AWERProcedia Information
Technology & Computer Science
Vol 03 (2013) 1175-1181
3
rdWorld Conference on Information Technology (WCIT-2012)
The Influence of the Ontological Status of Student Understanding on
Modelling Activities in a Virtual Learning Environment
Jaanika Piksööt *, Science Education Centre, University of Tartu, Tahe 4, Tartu 51010, Estonia. Tago Sarapuu, Science Education Centre, University of Tartu, Tahe 4, Tartu 51010, Estonia. Jérémy Castéra, Science Education Centre, University of Tartu, Tahe 4, Tartu 51010, Estonia. Suggested Citation:
Piksööt, J., Sarapuu, T. & Castéra, J. The Influence of the Ontological Status of Student Understanding on Modelling Activities in a Virtual Learning Environment, AWERProcedia Information Technology &
Computer Science. [Online]. 2013, 3, pp 1175-1181. Available from: http://www.world-education-center.org/index.php/P-ITCS Proceedings of 3rd World Conference on Information Technology
(WCIT-2012), 14-16 November 2012, University of Barcelon, Barcelona, Spain.
Received 23 Feburary, 2013; revised 4 May, 2013; accepted 8 September, 2013. Selection and peer review under responsibility of Prof. Dr. Hafize Keser.
©2013 Academic World Education & Research Center. All rights reserved.
Abstract
This paper addresses the effectiveness of computer-based scientific modelling from the perspective of the ontological status of modellers’ prior understanding. The participants of the study were 257 Estonian secondary school students who applied the virtual modelling environment “Cell World”. A pretest questionnaire was filled in for assessing students’ prior understanding within two ontological categories: objects and processes. The results indicated that students with lower domain-specific prior understanding had greater difficulties in scientific modelling. Moreover, learners’ understanding in the ontological category of processes was the most substantial difference between high-performing and low-performing modellers. The results of the study suggest that learners with low process understanding need scaffolds during scientific modelling that contribute to the category shifts.
Keywords: Virtual modelling, ontological categories, interactive learning environments;
* ADDRESS FOR CORRESPONDENCE: Jaanika Piksööt, Science Education Centre, University of Tartu, Tahe 4, Tartu 51010, Estonia,
1. Introduction 1.1. Virtual modelling
Virtual modelling is often claimed as a way to promote deeper understanding of complex scientific phenomena [e.g. 1, 2]. Jonassen, Storbel, and Gottdenker [3] have concluded that constructing technology-mediated models is a powerful tool for enhancing conceptual change of learners as these models scaffold and allow learners’ own internal, mental models to be externalised.
In addition, Reed’s theoretical framework for designing multimedia materials *4] suggests that active manipulation of objects, instead of just perception of objects, has a substantial effect on improving the learning process. For the current research, a virtual modelling environment “Cell World” has been applied to enable learners to actively manipulate the components of the scientific processes, and thereby reach a more appropriate, scientific understanding about the processes.
Scientific modelling, however, is a complex task and novice learners may face several difficulties in modelling. Sins, Savelsbergh, and van Joolingen [5] have concluded that students mostly encounter difficulties at three levels: the task perception, the content addressed and tools. At the task level, learning may be superficial as students may concentrate only on the outcome of the model and try to achieve the fitting result, and not on understanding the behaviour of a particular scientific process. At the content level, students often have difficulties in conceptualising the complex phenomena. And finally, at the tool level, learners may find complications with relating their own knowledge to the representational formalism of a modelling tool.
1.2. Ontological categories
This research defines understanding of a scientific phenomenon through the ontological categories of objects and processes. According to Chi, Slotta, and de Leeuw [6], all entities in the world belong to three main ontological categories:
matter, which involves natural objects and artefacts;
processes, with the subcategories of procedures, events and constraint-based interactions; mental states, which includes one’s emotions and intentions.
The authors [6] claim that conceptual change occurs when a concept is shifted from one category to another. If the two concepts belong ontologically to the same category, conceptual change occurs easily. However, if the two concepts are ontologically distinct, conceptual change is more difficult.
