Student learning of biology concepts in different university contexts
Elizabeth Hazel
Centre for Learning and Teaching, University of Technology, Sydney
Michael Prosser
Academic Development Unit, La Trobe University
Keith Trigwell
Centre for Learning and Teaching, University of Technology, Sydney
In a study (Hazel, Prosser and Trigwell) funded by the Australian Research Council of student learning in first year university science, we have been exploring the relations between students' conceptual understanding, their approaches to learning, and their perceptions of the learning context in Biology and Physics. This paper describes indicators of students learning of the concept of photosynthesis and the patterns of stability and development which were found in students' learning in Biology at Australian universities.
Introduction
The research reported here is underpinned by the presage-process-product model developed by Dunkin and Biddle and refined by Biggs (Hazel et al. 1994, Prosser et al. 1994). At this conference, we will further address the model - focussing on students' perceptions of the learning environment and their approaches to learning at the topic level in science (Trigwell, Prosser and Hazel).
In this paper we report on the development and use of qualitative and quantitative indicators of students' learning of the concept of photosynthesis. We describe a new way of analysing open-ended questions in biology using a method derived from the phenomenographic tradition and adapted for use with written responses. This builds on work for mathematics (Prosser 1994) and, in an earlier report on this project, for physics (Lyons and Prosser 1995).
We link the indicators of student learning to approaches to learning and features of the learning context. According to the literature (Ramsden, 1991; Biggs, 1991; Trigwell and Prosser, 1991), aspects of the learning context associated with fostering students' deep approaches to learning are good teaching, clear goals and standards, an appropriate workload, and assessment which measures understanding rather than rote recall and opportunities for independent learning. Our approach has been described earlier (Hazel et al., 1994).
Notes on the learning contexts for Biology at two settings are provided.
The settings, the students and their teachers, the methods used in this study
The project was set in four different contexts, with two first year science subjects Biology and Physics at each of two Australian metropolitan universities. Some 272 students completed pre-and post-tests using our outcome measures for Biology - 125 at University A and 147 at University B.
Accounts of the learning environment in the four contexts were obtained from analysis of documentation of the topic as well as interviews with the academic staff responsible for the lectures and administration of the laboratory classes.
Measures to describe students' approaches to learning (SPQ) and students' perceptions of the learning environment (SEQ) were specially developed in this project for use at topic level and were described earlier (Prosser et al., 1994).
Qualitative measures of student learning outcomes in photosynthesis were developed for two open-ended questions and a concept map. The open-ended questions were developed by the researchers together with several academics who were subject matter experts. Question 1 was:
The Blackwood River in Western Australia had been infected with a rapidly - growing green alga. In terms of photosynthetic reactions, describe what happens when light falls on the algae, what happens at night, and any differences between these two situations.
Responses were analysed using a method derived from the phenomenographic tradition adapted for use with written responses. Deciding on the categories of description was an iterative process involving two of the researchers and two of the research assistants and a large sample of scripts. Analysis of all scripts was then done by one research assistant (who was an expert in the subject matter area). An example of the results for Question 1 is shown in Figure 1. A similar approach was taken with Question 2.
Figure 1: Categories of description for students' conceptions of photosynthesis
Category 0
Not on photosynthetic reactions
Students do not address the central issue of the question - "In terms of photosynthetic
reactions ..". Students give isolated and often incorrect responses, eg. "When light falls on alga, CO2 is produced", or discuss some general aspects of algae or of pollution but do not discuss photosynthetic reactions.
Category 1
Photosynthesis involves energy interconversion
Students may discuss the conversion of light energy into chemical energy, may use specific energy terms such as Glucose or ATP, may use an equation form of expression.
Students do not discuss the two key stages in the process, Light and 'Dark' reactions.
Category 2A
Photosynthesis involves energy interconversion and
Key Light and 'Dark 'reactions recognised but undefined
Light and 'Dark' reactions recognised. There may be confusion between 'Dark' reactions and what happens at night.
Category 2B
As for 2A. With a limited biochemical explanation
Further explanation is offered of one or both the key reactions. Examples would be mentioning Photosystems I and II, or the flow of electrons, or light initiating splitting of water and transfer of energy, or a description of the 'Dark' reaction that clearly describes carbon dioxide fixation.
