J Sci Teacher Educ (2014) 25:519–541 DOI 10.1007/s10972-014-9389-9
The Intersection of Inquiry-Based Science and Language: Preparing Teachers for ELL Classrooms Molly Weinburgh • Cecilia Silva • Kathy Horak Smith • Judy Groulx • Jenesta Nettles
Published online: 7 May 2014 The Association for Science Teacher Education, USA 2014
Abstract As teacher educators, we are tasked with preparing prospective teachers to enter a field that has undergone significant changes in student population and policy since we were K-12 teachers. With the emphasis placed on connections, mathematics integration, and communication by the New Generation Science Standards (NGSS) (Achieve in Next generation science standards, 2012), more research is needed on how teachers can accomplish this integration (Bunch in Rev Res Educ 37:298–341, 2013; Lee et al. in Educ Res 42(4):223–233, 2013). Science teacher educators, in response to the NGSS, recognize that it is necessary for preservice and in-service teachers to know more about how instructional strategies in language and science can complement one another. Our purpose in this study was to explore a model of integration that can be used in classrooms. To do this, we examined the change in science content knowledge and academic vocabulary for English language learners (ELLs) as they engaged in inquiry-based science experience utilizing the 5R Instructional Model. Two units, erosion and wind turbines, were developed using the 5R Instructional Model and taught during two different years in a summer school program for ELLs. We analyzed data from interviews to assess change in conceptual understanding and science academic vocabulary over M. Weinburgh (&) C. Silva J. Groulx J. Nettles Texas Christian University (TCU), Box 297920, Fort Worth, TX 76129, USA e-mail:
[email protected] C. Silva e-mail:
[email protected] J. Groulx e-mail:
[email protected] J. Nettles e-mail:
[email protected] K. H. Smith Department of Mathematics, Tarleton State University, Box T-0470, Stephenville, TX 76402, USA e-mail:
[email protected]
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the 60 h of instruction. The statistics show a clear trend of growth supporting our claim that ELLs did construct more sophisticated understanding of the topics and use more language to communicate their knowledge. As science teacher educators seek ways to prepare elementary teachers to help preK-12 students to learn science and develop the language of science, the 5R Instructional Model is one pathway. Keywords Science teacher education English language learners 5R Instructional Model
Introduction As teacher educators, we are tasked with preparing prospective teachers to enter a field that has undergone significant changes in student population and policy since we were K-12 teachers. In the last decade, science education reform documents have provided ‘‘visions not only of what we should teach, but also how we should teach and how we should teach teachers to teach’’ (Yore & Treagust, 2006, p. 297). Diaz, Eick and Diaz (2014) further highlighted, in an ASTE monograph, that science teacher educators should return to the classroom in order to ‘practice what we preach/teach’, enhance our practice, and better understand the impact of these practices. In doing so, we may be able to provide evidence to our students of the effectiveness of the proposed teaching methods in real elementary classrooms. This evidence of K-6 student growth when we use the practices we are advocating can strengthen our credibility and produce better science teachers. Changing Demographics and Policies The increasing populations of English language learners (ELLs) in US classrooms (Camarota, 2007; US Census, 2010), the increased demand for inquiry-based science instruction (Achieve, 2012; National Research Council [NRC], 2000, 2012), and a call for increased communication skills in science (NRC, 2012) have produced an environment in which it is necessary for teacher educators, pre-service and in-service teachers to know more about instructional strategies in language and science. Currently, teacher educators face the challenge of supporting pre-service teachers in learning to teach in ways that are different from their own learning experiences. The next generation of elementary teachers must assist students in developing language skills while acquiring content knowledge (Bunch, 2013). This dual requirement is particularly demanding for elementary science teachers (Lee, Quinn, & Valdes, 2013) who may have learned different strategies in different methods courses. Therefore, elementary science educators must help pre-service and in-service teachers consider methodologies that can complement one another when integrating science and language instruction. As teacher educators and researchers, we have been considering the best ways to help teachers integrate science and language instruction (Brown & Ryoo, 2008; Lee et al., 2013; Settlage, Madsen, & Rustad, 2005). In this paper we describe a summer experience where, as teacher educators, we attempted to think about how we could
