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Session Overview
Session
31 SES 12 A JS: Researching Multiliteracies in Intercultural and Multilingual Education XIII
Time:
Thursday, 24/Aug/2023:
3:30pm - 5:00pm

Session Chair: Irina Usanova
Location: James McCune Smith, 429 [Floor 4]

Capacity: 20 persons

Joint Paper Session NW 07, NW 20, NW 31

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Presentations
31. LEd – Network on Language and Education
Paper

One Fits Them All? – Metaphors in Multilingual Biology Classes

Ronja Sowinski, Simone Abels

Leuphana University Lüneburg, Germany

Presenting Author: Sowinski, Ronja

Language is a central element of learning and essentially influences the development of students’ conceptions. Students do not only have to understand a language itself but must also apply it to structure their knowledge and conceptions (Beger & Jäkel, 2015; Ikuta & Miwa, 2021). Since the national language is predominant in most biology classes, students need to have a high level of national language proficiency to participate. This monolingual habitus (Gogolin, 1997) constitutes one of the main barriers for second language learners (SLL) in a considerable number of European countries when attending science classes.

As biological phenomena are often complex and abstract, the use of metaphors in biology is common to describe or explain the phenomena (Niebert et al., 2014). According to Conceptual Metaphor Theory (Lakoff & Johnson, 2003), people use embodied, physical experiences to understand abstract phenomena in an analogical way. Therefore, metaphors are used in biology education to support students in understanding abstract phenomena (Aubusson et al., 2006). This support can be realised when students transfer (embodied) experiences (source domain) to an unknown/abstract phenomenon (target domain) (Schmitt, 2005).

Due to the fact that metaphors must be understood in a transferred sense, metaphors might even impede conceptual understanding, especially for SLL. In addition, metaphors differ depending on language as well as culture (Danielsson et al., 2018). Therefore, students might not understand (some) metaphors if the language of instruction differs from their first language. In this way, the monolingual habitus would be putting them in a disadvantage in biology classes.

All biology textbooks contain metaphors and with respect to biology education, students need to understand those metaphors (Jahic Pettersson et al., 2020). However, even though metaphors play an important role for conceptual learning in biology, other science language characteristics, such as sentence structure and the use of biological jargon, are more prominently researched (e.g., Zukswert et al., 2019).

The study by Jahic Pettersson et al. (2020) shows first indications that students adapt metaphors of their teachers and textbooks and use their own-built metaphors to understand abstract biological phenomena. However, students often understand metaphors literally or misinterpret them (Beger & Jäkel, 2015) resulting in challenges according to their content learning. Therefore, metaphors put an especially high barrier on learning for SLL.

Since metaphors depend on both languages and cultures (Lakoff & Johnson, 2003), it can be hypothesised that the constructed metaphors of SLL differ from those of native speakers. First indications are shown in the research of Haddad and Montero Martínez (2019) as well as Conrad and Libarkin (2021). This research, however, refers to chemistry, physics or geoscience education and does not differentiate on different first languages.

This study aims at exploring which metaphorical expressions are used by native and non-native German students as an example while talking about biological phenomena and to what extent these metaphorical expressions differ. Beyond that, identified (linguistic) characteristics of students’ conceptions will be compared with those of their science teachers. By doing this, I will discuss to what extent the inclusion of different languages within student conceptions research may be important for biology education. The influence of teachers’ language will be identified by comparing the conceptions of all participants.

This leads to the following research questions:

(1) Which metaphors/ metaphorical expressions are used by students to explain biological phenomena?

(2) What differences can be seen according to characteristics and frequency of the use of metaphorical language between native and non-native students?

(3) To what extent do students use the metaphors of their teachers instead of their own-built metaphors?


Methodology, Methods, Research Instruments or Sources Used
To answer the research questions, guideline-based interviews (Cohen et al., 2011) with 24 high school students (grade 10, 15-17 years) were conducted. For these interviews, two biological phenomena were chosen: (1) decomposition of leaves as an experienceable topic and (2) being diseased by influenza as an abstract topic. By choosing these two topics, the use of metaphors depending on the level of abstraction could be compared within further analyses.  
Additionally, demographical data, such as age, migration background, and language background, were gathered with a questionnaire. In this way, information about students’ first languages and language use could be collected as external conditions of biology learning. Hereby, a comparison between students with different first languages was possible.
As the influence of teachers’ language on students’ metaphor use is described in the state of research, the interviews and the questionnaire were also conducted with the teachers of the interviewed students. Thus, possible influences between the teachers’ and the students’ use of metaphors could be established.
The interviews were analysed by Qualitative Content Analysis (Kuckartz, 2014) and Systematic Metaphor Analysis (Schmitt, 2005).
As a first step, the data was structured according to thematic aspects using a content-structuring Qualitative Content Analysis (Kuckartz, 2014). Therefore, a category system was developed. For the topic of decomposition of leaves as example categories like “animals as macroscopical players” or “accumulation of leaves as consequence for missing decomposition” were used for the analysis.
The results of this Qualitative Content Analysis were used as target domains for metaphor analysis as a second step. Within the metaphor analysis, types of metaphors (e. g., personifications, container concept, transmitter-receiver concept) were coded within the interviews. These types of metaphors served as source domains. By combining the results of the first and the second step, an overview of metaphorical concepts (e. g. microorganisms are persons) can be given.
As a last step of the analysis, the metaphorical concepts of the participants were analysed regarding similarities and differences between native and non-native students as well as their teachers.