Several authors [6, 7] have stressed that for understanding scientific conceptions, the process category is essential, as most scientific concepts belong to the ontological category of processes. Students’ initial scientific concepts, on the other hand, are explained mainly by the category of objects *8+. For instance, the comparison of experts’ and novices’ explanations to physics problems involving light, heat and electrical current has demonstrated that novices conceptualise these concepts as material substances *9+. This mismatch explains learners’ difficulties in understanding science, as very often there is an ‘incompatibility’ between students’ conceptions and scientific conceptions *6+.
1.3. Research questions
The following research questions were set for the study:
What is the effect of prior understanding on students’ modelling activities in a virtual learning environment?
What is the ontological status of prior understanding of high-performing and low-performing modellers in the virtual learning environment?
2. Methods
2.1. Learning environment
In order to fulfil the aims of the study, a Web-based modelling environment “Cell World” composed in the Science Education Centre at the University of Tartu, was used. In this environment, learners can study various complicated processes of cell biology (e.g. photosynthesis, molecular genetics, etc). The environment consists of 10 different models that enable modellers to construct microscopic processes virtually. For that, they have to watch the animation of a particular process, and, when needed, add new components or change the existing ones, so that the process can go further (for more details, see “Cell World” at http://bio.edu.ee/models). As the environment enables learners to actively explore the functions of the components and their interactions during the processes, it is expected that it would enable category shifts from the ontological category of objects to processes.
When using “Cell World”, students can perform two types of modelling operations:
to add new components to the model so that the animation can continue (Type 1 operations); to replace existing components of the animation in order to modify the process (Type 2).
In the current study, the model of translation was used, where students can add tRNA molecules to the animation (Type 1) or replace mRNA nucleotides on the animation (Type 2).
2.2. Participants
The participants of the study were 257 students (aged 17-18) from the 11th grade of nine secondary schools from Estonia. The topic of translation was familiar to the students, as the study was conducted after they had already learned about molecular genetics. None of the participants, however, were familiar with the model of translation applied in this study.
2.3. Procedures
An individual 20-minute pretest questionnaire was filled in for assessing the ontological status of the students’ prior understanding in the categories of objects and processes. In the second lesson, participants used the model of translation for 45 minutes. All students worked individually.
2.4. Data collection
The students’ prior understanding was assessed on the basis of two items from the pretest – one for each ontological category. Student understanding within the objects category was analysed on the basis of a screenshot of the model of translation, where they had to name the required objects. Understanding in the category of processes was assessed by a question where students had to write about the participation of different molecules in the translation.
(‘High-performing modellers’); (2) students that made one mistake; and (3) students that made mistakes in at least two operations of the same type (‘Low-performing modellers’). For finding differences between the high- and low-performing modellers, an independent samples t-test was applied using IBM SPSS Statistics software.
3. Results and Discussion
3.1. Correctness of modelling operations
At first, the data about the correctness of the modelling in the learning environment “Cell World” were analysed. The participants made two types of modelling operation: added necessary molecules to the animation (Type 1) or changed existing molecules on the model (Type 2) – they performed 16 operations in total. Figure 1 illustrates the proportion of students who made mistakes in both types of operations. 12 5 5 2 2 1 5 57 3 2 1 0 1 0 38 1 0 10 20 30 40 50 60 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Operation no % o f in co rr ec t op er at io ns Figure 1. Ratio of students’ (N=257) mistakes in two types of modelling operations
( Type 1 – adding, Type 2 – replacing).
As can be seen from Figure 1, students made substantially more mistakes in Type 2 operations. In the first operation of Type 1, only 12% of students made mistakes, while in Type 2 modelling, 57% of students were incorrect. This can be explained by the fact that it was easier to add new molecules to the animation than to change the components on the model.
As the two types of modelling operations require different levels of cognitive processing, the data concerning high-performing and low-performing modellers’ ontological status are presented separately for the two distinct types of modelling operations.
3.2. Prior understanding of high- and low-performing modellers
mistakes in at least two operations of the same type. As our purpose was to compare diverse groups of modellers, the rest of the students were left out of the analysis.