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teach science and language at the same time. Specifically, the research reported here explores the effectiveness of an alternative model for integrating inquiry-based science and language. In an attempt to understand the complex nature of science and language instruction, several lines of research have focused on different facets of this issue: (a) the complex nature of the language of science (Gee, 2004, 2008; Lemke, 2004; Snow, 2008); (b) the amount of vocabulary found in secondary level textbooks (Groves, 1995; Yager, 1983); (c) the time required to develop academic language proficiency (Cummins, 2000; Hakuta, Butler, & Witt, 2000); (d) the lack of training in language acquisition for science teachers (Lee, 2005; Scarcella, 2002, 2003; Slater & Mohan, 2010) and (e) the context-bound nature of academic language (Brown & Ryoo, 2008; Gee, 2008; Snow, 2008). This growing corpus of research illuminates a need to examine the instruction that occurs at the intersection of inquiry-based science and language. With the emphasis placed on connections, mathematics integration, and communication by the New Generation Science Standards (NGSS) (Achieve, 2012), more research is needed on how teachers can accomplish this integration (Bunch, 2013; Lee et al., 2013). Science Instruction Science educators have used constructivist learning theory (Luria, 1976; Steffe & Gale, 1995; Vygotsky, 1978, 1986) as a theoretical framework for research and practice for several decades. Constructivist learning theory suggests that learners build new knowledge by integrating new ideas into what has been previously learned. Luria and Vygotsky focused on social interactions and language as tools that scaffold understanding. This sociocultural view of learning suggests that ‘‘language, literacy, and science education are viewed as the products of socioculturally mediated discourse processes among individuals and groups in both formal and informal instructional settings’’ (Sweeney & Tobin, 2000, p. 15). The core of this theory is one that ‘‘defines human learning as a dynamic, social activity that is situated in physical and social contexts, and is distributed across persons, tools, and activities’’ (Johnson, 2009, p. 1). The Framework for K-12 Science Education [Framework] (NRC, 2012) stresses that as students participate in inquiry they must be engaged in the practices of science which include active discourse around a scientific model or phenomenon. To teach science effectively using the principles of inquiry-based instruction, science educators understand that elementary teachers need to have definite content and process objectives in mind. The NGSS (Achieve, 2012) stress this by combining content and practice into a single objective. Teachers must establish environments in which there are phenomena to study that help students meet the objective for content and practice. However, because students are engaged in an investigation in which the purpose is to find the answer to a scientifically oriented question, they do not need to be told the specific objective in advance of the investigation. By providing a shared, hands-on experience, teachers give students an anchor or context for more questions, authentic use of mathematics in data collection and analysis, and relevance for new scientific language. Teacher educators know that the context of science inquiry can provide a rich environment where content and language can develop.
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From a science inquiry perspective, lessons often begin with an experience during which data are collected, recorded, and transposed and then followed by discussions and explanations that originate with student data and build toward canonical understanding of science. [See learning cycle literature; i.e., Lawson, Abraham, & Renner, 1989; Marek & Cavallo, 1997] Such lessons engage students cognitively and emotionally with ideas as well as physically with materials. Language Instruction Language instruction has also been influenced by constructivist learning theory. For example, Krashen (1985), a language educator, proposed a theory of second language acquisition that emphasizes the importance of comprehensible input. This theory suggests that students need to be instructed in a way that includes language already known (i) with new structures (i ? 1) in combinations allowing the learner to understand the message within the language. A major movement in language acquisition that emerged during the 1980s is ‘sheltered instruction’. The intent of sheltered instruction is to make grade-level content comprehensible to ELLs using strategies that are language acquisition driven (Freeman & Freeman, 1988). Teachers using sheltered instruction emphasize key vocabulary and provide scaffolded activities that contextualize mathematics, science, and social studies instruction in a way that lowers the linguistic demand but does not diminish the rigor of the content. The premise is that language and content develop in tandem. Elementary teachers concentrating on language acquisition have often relied on strategies that stress explicit and intentional practices. From this perspective, explicit vocabulary instruction is required in order for students to develop English skills (California Department of Education, 2010; Genesee, Lindholm-Leary, Saunders, & Christian, 2006). The need to help students learn language skills, especially vocabulary, is accepted within the science education community. One of the best-known and most used approaches to help classroom teachers utilize sheltered instruction in the content areas is the Sheltered Instruction Observation Protocol (SIOP) (Echevarrı´a, Vogt, & Short, 2004). However, this approach has been questioned by the science education community. The SIOP protocol consists of 30 elements grouped in eight categories. One of the categories is using special activities at the beginning of a unit to build vocabulary related to the content (frontloading language). Another SIOP category is developing content and language objectives which are articulated to the students prior to the lesson (Echevarrı´a, Vogt, & Short, 2008; Short, Vogt, & Echevarrı´a, 2011). While these strategies appear to be effective for scaffolding instruction for ELLs across a variety of content areas, they are problematic for inquiry-based science instruction. Science and Language Integration Pre-service elementary teachers are taught methods in all subjects and may get conflicting views from the language arts and science educators. Similarly, elementary teachers in many districts now have professional development in both SIOP and inquiry science and appear to struggle with the ‘‘misalignment between