Category 3A
The process as a whole. Photosynthesis involves energy interconversion and
Key Light and 'Dark 'reactions recognised, defined and described in both day and night settings
Here there is clear recognition of Light and 'Dark' reactions, recognition that the Light reaction only occurs in the presence of light, and its products are required by the 'Dark' reaction. The 'Dark' reaction (Calvin cycle) does not require light, but will not occur at night because it is dependent on the products from the Light reaction (as energy source). Photosynthesis cannot occur in the dark, at night.
Category 3B
As for 3A. With a biochemical explanation.
The biochemical explanation might explain one or both of the key reactions and their relationship, eg. light energy conversion in Photosystems, splitting of water and release of H, flow of electrons and capturing of chemical energy into ATP and NADPH which are being used as energy sources in fixation of carbon dioxide in the 'Dark' reaction, resulting in the production of sugar.
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Responses to the two open-ended questions were also analysed using a development of the SOLO scheme (Biggs 1991).
A concept mapping task was used where students were provided with a list of some 13 concepts about photosynthesis and asked to sort them into groups of closely related concepts, choose the most general arrange the other concepts to best represent the relationships between them. Lines indicating relationships were accompanied by propositional statements in scientific terms. Quantitative analyses were done of students' concept maps of photosynthesis where branches, relations, cross-links and hierarchy were scored, using some further development of our techniques (Hazel and Prosser 1994).
The concept maps were also analysed qualitatively but we do not have the opportunity to describe that approach in this brief report.
Results
Using document analysis and interviews with teachers, it was found that the teaching and learning contexts for the topic photosynthesis in the two university settings was similar in some ways. Both took a fairly traditional approach with little active involvement or independence for students and an emphasis on information transfer. At both, coursework on photosynthesis was intended to involve about 15 hours of students' class attendance and private study. However, academic staff pointed out that the effective workload could be very much higher for those students who had not done matriculation level biology.
It was not evident that there was a high emphasis on any of the variables we had associated with fostering students' deep approaches to learning - good teaching, clear goals and standards, appropriate workload and assessment, independence. In the areas of good teaching and appropriate assessment there were some features which were of interest in interpretation of our findings on student outcome measures.
Good teaching
In context A it seemed that planned activities had not cohered in the teaching though there were mechanisms for student support. Things seemed more personally directed at B but there was less support for students.
- The academic who gave the lectures explained that he did not usually deal with the topic at first year level, he was replacing someone on leave. Whilst having lecture notes handed on was helpful, he identified a number of problems - the lecture time allowed seemed too little for the material, there did not seem to be scope for the lectures to be made interesting for students and the planned links with the laboratory classes did not work properly. Lecture summaries were distributed. The taped lectures and the lecture overheads together with material from the laboratories were made available for student perusal and revision. In the laboratory, ways had been planned to encourage the students to engage with the topic of photosynthesis. As well as any assistance from their regular demonstrator in the lab, students could see a duty tutor and use the revision materials. At a broader level they could attend special courses run by a students learning centre.
- The teacher responsible for the lectures on photosynthesis judged that the quality of teaching on this section of the course was "not particularly good" - classes were large (500-700) and students did not seem to engage or pay attention in lectures. He had several strategies to counteract these problems where he introduced appropriate humour and avoided teaching about photosynthesis as a disembodied process by referring to the work of individual scientists who were involved in the discovery of processes and to Calvin's Nobel lecture where he referred to the painstaking nature of his famous work. Several lecture overheads were devoted to this process. Students had the opportunity to attend a practical class and a tutorial on the topic where they could obtain feedback on their performance.
Assessment for understanding
The assessment seemed more appropriate in context A than B. At A there was a range of assessment types including an emphasis on qualitative responses whereas at B all assessment was in the form of multiple choice questions.
- In the photosynthesis lab, there were practical exercises, a quiz with varied format and feedback provided, a set of extension questions and a self test. The lab quiz had both short answer questions and MCQ, the practical exam had both practical tasks and short answer questions and the final examination had both short answer questions and MCQ on photosynthesis. At least half of the assessment items were of short answer type and the teaching staff felt these would require students to demonstrate meaningful understanding. They felt that the topic was too complex to be learnt by rote.
- The was no range of assessment on photosynthesis. Only MCQ were used in a tutorial test and in the final examination. As in previous years at University B, the lecturer made an effort to set MCQ on photosynthesis which would require integration and would be unlikely answered correctly by rote learning. Availability of trial and past exam questions could alert students to the types of questions asked - difficult questions requiring integration, evaluation and synthesis on difficult topics such as photosynthesis.