Table 1. Differences in the prior understanding of high- and low-performing modellers in Type 1 operations according to pretest answers analysed with an independent samples t-test
Group N
Category of objects Category of processes Mean (max 10) SD t p Mean (max 10) SD t p High-performing modellers 198 6.55 3.39 2.59 <0.05 4.57 3.73 4.53 <0.001 Low-performing modellers 23 4.33 2.74 1.91 2.50
We can see from this that there were statistically significant differences between high- and low-performing modellers’ pre-understanding within both ontological categories. For the category of objects, the average score for high-performing modellers was 6.55 and for low-performing modellers 4.33 out of 10. According to the independent samples t-test, this difference was statistically significant (t=2.59; p<0.05).
In the category of processes, the high performers’ results were again higher (m=4.57) than the low-performing modellers’ scores (m=1.91). Again, this difference was statistically significant (t=4.53; p<0.001).
From Table 1, we can also conclude that there were higher differences between these groups in the category of processes (t=4.53; p<0.001) than in the category of objects (t=2.59; p<0.05). Here we can hypothesise that modellers’ success was provoked primarily by their better understanding within the ontological category of processes.
Next, we investigated the pretest data of high- and low-performing modellers in respect of Type 2 operations – replacing a component of the model. The results are presented in Table 2.
Table 2. Differences in the prior understanding of high- and low-performing modellers in Type 2 operations according to pretest answers analysed with an independent samples t-test
Group N
Category of objects Category of processes Mean (max 10) SD t p Mean (max 10) SD t p High-performing modellers 93 7.89 2.83 3.34 <0.01 5.77 3.73 7.19 <0.001 Low-performing modellers 80 5.01 3.14 2.16 2.86
According to Table 2, the high-performing modellers’ mean score for the category of objects was 7.89 and the score of low performers 5.01 out of 10. Again, the high-performing modellers’ pre-understanding was statistically significant (t=3.34; p<0.01). Their prior pre-understanding was also significantly higher in the category of processes (t=7.19; p<0.001). The comparison of prior understanding of high- and low-performers in respect of Type 2 operations confirmed the finding about Type 1 operations – the most significant differences between the two groups’ prior understanding were in the category of processes (t=7.19; p<0.001) when compared to the category of objects (t=3.34; p<0.01).
modellers used their prior knowledge more than low-performing modellers during model-based reasoning.
The analysis of the ontological status of learners’ understanding revealed that the biggest difference between high-performing and low-performing modellers concerns the ontological category of processes. This finding indicates that for successful scientific modelling, learners’ conceptions in the processes category are crucial. Also, Barak et al. [7] stressed that understanding the systematic nature of biology and the complex interactions in natural systems ‒ the conception of biological concepts through the category of processes ‒ is essential.
The results of the study also suggest that learners with low process understanding need scaffolds during scientific modelling that contribute to the category shifts. Attempts to elaborate learning support for facilitating ontological shifts have been made before. For instance, Chi and Slotta [10] developed a computer-based module for training students in the appropriate ontology. The results demonstrated the effectiveness of such ontology training on developing students’ conceptions about complicated, emergent physics processes. Future research, therefore, should invest more effort in supporting learners to create appropriate, process-based scientific conceptions.
4. Conclusions
In the present study, students’ performance in computer-based modelling in the learning environment “Cell World” was investigated to clarify the differences between successful and less successful modellers’ prior understanding within the ontological categories of objects and processes.
As expected, the level of students’ prior understanding had a positive effect on learners’ virtual modelling activities. The results clearly indicate that students with poor previously acquired knowledge in a specific domain have greater difficulty in scientific modelling.
More importantly, we undertook an investigation into the influence of the ontological status of learners’ prior understanding on their performance in virtual modelling. The analysis of students’ ontological status revealed that successful modellers differed from less successful ones most in their understanding within the category of processes. This leads us to the conclusion that when learning complex phenomena during virtual modelling, learners need scaffolding that facilitates category shifts from their initial, mainly object-based conceptions to scientific, more process-based ones.
Acknowledgements
This work was supported by the Estonian Science Foundation, grant 7739, and the European Social Fund.
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