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the thirty-item SIOP checklist’’ (Settlage et al., 2005, p. 51) and inquiry-based science (Lee et al., 2013). Pre-service and in-service teachers walk away from methodology courses and workshops confused because the two approaches ‘‘typically place different emphasis within a lesson’’ (Zwiep, Straits, Stone, Beltran, & Furtado, 2011, p. 783). Therefore teachers are faced with a fundamental disagreement between language and science as to when/how to introduce language, when/how to present science learning objectives and how explicit to be with instructions/directions. Few teacher educators would argue that reading, writing, listening and speaking are not constitutive parts of science (Norris & Philips, 2003; NRC, 2013). In fact, independent of science education research, those interested in reading education ‘‘have started to reconsider the role of content knowledge and genre in learning to read’’ (Cervetti, Barber, Dorph, Pearson, & Goldschmidt, 2012, p. 632). Some see acquiring information about the world, as found in science experiences, as the basis for future reading and writing (Neuman, 2006). In addition, being proficient in the literary style of the discipline is also necessary (Gee, 2004; Yore, Florence, Pearson, & Weaver, 2006). Others see the need to move students from fictional narrative texts to expository texts (Fang, 2006; Palincsar, 2005) in order to build language arts competency as well as content. More recently, the Framework (NRC, 2012) stresses communication and language skills as part of the practices of science. During the last decade, researchers have suggested, and even attempted, instruction that blends inquiry-based science and language development for ELLs (Weinburgh, & Silva, 2011a, b; Cervetti et al., 2012; Lee, Deaktor, Hart, Cuevas, & Enders, 2005; Stoddart, Pinal, Latzke, & Canaday, 2002; Yore et al., 2006; Zwiep et al., 2011). Several specific areas for further examination have been noted: SIOP/ Inquiry interface (Settlage et al., 2005) and the timing of inserting academic vocabulary into the context of inquiry science lessons (Banchi & Bell, 2008; Beck, McKeown, & Kucan, 2002). In response to these calls for more research, we used the work of others (Brown & Ryoo, 2008; Cummins, 1996, 2000; Fang, Lamme, & Pringle, 2010; Genesee, 1994; Gibbons, 2008; NRC, 1996, 2000) as a foundation to design instruction that interrogated the overlap of inquiry-based science and language development. Like Brown and Ryoo (2008), we questioned the use of complex terminology prior to having experienced the scientific ideas. The model that emerged from research begun in 2007 is an instructional model—5R Instructional Model—that problematized the frontloading of vocabulary and the deliberate discussion of lesson objectives (Weinburgh, 2009; Weinburgh et al., 2009; Weinburgh, & Silva, 2012; Silva et al. 2012). As we went back to the classroom we concentrated on these two aspects because frontloading vocabulary and stating the objective are problematic in science instruction but are common in second language instruction. Our alternative to frontloading is to have the language emerge during an inquiry-based lesson with reloading of language occurring the next day/lesson. In addition to reloading, other facets of instruction emerged to complete the 5R model as we examined the space in which science teaching and language instruction can co-exist and complement one another. The model includes repeating, revealing, repositioning, replacing and reloading language. The model is theoretically grounded on Gee’s (2004, 2008)
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notion that language constructs and reflects specific socioculturally defined contexts. It also builds on Lemke’s (2004) notion that the language of science is a unique hybrid in which natural language comes together with mathematics symbols, graphic representations and specialized action to communicate scientific meaning. Our research agenda also addresses the criticisms expressed by Clarke (1994) that educational research tends to be conducted outside real classrooms while using experimental designs that are difficult for teachers to reproduce in schools. We grounded our work in actual classrooms during a summer-school program for 5th grade ELLs where science, mathematics, and language were integrated into one relatively seamless unit. At the time of this research, the 5R Instructional Model had been used as the basis for a unit on erosion (Year 1) and a unit on wind turbines (Year 2). Our purpose in this study was to explore a model of integration that can be used in classrooms. To do this, we examined the change in science content knowledge and academic vocabulary for ELLs as they engaged in inquiry-based science experience utilizing the 5R Instructional Model (Weinburgh, & Silva, 2011a, 2012). Our hypothesis for this study was that elementary students taught using the 5R Instructional model would show significant gains in scientific academic vocabulary and conceptual understanding, as reflected in pre-post oral interviews. Furthermore, we tested whether gains would be similar for two different science topics (erosion and wind energy). We recognize that within an academic setting, teachers must support students to ‘‘use language to do things such as describe complexity and abstractions, use figurative expressions, be appropriately explicit for different audiences and use evidence for arguments’’ (Galguera, 2011, p. 90). However, for the purpose of this study, we define ‘academic’ vocabulary as language that will be necessary for teachers and students to communicate about a specific topic within a science classroom.