Conclusions, Expected Outcomes or Findings
The following results as well as the sample (24 students with 12 different variations of first languages) reflect the diversity of today’s classrooms in Europe.
So far, some interesting findings of the student interviews can be outlined. First, it can be confirmed that students are using more metaphors while talking about abstract phenomena (here: immunological processes), as while talking about experienceable topics. As expected, the students use some of the metaphors of their teachers while talking about the topics as well.
Furthermore, it became clear, that the influence between students’ experiences, conceptions and metaphor use may be a crucial factor for biology learning. During an interview with a student with German and Turkish as first languages (born in Germany) and a student with Arabic as first language (born in Syria), very different ideas about health and illness occurred. While the German and Turkish speaking student explained that having influenza is caused by bacteria, the student with Arabic as a first language explained different lifestyles as reason for illness. This student did not mention the function of the immune system as an important part of our health either. As a result, the use of metaphors differed between those students exceptionally. The student talking about the immune system was more likely to use metaphors which are also used in science. Seeing this, it could be important to keep – next to the languages – the cultural background of the students in mind during analysis regarding the idea of western science.
Following the hypotheses and results, it is expected to find more differences according to metaphors used by students with different first languages within other topics and other first languages as well. Beyond that, first indications of the necessity to implement metaphors in biology education will be shown and lead to further research.

References
Aubusson, P. J., Harrison, A. G., & Ritchie, S. M. (2006). Metaphor and Analogy. Serious thought in science education. In P. J. Aubusson, A. G. Harrison, & S. M. Ritchie (Eds.), Metaphor and Analogy in Science Education (pp. 1–10). Springer-Verlag. https://doi.org/10.1007/1-4020-3830-5

Beger, A., & Jäkel, O. (2015). The cognitive role of metaphor in teaching science: Examples from physics, chemistry, biology, psychology and philosophy. Philinq, 3(1966), 89–112.

Cohen, L., Manion, L., & Morrison, K. (2011). Research Methods in Education (7th ed.). Routledge.

Conrad, D., & Libarkin, J. C. (2021). Using Conceptual Metaphor Theory within the Model of Educational Reconstruction to identify students’ alternative conceptions and improve instruction. A plate tectonics example. Journal of Geoscience Education, 70(2), 262–277. https://doi.org/10.1080/10899995.2021.1983941

Danielsson, K., Löfgren, R., & Pettersson, A. J. (2018). Gains and Losses: Metaphors in Chemistry Classrooms. In K.-S. Tang & K. Danielsson (Eds.), Global Developments in Literacy Research for Science Education (pp. 219–235). Springer International Publishing. https://doi.org/10.1007/978-3-319-69197-8_14

Gogolin, I. (1997). The “monolingual habitus” as the common feature in teaching in the language of the majority in different countries. Per Linguam, 13(2), 38–49. http://perlinguam.journals.ac.za

Haddad, A. H., & Montero Martínez, S. (2019). “Radiative Forcing” Metaphor. An English-Arabic Terminological and Cultural Case Study. International Journal of Arabic-English Studies (IJAES), 19(1), 139–158. https://doi.org/https://doi.org/10.33806/ijaes2000.19.1.8

Ikuta, M., & Miwa, K. (2021). Structure Mapping in Second-Language Metaphor Processing. Metaphor and Symbol, 36(4), 288–310. https://doi.org/10.1080/10926488.2021.1941971

Jahic Pettersson, A., Danielsson, K., & Rundgren, C.-J. (2020). “Traveling nutrients”. How students use metaphorical language to describe digestion and nutritional uptake. International Journal of Science Education, 1–21. https://doi.org/10.1080/09500693.2020.1756514

Kuckartz, U. (2014). Qualitative Text Analysis: A Guide to Methods, Practice & Using Software. SAGE Publications Ltd. https://doi.org/10.4135/9781446288719

Lakoff, G., & Johnson, M. (2003). Metaphors we live by. With a new afterword. University of Chicago Press.