In the phenomenographically based analysis of responses to Question 1, the categories derived (Figure 1) ranged from a simple overview of photosynthesis as energy interconversion (Category 1) to views where the key structures of light and 'dark' reactions were described in a commonplace or scientific way (Category 2A or B) and, further, to views where the interrelationship of light and 'dark' reactions were described in a commonplace or scientific way (Category 3A or B). The teachers whom we interviewed felt that performance at the level of Category 3 was desirable for these university students and responded accordingly when they wrote model answers for our questions. However we found that most students did not go beyond Category 1 (Table 1). For Biology in context A, patterns of response remained quite stable over our pre- and post-tests, whereas in context B, there was significant development (compare Tables 1 and 2). Similar patterns of stability at A and development at B were found with the quantitative analysis of the concept mapping task (compare Tables 3 and 4).
Table 1: Summary of the distribution of the phenomenographic categories
of description of Question 1 (algae in a river) for University A.
University A
Question 1 | Post |
| 0 | 1 | 2A | 2B |
3A | 3B | Total |
| Pre | 0 |
6 | 6 | 1 | 0 | 0 | 0 | 13 |
| 1 |
10 | 80 | 10 | 0 | 0 | 0 | 100 |
| 2A |
0 | 3 | 5 | 1 | 0 | 0 | 9 |
| 2B |
0 | 1 | 1 | 0 | 0 | 0 | 2 |
| 3A |
0 | 1 | 0 | 0 | 0 | 0 | 1 |
| 3B |
0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Total |
16 | 91 | 17 | 1 | 0 | 0 | 125 |
| Sign test: NS |
Table 2: Summary of the distribution of the phenomenographic categories
of description of Question 1 (algae in a river) for University B.
University B
Question 1 | Post |
| 0 | 1 | 2A | 2B |
3A | 3B | Total |
| Pre | 0 |
5 | 6 | 10 | 1 | 1 | 0 | 23 |
| 1 |
12 | 39 | 29 | 7 | 3 | 2 | 92 |
| 2A |
0 | 3 | 9 | 6 | 4 | 0 | 22 |
| 2B |
0 | 0 | 1 | 4 | 0 | 0 | 5 |
| 3A |
0 | 0 | 1 | 0 | 2 | 0 | 3 |
| 3B |
0 | 1 | 0 | 1 | 0 | 0 | 2 |
| Total |
17 | 49 | 50 | 19 | 10 | 2 | 147 |
| Sign test: p<.01 |
Table 3: Summary of the distribution of the quantitative analysis
variables for concept mapping at University A.
University A
Concept mapping | Pre | Post |
| Mean | SD | Mean | SD |
| Hierarchy |
3.11 | 1.04 | 3.30 | 1.21 |
| Relation |
6.63 | 1.98 | 6.92 | 2.29 |
| Branch |
1.26 | 1.19 | 1.27 | 1.05 |
| Cross-link |
.30 | .79 | .26 | .54 |
| *P<.05, **P<.01 |
Table 4: Summary of the distribution of the quantitative analysis
variables for concept mapping at University B.
University B
Concept mapping | Pre | Post |
| Mean | SD | Mean | SD |
| Hierarchy |
3.53** | 1.36 | 4.03** | 1.68 |
| Relation |
6.52** | 2.04 | 7.39** | 2.71 |
| Branch |
1.28 | 1.27 | 1.42 | 1.34 |
| Cross-link |
.20 | .51 | .29 | .59 |
| *P<.05, **P<.01 |
Relationships between major variables for the study were explored using hierarchical cluster analysis (Ward's method). Table 5 shows the two cluster result for all students who had completed the SEQ and SPQ as well as qualitative and quantitative measures of learning before and after the teaching segment on photosynthesis. Cluster 2 shows that students with strong results on our pre- and post- measures of meaningful learning also said they adopted deep approaches to study and did not adopt surface approaches. They perceived that their learning environment fostered deep and not surface approaches. There was a striking effect of prior learning where those who were well prepared, continued to do well. Those who were not well prepared (Cluster 1) and did not adopt deep approaches, nor feel encouraged to do so by their environment, scored poorly on our qualitative and quantitative measures.
Further analysis shows different patterns in the two different contexts (Table 6). Significantly more of the students in Cluster 2 were doing Biology at University B. This poses something of a dilemma for interpretation. A strong possibility is that although the assessment was consistently MCQ only, the availability of past papers with difficult questions on photosynthesis, students were encouraged to take deep approaches. The lecturer's ways of showing science as a human endeavour may also have caught students' attention (even though the lecturer feared the reverse).