Method Context Three of the five authors were also the teachers of the summer school program prior to and throughout the two summers during which the data were collected. In addition, two district teachers were a part of the instructional team. The curriculum using the 5R Instructional Model purposely blended inquiry-based science with authentic language and mathematics instruction for fifth grade ELL students. We established science, mathematics, and language objectives prior to the unit but did not write them on the board or verbally give them to the students. As a team, we identified the content and a set of science vocabulary words necessary for understanding the science topic. The language was highlighted during both explicit science and language arts instruction but was not frontloaded. While planning each unit we incorporated the essential features of inquiry (posing a question, developing a plan, making evidence-based arguments, revising and clarifying student ideas) as recommended by the NRC (2000). [Planning
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occurred prior to the release of the Framework (NRC, 2012) and NGSS (Achieve, 2012)]. Students engaged with cross-cutting ideas as well as experiencing the specific phenomena they studied (Bybee et al., 2006; Lawson et al., 1989). The science emphasis for both years was on the cross-cutting concepts of models and scientific investigation/experimentation. In the first year, the specific science topics were erosion, experimentation, and using explanatory models while in the second year, we again focused on experimentation and models, this time relating to wind energy and turbines. We selected these topics because they provided a rich context for inquiry-based lessons and language development, followed the district framework, met the state standards, and allowed us to test the 5R Instructional Model with more than one topic. The language emphases were listening, speaking, writing, and reading using specific academic vocabulary and features of discourse in the science classroom. Specific strategies included journal writing, learning to interview others, examination of types of texts, sentence starters, and sustained silent reading with books related to the science and mathematics topics. Participants The study was conducted over 2 years during a 3-week summer school with 110 fifth grade newcomers to a large urban school district in north Texas. During the academic year, the students were enrolled in the district’s Language Center (Weinburgh et al., 2009). The summer school program was voluntary and intended as enrichment rather than remediation. The students attended class from 8:00 to 12:30 with breakfast and lunch provided resulting in approximately 60 h of instruction integrating science, mathematics, and language. In order to qualify for the district summer program, the students met the advanced language proficiency requirement within the Texas English Language Proficiency Assessment System (TELPAS) (Texas Education Agency, 2009). For this study we analyzed data from the 45 students who were present for at least 13 of the 15 days and on all days of data collection. All participants completed the consent procedures. Table 1 provides additional information about the two groups of students (n = 23 in Year 1 investigating erosion and n = 22 investigating turbines in Year 2). Procedures We structured the 15-day summer school around the science topic (erosion or wind turbines) with specific mathematics and language arts support. This structure allowed for mathematics instruction needed to fully represent the data collected during science. During each science lesson, the students engaged in inquiry-based instruction. Students were given a question on the second day [‘How will different environmental factors affect my newly landscaped yard?’ ‘What is the best design for a wind turbine for me use at my house?’] and systematically examined one variable in each trial to determine how it changed the system. We used a stream table with local sand (Year 1) and a 22-inch, plastic wind turbine (Year 2) as the model for each year’s investigation. We wove language instruction throughout both
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Table 1 Demographic information of students in two years M:F
Ages
Home languages
Year in US
Year 1 (2010)
15:8 (N = 23)
9–12 (most 11)
Spanish, Nepali, Burmese, Farsi, Thai, Vietnamese, Arabic, Congolese, Amharic
Average of 2.5 years
Year 2 (2011)
17:5 (N = 22)
9–12 (most 11)
Burmese, Spanish, Chin, Nepali, Chinese, Hindi, Lisu, Somali
Average of 2.5 years
science and mathematics by writing new words on paper strips and adding them to a word wall to return to later and by stopping to explicitly talk about important words as they emerged in context. The students regularly engaged in sustained silent reading and literature circles using thematically related texts. The mathematics instructor reminded the students that they were calculating information to help understand the science and the science instructor reminded the students that good science conclusions required using good mathematical skills. Throughout, we introduced and used language in both verbal and written modes. Data Collection Data (students’ journals, pre/post interviews, pre/post-tests, videos, photographs, student audio, and students’ artifacts) were collected throughout the summer sessions. Although students used authentic discourse throughout the inquiry-based science, for this study, we only analyzed data from interviews conducted at the beginning and end of each summer program. This allowed us to assess change in conceptual understanding and targeted academic vocabulary over the 60 h of instruction. Oral interviews were utilized to help lower the affective filter that could have prevented a student from fully expressing his/her knowledge. In order to honor the context-bound nature of the language experience, realia (stream table and wind turbine) were present during the interview to act as stimuli for student responses. In addition, the summer program was designed as an enriching experience that we felt should not include assessments that resembled the high-stakes testing that students are required to take during the academic year. Graduate assistants interviewed the students individually during the second and fourteenth day of the program. To ensure that all students responded to the same questions in both interviews we used a structured protocol, which began with a general question and moved to more specifics about the topic (‘‘Appendix 1’’). Each interview lasted for approximately 20 min and was audio and video taped. Audio was sent to a professional transcribing service and returned as a word file. Data Analysis The research team (consisting of all authors) developed and piloted a coding system (Weinburgh, & Silva, 2011a, b) for word use and conceptual understanding. The team used interviews from participants who had data points missing to practice
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coding, discuss disagreement on ratings, and share exemplars. Rating sessions always consisted of time for each team member to code alone, then report out. Any differences were discussed and consensus was used as the way of selecting a final code. Where needed, rules were set using excerpts from student work as examples. Target words were selected using the state’s framework (Texas Education Agency, 2014) and Marzano (2004). At the word level, students were rated as 0 (no use of word or parroted the word from the interview prompt) or 1 (acceptable use of the word in response to the interview prompt). This resulted in a content word score of academic vocabulary. At the conceptual level, students were rated as 0 (no understanding), 1 (preliminary understanding), 2 (partial understanding), 3 (adequate understanding), or 4 (proficient understanding). See ‘‘Appendix 2’’ for examples of the coding system. Our dependent measures were (a) content word scores; (b) content core concept scores; and (c) science concept (cross-cutting ideas) composite scores. Content word scores (a) are represented as percentages, with a top score of 100 % representing 12/12 erosion topic words mentioned appropriately; or 10/10 words for the wind turbine topic, respectively. For conceptual understanding scores (b), in the erosion group the core concept was ‘‘earth materials move from one location and are deposited in another location by a natural force’’ while the core concept for turbines was ‘‘energy can change types (wind, mechanical, electrical) and can be harnessed to help humans.’’ For cross-cutting ideas science concepts (c) our targets were ‘‘a model helps scientists explain how something [such as erosion or a turbine] works; scientists observe what happens by changing one thing at a time [fair test] in their model; testing how that one thing has an effect on the outcome.’’ We combined these three ideas into a composite score because they are conceptually highly inter-related. We first used descriptive statistics from the oral data to get a general picture of the change over time. The oral interview scores were then submitted to a series of 2-way repeated-measures ANOVAs with topic (erosion vs. turbine) as a betweensubjects factor and time (pre- versus post-intervention) as the within-subject factor.