Niebert, K., Dannemann, S., & Gropengiesser, H. (2014). Metaphors, Analogies and Representations in Biology Education. In I. Baumgardt (Ed.), Forschen, Lehren und Lernen in der Lehrerausbildung (pp. 145–157). Schneider. https://www.researchgate.net/publication/277956201

Schmitt, R. (2005). Systematic Metaphor Analysis as a Method of Qualitative Research. The Qualitative Report, 10(2), 358–394. https://doi.org/10.46743/2160-3715/2005.1854

Zukswert, J. M., Barker, M. K., & McDonnell, L. (2019). Identifying troublesome jargon in biology: Discrepancies between student performance and perceived understanding. CBE Life Sciences Education, 18(1). https://doi.org/10.1187/cbe.17-07-0118


31. LEd – Network on Language and Education
Paper

DICE in the Classroom: Disaggregate Instruction in Chemistry Education for Multilingual Learners

Robert Gieske, Sabine Streller, Claus Bolte

Freie Universität Berlin, Germany

Presenting Author: Gieske, Robert

Students’ language competences evidentially determine their learning outcomes in STEM subjects to a substantial degree (Bird & Welford, 1995). Societies, particularly in Europe but also beyond, have experienced a constant influx of migrants and refugees in recent decades, which has resulted in an increasing degree of linguistic and cultural diversity in school classrooms (OECD, 2019). Apart from migrant students acquiring the language of schooling and its academic register, large-scale assessment has additionally identified monolingual students with a low socioeconomic status and/or from households with a low level of education in the parental generation, who also struggle to meet the academic objectives (Reiss et al., 2019, S. 77). Consequently, teachers should try to scale down the burdens for an increasingly diverse population of monolingual as well as multilingual students and the integration of language instruction and subject-matter learning is regarded a key strategy to achieve educational justice (Gogolin & Lange, 2011).

To account for this challenge, researchers have developed approaches to the integration of subject- and language-learning like scaffolding (Hammond & Gibbons, 2005) or Translanguaging (García, 2011). So far, there is only scarce evidence from systematically planned intervention studies on the effectiveness of language-responsive teaching approaches from the perspective of individual subject didactics. We want to add to the scientific debate on subject- and language-integrated instruction in STEM subjects by presenting findings from a study that centers the promising Disaggregate Instruction approach (Brown et al., 2010) which has not been widely used in Europe to this day. In the present study we utilize an adapted and optimized version of Brown et al.’s (2010) Disaggregate Instruction which we named Disaggregate Instruction in Chemistry Education (DICE) to tackle the following research questions:

1) To what extent does teaching in accordance with the DICE result in higher student learning growths compared to Scaffolded, language-responsive science teaching designed for this purpose?

2) To what degree do students with diverging competences regarding the language of schooling benefit from the DICE?

The DICE distinguishes itself from other language-responsive approaches (e.g., Scaffolding) as students initially negotiate novel scientific concepts with the help of terminology that they are already familiar with. Only after the learners have developed a general understanding of the concepts, the teacher introduces the corresponding scientific terms and provides opportunities to practice those (Brown et al., 2010, p. 1474). This disaggregation of concept and scientific language learning prevents students from acquiring complex science concepts using abstract mental models and new terminology simultaneously and supports them to purposefully apply the limited capacities of their working memories (Brown et al., 2019). Brown and colleagues (2010) implemented and evaluated the approach with multilingual students studying the concept of photosynthesis in a digital learning environment where the use and presentation of everyday and scientific language varied according to the students allocation to either the treatment (‘disaggregate’) group or the control (‘aggregate’ also called ’textbook’) group (Brown et al., 2010).

Students in the treatment group, who received instruction in accordance with the ideas of Disaggregate Instruction, developed a superior conceptual understanding compared to the control group and were also able to communicate their understanding of the novel concepts in a superior way compared to control group students (Brown et al., 2010).


Methodology, Methods, Research Instruments or Sources Used
To answer our research question and to evaluate the implementation of our optimized version of DICE we developed a teaching intervention called “The Dead Sea is Dying” for secondary chemistry learners in grades 8/9 at public (regular and academic) high schools (Gieske et al., 2022). The intervention has been designed as two different teaching sequences (4 times 90 minutes each): (a) language-responsive in accordance with the design principles of DICE (treatment group) and (b) language-responsive without a disaggregation of concept and scientific language learning (control group). The language-responsive nature of both teaching interventions stems from the adherence to scaffolding design principles (Hammond & Gibbons, 2005). The two interventions cover identical subject-matter contents, apply the same teaching methods and both introduce the same ten novel scientific concepts relevant to the topic chemical structure and dissolving of salts. The type of intervention serves as the independent variable; the students’ subject-matter knowledge growth as the dependent variable.