Table 5: Summary statistics for a two cluster solution for the outcome measures
on photosynthesis, approaches to study and perceptions of the learning
environment for all students in Biology at both A and B Universities
| Variable | Cluster 1
(n=210) | Cluster 2
(n=62) |
Post knowledge and
understandings | Qualitative (phenomenographic, SOLO) (r=.45) | -0.29 | 0.96 |
| Quantitative, concept maps (a=.76) | -0.27 | 0.90 |
Approaches and
perceptions | Surface approaches[1] (a=.73) | 0.23 | -0.77 |
| Deep approaches[1] (a=.84) | -0.10 | 0.34 |
| Surface perceptions[2] (a=.76) | 0.19 | -0.64 |
| Deep perceptions[2] (a=.79) | -0.15 | 0.50 |
Pre knowledge and
understandings | Qualitative (phenomenographic, SOLO) (r=.47) | -0.25 | 0.84 |
| Quantitative, concept maps (a=.72) | -0.08 | 0.26 |
Hierarchical Cluster Analysis using Ward's method, n=272
1 Includes appropriate scores from SPQ
2 Includes appropriate scores from SEQ |
Table 6: Summary of the cluster groupings for biology students at Universities A and B
| Institution | Cluster 1 | Cluster 2 |
| A | 108 | 17 |
| B | 102 | 45 |
| Mann-Whitney U, p <.001. |
Conclusions
Firstly, we were successful in developing a range of qualitative and quantitative indicators of students learning in Biology which proved to be robust and suitable for use as outcome measures in tests based on the process-presage-product model. Secondly, our results affirm the strong links between prior learning, students' approaches to learning and outcomes which we found in our earlier studies. They move further in looking at students' intentions as well as study strategies. Thirdly, we have found that students who had good prior understanding, took deep approaches to their learning, felt their environment fostered deep approaches, were those who did well on our measures of meaningful learning about photosynthesis. However there was only quite a small proportion of students who showed this pattern (less than one quarter) and most of these students were in context B. In this paper, we have briefly explored possible reasons. At this conference (Trigwell, Prosser and Hazel) we explore the finding of a large proportion of students who do not perceive any particular influence from either context.
Acknowledgments
The project received research funding from the Australian Research Council. The research assistance of Alex Pulkownik, Deryn Alpers, Patricia Gallagher and Andrew Hart is acknowledged. As well the authors wish to thank all those academic staff members at University A and University B, together with a number at the University of Technology, Sydney, who helped the project in a number of ways.
Bibliography
Biggs J. B. (1991). Teaching for Learning: the View from Cognitive Psychology. Hawthorn, Victoria: Australian Council for Educational Research.
Hazel E. and Prosser M. (1994). First-year university students' understanding of photosynthesis, their study strategies and learning context. The American Biology Teacher, 56, 274-279.
Hazel E., Prosser M., Trigwell K. and Gallagher P. (1994). The learning context and students' perceptions. Research and Development in Higher Education, 16, 253-258.
Lyons F. and Prosser M. (1995). Qualitative differences in student learning of electrical phenomena. In Research into Higher Education: Dilemmas, Directions and Diversions, Proceedings. Melbourne: HERDSA Victoria.
Prosser M. (1994). Using phenomenographic research methods in large scale studies of student learning in higher education. In Phenomenography: Philosophy and Practice, Proceedings. Brisbane: QUT.
Prosser, M., Trigwell, K., Hazel, E. and Gallagher, P. (1994). Students' experiences of teaching and learning at the topic level. Research and Development in Higher Education, 16, 305-310.
Ramsden, P. (1991). A performance indicator of teaching quality in higher education: The course experience questionnaire. Studies in Higher Education, 16,129-150.
Trigwell, K. and Prosser M. (1991). Improving the quality of student learning: The influence of learning context and students approaches to learning on learning outcomes. Higher Education, 22, 251-266.
| Please cite as: Hazel, E., Prosser, M. and Trigwell, K. (1996). Student learning of biology concepts in different university contexts. Different Approaches: Theory and Practice in Higher Education. Proceedings HERDSA Conference 1996. Perth, Western Australia, 8-12 July.
http://www.herdsa.org.au/confs/1996/hazel.html |
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