Findings Using Content Words During the interviews on Day 2 before their investigations began, all but one student in Year 1 (n = 23) used the term ‘‘erosion’’ in discussing the picture; however, only a few referred to terms such as ‘‘soil’’, ‘‘gully’’, or ‘‘hole’’, and none mentioned more than 3 of the 12 targeted words. At end of program, more than of the students correctly used at least 50 % of the targeted words; for example 20 students talked about an ‘‘alluvial fan’’, 19 students referred to a ‘‘gully’’, 17 students to a ‘‘hole’’, and 6 to ‘‘deposition’’. Before the wind turbine investigations in Year 2 (n = 22), 4 students correctly used more than 50 % of the targeted words and 10 students mentioned close to half of the target words. For example, on Day 2, all of them referred to ‘‘energy’’, 11 used the term ‘‘turbine’’, and 9 mentioned ‘‘electrical power’’. After their investigations, 2/3 of the students correctly used more than 50 % of the targeted words, with five students correctly mentioning more than 90 %
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Table 2 Means and standard deviations (in parentheses) for word scoresa, pre- and post program Pre
Post
Erosion (n = 23)
19.93 (10.27)
58.33 (12.56)
Turbines (n = 22)
45.96 (17.91)
59.09 (18.11)
Topic
a
Top possible score = 100 % or all targeted topic words mentioned accurately
Fig. 1 Mean scores for accurate use of content words before and after instruction
of the target words. During the post-program interviews, all but one student also talked about ‘‘electrical energy’’. Table 2 provides group means for use of science words during oral interviews with students before and after each summer program. A repeated-measures ANOVA with topic as a between-subjects factor revealed that word scores rose significantly overall from pre- to post program F(1, 43) = 145.58; p \ .001 (M = 32.95 vs. 58.71). Topic (erosion/turbine) differences (M = 39.13 vs. 52.53) were also significant F(1, 43) = 19.47; p \ .001, as was the interaction between group and time F(1, 43) = 15.07; p \ .001. Figure 1 depicts these results. The students who investigated wind turbines performed better overall than those who studied erosion, but gains from pre to post were stronger for erosion than for turbines. The turbine group started with more language and made smaller gains, while the erosion group made greater gains from a lower starting-point. Expressing Content Concepts Table 3 provides group means for core conceptual understanding for each topic as derived from pre- and post-interviews. Before investigations, no student in either group scored above a ‘‘2’’ rating (partial understanding) for core conceptual
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Table 3 Means and standard deviations (in parentheses) for core concept scoresa, pre- and post program Pre
Post
Topic
a
Erosion (n = 23)
.74 (.86)
2.65 (1.11)
Turbines (n = 22)
.73 (.78)
1.45 (.96)
Top possible score = 4 ‘‘proficient understanding’’
Fig. 2 Mean proficiency scores for content core concepts before and after instruction
understanding and nearly half of each group received zeros. At end of program, 2/3 of the erosion students showed evidence of adequate or proficient understanding, while in the turbine group, of the students scored at partial or adequate levels, while the rest received zeros or 1’s (preliminary understanding). A repeated-measures ANOVA with group (topic) as the between-subjects factor revealed a significant pre-post gain (M = .735 vs. 2.05) in core concept scores overall F(1, 43) = 64.59 p \ .001; a significant difference between groups F(1, 43) = 7.16 p = .01 (M = 1.70 vs. 1.09); and a significant interaction F(1, 43) = 13.03 p = .001. Both groups began with similarly low levels of conceptual understanding, and after the program, performance was higher overall, but the erosion group showed greater gains than the turbine group. These data are depicted in Fig. 2. Expressing Cross-Cutting Ideas Science Concepts We also found evidence of growth in conceptual understanding for the cross-cutting ideas regarding a ‘‘model’’, ‘‘experiment’’, and ‘‘fair test’’ for the two topics. Inspection of the frequencies for each level of understanding revealed that in both groups, levels of understanding moved up, although not uniformly and not similarly
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Fig. 3 Levels of proficiency for 3 cross-cutting concepts before instruction, erosion group
Fig. 4 Levels of proficiency for 3 cross-cutting concepts after instruction, erosion group
across groups (see Figs. 3, 4, 5, 6). In the erosion group, every student showed an increase of at least one level of understanding from pre-to post-interview, while in the turbine group all but two students showed gains. Group means for cross-cutting ideas concept score composites are provided in Table 4. A repeated-measures ANOVA revealed that the cross-cutting ideas concept scores rose significantly overall F(1, 43) = 64.91 p \ .001, (M = 12.46 vs. 36.48) but there were no significant group differences or group-by-time interaction, as depicted in Fig. 7.