To retrace students’ subject-matter knowledge growth, we apply a test with 16 multiple-choice items, which we developed for this purpose, in a pre-post design. Referring to our research questions, we furthermore capture students’ language competences as a control variable by means of an established c-test instrument consisting of four texts and 100 gaps in total (ifbq Hamburg, 2008) covering the academic register of the language of schooling (German in this case). C-tests are considered a reliable and robust measurement for language competences (Eckes & Grotjahn, 2006).

To analyze the data, we apply regression analyses using linear mixed models taking into account intra class correlations for the participating learning groups as individual clusters (Leyrat et al., 2018). After checking the comparability of our treatment and control group samples prior to taking part in the intervention (i.e., absence of statistically significant differences in subject-matter knowledge and language competences), we can examine the students’ learning growth as the difference of the score in posttest and the pretest. Therefore, we calculate t-tests using the Satterthwaite approximation for the influence of the type of intervention on the subject-matter knowledge growth. Afterwards we add the c-test score interacting with the type of intervention to the model in order to identify the relevance of students’ language proficiency on our investigations.

Conclusions, Expected Outcomes or Findings
The teaching intervention was put into practice in 16 classes at seven different schools in 2021 and 2022 and the data of N = 228 students could be included in our statistical analyses.

Prior to the intervention we detect an expected low level of subject-matter knowledge as students in both groups score around six points on average in the pretest. The c-test performances are very similar with a mean score slightly under 72 as well and our calculations do not show any significant differences at this point.
Through the intervention, the students, again in both groups, reach subject-matter knowledge scores which are more than five points higher in the posttest than in the pretest which exhibits a statistically significant learning growth (p < .001). Comparing the knowledge growth of the DICE group (n = 113, M = 5.4) with that of the control group (n = 115, M = 5.1), however, we do not detect a statistically significant difference (p = .45).

Adding the c-test score to the linear mixed regression model as a multiplicative interaction term with the type of intervention, we detect a negative coefficient which is statistically significant (p = 0.018). This result indicates that the effect of the teaching intervention differs between students with lower and higher language competences as measured by their c-test scores. More specifically, students in the treatment group reach very similar knowledge growth scores almost regardless of their c-test performance whereas the knowledge growth of control group students increases with an improving c-test performance. This central finding supports our claim that a disaggregation of concept and scientific language learning can assist learners at risk of failure in science learning. Moreover, it is remarkable that students with higher language competences benefit from both conditions in a similar way.

References
Bird, E., & Welford, G. (1995). The effect of language on the performance of second‐language students in science examinations. International Journal of Science Education, 17(3), 389–397. https://doi.org/10.1080/0950069950170309
Brown, B. A., Donovan, B., & Wild, A. (2019). Language and cognitive interference: How using complex scientific language limits cognitive performance. Science Education, 103(4), 750–769. https://doi.org/10.1002/sce.21509
Brown, B. A., Ryoo, K., & Rodriguez, J. (2010). Pathway Towards Fluency: Using ‘disaggregate instruction’ to promote science literacy. International Journal of Science Education, 32(11), 1465–1493. https://doi.org/10.1080/09500690903117921
Eckes, T., & Grotjahn, R. (2006). A closer look at the construct validity of C-tests. Language Testing, 23(3), 290–325. https://doi.org/10.1191/0265532206lt330oa
García, O. (2011). Bilingual Education in the 21st Century: A Global Perspective. John Wiley & Sons.
Gieske, R., Streller, S., & Bolte, C. (2022). Transferring language instruction into science education: Evaluating a novel approach to language- and subject-integrated science teaching and learning. RISTAL, 5, 144–162. https://doi.org/10.2478/ristal-2022-0111
Gogolin, I., & Lange, I. (2011). Bildungssprache und Durchgängige Sprachbildung. In S. Fürstenau & M. Gomolla (Hrsg.), Migration und schulischer Wandel: Mehrsprachigkeit (S. 107–127). VS, Verl. für Sozialwiss.
Hammond, J., & Gibbons, P. (2005). Putting scaffolding to work: The contribution of scaffolding in articulating ESL education. Prospect, 20(1), 6–30.
Institut für Bildungsmonitoring und Qualitätsentwicklung Hamburg. (2008). C-Test Klasse 7/8 „Überfall +3“.
Leyrat, C., Morgan, K. E., Leurent, B., & Kahan, B. C. (2018). Cluster randomized trials with a small number of clusters: Which analyses should be used? International Journal of Epidemiology, 47(1), 321–331. https://doi.org/10.1093/ije/dyx169
OECD. (2019). PISA 2018 Results (Volume II): Where All Students Can Succeed. OECD. https://doi.org/10.1787/b5fd1b8f-en
Reiss, K., Weis, M., Klieme, E., & Köller, O. (Hrsg.). (2019). PISA 2018. Waxmann. https://doi.org/10.31244/9783830991007


 
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