questions individually. For about 90% of the exam questions, indexation resulted in the same cognitive dimensions. The remaining 10% of questions were indexed by mutual agreement between the authors.
- 7.
The categorisation of questions in three different types can be compared to Bloom’s (1956) taxonomy in the following manner: knowledge questions constitute level 1.0 and 2.0 of Bloom’s taxonomy, application questions constitute level 3.0 and insight questions constitute level 4.0.
- 8.
The categorisation of prior education levels is based on the OUNL’s student database.
- 9.
Dutch higher tertiary education can be divided in two groups: colleges and universities. Colleges mainly have educational activities, while universities also have research activities. The educational level of colleges is somewhat below that of universities.
- 10.
This includes specialist studies like assistant controller and certified accountants, but not at university level.
- 11.
Such scores typically represent student who have registered, but not taken part in the exam. The blended learning students exhibit more “0” scores due to different exam registration procedures at the colleges.
- 12.
In order to pass the exam, students have to attain a minimum score of 55%. The average overall scores indicate that almost half of the students pass the exam.
- 13.
These differences in scores on different types of questions may also be affected by a self-selection bias, as indicated by Colliver (2000). To control for this, our empirical design uses control variables. Supplemental analyses (not reported) do not show large differences between individual colleges exam scores.
- 14.
As our data sample includes re-sit students, we include a dummy variable to examine whether re-sit students have different scores compared to first-time students. The results (not reported) indicate that re-sit students have lower overall exam scores, but do not affect the interaction between the blended learning variable and different cognitive dimensions.
- 15.
A number of students have studied previously at another university. Typically, this prior study is in a completely different area, like engineering. Despite this possible advantage, they do not outperform students with other educational backgrounds.
- 16.
Analyses using an ANOVA design (not reported) provide similar results.
References
Anderson, J. R. (1995). Cognitive psychology and its implications (6th eed.). New York: Worth Publishers.
Bernard, R. S., Abrami, P. C., Lou, Y. Borokhovski, B., Wade, A., Wozney, L., Wallet, P. A., Fiset, M., & Hang, B. (2004). How does distance education compare with classroom instruction? A meta-analysis of the empirical literature. Review of Educational Research, 74(3), 379–439.
Bloom, B. S., Engelhart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy of Educational Objectives: The Classification of Educational Goals; Handbook 1: Cognitive Domain. New York: David MacKay.
Bollen, L., Janssen, B., & Gijselaers, W. (2002). Measuring the effect of innovations in teaching methods on the performance of accounting students. In A., Bentzen-Bilkvist W.H.Gijselaers, & R.G. Milter (Eds.), Educational innovation in economics and business: Educating knowledge workers for corporate leadership: Learning into the future, 21–39. Dordrecht: Kluwer Academic Publishers.
Bonner, S. E. (1999). Choosing teaching methods based on learning objectives: an integrative framework. Issues in Accounting Education, 14(1), 11–39.
Booth, P., Luckett, P., & Mladenovic, R. (1999). The quality of learning in accounting education: the impact of approaches to learning on academic performance. Accounting Education, 8(4), 277–300.
Bryant, S. M., & Hunton, J. E. (2000). The use of technology in the delivery of instruction: implications for accounting educators and education researchers. Issues in Accounting Education, 15(1), 129–162.
Byrne, M., Flood, B., & Willis, P. (2002). The relationship between learning approaches and learning outcomes: a study of Irish accounting students, Accounting Education, 11(1), 27–42.
Cheung, L. L. W., & Kan, A. C. N. (2002). Evaluation of factors related to student performance in a distance-learning business communication course, Journal of Education for Business, May/June, 257–263.
Clark, R. (1983). Reconsidering research on learning from media. Review of Educational Research, 53(4), 445–459.
Colliver, J. A. (2000). Effectiveness of problem-based learning curricula: research and theory. Academic Medicine, 75(3), 259–266.
Davidson, R. A. (2002). Relationship of study approach and exam performance. Journal of Accounting Education, 20(1), 29–45.
Dowling, C., Godfrey, J. M., & Gyles, N. (2003). Do hybrid flexible delivery teaching methods improve accounting students’ learning outcomes? Accounting Education, 12(4), 373–391.
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.
English, L., Luckett, P., & Mladenovic, R. (2004). Encouraging a deep approach to learning through curriculum design. Accounting Education, 13(4), 461–488.
Flavell, J. H. (1979). Metacognition and Cognitive Monitoring: A new Area of Cognitive-Developmental Inquiry. American Psychologist, 34(10), 906–911.
Hall, M., Ramsey, A., & Raven, J. (2004). Changing the learning environment to promote deep learning approaches in first-year accounting students. Accounting Education, 13(4), 489–505.
Jones, S. H., & Davidson, R. A. (1995) Relationship between level of formal reasoning and students’ performance in accounting examinations. Contemporary Accounting Research, 12(1), 163–181.
Koh, M. Y., & Koh, H. C. (1999). The determinants of performance in an accountancy degree programme, Accounting Education, 8(1), 13–29.
Krathwohl, D. R. (2002). A revision of Bloom's Taxonomy: an overview. Theory into Practice, 41(4), 212–218.
Kuhn, D. (2000). Metacognitive development. Current Directions in Psychological Science, 9(5), 178–181.
Lane, A., & Porch, M. (2002). The impact of background factors on performance of nonspecialist undergraduate students on accounting modules—a longitudinal study: a research note, Accounting Education, 11(1), 109–118.
Philips, F. (1998). Accounting students’ beliefs about knowledge: associating performance with underlying belief dimensions. Issues in Accounting Education, 13(1), 113–126.
Prins, F. J., Veenman, M. V. J., & Elshout, J. J. (2006). The impact of intellectual ability and metacognition on learning: New support for the threshold of problematicity theory, Learning and instruction, 16(4), 374–387.
Reeve, F., & Gallacher, J. (2005). Employer-university “partnerships”: a key problem for work-based learning programmes? Journal of Education and Work, 18(2), 219–233.
Watson, S. F., Apostolou, B., Hassell, J. M., & Webber, S. A. (2003). Accounting education literature review. Journal of Accounting Education, 21, 267–325.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Appendix A Mean Exam Scores for Different Types of Prior Education
Appendix A Mean Exam Scores for Different Types of Prior Education
Prior education level | Number of observations | Mean exam score | Mean score knowledge questions | Mean score on application questions | Mean score on insight question |
|---|---|---|---|---|---|
Not available | 158 | 55.7% | 57.9% | 52.9% | 50.6% |
High School | 41 | 55.5% | 59.3% | 49.9%^ | 36.7% |
Middle-level vocational training | 36 | 51.8% | 52.6% | 49.9% | 51.8% |
Colleges | 1,422 | 53.7%* | 54.8%* | 52.4%* | 41.4% |
Specialist vocational training | 299 | 56.6%* | 55.2% | 57.7%*^# | 38.1% |
University | 105 | 55.2% | 58.2%* | 51.4%# | 49.0% |
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media B.V.
About this chapter
Cite this chapter
Castelijn, P., Janssen, B. (2008). Effectiveness of Blended Learning in a Distance Education Setting. In: Barsky, N.P., Clements, M., Ravn, J., Smith, K. (eds) The Power of Technology for Learning. Advances in Business Education and Training, vol 1. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8747-9_4
Download citation
DOI: https://doi.org/10.1007/978-1-4020-8747-9_4
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-8746-2
Online ISBN: 978-1-4020-8747-9
eBook Packages: Humanities, Social Sciences and LawEducation (R0)