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Fig. 5 Levels of proficiency for 3 cross-cutting concepts before instruction, turbine group
Fig. 6 Levels of proficiency for 3 cross-cutting concepts after instruction, turbine group
Discussion Teaching science to ELLs during summer school for a local school district was an invaluable experience for us (Griffith et al., 2014). We were able to integrate science, mathematics, and language in ways that were documented and resulted in student growth. In order to help us understand the impact of these practices on children, this study examined the change in science content knowledge and academic vocabulary for ELLs as they engaged in inquiry-based science experience utilizing the 5R Instructional Model (Weinburgh, & Silva, 2011a, 2012). As would be expected across different units of instruction, all children did not show the same amount of change in word use and conceptual understanding. However, the statistics
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Table 4 Means and standard deviations (in parentheses) for cross-cutting concept composite scoresa, pre- and post program Pre
Post
Topic
a
Erosion (n = 23)
9.78 (11.14)
36.59 (24.20)
Turbines (n = 22)
15.15 (13.52)
36.36 (19.51)
Top possible score = 100 % or all three concepts expressed proficiently
Fig. 7 Mean composite scores for percent of cross-cutting concepts expressed proficiently before and after instruction
show a clear trend of growth. Thus our claim that they did construct more sophisticated understanding of the topics and used more language to communicate their knowledge is supported. Our results are consistent with recent work by Krashen (2013) who also questions the frontloading of vocabulary as suggested by the SIOP model. The results, we hope, will provide teacher educators a new model for helping their students integrate language into inquiry-based instruction. Gains in Academic Vocabulary and Conceptual Understanding We believe language and conceptual understanding are the two sides of the same coin and, as such, complement and reinforce each other. Also, as one increases in sophistication, the other is more apt to grow. Therefore, looking at the change in language is important as a correlate to conceptual understanding. The increased academic language can be explained by several of the ‘‘Rs’’ in the 5R model. First is the mindful reloading. This R is dedicated time each day to revisit the language associated with science concepts—Tier 3 words—and language needed for more general academic discourse—Tier 2 words (Beck et al., 2002). For example,
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students learning about erosion discussed the difference between the word ‘hole’ (a feature observed in the stream table as a result of water moving the sand) and ‘whole’ (a concept necessary to understand fractions). They also discussed sequence words (1st, 2nd, 3rd; first, next, last) and words or phrases that change the meaning (on the other hand, therefore, in so much as). Two other ‘‘Rs’’, repeat and reposition, may also help to explain the increase in language. The teachers used scientific vocabulary as often as possible (repeat) and restructured student utterances to more closely adhere to the Discourse (Gee, 2004) of science (reposition). To help with repeat and reposition, word stems such as ‘‘My manipulated variable is ___. My responding variable is ___. I predict ___ because ___’’ were used to encourage and require students to say and hear scientific Discourse. With each topic, we encouraged students to think about and discuss their own knowledge, experimental design, the initial observation and explanations using informal terms. The teachers would replace the informal term with a more academic one. Quickly the everyday terms (pile of sand, stem) were replaced with specific and precise academic terms (alluvial fan, tower). However, the informal terms were never denigrated but were used to scaffold toward the Discourse of science. As students encounter concepts or materials for which they have no everyday word, the new term is revealed to them. We introduced students to the idea of specialized language for use not only in science but in jobs, sporting events, art and other areas in which they engage. We believe that we found ways to examine genuine vocabulary use and conceptual understanding as opposed to ‘‘parroting’’ of words or mere recognition of terms as often occurs on multiple choice or matching assessments. We also hypothesized gains in conceptual understanding. The coding system for concept acquisition ranged from 0 to 4. To rate a 4, students had to display a strong understanding as predetermined by the research group. As with language, conceptual growth in both language and content can be explained by the ‘‘Rs’’. The students engage in systematic repeating of science ideas such as (1) discussing the baseline components prior to each test to establish a fair test situation, (2) setting up the equipment (stream table or wind turbine) for several trials of an investigation, and (3) deciding the one variable to test (i.e., rain type or number of blades) to be tested each time. This allowed the students to have multiple exposures to general cross-cutting concepts and ‘‘big-ideas’’ such as fair test, experiments, and models (see Figs. 3, 4, 5, 6) as well as newly observed experimental results. Evidence showed that students not only learned topic words about erosion or about wind turbines but also the more general science terms. Understanding these terms at a conceptual level is not easy even for adult learners so we believe that even these modest gains are noteworthy. Fair test is an abstract concept and Piaget (1926) would suggest that 12 year olds are typically not able to separate or isolate variables. Students also repeated the skill of creating a data chart with title and labels for each new variable tested. These experiences required students to speak and listen during each experiment and to write during and after the experiment.
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Different Gains for Different Topics The difference in language acquisition found between topics (erosion versus wind turbines) may have several explanations. One reason may be simply that the two groups were not comparable in some way not measured at the beginning of each summer program. Another reason may be more subtle. At the word level, the group studying turbines knew more words coming into the experience than did the group who studied erosion, but in the end academic vocabulary between the two topics was about the same. A close look at the targeted language suggests that the words relevant to wind turbines are used (often incorrectly) in everyday speech. The students have heard these words often and can use them in simple sentences in colloquial ways. However, exchanging the novice scientific understanding for more advanced understanding takes time and many experiences. Terms relevant to erosion, such as alluvial fan and deposition, did not have the ‘baggage’ of prior incorrect meaning. The data indicate that the students could say and somewhat correctly position in a sentence a limited number of the words necessary to understand and communicate about erosion and wind turbines. The program used the strategy of reloading words each day to go back over what a word means, how to use it in a sentence, words that mean similar ideas, words that mean the opposite, and how the word connects with other words. We attribute the increase in word knowledge to the emphasis on using the words in written and oral situations throughout the 15 days. This research is particularly important to science educators who work with preservice and in-service teachers in that it both provides evidence for using an alternative model for the helping ELLs in science classrooms and suggests a model for supporting the emphasis placed on connections, mathematics integration and communication by the New Generation Science Standards (NGSS). Elementary science teachers need to have knowledge of how to integrate inquiry-based science with language and communication instruction, particularly, but not exclusively with ELLs. Science teacher educators have voiced the need for models that help teachers accomplish the NGSS of communication. This research may provide a model to assist science teacher educators prepare teachers who are better able to help ELLs meet the dual need of learning science and learning English, while helping all students use academic science Discourse to communicate scientific questions, arguments and findings.
Limitations The study did not directly test our 5R Instruction Model against SIOP so we cannot claim that it is ‘‘more effective’’, but the data do suggest that using an instructional model that reloads rather than frontloads language and that does not explicitly state the objective of the unit is effective. The 5R Instructional Model has the language emerge through the experience which supports the students in
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developing language and conceptual understanding while holding true to the 5E learning cycle promoted for science instruction (Bybee et al., 2006). Other limitations include that the interviews, while following a protocol, may have lacked consistency because not all interviewers asked follow-up questions that led the students to elaborate. Interviews were used as the primary source of data because we wanted to honor the language proficiency of the students and to allow students to use contextual clues to describe their new science knowledge. Standardized measures would not have allowed for this type of discourse. In addition, the sample was small and we accept that using a larger sample would allow for a more robust analysis.
Conclusions As science teacher educators seek ways to prepare pre-service and in-service teachers to help preK-12 students to learn science and develop the language of science, the 5R Instructional Model is one pathway. In using this model (replacing, revealing, repositioning, repeating and reloading of science and language experience/concepts), teachers can enhance experiences for students who are learning science and English. Our study makes a significant contribution to the teacher education literature by alleviating the confusion that inquiry-based science and sheltered instruction are incompatible. The 5R is not a series of steps or linear process but is a way for teachers to think about lesson/unit planning so that ELL students benefit from effective instruction in both science and language. As stated in the first paragraph, a convergence of events in the US makes it necessary for science teachers to have models of ways to infuse language and literacy with science instruction. We found that the 5R model provides a ‘space’ in which science and language instruction can co-exist and complement one another. In this model content-specific language becomes the target of instruction only after the students have had the opportunity to first encounter it as they participate within a meaningful, science experience. The findings indicate that the 5R Instructional Model supports conceptual as well as linguistic development for ELLs engaged in inquiry-based science instruction. Acknowledgments Authors wish to thank the urban district and the Language Center director for contributions to this study. This research forms part of a larger study on acquisition of language and content knowledge for English language learners partially funded by the JPMorgan Chase foundation and the Andrews Institute of Mathematics & Science Education and Center for Public Education at TCU.
Appendix 1: Structured Interview Protocol A structured interview protocol was used for erosion and wind energy. The same questions were asked on Day 2 and Day 14.
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Day 2/14: Pull Out for Interview on Erosion Welcome (Name of child). My name is ___________. Pre: We are trying to see how much you already know about our lessons before we get started. This is for us to see if we do a good job of teaching this summer. Post: We are trying to see if we did a good job teaching about erosion this summer. Yesterday, you saw a picture of a field (show picture of erosion) and wrote in your journal about what you saw in the picture. Tell me everything you know about erosion. [Allow student to talk as long as he/she can; allow student to use of realia to help situate the knowledge. If student does not display conceptual understanding, prompt with question #2] 1. 2. 3. 4.
Can you tell me anything about (a) a gully? (b) a delta? (c) an alluvial fan? Have you heard the word ‘model’? What can you tell me about models? What do scientists do to test ideas? What can you tell me about an experiment? Tell me more about what experiments are.
Day 2/14: Pull Out for Interview on Wind Energy Welcome (Name of child). My name is ___________. Pre: We are trying to see how much you already know about our lessons before we get started. This is for us to see if we do a good job of teaching this summer. Post: We are trying to see if we did a good job teaching about wind energy this summer Yesterday, you saw a picture (show picture of wind turbine) and wrote in your journal about what you saw in the picture. Tell me everything you know about a wind turbine. [Allow students to talk as long as he/she can; allow student to use of realia to help situate the knowledge. If student does not display conceptual understanding, prompt with question #2] 1. 2. 3. 4.
This is a picture of a real wind turbine (show picture), what would you call this? What do you tell me about models? What do you think scientists do to test an idea? What can you tell me about an experiment? Tell me more about what experiments are.
Appendix 2: Vocabulary and Concept Rubric with Exemplars The words and concepts were selected prior to the teaching of the unit using the state content framework and Marzano (2004) as a guide. Conceptual understanding and scientific language grow in parallel. If the student used the targeted vocabulary related to the topic during the interview, the student received a score of 1. If the student did not use the word, he/she received a score of 0.
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Erosion vocabulary
Wind energy vocabulary
Alluvial fan
Blade
Deposition
Control
Earth materials
Electrical
Erosion
Mechanical
Force
Tower
Gully
Turbine
Hole
Volt Meter
Soil
Experiment
Stream table
Variable
Experiment
Model
Variable Model
For each instance of a score of 1, a qualitative assessment was used to evaluate the student’s conceptual understanding of the targeted vocabulary. The interview protocol gave the student the freedom to use the scientific vocabulary in ways that he/she thought would best convey meaning. This assessment resulted in a score from 0 to 4. Unit on Erosion and Energy Brackets [] indicate examples from student interview transcripts. Erosion
0 (no understanding)—no use of the word 1 (preliminary understanding)—said word but gives no indication of what it is 2 (partial understanding)—movement of materials 3 (adequate understanding)—movement of earth materials; 4 (proficient understanding)—movement of earth materials by natural force (wind, water, air) from one place to another
Model
0 (no understanding)—no use of the word 1 (preliminary understanding)—not real 2 (partial understanding)—represents a real thing [A model is almost the same as the real thing.] 3 (adequate understanding)—represents a real thing and can elaborate the representation. [Models are kind of like the real thing, the model is something you can make for yourself] 4 (proficient understanding)—represents a real thing: dynamic or not, larger or smaller, explanatory [It’s not made a real thing. It’s just proportional to the real thing and you could study that instead of having to go somewhere else to find it]
Experiment
0 (no understanding)—no use of the word 1 (preliminary understanding)—some kind of investigation [you’re making something, if scientists make experiments, like they’re trying to find something out] 2 (partial understanding)—states an investigation and gives examples [it’s like a test, but they want to do something like the other things…like when we work with wind] 3 (adequate understanding)—something is manipulated [Just to test it or something …. how big the blades can be before they touch the floor or the ground, so you change it] 4 (proficient understanding)—independent and dependent variable conceptually; replicated; trials (not all but combination) [First, they think of what the idea has to do with something else and they start testing it. Like making a list of your standard, a list of your variables… and then you change one variable… but you do it again.]
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Appendix 2 continued Fair test
0 (no understanding)—no use of the word 1 (preliminary understanding)—variable (something manipulated) 2 (partial understanding)—some sense of standardization [we do it alike, together] 3 (adequate understanding)—imply one variable changed at a time [So we did fast rain. A fair test, I think, well we made the hill and then we did the rain, the fast rain.] 4 (proficient understanding)—explicitly state one variable changed at a time [We tested variables. First, you have to make a list of your standard which is the things you got in your normal model. Then you make a list of the things you could change. And you have to know your limiting variable… your limiting factor. Because if you don’t, then you might put something in the list that’s not supposed to go there. you could only change one variable at a time because then you won’t… Say you want to know what happened if you changed the blades and you change the height, if you do … if the voltage changed, you wouldn’t know what changed it.]
Energy
0 (no understanding)—no use of the word 1 (preliminary understanding)—said word but gives no indication of it is 2 (partial understanding)—does something and gives examples; moves something; [Everybody has energy. Because when you walk, run or skip or something, you’re using energy.] [The wind moves things. Like the blades, I think it moves.] 3 (adequate understanding) – does something, gives examples thoroughly laid out. [Energy is like electric…it make the light go on and TV work.] 4 (proficient understanding)—ability to do work or thorough explanation w/examples throughout; ability to move something [They help us by taking energy from wind. Here’s how it works—When wind blows, the blades spin. If the blades spin, it is turned into electricity by the generator.]
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